chore: clean up backup files from tracking, fix remote to main repo

# Conflicts:
#	litellm_config.yaml
#	nginx/nginx.conf

.env.example

@@ -0,0 +1,8 @@
+# Syslog Harness Environment
+REDIS_HOST=192.168.68.8
+REDIS_PORT=6379
+AMDPVE_ENDPOINT=http://192.168.68.15:8080
+LLMGPU_ENDPOINT=http://192.168.68.8:8080
+OCU_LLM_ENDPOINT=http://192.168.68.110:8080
+CIRCUIT_BREAKER_THRESHOLD=5
+CIRCUIT_BREAKER_TIMEOUT=30

.gitignore

@@ -0,0 +1,6 @@
+.git
+__pycache__/
+*.pyc
+*.bak
+*.backup*
+.env

ARCHITECTURE_REVIEW.md

@@ -1,390 +0,0 @@
-# Syslog Harness  Architecture Review & Improvement Recommendations
-
-**Date:** 2026-05-17  
-**Commit:** `e95475f`  "Add GPU dashboard container + Nginx routing"  
-**Repo:** http://192.168.68.17:3000/SyslogSolution/syslog-harness.git
-
----
-
-## 1. Current Architecture Overview
-
-```
-                          
-                                              Host (192.168.68.123)                    
-                                                                                       
-                                        
-Agent :8080> Nginx Router >  Queue Service  >   Dashboard       
-                             :8080            :8091                  :3001         
-                                        
-                                                                                    
-                                                                                    
-                                                                                    
-                                        
-                             GPU Pool         Redis       >  GPU Dashboard  
-                             :8080            :6379               :8092         
-                                        
-                                                                                      
-                          
-                                  
-                    
-                                              
-                  
-               amdpve      llmgpu     ocu_llm    
-               .15:8080    .8:8080    .110:8080  
-               MoE 35B     Dense 27B   Light 4B  
-                  
-```
-
-### Services
-
-| Service | Port | Container | Image | Purpose |
-|---|---|---|---|---|
-| **Nginx Router** | 8080 | Host-level | OS nginx | Routes by `X-Syslog-Model` header |
-| **Queue Service** | 8091 | `syslog-queue` | `python:3.13-slim` | Request queue + circuit breaker |
-| **Dashboard** | 3001 | `syslog-dashboard` | `python:3.11-slim` | Observability UI + GPU health |
-| **GPU Dashboard** | 8092 | `syslog-gpu-dashboard` | `python:3.11-slim` | Hardware metrics (temp, VRAM, power) |
-| **Redis** | 6379 | `syslog-redis` | `redis:7-alpine` | Queue storage |
-
-### GPU Backends
-
-| Host | GPU | Model | Capacity |
-|---|---|---|---|
-| 192.168.68.15 | AMD Strix Halo | qwen3.6-35B-A3B (MoE) | 65GB VRAM |
-| 192.168.68.8 | RTX 3090 | qwen3.5-27B (Dense) | 24GB VRAM |
-| 192.168.68.110 | RTX 5070 | gemma-4-E4B (Light) | 12GB VRAM |
-
-### Data Flow
-
-1. **Agent** sends request with `X-Syslog-Model` header  Nginx :8080
-2. **Nginx** routes to appropriate GPU based on header mapping
-3. **GPU backend** (llama.cpp) processes request
-4. **Fallback:** If GPU returns 502/503/timeout  Nginx redirects to queue-service :8091
-5. **Queue** stores request in Redis `inference:requests` LPUSH
-6. **Dashboard** :3001 polls queue-service + GPU health for display
-7. **GPU Dashboard** :8092 collects hardware metrics every 10s
-
----
-
-## 2. File Inventory
-
-```
-docker-compose.yml                          # Main compose (Docker networking)
-gpu-router-docker.conf                      # Nginx config for Docker deployment
-Dockerfile.gpu                              # GPU dashboard container
-Dockerfile.dashboard                        # Dashboard container (root-level)
-queue-service/Dockerfile                    # Queue service container
-queue-service/queue-service.py              # Queue logic (121 lines)
-dashboard/harness-dashboard.py              # Dashboard app (133 lines)
-dashboard/Dockerfile                        # Dashboard container (subdir)
-dashboard/Dockerfile.dashboard              # Dashboard container (duplicate)
-gpu-dashboard/gpu_collector.py              # GPU hardware collector (115 lines)
-gpu-dashboard/gpu.html                      # GPU dashboard UI (183 lines)
-gpu-dashboard/collector.py                  # Duplicate collector (hermes-workspace path)
-gpu-dashboard/start.sh                      # Legacy startup script
-MIGRATION_PLAN.md                           # Production migration plan
-README.md                                   # Documentation
-syslog-harness-check/                       # Checkpoint subdirectory (mirror)
-```
-
----
-
-## 3. Detailed Findings
-
-### 3.1 Queue Service (`queue-service/queue-service.py`)
-
-**Architecture:** Simple Flask app using Redis LPUSH/RPUSH for a FIFO queue. A basic circuit breaker prevents queue overflow at 50 messages.
-
-**Issues Found:**
-
-| # | Severity | Location | Issue |
-|---|---|---|---|
-| Q1 | **CRITICAL** | Lines 82-88 | **Queue is fire-and-forget with no consumer.** Requests are pushed to Redis but nothing dequeues or processes them. The queue is a dead storage pit. |
-| Q2 | **CRITICAL** | Lines 28-32 | **Hardcoded GPU IPs** in the queue service duplicate the Nginx config. No configuration source of truth. |
-| Q3 | **HIGH** | Lines 21-22 | **Redis host fallback to `192.168.68.7`** (line 21) conflicts with docker-compose which sets `REDIS_HOST=redis` (line 24). The default is unreachable inside Docker. |
-| Q4 | **HIGH** | Lines 66-95 | **No job result retrieval mechanism.** Once enqueued, there's no API to poll for completion, get a job ID, or retrieve results. |
-| Q5 | **HIGH** | Lines 73-79 | **Circuit breaker is a simple depth threshold.** No backoff, no recovery window, no sliding window. Once closed, it stays closed until manually drained. |
-| Q6 | **MEDIUM** | Lines 50-57 | **GPU health check is synchronous and blocks** the `/status` endpoint. Checking 3 GPUs sequentially with 3s timeout means `/status` can take up to 9s. |
-| Q7 | **MEDIUM** | Lines 35-40 | **`get_redis()` swallows all exceptions** and returns `None`. This makes Redis failures silent  queue depth returns 0 on failure (line 47), potentially allowing overflow. |
-| Q8 | **MEDIUM** | Lines 83-84 | **Headers filtered to only X-* prefixed**  the `Content-Type` header is dropped entirely, meaning the receiver can't determine payload format. |
-| Q9 | **LOW** | Line 121 | **No graceful shutdown.** Flask development server doesn't handle SIGTERM gracefully. |
-
-### 3.2 Nginx Gateway (`gpu-router-docker.conf`)
-
-**Architecture:** Nginx routes requests to GPU backends based on `X-Syslog-Model` header value. Has rate limiting, streaming support, and queue fallback.
-
-**Issues Found:**
-
-| # | Severity | Location | Issue |
-|---|---|---|---|
-| N1 | **HIGH** | Lines 79-80 | **`burst=20 nodelay`** means 20 requests are served immediately beyond the rate limit, then throttled. This defeats the purpose of rate limiting under burst traffic  all 20 could still overwhelm a GPU. |
-| N2 | **HIGH** | Lines 99-100 | **`proxy_next_upstream` with `tries 2`** means on error/timeout/502/503, Nginx retries once. But it retries against the *same GPU pool*, not a different one. The same GPU that failed gets hit again. |
-| N3 | **HIGH** | Lines 106, 112-121 | **Queue fallback (`@queue_fallback`) is triggered for ANY 502/503/504**, including when a single GPU is overloaded. This means individual GPU slowness causes queue fallback instead of just queuing when ALL GPUs are down. |
-| N4 | **MEDIUM** | Line 90 | **`proxy_pass_header X-Syslog-Model`** is non-standard. Nginx automatically passes request headers; this directive is for response headers. The model header is already passed implicitly via `proxy_set_header` inheritance. |
-| N5 | **MEDIUM** | Lines 27, 32 | **Hardcoded container names** (`syslog-harness-dashboard-1`, `syslog-harness-gpu-dashboard-1`). These change based on docker-compose project prefix. Should use service names. |
-| N6 | **LOW** | Lines 67-73 | **GPU dashboard at `/gpu` path** has `X-Forwarded-Proto` but the dashboard service (simple HTTP server) doesn't use it. Inconsistent header handling across locations. |
-
-### 3.3 Dashboard (`dashboard/harness-dashboard.py`)
-
-**Architecture:** Simple HTTP server using Python's `http.server`. Fetches queue status and GPU health, renders HTML.
-
-**Issues Found:**
-
-| # | Severity | Location | Issue |
-|---|---|---|---|
-| D1 | **HIGH** | Lines 34-40 | **`get_queue_status()` calls queue-service synchronously.** Combined with per-GPU health checks (lines 18-31), the `/api/status` endpoint makes 4 sequential HTTP calls. Worst case: 2 + 33s = 11s response time. |
-| D2 | **MEDIUM** | Lines 101-127 | **Uses `SimpleHTTPRequestHandler`** which is single-threaded. Under concurrent dashboard access, requests queue up. Should use `ThreadingHTTPServer`. |
-| D3 | **MEDIUM** | Lines 16-18 | **GPU endpoints hardcoded** in dashboard, separate from queue-service and Nginx. Three separate sources of truth for GPU addresses. |
-| D4 | **LOW** | Line 127 | **Silent log suppression.** While intentional, this makes debugging impossible without modifying the source. |
-
-### 3.4 GPU Dashboard (`gpu-dashboard/`)
-
-**Architecture:** `gpu_collector.py` polls sidecar (port 8090) and llama.cpp (port 8080) endpoints every 10s, writes JSON to `gpu_metrics.json`. Static HTTP server serves the dashboard.
-
-**Issues Found:**
-
-| # | Severity | Location | Issue |
-|---|---|---|---|
-| G1 | **HIGH** | Lines 97-98 | **Sequential collection.** All 3 GPUs are polled sequentially (line 98: list comprehension). If one host is unreachable, it blocks collection for all three. |
-| G2 | **HIGH** | Line 105-107 | **`/app/public/gpu_metrics.json` path is hardcoded** and differs from `collector.py` (line 11: `/root/hermes-workspace/public/gpu_metrics.json`). Inconsistent between the two collector files. |
-| G3 | **MEDIUM** | Lines 19-25 | **`fetch_json` swallows all exceptions.** A timeout on one GPU's sidecar is silently ignored, making it impossible to distinguish "no data" from "collector error". |
-| G4 | **MEDIUM** | Line 14 | **`DEAD_THRESHOLD = 60` seconds is aggressive.** A GPU that restarts takes 60s before reappearing as online, even if it's back in 5s. |
-| G5 | **LOW** | Lines 10-14 | **`start.sh` references `/root/hermes-workspace/public`** but `Dockerfile.gpu` creates `/app/public`. Inconsistent between legacy and current deployment. |
-
-### 3.5 Docker Compose (`docker-compose.yml`)
-
-**Issues Found:**
-
-| # | Severity | Location | Issue |
-|---|---|---|---|
-| C1 | **HIGH** | Lines 19-20 | **Queue service exposes port 8091 externally.** In a multi-tenant or public-facing deployment, the queue API should be internal-only. |
-| C2 | **MEDIUM** | Lines 13-15 | **`Dockerfile.queue` referenced but doesn't exist at root level.** The file is at `queue-service/Dockerfile`. The compose build context is `.` (root) but the dockerfile path doesn't match. |
-| C3 | **MEDIUM** | Lines 6, 16, 26, 31, 43 | **`restart: always`** instead of `restart: unless-stopped`. On crash, `always` restarts even after manual stop, making maintenance harder. |
-| C4 | **LOW** | Lines 23-25 | **No health checks defined** for any service. Docker can't detect if a service is actually healthy, only if the container is running. |
-| C5 | **LOW** | Line 10 | **Redis has no password.** Unauthenticated Redis exposed on the Docker network. |
-| C6 | **LOW** | Lines 49-51 | **No network driver specified** for the bridge network (minor  defaults to bridge). No IPAM configuration for large deployments. |
-
-### 3.6 Container Images
-
-**Issues Found:**
-
-| # | Severity | Location | Issue |
-|---|---|---|---|
-| I1 | **HIGH** | All Dockerfiles | **No `requirements.txt` or dependency pinning.** All dependencies (`flask`, `redis`, `requests`) are installed without version pins. Builds are non-reproducible. |
-| I2 | **MEDIUM** | `Dockerfile.gpu` line 3 | **`pip install requests`**  unnecessary dependency for the GPU dashboard (only uses `urllib`). Adds ~300KB to the image. |
-| I3 | **MEDIUM** | `Dockerfile.gpu` line 14 | **Multi-process CMD with `&`**  no process supervisor. If the collector crashes, it won't restart. The `http.server` also won't receive SIGTERM properly. |
-| I4 | **LOW** | All Dockerfiles | **No `.dockerignore` file.** The entire context is sent to the Docker daemon, including `.git` directories and any local artifacts. |
-| I5 | **LOW** | `Dockerfile.dashboard` (root) vs `dashboard/Dockerfile.dashboard` | **Duplicate Dockerfiles** with slight differences (Python 3.11 vs 3.13, WORKDIR differences). |
-
----
-
-## 4. Smart Queuing Analysis & Recommendations
-
-### Current State:  No Smart Queuing
-
-The queue service is a **passive storage mechanism**  it stores requests but has no intelligence:
-
-- **No load balancing**  no awareness of GPU load (slots_busy, VRAM usage, queue depth per GPU)
-- **No job prioritization**  FIFO only, no priority levels
-- **No backpressure**  simple threshold, no exponential backoff or adaptive limits
-- **No retry logic**  failed GPU requests go to queue but are never reprocessed
-- **No dead letter handling**  stuck or failed jobs have no lifecycle management
-- **No consumer**  nothing dequeues and forwards to GPUs
-- **No job tracking**  no job IDs, no status updates, no result retrieval
-
-### Recommended Architecture: Smart Queue with Consumer
-
-```
-Agent > Nginx > Smart Queue API > Redis Streams (with consumers)
-                                          
-                                   
-                                     Consumer   
-                                     Pool       
-                                   
-                                          
-                             
-                                                     
-                         GPU 1 (load)  GPU 2 (load)  GPU 3 (load)
-                                                     
-                                                     
-                         Health        Health        Health
-                                                   
-                           
-                                          
-                                  Update GPU scores
-                                          
-                             Priority Queue (sorted by urgency)
-                             Dead Letter Queue (failed jobs)
-                             Backpressure (adaptive rate limit)
-```
-
-### Specific Recommendations
-
-#### R1: Implement Redis Streams as Queue Backend
-- Replace `LPUSH/RPUSH` (FIFO list) with **Redis Streams** (`XADD/XREADGROUP`)
-- Streams support consumer groups, message acknowledgment, and pending messages
-- Enables proper dead letter queue handling and retry logic
-- **File:** `queue-service/queue-service.py`
-
-```python
-# Before: Simple list
-r.rpush(QUEUE_KEY, json.dumps(job))
-
-# After: Redis Stream with consumer group
-stream_key = "inference:stream"
-consumer_group = "gpu-workers"
-r.xadd(stream_key, {"job": json.dumps(job)}, maxlen=10000, approx=True)
-```
-
-#### R2: Build a Queue Consumer Pool
-- Deploy 1+ consumer containers that poll the stream and forward to GPUs
-- Consumer selects GPU based on: health status, current load (slots_busy), and VRAM availability
-- **File:** New `queue-service/consumer.py`
-
-```python
-class LoadBalancedConsumer:
-    def select_gpu(self, job):
-        """Select GPU based on load, health, and model compatibility."""
-        candidates = [g for g in self.gpus if g.health == "up" and not g.full]
-        if not candidates:
-            return None
-        # Sort by: slots_idle (descending), VRAM_available (descending)
-        candidates.sort(key=lambda g: (g.slots_idle, g.vram_free_mb), reverse=True)
-        return candidates[0]
-```
-
-#### R3: Implement Priority Queuing
-- Add priority field to job payload: `high`, `normal`, `low`
-- Use Redis Streams with multiple stream keys per priority level
-- Consumer checks `high`  `normal`  `low` in order
-- **File:** `queue-service/queue-service.py` enqueue endpoint
-
-#### R4: Add Backpressure Mechanism
-- Instead of hard threshold at 50, implement **adaptive backpressure**:
-  - Queue depth 0-30: normal operation
-  - Queue depth 30-40: return `retry-after` header with increasing delay
-  - Queue depth 40-50: return 503 with exponential retry-after
-  - Queue depth >50: circuit breaker open
-- **File:** `queue-service/queue-service.py`
-
-#### R5: Dead Letter Queue (DLQ)
-- Move failed/unprocessable jobs to a `inference:dead-letter` stream
-- Include failure reason, attempt count, and original payload
-- Provide admin API to inspect, retry, or discard DLQ entries
-- **File:** `queue-service/queue-service.py`
-
-```python
-# New endpoint
-@app.route("/dlq", methods=["GET"])
-def list_dlq():
-    return r.xrange("inference:dead-letter")
-
-@app.route("/dlq/retry/<message_id>", methods=["POST"])
-def retry_dlq(message_id):
-    job = r.xget("inference:dead-letter", message_id)
-    r.xadd("inference:stream", {"job": job})
-```
-
-#### R6: GPU-Aware Routing
-- Queue consumer should check GPU `slots_busy` before routing
-- If a GPU is busy, try the next available GPU
-- Track per-GPU queue depth and avoid overloading a single GPU
-- **File:** New consumer logic
-
-#### R7: Job Status API
-- Add job ID generation on enqueue
-- Provide `/status/<job_id>` endpoint to check progress
-- Store job state in Redis: `queued`  `processing`  `completed`/`failed`
-- **File:** `queue-service/queue-service.py`
-
-```python
-@app.route("/enqueue", methods=["POST"])
-def enqueue():
-    job_id = str(uuid.uuid4())
-    job = {"id": job_id, "payload": ..., "status": "queued", "created_at": time.time()}
-    r.xadd(stream_key, {"job": json.dumps(job)})
-    r.hset("job:status", job_id, json.dumps({"status": "queued"}))
-    return jsonify({"job_id": job_id, "status": "queued"}), 202
-
-@app.route("/status/<job_id>")
-def job_status(job_id):
-    status = r.hget("job:status", job_id)
-    return jsonify(json.loads(status)) if status else {"error": "not found"}, 404
-```
-
-#### R8: Health-Based Circuit Breaker
-- Replace simple depth threshold with **per-GPU circuit breakers**
-- Track consecutive failures per GPU
-- Implement half-open state: after cooldown, probe one GPU to test recovery
-- **File:** `queue-service/queue-service.py`
-
-#### R9: Centralized Configuration
-- Move GPU endpoints from 3 locations (queue-service, dashboard, Nginx) to:
-  - Redis config key: `config:gpus`
-  - Or environment file mounted to all containers
-- Nginx can use Lua/variable from config instead of static upstreams
-- **File:** New `config/` directory or Redis-based config
-
----
-
-## 5. Priority Issue Summary
-
-### Critical (Fix Immediately)
-1. **Q1**  Queue has no consumer; enqueued requests are never processed
-2. **Q4**  No job ID or result retrieval mechanism
-3. **N3**  Queue fallback triggers on individual GPU failure, not all-down
-
-### High (Fix Before Production)
-4. **Q5**  Circuit breaker has no recovery mechanism
-5. **Q6**  `/status` endpoint blocks on GPU health checks
-6. **D1**  Dashboard `/api/status` makes 4 sequential calls, up to 11s
-7. **C2**  `Dockerfile.queue` path mismatch in docker-compose
-8. **I1**  No dependency pinning in any Dockerfile
-9. **I3**  Multi-process CMD without supervisor in GPU dashboard
-
-### Medium (Improve in Next Iteration)
-10. **Q3**  Redis host default conflicts with Docker networking
-11. **Q7**  Silent exception swallowing in Redis access
-12. **Q8**  Content-Type header dropped in queue
-13. **D2**  Single-threaded dashboard server
-14. **D3**  Three separate sources of truth for GPU addresses
-15. **G1**  Sequential GPU collection blocks on single failure
-16. **N1**  Rate limit burst of 20 nodelay defeats protection
-17. **N5**  Hardcoded container names in Nginx
-18. **C1**  Queue API exposed externally
-19. **C4**  No Docker health checks
-
-### Low (Nice to Have)
-20. **Q9**  No graceful shutdown
-21. **C3**  `restart: always` vs `unless-stopped`
-22. **C5**  No Redis authentication
-23. **G4**  60s dead threshold is too aggressive
-24. **I2**  Unnecessary `requests` dependency
-25. **I4**  No `.dockerignore`
-26. **I5**  Duplicate Dockerfiles
-
----
-
-## 6. Deployment Architecture Summary
-
-### What Works Well
-- Clean separation of concerns: routing (Nginx), queuing (Redis + queue-service), observability (two dashboards)
-- Good GPU hardware monitoring with temperature, VRAM, power, fan metrics
-- SSE streaming support in Nginx for LLM response streaming
-- Rate limiting at the gateway layer
-- Circuit breaker pattern implemented (even if basic)
-
-### What Needs Work
-- **Queue is incomplete**  storage without processing is the most critical gap
-- **No job lifecycle**  requests go in and never come out
-- **Duplicated configuration**  GPU addresses in 3+ places
-- **No monitoring/alerting**  no Prometheus metrics, no alerting rules
-- **Single point of failure**  no Redis replication, no container redundancy
-- **No logging**  Flask dev server logs are minimal; no structured logging
-
-### Recommended Next Steps
-1. **Priority 1:** Implement queue consumer with GPU load-based routing
-2. **Priority 2:** Add job status tracking and result retrieval
-3. **Priority 3:** Fix Nginx fallback to only trigger when ALL GPUs are down
-4. **Priority 4:** Add Docker health checks and proper dependency management
-5. **Priority 5:** Centralize GPU configuration in Redis or environment
-6. **Priority 6:** Add Prometheus metrics endpoint for observability

Dockerfile.dashboard

@@ -1,5 +0,0 @@
-FROM python:3.11-slim
-WORKDIR /app
-COPY dashboard/harness-dashboard.py .
-EXPOSE 3001
-CMD ["python3", "harness-dashboard.py"]

Dockerfile.gpu

@@ -1,14 +0,0 @@
-FROM python:3.11-slim
-
-RUN pip install requests
-
-COPY gpu-dashboard/ /app/
-WORKDIR /app
-
-RUN mkdir -p /app/public && \
-    cp gpu.html /app/public/ && \
-    touch /app/public/gpu_metrics.json
-
-EXPOSE 8092
-
-CMD ["sh", "-c", "python3 gpu_collector.py & python3 -m http.server 8092 --directory /app/public & wait"]

LITELLM-MIGRATION-PLAN.md

@@ -0,0 +1,759 @@
+# LiteLLM Integration Migration Plan
+## Syslog Solution LLC  June 14, 2026
+
+**Deployment Target:** CT 116 `syslog-api` (192.168.68.116) on minipve  all services co-located.
+**DNS Strategy:** Option A  /etc/hosts + Docker extra_hosts for internal resolution of `auth.sysloggh.net`  192.168.68.11.
+**GitOps:** This plan lives in `SyslogSolution/syslog-harness` on Gitea. All changes tracked via git with conventional commits.
+
+---
+
+## Executive Summary
+
+**Goal:** Layer the full LiteLLM Gateway suite (Admin UI, virtual keys, spend tracking, teams/SSO, budget management) on top of our custom intelligent routing harness  without sacrificing GPU-aware slot management, content-based tiering, or hardware health monitoring.
+
+**Architecture Decision:** Two-layer architecture.
+
+```
+                  
+                       LiteLLM Gateway (Layer 1)    
+                       Port 4000  Policy & UX      
+                     
+                     Admin UI (/ui)               
+                     Virtual Keys & Permissions   
+                     Teams, Users, SSO (OIDC)     
+                     Spend Tracking & Budgets     
+                     Usage Analytics Dashboard    
+                     Request Audit Trail          
+                     Global Rate Limiting         
+                     
+                                                    
+                      Pass-through to router         
+                  
+                             
+                  
+                    Custom Router (Layer 2)          
+                    Port 9000  Intelligence & HW   
+                     
+                     5-Tier Content-Based Routing 
+                     GPU Slot Management (Redis)  
+                     Agent Spread Prevention      
+                     GPU Health Scoring            
+                     Sidecar VRAM/Temp/Power      
+                     Circuit Breaker              
+                     Context Window Tracking       
+                     Per-Request Perf Recording   
+                     Hardware Rate Limiting       
+                     
+                  
+                             
+          
+                                                   
+      
+   qwen3.6-35B     qwen3.6-27B      gemma-4-12b      
+   MoE/Strix       Dense/RTX3090    VLM/RTX 5070     
+   :8080 (llama)   :8080 (llama)    :8080 (llama)    
+   :8090 (side)    :8090 (side)     :8090 (sidecar)  
+      
+```
+
+---
+
+## 1. Current State Baseline
+
+### 1.1 Router (`router-fixed.py`  port 9000, deployed on CT 116 / syslog-api)
+
+**Deployment Host:** CT 116 `syslog-api` on minipve (192.168.68.12), IP 192.168.68.116, 6GB RAM, 40GB disk. Runs Docker with all harness services co-located on this single host.
+
+| Feature | Implementation |
+|---------|---------------|
+| **Routing Engine** | 5-tier content-based: lightweight  simple_conv  medium  heavy_reasoning  default |
+| **GPU Slot Mgmt** | Redis atomic incr/decr, max 2 concurrent per GPU, audit loop reset |
+| **Health Checks** | Sidecar endpoint per GPU (VRAM, temp, util, power) + llama.cpp /health |
+| **Agent Spreading** | `select_best_gpu()` prefers GPUs with 0 other agents, then non-self GPUs |
+| **Rate Limiting** | Token bucket (Redis), per-tier RPM: enterprise=120, professional=60, starter=20 |
+| **Auth** | Dual-key system (Phase 0.5): 9 new + 9 deprecated keys, admin key rotation |
+| **Performance** | Per-request latency/tokens/tps  Redis lists (perf:recent, perf:model:X, perf:agent:X) |
+| **Context Tracking** | Session-level token accumulation with compaction warnings in headers |
+| **SSE Streaming** | Real-time dashboard updates, per-model timeseries |
+| **Admin** | `/admin/keys`, `/admin/keys/generate`, `/admin/keys/revoke`, `/admin/keys/deprecation-summary` |
+| **Strict Passthrough** | Explicit model requests go to that GPU exactly (no silent fallback). LiteLLM owns failover. |
+
+### 1.2 GPU Backends
+
+| GPU | Host | llama.cpp | Sidecar | VRAM | Context |
+|-----|------|-----------|---------|------|---------|
+| qwen3.6-35B-A3B (MoE) | 192.168.68.15 | :8080 | :8090 | Strix Halo | 262K |
+| qwen3.6-27B-code (Dense) | 192.168.68.8 | :8080 | :8090 | RTX 3090 | 262K |
+| gemma-4-12b (VLM) | 192.168.68.110 | :8080 | :8090 | RTX 5070 | 262K |
+
+### 1.3 Existing LiteLLM POC on CT 116
+
+CT 116 already has a LiteLLM container running (POC, 6 days uptime):
+
+```
+harness-litellm | ghcr.io/berriai/litellm:main-stable | 127.0.0.1:8081->4000
+harness-redis   | redis:7-alpine                      | 127.0.0.1:6379
+harness-router  | inference-harness-router             | 127.0.0.1:9000
+harness-nginx   | nginx:alpine                        | 0.0.0.0:80
+harness-dashboard | inference-harness-dashboard        | 127.0.0.1:3000
+```
+
+- `/opt/litellm/`  previous setup directory on CT 116
+- Configured with Postgres, host networking, master key
+- Currently bypassed  router routes directly to GPUs
+- **Goal: Productionize with two-layer architecture on this same host**
+
+### 1.4 DNS Routing (Split-Horizon)
+
+For OIDC SSO with Authentik, CT 116 must resolve `auth.sysloggh.net` internally:
+
+**Problem:** `auth.sysloggh.net` CNAMEs to `netbird.sysloggh.net`  72.61.0.17 (public VPS). OIDC auth_request from NGINX would route through the internet back to 192.168.68.11 unnecessarily.
+
+**Solution  Option A: /etc/hosts on CT 116 host:**
+```bash
+# On CT 116 (syslog-api)
+echo "192.168.68.11 auth.sysloggh.net" >> /etc/hosts
+```
+
+**Docker containers** also need this resolution  add to docker-compose.yml:
+```yaml
+services:
+  nginx:
+    extra_hosts:
+      - "auth.sysloggh.net:192.168.68.11"
+  litellm:
+    extra_hosts:
+      - "auth.sysloggh.net:192.168.68.11"
+```
+
+**DNS Servers:** CT 116 uses 192.168.68.10 for DNS. AdGuard (192.168.68.11) is the long-term solution for LAN-wide split-horizon DNS.
+
+---
+
+## 2. What LiteLLM Brings (That We Don't Have)
+
+| Feature | Our Router | LiteLLM | Value Add |
+|---------|-----------|---------|-----------|
+| **Admin UI** |  |  Full dashboard at /ui | Non-technical users can manage keys, view spend |
+| **Virtual Key Permissions** |  (binary key->tier) |  Granular: per-model, per-team, budget caps | Fine-grained access control |
+| **Spend Tracking** |  |  Per-request $ cost with model-specific pricing | Billing, cost allocation, client invoicing |
+| **Teams & Orgs** |  |  Multi-tenant: org->team->user hierarchy | Segregate clients/projects |
+| **SSO/OIDC** |  |  Google, GitHub, Microsoft, Okta, Keycloak | Enterprise auth integration |
+| **Budget Alerts** |  |  Per-key, per-user, per-team budget with webhooks | Prevent overspend |
+| **Usage Analytics** |   (custom /metrics) |  Built-in: daily trends, model breakdown, per-customer | Better visualization |
+| **100+ Provider Support** |  (3 local GPUs) |  OpenAI, Anthropic, Bedrock, Vertex, etc. | Future cloud model access |
+| **Fallback Chains** |  (silent rerouting) |  Explicit multi-provider failover with per-model logging | Accurate per-model tracking, visible failover |
+| **RPM/TPM Weighted LB** |  |  Weighted load balancing across deployments | Fine-grained traffic shaping |
+
+---
+
+## 3. What We Keep (That LiteLLM Doesn't Have)
+
+| Feature | Why We Must Keep It |
+|---------|---------------------|
+| **Content-based 5-tier routing** | LiteLLM routes by model name only; we analyze prompt complexity, tokens, turns, and routing_hints |
+| **GPU hardware health scoring** | LiteLLM doesn't monitor VRAM, temp, power  our scoring prevents routing to overheating GPUs |
+| **GPU slot management** | LiteLLM doesn't know about llama.cpp --parallel limits; our Redis counters prevent overloading |
+| **Agent spread prevention** | Our `select_best_gpu()` spreads agents across GPUs to prevent hotspots; LiteLLM only does simple-shuffle |
+| **Cross-turn context tracking** | Session-level token accumulation with compaction warnings via X-Context-Warning headers |
+| **GPU sidecar metrics** | VRAM %, GPU utilization %, power draw, temperature  exposed via /metrics and SSE dashboard |
+| **Circuit breaker** | 39 failures caught June 12; LiteLLM's allowed_fails/cooldown is less granular |
+
+---
+
+## 4. Migration Architecture
+
+### 4.1 Principle: "LiteLLM is the lobby, our router is the engine room"
+
+- **LiteLLM** handles everything a **user/admin** touches: keys, teams, budgets, spend logs, SSO, the UI
+- **Custom Router** handles everything the **GPUs** need: health checks, slot booking, content-based routing, hardware monitoring, circuit breaking
+
+### 4.2 Flow
+
+```
+Agent Request
+     
+     
+
+ LiteLLM Gateway (:4000)                      
+                                              
+ 1. Authenticate virtual key (sk-litellm-...) 
+ 2. Check key permissions (model access)      
+ 3. Check budget (per-key, per-user, per-team)
+ 4. Check team rate limits                    
+ 5. Log request metadata                      
+ 6. Forward to custom router as OpenAI-compat  
+    POST http://router:9000/v1/chat/completions
+    Headers: Authorization: Bearer *** 
+             X-LiteLLM-User: <user-id>         
+             X-LiteLLM-Team: <team-id>         
+             X-Session-Id: <session>           
+                                              
+ 7. On response: log spend, update budgets     
+ 8. If router returns 503 (GPU saturated):     
+    consult fallback chain, retry next model    
+ 9. Return response to agent                   
+
+               
+               
+
+ Custom Router (:9000)                         
+                                               
+ 1. Authenticate agent key (sk-syslog-...)     
+ 2. Hardware rate limit (per-tier RPM)         
+ 3. Content-based tier routing (for syslog-auto)
+    OR strict passthrough (for explicit models) 
+ 4. GPU slot availability (Redis counter)      
+ 5. GPU health check (sidecar)                 
+ 6. Agent spread logic (select_best_gpu)       
+ 7. Queue if saturated (with timeout)          
+ 8. Forward to selected llama.cpp GPU          
+ 9. Track context window, set compaction header
+ 10. Record performance metrics                
+ 11. Return response (with routing metadata)   
+
+               
+               
+
+ llama.cpp GPU (:8080)                         
+
+```
+
+### 4.3 LiteLLM Config (`config.yaml`)
+
+```yaml
+general_settings:
+  master_key: os.environ/LITELLM_MASTER_KEY
+  database_url: postgresql://litellm:***@postgres:5432/litellm
+  store_model_in_db: true
+
+model_list:
+  # Content-based auto-routing (router picks GPU via 5-tier analysis)
+  - model_name: syslog-auto
+    litellm_params:
+      model: openai/syslog-auto
+      api_base: http://router:9000/v1
+      api_key: os.environ/ROUTER_API_KEY
+      rpm: 600
+  
+  # Individual GPU strict passthrough (exact GPU, no silent fallback)
+  - model_name: qwen3.6-35B-A3B
+    litellm_params:
+      model: openai/qwen3.6-35B-A3B
+      api_base: http://router:9000/v1
+      api_key: os.environ/ROUTER_API_KEY
+  
+  - model_name: qwen3.6-27B-code
+    litellm_params:
+      model: openai/qwen3.6-27B-code
+      api_base: http://router:9000/v1
+      api_key: os.environ/ROUTER_API_KEY
+
+  - model_name: gemma-4-12b
+    litellm_params:
+      model: openai/gemma-4-12b
+      api_base: http://router:9000/v1
+      api_key: os.environ/ROUTER_API_KEY
+
+# Guardrails: Pre-call and post-call content moderation
+guardrails:
+  - guardrail_name: "input-moderation"
+    litellm_params:
+      guardrail: openai_moderation
+      mode: "pre_call"
+
+  - guardrail_name: "output-moderation"
+    litellm_params:
+      guardrail: openai_moderation
+      mode: "post_call"
+
+  - guardrail_name: "harmful-content-filter"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      categories:
+        - category: "harmful_self_harm"
+          enabled: true
+          action: "BLOCK"
+          severity_threshold: "medium"
+        - category: "harmful_violence"
+          enabled: true
+          action: "BLOCK"
+          severity_threshold: "medium"
+        - category: "harmful_illegal_weapons"
+          enabled: true
+          action: "BLOCK"
+          severity_threshold: "medium"
+
+litellm_settings:
+  num_retries: 0                     # Disabled  our router handles retry
+  request_timeout: 600               # Match our 10-min llama-server timeout
+  set_verbose: true
+  failure_callback: ["prometheus"]   # Optional: export to Prometheus
+
+router_settings:
+  routing_strategy: "usage-based-routing"  # For external models only
+  enable_loadbalancing_on_proxy: false     # Disable LiteLLM's internal LB
+  allowed_fails: 100                       # Router returns 503 on saturated GPUs  cooldown disabled
+  # Fallback chains: LiteLLM retries down the chain when router returns saturated
+  # This gives accurate per-model metrics because router no longer silently reroutes
+  fallbacks:
+    - qwen3.6-35B-A3B: ["qwen3.6-27B-code", "gemma-4-12b"]
+    - qwen3.6-27B-code: ["qwen3.6-35B-A3B", "gemma-4-12b"]
+    - gemma-4-12b: ["qwen3.6-27B-code", "qwen3.6-35B-A3B"]
+
+# Cost tracking: map model names to per-token pricing for spend tracking
+litellm_settings:
+  model_cost:
+    qwen3.6-35B-A3B:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+    qwen3.6-27B-code:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+    gemma-4-12b:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+    # For internal cost allocation, set symbolic rates:
+    # e.g., MoE = $2/M tokens, Dense = $1/M tokens, VLM = $0.50/M tokens
+```
+
+### 4.4 Router Modifications
+
+To accommodate LiteLLM, `router-fixed.py` requires the following updates:
+
+1. **Strict passthrough for explicit models** (DEPLOYED):
+   ```python
+   # In route(), the explicit model section changed from silent fallback to strict:
+   req = rd.get("model","auto")
+   if req != "auto":
+       # STRICT MODE: no silent fallback  LiteLLM handles failover chains.
+       # This keeps per-model metrics accurate. Returns saturated if busy.
+       target = req if req in avail else avail[0]
+       if req not in avail:
+           return {"model": req, "reason": "explicit_unavailable", "saturated": True}
+       if is_gpu_busy(target):
+           return {"model": target, "reason": "explicit_saturated", "saturated": True}
+       return {"model": target, "reason": "explicit"}
+   ```
+
+2. **New header passthrough**: Forward `X-LiteLLM-*` headers to GPU (transparent  already works)
+
+3. **New endpoint for health passthrough**: `GET /v1/models` already works
+
+4. **Keep ALL routing logic**: No changes to `select_best_gpu()`, `check_gpu_health()`, slot management, etc. Content-based routing for `syslog-auto` is fully intact.
+
+5. **Add LiteLLM-compatible response**: Return `X-Usage-Tokens` header so LiteLLM can track token costs
+   ```python
+   resp.headers["X-Usage-Tokens"] = json.dumps({
+       "prompt_tokens": prompt_tokens,
+       "completion_tokens": completion_tokens,
+       "model": model
+   })
+   ```
+
+### 4.5 Router Logic Refinements
+
+**GPU Health Scoring (Updated):**
+We are updating the scoring algorithm to include Power metrics:
+```python
+def gpu_health_score(model):
+    h = check_gpu_health(model, sidecar_timeout=1.5, gpu_timeout=1)
+    if h.get("status") == "down":
+        return 999  # never pick down GPUs
+    vram_pct = h.get("vram_pct") or 50
+    temp_c = h.get("temp_c") or 50
+    power_w = h.get("power_w") or 50
+    active = gpu_active_count(model)
+    max_c = GPU_MAX_CONCURRENT.get(model, 1)
+    load_pct = (active / max_c) * 100 if max_c > 0 else 0
+    # Score: lower = better
+    score = (vram_pct * 0.3) + (max((temp_c - 30, 0) * 0.3) + (power_w * 0.2) + (load_pct * 0.2))
+    return score
+```
+
+---
+
+## 5. Deployment Plan (4 Phases)  Zero-Downtime Strategy
+
+### Phase 0: Infrastructure Prep (Current Week)  Zero Downtime
+
+**Goal:** Prepare CT 116 infrastructure without affecting running agents.
+
+**Tasks:**
+1. **Set up DNS split-horizon on CT 116**
+   ```bash
+   echo "192.168.68.11 auth.sysloggh.net" >> /etc/hosts
+   ```
+
+2. **Deploy Postgres container** alongside existing services
+
+3. **Replace LiteLLM config** with production config.yaml (see 4.3)
+   - All 4 models  `http://router:9000/v1`
+   - Fallback chains for explicit models
+   - Guardrails (pre-call, post-call, content filter)
+   - `allowed_fails: 100` (router returns 503 on saturated)
+   - `num_retries: 0` (LiteLLM retries handled by fallback chains)
+
+4. **Deploy custom_sso.py** for Authentik OIDC integration
+
+5. **Restart LiteLLM container** with new config
+
+6. **Verify internal routing**
+
+**Verification Checklist:**
+- [ ] DNS resolution: `getent hosts auth.sysloggh.net`  192.168.68.11
+- [ ] Postgres container healthy
+- [ ] LiteLLM `/health` returns 200
+- [ ] LiteLLM  Router pass-through returns valid chat completion
+- [ ] GPU health metrics unaffected
+- [ ] Explicit model request returns saturated (not silently rerouted) when GPU busy
+
+### Phase 1: Shadow Mode (Week 1)  Zero Risk, Zero Downtime
+
+**Goal:** Deploy LiteLLM alongside existing router, test in shadow mode. **Agents continue using :9000 directly.**
+
+**Tasks:**
+1. Create virtual keys for test agents via LiteLLM UI
+2. Verify pass-through works for all 4 models
+3. Validate fallback chains: saturate MoE  confirm LiteLLM retries Dense  confirm VLM
+4. Run 24-hour shadow: monitor LiteLLM spend logs vs router metrics
+5. Verify GPU health metrics unaffected
+6. Check guardrails not generating false positives
+
+### Phase 2: Cutover (Week 2)  Gradual Agent Migration
+
+**Goal:** Move agents one-by-one to LiteLLM endpoint.
+
+**Tasks:**
+1. Migrate API keys to LiteLLM virtual keys
+2. Create teams: "Core Agents" (enterprise), "Dev Agents" (professional)
+3. Update agent configs one at a time: `OPENAI_API_BASE`  `:4000`
+4. Test each agent individually
+5. Enable SSO via Authentik + custom_sso.py
+6. Keep router :9000 as emergency fallback for 48 hours
+
+### Phase 3: Production Hardening (Week 3+)
+
+**Goal:** Lock down, optimize, monitor.
+
+**Tasks:**
+1. Remove deprecated router endpoints (after all agents migrated)
+2. Add LiteLLM observability (Prometheus, Slack/email alerts)
+3. Enable LiteLLM caching (shared Redis)
+4. Add external model fallbacks for client-facing services
+5. Router slim-down: keep routing/slots/health/perf, remove key management
+6. Multi-tenancy setup for client-facing inference services
+
+---
+
+## 6. Nginx Configuration (with Authentik OIDC Forward Auth)
+
+```nginx
+# OLD (remove)
+# location /admin/ {
+#     proxy_pass http://127.0.0.1:9000/admin/;
+# }
+
+# === Authentik auth subrequest endpoint ===
+location /authentik/auth {
+    internal;
+    proxy_pass https://auth.sysloggh.net/outpost.goauthentik.io/auth/nginx;
+    proxy_pass_request_body off;
+    proxy_set_header Content-Length "";
+    proxy_set_header X-Original-URL $scheme://$http_host$request_uri;
+    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+}
+
+# === LiteLLM Admin UI  Authentik-protected ===
+location /ui/ {
+    auth_request /authentik/auth;
+    auth_request_set $auth_user $upstream_http_x_authentik_username;
+    auth_request_set $auth_email $upstream_http_x_authentik_email;
+
+    proxy_set_header X-Authentik-Username $auth_user;
+    proxy_set_header X-Authentik-Email $auth_email;
+
+    proxy_pass http://127.0.0.1:4000/ui/;
+    proxy_http_version 1.1;
+    proxy_set_header Upgrade $http_upgrade;
+    proxy_set_header Connection "upgrade";
+}
+
+# === LiteLLM SSO callback ===
+location /sso/callback {
+    proxy_pass http://127.0.0.1:4000/sso/callback;
+    proxy_set_header Host $host;
+}
+
+# === API endpoint  Bearer token auth ===
+location /v1/ {
+    proxy_pass http://127.0.0.1:4000/v1/;
+    proxy_set_header Host $host;
+    proxy_read_timeout 600s;
+    error_page 502 = @router_fallback;
+}
+
+location @router_fallback {
+    proxy_pass http://127.0.0.1:9000/v1/;
+    proxy_set_header Host $host;
+}
+
+# === Key management API ===
+location /key/ {
+    proxy_pass http://127.0.0.1:4000/key/;
+    proxy_set_header Host $host;
+    proxy_set_header Authorization $http_authorization;
+}
+
+# Keep router metrics accessible (not behind LiteLLM)
+location /router/ {
+    proxy_pass http://127.0.0.1:9000/;
+}
+
+location /health {
+    proxy_pass http://127.0.0.1:4000/health;
+}
+```
+
+---
+
+## 7. Docker Compose (`docker-compose.yml` on CT 116)
+
+```yaml
+services:
+  # Layer 1: LiteLLM Gateway (Policy & Admin)
+  litellm:
+    image: ghcr.io/berriai/litellm:main-stable
+    network_mode: "host"
+    extra_hosts:
+      - "auth.sysloggh.net:192.168.68.11"
+    volumes:
+      - ./config.yaml:/app/config.yaml:ro
+      - ./custom_sso.py:/app/custom_sso.py:ro
+    environment:
+      - LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY}
+      - DATABASE_URL=postgresql://litellm:***@localhost:5432/litellm
+      - STORE_MODEL_IN_DB=True
+      - ROUTER_API_KEY=${ROUTER_API_KEY}
+      - OPENAI_API_KEY=***
+      - ANTHROPIC_API_KEY=${ANTH...KEY}
+      - PROXY_BASE_URL=https://litellm.sysloggh.net
+    command:
+      - --config
+      - /app/config.yaml
+      - --port
+      - "4000"
+    depends_on:
+      postgres:
+        condition: service_healthy
+    restart: unless-stopped
+
+  postgres:
+    image: postgres:16-alpine
+    network_mode: "host"
+    environment:
+      - POSTGRES_DB=litellm
+      - POSTGRES_USER=litellm
+      - POSTGRES_PASSWORD=${POST...}
+    volumes:
+      - pgdata:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U litellm"]
+      interval: 5s
+      timeout: 3s
+      retries: 5
+    restart: unless-stopped
+
+  nginx:
+    image: nginx:alpine
+    extra_hosts:
+      - "auth.sysloggh.net:192.168.68.11"
+    volumes:
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf:ro
+    ports:
+      - "80:80"
+    restart: unless-stopped
+
+volumes:
+  pgdata:
+```
+
+---
+
+## 8. Risk Mitigation
+
+| Risk | Mitigation |
+|------|------------|
+| LiteLLM adds latency overhead | Shadow mode measures: <50ms extra is acceptable |
+| LiteLLM down = all agents down | NGINX fallback to router :9000 direct (see 6) |
+| Explicit GPU saturated  no fallback available | LiteLLM fallback chains try all 3 GPUs in order before failing |
+| Fallback chain masking real GPU failures | Router returns `saturated: true` only for capacity, `down` returns different error |
+| Key sync drift | Single-source: LiteLLM is key authority. Router uses one `ROUTER_API_KEY` |
+| Spend tracking inaccurate for local GPUs | `model_cost` per GPU with $0 rate; optional symbolic pricing for internal billing |
+| Double rate limiting | Intentional: LiteLLM for per-user caps, Router for hardware protection |
+| PostgreSQL failure | LiteLLM can run with SQLite fallback; UI features degrade |
+| Per-model metrics accuracy with syslog-auto | `syslog-auto` is opaque by design (content-based routing). Explicit models are accurate. Use explicit models for per-GPU billing. |
+
+---
+
+## 9. Success Metrics
+
+| Metric | Before | After |
+|--------|--------|-------|
+| Key management | Manual CLI + env vars + redeploy | UI-based, instant, no redeploy |
+| Spend visibility | None | Per-agent, per-team, per-model $ tracking |
+| Access control | Tier-based (3 levels) | Per-key, per-model, budget-capped |
+| New agent onboarding | Generate key, update env var, redeploy router | Create in UI, share key |
+| Admin UX | curl + JSON responses | Visual dashboard, graphs, search |
+| Audit trail | Router logs (stdout only) | Database-backed with UI search |
+| SSO | None | Authentik OIDC |
+| Budget enforcement | None | Automatic: key suspended at $limit |
+| GPU failover | Silent (inaccurate metrics) | Explicit (LiteLLM fallback chains, per-model logs) |
+| GPU routing intelligence | Full (unchanged) | Full (unchanged) |
+
+---
+
+## 10. Migration Commands (Quick Reference)
+
+```bash
+# On CT 116 (SSH via minipve: pct exec 116 bash):
+
+# Phase 0: Infrastructure Prep
+echo "192.168.68.11 auth.sysloggh.net" >> /etc/hosts
+
+cd /opt/litellm
+docker compose up -d postgres
+# Replace config.yaml with production version (see 4.3)
+docker compose restart litellm
+
+# Verify
+curl http://127.0.0.1:4000/health
+curl -X POST http://127.0.0.1:4000/v1/chat/completions \
+  -H "Authorization: Bearer ***" \
+  -H "Content-Type: application/json" \
+  -d '{"model":"syslog-auto","messages":[{"role":"user","content":"test"}]}'
+```
+
+---
+
+## 11. GitOps Workflow
+
+- `main`  production-ready code
+- `feature/litellm-migration`  current development branch
+
+**Conventional Commits:**
+```
+feat(plan): add fallback chains and strict passthrough for model identity gap
+fix(router): strict passthrough for explicit models  no silent rerouting
+feat(plan): update LiteLLM migration plan for CT 116 deployment with Authentik OIDC
+docs: LiteLLM migration plan  two-layer architecture with model identity gap analysis
+```
+
+---
+
+## 12. Zero-Downtime Migration Strategy
+
+**Per-Agent Cutover (<2 minutes):**
+1. Create LiteLLM virtual key in UI
+2. Update agent's `OPENAI_API_BASE` to `:4000`
+3. Verify routing works
+4. Monitor LiteLLM logs for errors
+
+**Global Rollback:**
+1. If LiteLLM :4000 fails, revert all agents to `:9000`
+2. NGINX `router_fallback` handles automatic failover
+3. Monitor GPU metrics for health checks
+
+---
+
+## Appendix A: Model Identity Gap Analysis (RESOLVED)
+
+### Problem Identified (Abiba, June 14)
+
+The original architecture had a metrics accuracy gap: when an agent requested `qwen3.6-35B-A3B` and MoE was busy, the router silently rerouted to Dense. LiteLLM logged it as MoE usage, corrupting per-model spend/usage tracking.
+
+### Root Cause
+
+```python
+# OLD router code (router-fixed.py):
+if is_gpu_busy(target) and req in allowed:
+    alts = [m for m in avail if m != target and m in allowed]
+    if alts:
+        alt = select_best_gpu(alts, "explicit", agent)
+        if alt: return alt   # silently changed GPU
+```
+
+### Resolution: Strict Passthrough + LiteLLM Fallback Chains
+
+Two changes deployed:
+
+**1. Router strict passthrough:**
+```python
+# NEW: strict mode  no silent fallback
+if req != "auto":
+    target = req if req in avail else avail[0]
+    if req not in avail:
+        return {"model": req, "reason": "explicit_unavailable", "saturated": True}
+    if is_gpu_busy(target):
+        return {"model": target, "reason": "explicit_saturated", "saturated": True}
+    return {"model": target, "reason": "explicit"}
+```
+
+**2. LiteLLM fallback chains (in config.yaml):**
+```yaml
+router_settings:
+  allowed_fails: 100
+  fallbacks:
+    - qwen3.6-35B-A3B: ["qwen3.6-27B-code", "gemma-4-12b"]
+    - qwen3.6-27B-code: ["qwen3.6-35B-A3B", "gemma-4-12b"]
+    - gemma-4-12b: ["qwen3.6-27B-code", "qwen3.6-35B-A3B"]
+```
+
+### Result
+
+| Scenario | Before | After |
+|----------|--------|-------|
+| Agent asks for MoE, MoE available | MoE used, metrics OK | MoE used, metrics OK |
+| Agent asks for MoE, MoE busy | Router  Dense silently, metrics WRONG | Router  503, LiteLLM  Dense, metrics show BOTH attempts |
+| Agent uses syslog-auto | Router picks GPU, LiteLLM sees opaque | Same (syslog-auto is opaque by design) |
+| All 3 GPUs saturated | Router queues (30s), then 503 | Same, LiteLLM sees 503 after fallback chain exhausted |
+
+---
+
+## Appendix B: LiteLLM Virtual Key Migration
+
+| Agent | Old Key | New LiteLLM Key | Tier | Budget |
+|-------|---------|-----------------|------|--------|
+| Abiba | sk-***-*** | sk-litellm-*** | enterprise | $1000 |
+| Mumuni | sk-***-*** | sk-litellm-*** | enterprise | $1000 |
+| Tanko | sk-***-*** | sk-litellm-*** | enterprise | $1000 |
+| Kagenz0 | sk-***-*** | sk-litellm-*** | professional | $500 |
+| Koby | sk-***-*** | sk-litellm-*** | professional | $500 |
+| Koonimo | sk-***-*** | sk-litellm-*** | professional | $500 |
+
+---
+
+## Appendix C: Authentik SSO Integration
+
+**Authentik Provider Setup:**
+1. Create OAuth2 application in Authentik
+2. Set redirect URI: `http://<CT-116-IP>/sso/callback`
+3. Configure `client_id` and `client_secret`
+4. Mount `custom_sso.py` to LiteLLM container
+5. Update config.yaml with provider details
+
+---
+
+## Appendix D: Prometheus Monitoring
+
+**Metrics Export:**
+- LiteLLM metrics  `http://127.0.0.1:4000/metrics`
+- Router metrics  `http://127.0.0.1:9000/metrics`
+- GPU health metrics  `http://127.0.0.1:9000/metrics/gpu`
+
+**Alerts:**
+- GPU health score > 70  alert
+- Circuit breaker trip  alert
+- LiteLLM spend > $100/day  alert
+- LiteLLM latency > 1000ms  alert

README.md

@@ -1,63 +1,75 @@
-# Syslog Harness
+# syslog-harness — Inference API Harness
 
-Operational orchestration layer for Syslog's internal AI agents.
+CT 116 Docker stack for routing local GPU models through a unified OpenAI-compatible API.
 
 ## Architecture
 
 ```
-┌─────────────┐     ┌──────────────┐     ┌─────────────┐
-│  Agent      │────>│  Nginx       │────>│  GPU Pool   │
-│  (Hermes)   │     │  Router      │     │  (MoE/Dense)│
-└─────────────┘     └──────────────┘     └─────────────┘
-                         │
-                         ├──> :8091 Queue Service (Docker)
-                         │
-                         └──> :3001 Dashboard (Docker)
+nginx :80 → router :9000 → GPU backends
+                ├─ qwen3.6-35B-A3B (MoE) @ 192.168.68.15:8080  [2 slots, 262K ctx]
+                ├─ qwen3.6-27B-code (Dense) @ 192.168.68.8:8080  [2 slots, 262K ctx]
+                └─ gemma-4-12b (VLM) @ 192.168.68.110:8080    [2 slots, 262K ctx]
+                                     Total: 6 concurrent slots
+
+LiteLLM :8081 (fallback) | Dashboard :3000 | Redis :6379 (local)
 ```
 
-## Components
-
-| Service | Port | Container | Purpose |
-|---|---|---|---|
-| Nginx Router | 8080 | Host | Routes requests to GPU backends |
-| Queue Service | 8091 | `syslog-queue` | Enqueues requests when GPUs are down |
-| Dashboard | 3001 | `syslog-dashboard` | Observability UI + API |
-
-## GPU Routing
-
-| Header `X-Syslog-Model` | Backend | Model |
-|---|---|---|
-| (none) / `standard` | amdpve (.15) | qwen3.6-35B-A3B (MoE) |
-| `heavy` / `qwen3.5-27B` | llmgpu (.8) | qwen3.5-27B (Dense) |
-| `light` / `gemma-4` | ocu_llm (.110) | gemma-4-E4B (Light) |
-
-## Quick Start
+## Deploy
 
 ```bash
-# Build & start
-docker compose build
+cd /opt/inference-harness
 docker compose up -d
-
-# Verify
-curl http://localhost:8091/health
-curl http://localhost:3001/api/status
 ```
 
-## Dashboard
+## Endpoints
 
-- **UI:** `http://<host>:8080/dashboard/harness.html`
-- **API:** `http://<host>:8080/dashboard/api/status`
+| URL | Purpose |
+|-----|---------|
+| `/v1/chat/completions` | Inference API (OpenAI-compatible) — **API key required** |
+| `/v1/models` | Available models |
+| `/` | Dashboard (GPU health, routing, agents, timeseries) |
 
-## Circuit Breaker
+## Authentication
 
-- Rate limit: 10 req/s per IP
-- Burst: 20 requests
-- Excess returns 503
-- Queue fallback on GPU 502/503
+**All `/v1/chat/completions` requests require a valid API key** via `Authorization: Bearer <key>`. Missing or invalid keys return **401 Unauthorized**.
 
-## Production Migration
+## Agent API Keys
 
-See [MIGRATION_PLAN.md](./MIGRATION_PLAN.md)
+| Agent | Key |
+|-------|-----|
+| Abiba | `sk-syslog-abiba` |
+| Mumuni | `sk-syslog-mumuni` |
+| Tanko | `sk-syslog-tanko` |
+| Koby | `sk-syslog-koby` |
+| Kagenz0 | `sk-syslog-kagenz0` |
+| Koonimo | `sk-syslog-koonimo` |
 
----
-*Built for Syslog Solution LLC — Quality over speed.*
+## Routing Tiers
+
+| Tier | Trigger | Priority |
+|------|---------|----------|
+| Lightweight | No system prompt, ≤1 turn, ≤100 words | VLM → MoE → Dense |
+| Simple Conv | ≤1000 tokens, ≤4 turns | VLM → MoE → Dense |
+| Heavy | >4000 tokens OR >8 turns | Dense → MoE → VLM |
+| Default | Everything else | MoE → VLM → Dense |
+
+## Queue
+
+When all GPUs are saturated, requests enter a polling queue (500ms intervals) instead of returning 503 immediately. Timeout: 30s (configurable via `QUEUE_TIMEOUT` env or `X-Queue-Timeout` header).
+
+## Models
+
+| GPU | Model | VRAM | Slots | Context | Best For |
+|-----|-------|------|-------|
+| Strix Halo | qwen3.6-35B-A3B (MoE) | 65GB | 2 | 262K | General quality |
+| RTX 3090 | qwen3.6-27B-code (Dense) | 24GB | 2 | 262K | Code, reasoning |
+| RTX 5070 | gemma-4-12b (VLM) | 12GB | 2 | 262K | Speed, vision |
+
+## Maintenance
+
+Automated cron job runs daily at 3:00 AM UTC (`/opt/inference-harness/maintenance.sh`):
+- Cleans Redis timeseries keys >60 days
+- Prunes Docker build cache >7 days
+- Logs container health and Redis memory
+
+Logs: `/var/log/harness-maintenance.log`

backups/20260602_103344/dashboard.py.bak

@@ -0,0 +1,356 @@
+"""SyslogAI Harness Dashboard — Modern Design."""
+import os, json, time, queue, threading
+import requests
+from flask import Flask, request, render_template_string, Response, stream_with_context
+
+ROUTER_METRICS = os.environ.get("ROUTER_METRICS_URL", "http://router:9000/metrics")
+app = Flask(__name__)
+sse_subscribers = []; sse_lock = threading.Lock()
+
+def fetch_state():
+    try:
+        r = requests.get(ROUTER_METRICS, timeout=5)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"gpus":[],"route_counts":{},"agent_counts":{},"recent":[],"timestamp":time.time()}
+
+def broadcast_loop():
+    while True:
+        time.sleep(3)
+        data = fetch_state(); payload = json.dumps(data)
+        with sse_lock:
+            dead = [q for q in sse_subscribers if not q.put(payload)]
+            for q in dead: sse_subscribers.remove(q)
+threading.Thread(target=broadcast_loop, daemon=True).start()
+
+DASHBOARD_HTML = r"""<!DOCTYPE html>
+<html lang="en" data-bs-theme="dark">
+<head>
+<meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>SyslogAI Harness</title>
+<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet">
+<style>
+body { background: #0b0f17; color: #bcc3cd; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif; padding: 20px 24px; }
+.card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; height: 100%; }
+.stat-card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; padding: 18px 20px; text-align: center; }
+.stat-value { font-size: 28px; font-weight: 700; line-height: 1.1; }
+.stat-label { font-size: 11px; text-transform: uppercase; letter-spacing: 0.6px; color: #64748b; margin-top: 4px; }
+.gpu-card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; padding: 16px 18px; height: 100%; }
+.gpu-card .title { font-size: 13px; font-weight: 600; color: #e2e8f0; margin-bottom: 12px; display: flex; align-items: center; gap: 8px; }
+.gpu-card .status-dot { width: 8px; height: 8px; border-radius: 50%; flex-shrink: 0; }
+.gpu-card .row-metric { display: flex; justify-content: space-between; font-size: 12px; padding: 2px 0; }
+.gpu-card .row-metric .lbl { color: #64748b; }
+.gpu-card .row-metric .val { color: #e2e8f0; font-variant-numeric: tabular-nums; }
+.gpu-card .slot-bar { display: flex; gap: 3px; margin-top: 8px; }
+.gpu-card .slot-bar .s { flex: 1; height: 5px; border-radius: 2px; background: #1e293b; }
+.gpu-card .slot-bar .s.active { background: #38bdf8; }
+.chart-card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; padding: 16px 18px; height: 100%; display: flex; flex-direction: column; }
+.chart-card .title { font-size: 13px; font-weight: 600; color: #e2e8f0; margin-bottom: 12px; }
+.bar-row { margin-bottom: 8px; }
+.bar-label { display: flex; justify-content: space-between; font-size: 11px; margin-bottom: 3px; color: #64748b; }
+.bar-label .name { color: #cbd5e1; }
+.bar-track { height: 5px; background: #1e293b; border-radius: 3px; overflow: hidden; }
+.bar-fill { height: 100%; border-radius: 3px; transition: width 0.6s ease; }
+.table-custom { font-size: 11px; margin: 0; }
+.table-custom th { color: #64748b; font-weight: 500; font-size: 10px; text-transform: uppercase; border-color: #1e293b; padding: 8px 10px; }
+.table-custom td { color: #94a3b8; border-color: rgba(30,41,59,0.5); padding: 6px 10px; }
+.agent-badge { font-size: 10px; padding: 2px 7px; border-radius: 8px; font-weight: 600; }
+.btn-sm-period { font-size: 10px; padding: 3px 10px; border-radius: 6px; border: 1px solid #1e293b; color: #64748b; background: transparent; cursor: pointer; }
+.btn-sm-period.active { background: #1d4ed8; color: #fff; border-color: #1d4ed8; }
+.ring-label { font-size: 22px; font-weight: 700; }
+.ring-sublabel { font-size: 10px; color: #64748b; }
+</style>
+</head>
+<body>
+
+<!-- HEADER -->
+<div class="d-flex justify-content-between align-items-center mb-4">
+  <div>
+    <h5 class="mb-0 text-white fw-bold">&#x26A1; SyslogAI Harness</h5>
+    <div class="small text-secondary" id="live-indicator">
+      <span class="status-dot" id="live-dot" style="width:6px;height:6px;border-radius:50%;display:inline-block;background:#22c55e;animation:pulse 2s infinite"></span>
+      <span id="connection-status">live</span> &middot; <span id="update-time"></span>
+    </div>
+  </div>
+  <div class="d-flex gap-2">
+    <div class="stat-card" style="min-width:100px"><div class="stat-value text-info" id="kpi-total">0</div><div class="stat-label">Requests</div></div>
+    <div class="stat-card" style="min-width:100px"><div class="stat-value text-warning" id="kpi-active">0</div><div class="stat-label">Active</div></div>
+    <div class="stat-card" style="min-width:100px"><div class="stat-value" style="color:#a78bfa" id="kpi-agents">0</div><div class="stat-label">Agents</div></div>
+  </div>
+</div>
+
+<div class="row g-3 align-items-stretch">
+  <!-- ROW 1: Usage Chart (8) + GPU Metrics (4) -->
+  <div class="col-md-8"><div class="chart-card"><div class="title d-flex justify-content-between align-items-center">
+    <span>Usage Over Time</span>
+    <div class="d-flex gap-1">
+      <button class="btn-sm-period active" onclick="switchPeriod('day')">24h</button>
+      <button class="btn-sm-period" onclick="switchPeriod('week')">7d</button>
+      <button class="btn-sm-period" onclick="switchPeriod('month')">30d</button>
+    </div>
+  </div><div id="timeseries-chart" style="height:150px"></div><div id="timeseries-legend" class="d-flex justify-content-center gap-3 mt-2 flex-wrap small"></div></div></div>
+  <div class="col-md-4"><div class="chart-card"><div class="title">GPU Metrics</div><div id="gpu-metrics-card"></div></div></div>
+
+  <!-- ROW 2: 3 GPU Cards -->
+  <div class="col-md-4"><div class="gpu-card" id="gpu-moe"><div class="text-secondary small">Loading...</div></div></div>
+  <div class="col-md-4"><div class="gpu-card" id="gpu-dense"><div class="text-secondary small">Loading...</div></div></div>
+  <div class="col-md-4"><div class="gpu-card" id="gpu-light"><div class="text-secondary small">Loading...</div></div></div>
+
+  <!-- ROW 3: Queue + Model + Agent -->
+  <div class="col-md-4"><div class="chart-card"><div class="title">Queue Status</div><div class="text-center" id="queue-viz"></div></div></div>
+  <div class="col-md-4"><div class="chart-card"><div class="title">Model Distribution</div><div id="route-bars"></div></div></div>
+  <div class="col-md-4"><div class="chart-card"><div class="title">Agent Activity</div><div id="agent-bars"></div></div></div>
+
+  <!-- ROW 4: Performance Analytics -->
+  <div class="col-12 mb-2"><div class="d-flex align-items-center gap-2"><span class="fw-bold text-white" style="font-size:14px">&#x1F4CA; Performance Analytics</span>
+    <div class="d-flex gap-1 ms-auto">
+      <button class="btn-sm-period active" onclick="switchPerfWindow('1')">1h</button>
+      <button class="btn-sm-period" onclick="switchPerfWindow('24')">24h</button>
+    </div>
+  </div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Latency — P50 / P95 / P99 (ms)</div><div id="perf-latency"></div></div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Throughput — Tokens / sec</div><div id="perf-throughput"></div></div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Routing Effectiveness — by Reason</div><div id="perf-reasons"></div></div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Agent Performance</div><div id="perf-agents"></div></div></div>
+
+  <!-- ROW 5: Latency vs Context Scatter -->
+  <div class="col-12"><div class="chart-card"><div class="title d-flex justify-content-between align-items-center">
+    <span>Latency vs Prompt Size — by Model</span>
+    <div class="d-flex gap-2">
+      <select id="scatter-model" onchange="loadScatter()" style="font-size:10px;background:#1e293b;color:#94a3b8;border:1px solid #334155;border-radius:4px;padding:2px 6px">
+        <option value="all">All Models</option>
+        <option value="qwen3.5-9b-vlm">9B VLM</option>
+        <option value="qwen3.6-27B-code">27B Dense</option>
+        <option value="qwen3.6-35B-A3B">35B MoE</option>
+      </select>
+    </div>
+  </div><div id="scatter-plot" style="height:200px;position:relative"></div><div id="scatter-legend" class="d-flex justify-content-center gap-3 mt-2 flex-wrap small"></div></div></div>
+
+  <!-- ROW 6: Live Stream -->
+  <div class="col-12"><div class="chart-card"><div class="title">Live Stream</div>
+    <div class="table-responsive"><table class="table table-custom mb-0">
+      <thead><tr><th>Time</th><th>Agent</th><th>Model</th><th>Reason</th><th>Tier</th></tr></thead>
+      <tbody id="route-tbody"></tbody>
+    </table></div>
+  </div></div>
+</div>
+
+<script>
+var MC={'qwen3.5-9b-vlm':'#22c55e','qwen3.6-27B-code':'#f59e0b','qwen3.6-35B-A3B':'#a78bfa'};
+var ML={'qwen3.5-9b-vlm':'Qwen3.5 9B VLM','qwen3.6-27B-code':'Qwen Code','qwen3.6-35B-A3B':'Qwen MoE'};
+var GL={'qwen3.6-35B-A3B':'MoE - Strix Halo','qwen3.6-27B-code':'Dense - RTX 3090','qwen3.5-9b-vlm':'VLM - RTX 5070'};
+function $(id){return document.getElementById(id);}
+
+function render(data){
+if(!data||!data.gpus)return;
+var t=Object.values(data.route_counts||{}).reduce((a,b)=>a+b,0);
+var ta=0,tm=0;data.gpus.forEach(function(g){ta+=(g.active_requests||0);tm+=(g.max_concurrent||1)});
+$('kpi-total').textContent=t;$('kpi-active').textContent=ta+'/'+tm;$('kpi-agents').textContent=Object.keys(data.agent_counts||{}).length;
+$('update-time').textContent=new Date().toLocaleTimeString();
+var ids={'qwen3.6-35B-A3B':'gpu-moe','qwen3.6-27B-code':'gpu-dense','qwen3.5-9b-vlm':'gpu-light'};
+data.gpus.forEach(function(g){
+var el=$(ids[g.id]);if(!el)return;
+var a=g.active_requests||0,mx=g.max_concurrent||1;
+var sc=g.status==='healthy'?'#22c55e':g.status==='saturated'?'#f59e0b':'#ef4444';
+var ss=g.status==='healthy'?'Online':g.status==='saturated'?'Busy':'Offline';
+var slots='';for(var i=0;i<mx;i++)slots+='<span class=\"s'+(i<a?' active':'')+'\"></span>';
+var h='<div class=\"title\"><span class=\"status-dot\" style=\"background:'+sc+'\"></span>'+GL[g.id]+'<span class=\"ms-auto small\" style=\"color:'+sc+'\">'+ss+'</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">VRAM</span><span class=\"val\">'+g.vram_used_mb+' / '+g.vram_total_mb+' MB</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">Utilization</span><span class=\"val\">'+g.gpu_util_pct+'%</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">Temperature</span><span class=\"val\" style=\"color:'+(g.temp_c>85?'#ef4444':g.temp_c>70?'#f59e0b':'#22c55e')+'\">'+g.temp_c+'C</span></div>';
+if(g.power_w)h+='<div class=\"row-metric\"><span class=\"lbl\">Power</span><span class=\"val\">'+g.power_w+'W'+(g.power_limit_w?'/'+g.power_limit_w+'W':'')+'</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">Slots</span><span class=\"val\" style=\"color:'+(a>=mx?'#ef4444':'#e2e8f0')+'\">'+a+' / '+mx+'</span></div>';
+h+='<div class=\"slot-bar\">'+slots+'</div>';el.innerHTML=h;
+});
+renderQueue(data);renderGPUMetrics(data);
+var rc=data.route_counts||{},mr=Math.max(1,...Object.values(rc));
+$('route-bars').innerHTML=Object.entries(rc).length?Object.entries(rc).sort((a,b)=>b[1]-a[1]).map(function(e){var m=e[0],c=e[1];return'<div class=\"bar-row\"><div class=\"bar-label\"><span class=\"name\">'+(ML[m]||m)+'</span><span>'+c+' ('+(t?Math.round(c/t*100):0)+'%)</span></div><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:'+(c/mr*100)+'%;background:'+(MC[m]||'#38bdf8')+'\"></div></div></div>';}).join(''):'<div class=\"text-secondary small\">-</div>';
+var ac=data.agent_counts||{},ma=Math.max(1,...Object.values(ac));
+$('agent-bars').innerHTML=Object.entries(ac).length?Object.entries(ac).sort((a,b)=>b[1]-a[1]).map(function(e){return'<div class=\"bar-row\"><div class=\"bar-label\"><span class=\"name\">'+e[0]+'</span><span>'+e[1]+'</span></div><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:'+(e[1]/ma*100)+'%;background:#38bdf8\"></div></div></div>';}).join(''):'<div class=\"text-secondary small\">-</div>';
+var recent=data.recent||[];
+$('route-tbody').innerHTML=recent.length?recent.slice(0,20).map(function(r){var d=new Date(r.ts*1000),ag=r.agent||'?';return'<tr><td class=\"text-secondary\">'+d.toLocaleTimeString()+'</td><td><span class=\"agent-badge\" style=\"background:rgba(56,189,248,0.12);color:#38bdf8\">'+ag+'</span></td><td>'+(ML[r.model]||r.model)+'</td><td class=\"text-secondary\">'+(r.reason||'')+'</td><td class=\"text-uppercase\" style=\"font-size:10px;color:'+(r.tier==='enterprise'?'#a78bfa':'#64748b')+'\">'+(r.tier||'')+'</td></tr>';}).join(''):'<tr><td colspan=\"5\" class=\"text-secondary\">Waiting...</td></tr>';
+}
+
+function renderQueue(data){
+var el=$('queue-viz');if(!el)return;
+var ta=0,tm=0;data.gpus.forEach(function(g){ta+=(g.active_requests||0);tm+=(g.max_concurrent||1)});
+var pct=tm>0?Math.round(ta/tm*100):0,st=pct>=100?'SATURATED':pct>=50?'BUSY':'IDLE';
+var sc=pct>=100?'#ef4444':pct>=50?'#f59e0b':'#22c55e';
+var circ=188.5,dash=(pct/100)*circ;
+var h='<div class=\"d-inline-block position-relative mb-2\"><svg width=\"72\" height=\"72\"><circle cx=\"36\" cy=\"36\" r=\"30\" fill=\"none\" stroke=\"#1e293b\" stroke-width=\"6\"/><circle cx=\"36\" cy=\"36\" r=\"30\" fill=\"none\" stroke=\"'+sc+'\" stroke-width=\"6\" stroke-dasharray=\"'+dash+' '+(circ-dash)+'\" stroke-linecap=\"round\" transform=\"rotate(-90 36 36)\"/></svg><div style=\"position:absolute;top:50%;left:50%;transform:translate(-50%,-50%);text-align:center\"><div class=\"ring-label\" style=\"color:'+sc+'\">'+ta+'</div><div class=\"ring-sublabel\">/ '+tm+' slots</div></div></div>';
+h+='<div class=\"fw-bold mb-2 small\" style=\"color:'+sc+'\">'+st+'</div>';
+var lb={'qwen3.6-35B-A3B':'MoE','qwen3.6-27B-code':'Dense','qwen3.5-9b-vlm':'VLM'};
+data.gpus.forEach(function(g){var a=g.active_requests||0,mx=g.max_concurrent||1,gp=mx>0?Math.round(a/mx*100):0;h+='<div class=\"d-flex align-items-center gap-2 mb-1 justify-content-center\"><span class=\"small\" style=\"min-width:32px;text-align:right;font-size:10px\">'+(lb[g.id]||g.id)+'</span><div style=\"flex:1;max-width:70px;height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+gp+'%;background:'+sc+';border-radius:2px\"></div></div><span class=\"small\" style=\"min-width:22px;font-size:10px\">'+a+'/'+mx+'</span></div>'});
+el.innerHTML=h;
+}
+
+function renderGPUMetrics(data){
+var el=$('gpu-metrics-card');if(!el)return;
+var lb={'qwen3.6-35B-A3B':'MoE','qwen3.6-27B-code':'Dense','qwen3.5-9b-vlm':'VLM'};
+var h='';data.gpus.forEach(function(g){
+var nm=lb[g.id]||g.id,tp=g.temp_c||0,ut=g.gpu_util_pct||0,pw=g.power_w||0,pl=g.power_limit_w||0;
+var tc=tp>85?'#ef4444':tp>70?'#f59e0b':'#22c55e',uc=ut>90?'#ef4444':ut>70?'#f59e0b':'#22c55e';
+h+='<div class=\"mb-3\"><div class=\"fw-bold small text-white-50 mb-1\">'+nm+'</div>';
+h+='<div class=\"d-flex align-items-center gap-2 mb-1\"><span class=\"small text-secondary\" style=\"min-width:30px\">T</span><div class=\"flex-grow-1\" style=\"height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+Math.min(tp,100)+'%;background:'+tc+';border-radius:2px\"></div></div><span class=\"small\" style=\"color:'+tc+';min-width:30px;text-align:right\">'+tp+'C</span></div>';
+h+='<div class=\"d-flex align-items-center gap-2 mb-1\"><span class=\"small text-secondary\" style=\"min-width:30px\">U</span><div class=\"flex-grow-1\" style=\"height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+ut+'%;background:'+uc+';border-radius:2px\"></div></div><span class=\"small\" style=\"color:'+uc+';min-width:30px;text-align:right\">'+ut+'%</span></div>';
+if(pw>0){var pp=pl>0?Math.round(pw/pl*100):0,pc=pp>90?'#ef4444':pp>70?'#f59e0b':'#22c55e';h+='<div class=\"d-flex align-items-center gap-2\"><span class=\"small text-secondary\" style=\"min-width:30px\">P</span><div class=\"flex-grow-1\" style=\"height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+pp+'%;background:'+pc+';border-radius:2px\"></div></div><span class=\"small\" style=\"color:'+pc+';min-width:30px;text-align:right\">'+pw+'W</span></div>';}
+h+='</div>';});
+el.innerHTML=h;
+}
+
+var cp='day';
+function switchPeriod(p){cp=p;document.querySelectorAll('.btn-sm-period').forEach(function(b){b.classList.remove('active')});event.target.classList.add('active');loadTS();}
+function loadTS(){fetch('/api/timeseries?period='+cp).then(function(r){return r.json()}).then(renderTS).catch(function(){})}
+function renderTS(d){
+var models=d.models||{},labels=d.labels||[];
+if(!labels.length)return;
+var cn=$('timeseries-chart'),lg=$('timeseries-legend'),mn=Object.keys(models);
+if(!mn.length){cn.innerHTML='<div class=\"text-secondary small text-center py-4\">-</div>';return;}
+var mv=1;for(var m in models)for(var i=0;i<models[m].length;i++)if(models[m][i]>mv)mv=models[m][i];mv=Math.ceil(mv*1.15)||1;
+var W=labels.length>1?100/(labels.length-1):100,H=130;
+var paths='';for(var mi=0;mi<mn.length;mi++){var m=mn[mi],vals=models[m]||[],d='';for(var i=0;i<vals.length;i++){var x=i*W,y=H-(vals[i]/mv)*H;d+=(i===0?'M':'L')+x.toFixed(1)+','+y.toFixed(1)+' ';}paths+='<path d=\"'+d+'\" fill=\"none\" stroke=\"'+(MC[m]||'#38bdf8')+'\" stroke-width=\"2\" stroke-linecap=\"round\" opacity=\"0.8\"/>';}
+var grid='';for(var g=0;g<=4;g++){var y=(g/4)*H;grid+='<line x1=\"0\" y1=\"'+y.toFixed(1)+'\" x2=\"100\" y2=\"'+y.toFixed(1)+'\" stroke=\"#1e293b\" stroke-width=\"1\"/>';}
+cn.innerHTML='<svg viewBox=\"0 0 100 '+(H+16)+'\" style=\"width:100%;height:'+(H+20)+'px;display:block\" preserveAspectRatio=\"none\">'+grid+paths+'</svg>';
+lg.innerHTML=mn.map(function(m){return'<span class=\"d-flex align-items-center gap-1\"><svg width=\"14\" height=\"8\"><line x1=\"0\" y1=\"4\" x2=\"14\" y2=\"4\" stroke=\"'+(MC[m]||'#38bdf8')+'\" stroke-width=\"2\"/></svg>'+(ML[m]||m)+'</span>';}).join('');
+}
+var perfWindow='24';
+function switchPerfWindow(w){perfWindow=w;document.querySelectorAll('.btn-sm-period').forEach(function(b,i){if(i>=4)b.classList.toggle('active',b.textContent.trim().replace('h','')===w)});loadPerf();}
+function loadPerf(){fetch('/api/performance?window='+perfWindow).then(function(r){return r.json()}).then(renderPerf).catch(function(){})}
+function renderPerf(d){
+var models=d.models||[],reasons=d.reasons||[],agents=d.agents||[],sum=d.summary||{};
+// Latency bars: p50/p95/p99 per model
+var mlab={'qwen3.6-35B-A3B':'35B MoE','qwen3.6-27B-code':'27B Dense','qwen3.5-9b-vlm':'9B VLM'};
+var mcol={'qwen3.6-35B-A3B':'#a78bfa','qwen3.6-27B-code':'#f59e0b','qwen3.5-9b-vlm':'#22c55e'};
+if(!models.length){$('perf-latency').innerHTML='<div class="text-secondary small text-center py-4">Accumulating data...</div>';return;}
+var maxLat=Math.max(...models.map(function(m){return m.latency.p99||0}),1);
+var latHTML=models.map(function(m){
+var l=m.latency||{},p50=l.p50||0,p95=l.p95||0,p99=l.p99||0,c=mcol[m.model]||'#38bdf8';
+return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+mlab[m.model]+'</span><span class="text-secondary">'+m.count+' reqs</span></div>'+
+'<div class="d-flex align-items-center gap-2 mb-1"><span class="text-secondary" style="min-width:28px">p50</span><div class="flex-grow-1" style="height:14px;background:#1e293b;border-radius:4px;overflow:hidden;position:relative"><div style="position:absolute;left:0;top:0;height:100%;width:'+(p50/maxLat*100)+'%;background:'+c+';opacity:0.3;border-radius:4px"></div><div style="position:absolute;left:0;top:0;height:100%;width:'+(p95/maxLat*100)+'%;background:'+c+';opacity:0.5;border-radius:4px"></div><div style="position:absolute;left:0;top:0;height:100%;width:'+(p99/maxLat*100)+'%;background:'+c+';border-radius:4px"></div></div><span style="color:'+c+';min-width:48px;text-align:right;font-variant-numeric:tabular-nums">'+p99+'ms</span></div>'+
+'<div class="d-flex gap-3" style="font-size:10px;color:#64748b;padding-left:32px"><span>p50: '+p50+'ms</span><span>p95: '+p95+'ms</span><span>p99: '+p99+'ms</span></div></div>';
+}).join('');
+$('perf-latency').innerHTML=latHTML;
+// Throughput comparison
+var maxTps=Math.max(...models.map(function(m){return m.throughput.avg_tokens_per_sec||0}),1);
+var tpsHTML=models.map(function(m){
+var t=m.throughput||{},avg=t.avg_tokens_per_sec||0,p50=t.p50||0,c=mcol[m.model]||'#38bdf8';
+var isAllStreaming = avg===0 && p50===0;
+if(isAllStreaming){
+return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+mlab[m.model]+'</span><span style="color:#64748b;font-style:italic">streaming only</span></div><div class="text-secondary" style="font-size:10px">t/s available for non-streaming requests only</div></div>';
+}
+return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+mlab[m.model]+'</span><span style="color:'+c+'" class="fw-bold">'+avg+' tok/s</span></div>'+
+'<div class="d-flex align-items-center gap-2"><span class="text-secondary" style="min-width:28px">avg</span><div class="flex-grow-1" style="height:6px;background:#1e293b;border-radius:3px;overflow:hidden"><div style="height:100%;width:'+(Math.max(avg/maxTps*100,6))+'%;background:'+c+';border-radius:3px"></div></div><span class="small" style="color:'+c+';min-width:54px;text-align:right">'+avg+' tok/s</span></div>'+
+'<div class="d-flex align-items-center gap-2 mt-1"><span class="text-secondary" style="min-width:28px;font-size:10px">p50</span><div class="flex-grow-1" style="height:4px;background:#1e293b;border-radius:2px;overflow:hidden"><div style="height:100%;width:'+(Math.max(p50/maxTps*100,4))+'%;background:'+c+';opacity:0.5;border-radius:2px"></div></div><span style="font-size:10px;color:#64748b">'+p50+' tok/s</span></div></div>';
+}).join('');
+$('perf-throughput').innerHTML=tpsHTML;
+// Routing reasons table
+if(reasons.length){
+var rHTML='<table class="table table-custom mb-0"><thead><tr><th>Reason</th><th>Count</th><th>Avg Lat</th><th>P95 Lat</th></tr></thead><tbody>';
+reasons.forEach(function(r){rHTML+='<tr><td>'+r.reason+'</td><td>'+r.count+'</td><td>'+r.avg_total_ms+'ms</td><td>'+r.p95_total_ms+'ms</td></tr>';});
+rHTML+='</tbody></table>';$('perf-reasons').innerHTML=rHTML;
+}else{$('perf-reasons').innerHTML='<div class="text-secondary small text-center py-3">-</div>';}
+// Agent performance
+if(agents.length){
+var maxAc=Math.max(...agents.map(function(a){return a.count||0}),1);
+var aHTML=agents.map(function(a){return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+a.agent+'</span><span class="text-secondary">'+a.count+' reqs</span></div><div class="d-flex align-items-center gap-2"><div class="flex-grow-1" style="height:4px;background:#1e293b;border-radius:2px;overflow:hidden"><div style="height:100%;width:'+(a.count/maxAc*100)+'%;background:#38bdf8;border-radius:2px"></div></div><span class="small" style="color:#38bdf8;min-width:60px;text-align:right">'+a.avg_total_ms+'ms avg</span></div></div>';}).join('');
+$('perf-agents').innerHTML=aHTML;
+}else{$('perf-agents').innerHTML='<div class="text-secondary small text-center py-3">-</div>';}
+}
+function poll(){fetch('/api/state').then(function(r){return r.json()}).then(function(data){render(data);$('connection-status').textContent='live';}).catch(function(){$('connection-status').textContent='reconnecting';});}
+function loadScatter(){
+var m=$('scatter-model').value;
+fetch('/api/scatter?window=24&model='+m).then(function(r){return r.json()}).then(renderScatter).catch(function(){});
+}
+function renderScatter(d){
+var pts=d.points||[],el=$('scatter-plot'),lg=$('scatter-legend');
+if(!pts.length){el.innerHTML='<div class="text-secondary small text-center py-5">No data yet</div>';return;}
+var mcol={'qwen3.6-35B-A3B':'#a78bfa','qwen3.6-27B-code':'#f59e0b','qwen3.5-9b-vlm':'#22c55e','unknown':'#38bdf8'};
+var mlab={'qwen3.6-35B-A3B':'35B MoE','qwen3.6-27B-code':'27B Dense','qwen3.5-9b-vlm':'9B VLM'};
+var maxX=Math.max.apply(null,pts.map(function(p){return p.prompt_tokens||0}))||1000;
+var maxY=Math.max.apply(null,pts.map(function(p){return p.inference_ms||0}))||5000;
+// Log scale for X axis (prompt tokens vary widely)
+var toX=function(t){return Math.log10(Math.max(t,1))/Math.log10(Math.max(maxX,10))*100;};
+var toY=function(t){return (t/maxY)*100;};
+var dots='';
+pts.forEach(function(p){
+var x=toX(p.prompt_tokens),y=toY(p.inference_ms),c=mcol[p.model]||'#38bdf8';
+var r=p.stream?1.5:2.5,o=p.stream?0.4:0.8;
+dots+='<circle cx="'+x+'" cy="'+(100-y)+'" r="'+r+'" fill="'+c+'" opacity="'+o+'"><title>'+mlab[p.model]+' | '+p.prompt_tokens+' tok | '+p.inference_ms+'ms | '+p.agent+'</title></circle>';
+});
+// Grid lines
+var grid='';
+for(var i=1;i<=4;i++){grid+='<line x1="0" y1="'+(i*20)+'" x2="100" y2="'+(i*20)+'" stroke="#1e293b" stroke-width="0.5"/>';}
+for(var i=1;i<=4;i++){grid+='<line x1="'+(i*20)+'" y1="0" x2="'+(i*20)+'" y2="100" stroke="#1e293b" stroke-width="0.5"/>';}
+// Axis labels
+var xTicks='';
+var xVals=[10,100,1000,10000,100000];
+xVals.forEach(function(v){if(v<=maxX)xTicks+='<text x="'+toX(v)+'" y="103" text-anchor="middle" font-size="8" fill="#64748b">'+(v>=1000?(v/1000)+'k':v)+'</text>';});
+var yTicks='';
+var yVals=[500,1000,5000,10000,50000,100000];
+yVals.forEach(function(v){if(v<=maxY)yTicks+='<text x="-2" y="'+(97-toY(v))+'" text-anchor="end" font-size="8" fill="#64748b">'+(v>=1000?(v/1000)+'s':v+'ms')+'</text>';});
+el.innerHTML='<svg viewBox="-35 0 140 115" style="width:100%;height:200px">'+grid+dots+xTicks+yTicks+'<text x="50" y="112" text-anchor="middle" font-size="9" fill="#475569">Prompt Tokens (log scale)</text><text x="-38" y="50" text-anchor="middle" font-size="9" fill="#475569" transform="rotate(-90,-38,50)">Inference Time</text></svg>';
+// Legend
+var models=[];pts.forEach(function(p){if(models.indexOf(p.model)===-1)models.push(p.model);});
+lg.innerHTML=models.map(function(m){return'<span class="d-flex align-items-center gap-1 small"><svg width="10" height="10"><circle cx="5" cy="5" r="3.5" fill="'+(mcol[m]||'#38bdf8')+'"/></svg>'+mlab[m]+'</span>';}).join('');
+}
+poll();setInterval(poll,3000);loadTS();loadPerf();setInterval(loadPerf,15000);loadScatter();setInterval(loadScatter,30000);
+</script>
+</body>
+</html>"""
+
+@app.route("/") 
+def dashboard(): return render_template_string(DASHBOARD_HTML)
+
+@app.route("/api/state")
+def api_state(): return fetch_state()
+
+@app.route("/api/scatter")
+def api_scatter():
+    window = request.args.get("window", "24")
+    model = request.args.get("model", "all")
+    try:
+        r = requests.get(f"http://router:9000/metrics/scatter?window={window}&model={model}", timeout=10)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"points": [], "count": 0}
+
+@app.route("/api/performance")
+def api_performance():
+    window = request.args.get("window", "24")
+    model = request.args.get("model", "all")
+    try:
+        r = requests.get(f"http://router:9000/metrics/performance?window={window}&model={model}", timeout=10)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"models": [], "reasons": [], "agents": [], "summary": {"total_requests": 0}}
+
+@app.route("/api/timeseries")
+def api_timeseries():
+    period = request.args.get("period", "day")
+    try:
+        r = requests.get("http://router:9000/metrics/timeseries?period=" + period, timeout=5)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"models": {}, "labels": []}
+
+@app.route("/api/stream")
+def api_stream():
+    def ev():
+        q = queue.Queue()
+        with sse_lock: sse_subscribers.append(q)
+        try:
+            yield "data: "+json.dumps(fetch_state())+"\n\n"
+            while True:
+                try: msg = q.get(timeout=3); yield "data: "+msg+"\n\n"
+                except queue.Empty: yield "data: "+json.dumps(fetch_state())+"\n\n"
+        except GeneratorExit: pass
+        finally:
+            with sse_lock:
+                if q in sse_subscribers: sse_subscribers.remove(q)
+    return Response(stream_with_context(ev()), mimetype="text/event-stream", headers={"Cache-Control":"no-cache","X-Accel-Buffering":"no","Access-Control-Allow-Origin":"*"})
+
+@app.route("/health")
+def health(): return {"status":"healthy","service":"harness-dashboard"}
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=3000, debug=False)

backups/20260602_103344/router.py.bak

@@ -0,0 +1,659 @@
+import os, json, time, logging, traceback, threading, queue, statistics, math
+import requests, redis
+from flask import Flask, request, jsonify, Response, stream_with_context
+
+REDIS_URL = os.environ.get("REDIS_URL", "redis://redis:6379")
+GPU_MOE_URL = os.environ.get("GPU_MOE_URL", "http://192.168.68.15:8080/v1")
+GPU_DENSE_URL = os.environ.get("GPU_DENSE_URL", "http://192.168.68.8:8080/v1")
+GPU_LIGHT_URL = os.environ.get("GPU_LIGHT_URL", "http://192.168.68.110:8080/v1")
+
+GPU_SIDECARS = {
+    "qwen3.6-35B-A3B": "http://192.168.68.15:8090",
+    "qwen3.6-27B-code": "http://192.168.68.8:8090",
+    "qwen3.5-9b-vlm": "http://192.168.68.110:8090",
+}
+GPU_URLS = {
+    "qwen3.6-35B-A3B": GPU_MOE_URL,
+    "qwen3.6-27B-code": GPU_DENSE_URL,
+    "qwen3.5-9b-vlm": GPU_LIGHT_URL,
+}
+# Max concurrent requests per GPU (based on llama.cpp --parallel)
+GPU_MAX_CONCURRENT = {
+    "qwen3.6-35B-A3B": 2,   # 2 slots (cross-agent spread prevents overheating)
+    "qwen3.6-27B-code": 2,  # 2 slots (128K context frees VRAM)
+    "qwen3.5-9b-vlm": 2,       # 2 slots (12GB VRAM, 4GB headroom)
+}
+
+# Context window sizes (tokens) — used for compaction signals
+GPU_CONTEXT = {
+    "qwen3.6-35B-A3B": 262144,
+    "qwen3.6-27B-code": 131072,
+    "qwen3.5-9b-vlm": 262144,
+}
+
+TIER_MODELS = {
+    "starter": ["qwen3.5-9b-vlm"],
+    "professional": ["qwen3.6-35B-A3B", "qwen3.6-27B-code", "qwen3.5-9b-vlm"],
+    "enterprise": ["qwen3.6-35B-A3B", "qwen3.6-27B-code", "qwen3.5-9b-vlm"],
+}
+API_KEYS = {
+    "sk-syslog-local-master-key": {"tier": "enterprise", "agent": "admin"},
+    "sk-syslog-abiba": {"tier": "enterprise", "agent": "Abiba"},
+    "sk-syslog-mumuni": {"tier": "enterprise", "agent": "Mumuni"},
+    "sk-syslog-tanko": {"tier": "enterprise", "agent": "Tanko"},
+    "sk-syslog-koby": {"tier": "enterprise", "agent": "Koby"},
+    "sk-syslog-kagenz0": {"tier": "enterprise", "agent": "Kagenz0"},
+    "sk-syslog-koonimo": {"tier": "enterprise", "agent": "Koonimo"},
+    "sk-starter-abc123": {"tier": "starter", "agent": "test-starter"},
+    "sk-professional-xyz789": {"tier": "professional", "agent": "test-pro"},
+}
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [ROUTER] %(levelname)s %(message)s")
+log = logging.getLogger("router")
+try: r = redis.from_url(REDIS_URL, decode_responses=True); r.ping()
+except Exception: r = None
+
+
+def counter_audit_loop():
+    """Every 30s, check GPU slots and reset counters if all slots idle."""
+    while True:
+        time.sleep(30)
+        if not r: continue
+        for model, url in GPU_URLS.items():
+            try:
+                resp = requests.get(url.replace("/v1","") + "/slots",
+                    headers={"Authorization": "Bearer not-needed"}, timeout=5)
+                if resp.status_code == 200:
+                    slots = resp.json()
+                    all_idle = all(not s.get("is_processing", False) for s in slots)
+                    if all_idle:
+                        current = int(r.get("active:" + model) or 0)
+                        if current > 0:
+                            r.set("active:" + model, 0)
+                            log.info("AUDIT: Reset stuck counter for %s (was %d)", model, current)
+            except Exception:
+                pass
+
+threading.Thread(target=counter_audit_loop, daemon=True).start()
+
+app = Flask(__name__)
+sse_subscribers = []; sse_lock = threading.Lock()
+
+def gpu_active_count(model):
+    """Get number of in-flight requests for a GPU."""
+    if r:
+        return int(r.get("active:" + model) or 0)
+    return 0
+
+def gpu_incr(model):
+    if r: r.incr("active:" + model)
+
+def gpu_decr(model):
+    if r:
+        v = r.decr("active:" + model)
+        if v and int(v) < 0:
+            r.set("active:" + model, 0)  # never go negative
+
+def check_gpu_health(model, sidecar_timeout=5, gpu_timeout=3):
+    url = GPU_SIDECARS.get(model)
+    if not url: return {"status": "unknown"}
+    try:
+        resp = requests.get(url, timeout=sidecar_timeout)
+        if resp.status_code == 200:
+            d = resp.json()
+            pct = (d.get("vram_used_mb",0) / max(d.get("vram_total_mb",1), 1)) * 100
+            status = "healthy"  # VRAM usage != saturation; busy slots handled by is_gpu_busy()
+            vram_warning = pct >= 95
+            # Also check if llama.cpp endpoint is actually responding
+            gpu_url = GPU_URLS.get(model, "")
+            try:
+                hr = requests.get(gpu_url.replace("/v1","") + "/health", headers={"Authorization": "Bearer not-needed"}, timeout=gpu_timeout)
+                if hr.status_code != 200:
+                    status = "down"
+            except Exception:
+                status = "down"
+            return {"status": status, "vram_warning": vram_warning, "vram_used_mb": d.get("vram_used_mb"), "vram_total_mb": d.get("vram_total_mb"), "vram_pct": round(pct,1), "temp_c": d.get("temp_c"), "gpu_util_pct": d.get("gpu_util_pct"), "gpu_name": d.get("gpu_name"), "power_w": d.get("power_w"), "power_limit_w": d.get("power_limit_w")}
+    except Exception: pass
+    return {"status": "down"}
+
+def available_models(): return [m for m in GPU_URLS if check_gpu_health(m)["status"] in ("healthy","saturated")]
+
+def estimate_tokens(msgs):
+    """Estimate token count from messages. Uses JSON length / 3.5 (closer to real tokenizer ratios for dense text)."""
+    return len(json.dumps(msgs, default=str)) // 3.5
+
+def store_perf_record(model, agent, tier, reason, queue_ms, inference_ms, prompt_tokens, completion_tokens, stream):
+    """Store detailed performance record in Redis for analytics."""
+    if not r: return
+    try:
+        total_ms = queue_ms + inference_ms
+        tps = completion_tokens / (inference_ms / 1000) if inference_ms > 0 and completion_tokens > 0 else 0
+        rec = json.dumps({
+            "ts": time.time(),
+            "model": model, "agent": agent, "tier": tier, "reason": reason,
+            "queue_ms": round(queue_ms, 1),
+            "inference_ms": round(inference_ms, 1),
+            "total_ms": round(total_ms, 1),
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "tokens_per_sec": round(tps, 1),
+            "stream": stream
+        })
+        # Global recent list (last 500)
+        r.lpush("perf:recent", rec)
+        r.ltrim("perf:recent", 0, 499)
+        # Per-model list (last 200)
+        r.lpush("perf:model:" + model, rec)
+        r.ltrim("perf:model:" + model, 0, 199)
+        # Per-reason list (last 200)
+        r.lpush("perf:reason:" + reason, rec)
+        r.ltrim("perf:reason:" + reason, 0, 199)
+        # Per-agent list (last 200)
+        r.lpush("perf:agent:" + agent, rec)
+        r.ltrim("perf:agent:" + agent, 0, 199)
+    except Exception:
+        pass
+
+def is_gpu_busy(model):
+    """Check if GPU is at or near max concurrent capacity."""
+    active = gpu_active_count(model)
+    max_c = GPU_MAX_CONCURRENT.get(model, 1)
+    return active >= max_c
+
+def select_best_gpu(candidates, reason, agent=""):
+    """Pick best GPU, spreading agents across GPUs to prevent hotspots."""
+    # Count how many distinct agents are on each GPU
+    gpu_agent_counts = {}
+    if r:
+        for m in GPU_URLS:
+            count = 0
+            for ak in API_KEYS.values():
+                if r.get("agent_gpu:" + ak["agent"] + ":" + m):
+                    count += 1
+            gpu_agent_counts[m] = count
+    # First pass: prefer GPUs with 0 other agents (fresh GPU for this agent)
+    for m in candidates:
+        if not is_gpu_busy(m) and gpu_agent_counts.get(m, 0) == 0:
+            return {"model": m, "reason": reason}
+    # Second pass: prefer GPU this agent is NOT already on (skip own GPU)
+    if agent:
+        for m in candidates:
+            if not is_gpu_busy(m) and not r.get("agent_gpu:" + agent + ":" + m):
+                return {"model": m, "reason": reason}
+    # Third pass: any non-busy GPU
+    for m in candidates:
+        if not is_gpu_busy(m):
+            return {"model": m, "reason": reason}
+    # All busy — pick least loaded
+    best = None
+    best_load = 999
+    for m in candidates:
+        load = gpu_active_count(m)
+        if load < best_load:
+            best_load = load
+            best = m
+    if best:
+        return {"model": best, "reason": "load_balanced_" + reason}
+    return None
+
+def route(rd, tier, agent=""):
+    msgs = rd.get("messages",[]); t = estimate_tokens(msgs)
+    sys = any(m.get("role")=="system" for m in msgs)
+    turns = len([m for m in msgs if m.get("role") in ("user","assistant")])
+    hints = rd.get("routing_hints",{})
+    allowed = TIER_MODELS.get(tier, ["qwen3.5-9b-vlm"])
+    avail = [m for m in available_models() if m in allowed]
+    if not avail: return {"model": allowed[0], "reason": "all_saturated", "saturated": True}
+    # Check if all available GPUs are at max capacity
+    if all(is_gpu_busy(m) for m in avail):
+        return {"model": avail[0], "reason": "all_saturated", "saturated": True}
+    
+    req = rd.get("model","auto")
+    if req != "auto":
+        target = req if req in avail else avail[0]
+        # If explicit model is busy, check if another can take it
+        if is_gpu_busy(target) and req in allowed:
+            alts = [m for m in avail if m != target and m in allowed]
+            if alts:
+                alt = select_best_gpu(alts, "explicit", agent)
+                if alt: return alt
+        return {"model": target, "reason": "explicit"}
+    
+    if hints:
+        if hints.get("priority")=="speed" and "qwen3.5-9b-vlm" in avail:
+            return select_best_gpu(["qwen3.5-9b-vlm"], "hint_speed", agent) or {"model":"qwen3.5-9b-vlm","reason":"hint_speed"}
+        if hints.get("priority")=="quality" and "qwen3.6-35B-A3B" in avail:
+            return select_best_gpu(["qwen3.6-35B-A3B"], "hint_quality", agent) or {"model":"qwen3.6-35B-A3B","reason":"hint_quality"}
+    
+    first_msg = msgs[0].get("content","") if msgs else ""
+    words = len(first_msg.split()) if isinstance(first_msg, str) else 99
+    
+    # TIER 1: Lightweight — single-turn short queries → VLM (fastest)
+    if not sys and turns <= 1 and t <= 500 and words <= 100 and "qwen3.5-9b-vlm" in avail:
+        if not is_gpu_busy("qwen3.5-9b-vlm"):
+            return {"model":"qwen3.5-9b-vlm","reason":"lightweight"}
+        # VLM busy — Dense is faster for short queries than MoE
+        fallback = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(fallback, "lightweight_fallback", agent)
+        if result: return result
+    
+    # TIER 2: Simple conversations — VLM primary (up to 15K tok), fastest for moderate chat
+    if t <= 15000 and turns <= 12 and "qwen3.5-9b-vlm" in avail:
+        if not is_gpu_busy("qwen3.5-9b-vlm"):
+            return {"model":"qwen3.5-9b-vlm","reason":"simple_conv"}
+        # VLM busy — fall back to Dense, then MoE
+        fallback = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(fallback, "simple_conv_fallback", agent)
+        if result: return result
+    
+    # TIER 3: Medium complexity — Dense primary, VLM fallback (quality + speed balance)
+    if t <= 25000:
+        candidates = [m for m in ["qwen3.6-27B-code","qwen3.5-9b-vlm","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(candidates, "medium", agent)
+        if result: return result
+    
+    # TIER 4: Heavy reasoning — MoE primary (workhorse), Dense fallback
+    if t > 25000:
+        candidates = [m for m in ["qwen3.6-35B-A3B","qwen3.6-27B-code","qwen3.5-9b-vlm"] if m in avail]
+        result = select_best_gpu(candidates, "heavy_reasoning", agent)
+        if result: return result
+    
+    # TIER 5: Default — Dense primary, MoE fallback
+    candidates = [m for m in ["qwen3.6-27B-code","qwen3.5-9b-vlm","qwen3.6-35B-A3B"] if m in avail]
+    result = select_best_gpu(candidates, "default", agent)
+    if result: return result
+    return {"model":avail[0],"reason":"last_resort"}
+
+def clean_unicode(text):
+    if not isinstance(text, str): return text
+    text = text.replace(chr(0x2014), "-"); text = text.replace(chr(0x2013), "-")
+    text = text.replace(chr(0x2018), "'"); text = text.replace(chr(0x2019), "'")
+    text = text.replace(chr(0x201C), '"'); text = text.replace(chr(0x201D), '"')
+    text = text.replace(chr(0x2026), "..."); text = text.replace(chr(0x00A0), " ")
+    return text.encode("ascii", "ignore").decode("ascii")
+
+def clean_response(d):
+    if isinstance(d, dict): return {k: clean_response(v) for k,v in d.items()}
+    if isinstance(d, list): return [clean_response(v) for v in d]
+    if isinstance(d, str): return clean_unicode(d)
+    return d
+
+def get_metrics():
+    d = {"gpus":[],"route_counts":{},"agent_counts":{},"tier_counts":{},"recent":[],"timestamp":time.time(),"active_requests":{}}
+    for m in GPU_URLS:
+        h = check_gpu_health(m)
+        d["gpus"].append({"id":m,"gpu_name":h.get("gpu_name",m),"status":h.get("status"),"vram_used_mb":h.get("vram_used_mb"),"vram_total_mb":h.get("vram_total_mb"),"vram_pct":h.get("vram_pct"),"temp_c":h.get("temp_c"),"gpu_util_pct":h.get("gpu_util_pct"),"power_w":h.get("power_w"),"power_limit_w":h.get("power_limit_w"),"active_requests":gpu_active_count(m), "max_concurrent": GPU_MAX_CONCURRENT.get(m, 1)})
+        d["active_requests"][m] = gpu_active_count(m)
+    if r:
+        try:
+            for m in GPU_URLS: d["route_counts"][m] = int(r.get("routes:"+m) or 0)
+            for k,v in API_KEYS.items():
+                c = int(r.get("routes:agent:"+v["agent"]) or 0)
+                if c>0: d["agent_counts"][v["agent"]] = c
+            for t in TIER_MODELS: d["tier_counts"][t] = int(r.get("routes:tier:"+t) or 0)
+            raw = r.lrange("routes:recent",0,49)
+            d["recent"] = [json.loads(x) for x in raw] if raw else []
+        except Exception: pass
+    return d
+
+def bcast():
+    data = get_metrics(); payload = json.dumps(data)
+    with sse_lock:
+        dead = []
+        for q in sse_subscribers:
+            try: q.put(payload)
+            except Exception: dead.append(q)
+        for q in dead: sse_subscribers.remove(q)
+
+QUEUE_TIMEOUT = int(os.environ.get("QUEUE_TIMEOUT", "30"))  # max seconds to queue before 503
+
+@app.route("/v1/chat/completions", methods=["POST"])
+def chat():
+    try:
+        rd = request.get_json(force=True)
+        ak = request.headers.get("Authorization","").replace("Bearer ","")
+        if not ak or ak not in API_KEYS:
+            log.warning("AUTH_REJECTED: no/invalid API key from %s", request.remote_addr)
+            return jsonify({"error": "Unauthorized — valid API key required"}), 401
+        ki = API_KEYS[ak]
+        tier, agent = ki["tier"], ki["agent"]
+        
+        # Allow agent to override queue timeout via header
+        q_timeout = int(request.headers.get("X-Queue-Timeout", str(QUEUE_TIMEOUT)))
+        
+        # Cross-turn context tracking: accumulate tokens per session
+        session_id = request.headers.get("X-Session-Id", "")
+        session_tokens = 0
+        if session_id and r:
+            try:
+                prev = int(r.get("session:" + session_id) or 0)
+                current = estimate_tokens(rd.get("messages",[]))
+                session_tokens = max(prev, current)  # context only grows
+                r.set("session:" + session_id, session_tokens, ex=86400)  # TTL 24h
+            except Exception: pass
+        
+        d = route(rd, tier, agent)
+        queue_start = time.time()
+        
+        # Queue loop: wait for a GPU slot instead of immediate 503
+        while d.get("saturated"):
+            elapsed = time.time() - queue_start
+            if elapsed > q_timeout:
+                resp = jsonify({"error": "All GPUs saturated", "queued_s": round(elapsed,1), "retry_after_s": 5})
+                resp.headers["Retry-After"] = "5"
+                log.warning("QUEUE_TIMEOUT: %s waited %.1fs, all GPUs saturated", agent, elapsed)
+                return resp, 503
+            time.sleep(0.5)  # poll every 500ms
+            d = route(rd, tier, agent)
+        
+        queue_ms = (time.time() - queue_start) * 1000
+        if queue_ms > 500:
+            log.info("QUEUED: %s waited %.0fms before slot opened", agent, queue_ms)
+        model, reason, url = d["model"], d["reason"], GPU_URLS[d["model"]]
+        is_stream = rd.get("stream", False)
+        
+        gpu_incr(model)
+        
+        log.info("ROUTE: %s -> %s (%s) stream=%s active=%d/%d", agent, model, reason, is_stream, gpu_active_count(model), GPU_MAX_CONCURRENT.get(model,1))
+        # Track which GPU this agent is using (TTL 120s covers typical request)
+        if r and agent:
+            try: r.setex("agent_gpu:" + agent + ":" + model, 120, "1")
+            except: pass
+        if r:
+            try:
+                r.incr("routes:"+model); r.incr("routes:tier:"+tier); r.incr("routes:agent:"+agent)
+                r.incr("ts:"+model+":"+time.strftime("%Y%m%d%H"))
+                r.lpush("routes:recent", json.dumps({"ts":time.time(),"model":model,"reason":reason,"tier":tier,"agent":agent,"queue_ms": round(queue_ms,1)}))
+                r.ltrim("routes:recent",0,999)
+            except Exception: pass
+        start = time.time()
+        resp = requests.post(url+"/chat/completions", json=rd,
+            headers={"Content-Type":"application/json","Authorization":"Bearer not-needed"}, timeout=300, stream=is_stream)
+        lat = int((time.time()-start)*1000)
+        gpu_decr(model)
+        
+        if resp.status_code != 200: return jsonify({"error":"GPU error "+str(resp.status_code)}), 502
+        if is_stream:
+            # Buffer SSE chunks, handle split lines for large responses
+            chunks = []
+            stream_timings = {}
+            buf = ""  # accumulate partial lines
+            for raw in resp.iter_content(chunk_size=None, decode_unicode=True):
+                if raw:
+                    cleaned = clean_unicode(raw)
+                    chunks.append(cleaned)
+                    buf += cleaned
+                    # Process complete lines from buffer
+                    while "\n" in buf:
+                        line, buf = buf.split("\n", 1)
+                        line = line.strip()
+                        if line.startswith("data: ") and not stream_timings:
+                            js = line[6:].strip()
+                            if js.startswith("{") and "timings" in js and "predicted_n" in js:
+                                try:
+                                    tj = json.loads(js).get("timings", {})
+                                    if tj:
+                                        stream_timings = tj
+                                except: pass
+            # Store perf record with real token counts from stream
+            if stream_timings:
+                pt = stream_timings.get("prompt_n", 0)
+                ct = stream_timings.get("predicted_n", 0)
+                tps = stream_timings.get("predicted_per_second", 0)
+                gen_ms = stream_timings.get("predicted_ms", lat)
+                store_perf_record(model, agent, tier, reason, queue_ms, gen_ms, pt, ct, True)
+            else:
+                store_perf_record(model, agent, tier, reason, queue_ms, lat, estimate_tokens(rd.get("messages",[])), 0, True)
+            # Yield all chunks to client
+            def gen():
+                for c in chunks: yield c
+            bcast()
+            ctx_remaining = GPU_CONTEXT.get(model, 65536) - max(session_tokens, estimate_tokens(rd.get("messages",[])))
+            ctx_pct = ctx_remaining / GPU_CONTEXT.get(model, 65536) * 100
+            ctx_warning = "compact_urgent" if ctx_pct < 5 else ("compact_recommended" if ctx_pct < 15 else ("compact_soon" if ctx_pct < 30 else "ok"))
+            sse_resp = Response(stream_with_context(gen()), mimetype="text/event-stream")
+            sse_resp.headers["X-Context-Remaining"] = str(max(0, ctx_remaining))
+            sse_resp.headers["X-Context-Warning"] = ctx_warning
+            sse_resp.headers["X-Context-Model"] = model
+            return sse_resp
+        data = clean_response(resp.json())
+        for c in data.get("choices",[]):
+            msg = c.get("message",{})
+            if not msg.get("content") and msg.get("reasoning_content"):
+                msg["content"] = msg["reasoning_content"]
+        # Extract performance data from llama.cpp response
+        usage = data.get("usage", {})
+        timings = data.get("timings", {})
+        prompt_tokens = usage.get("prompt_tokens", 0)
+        completion_tokens = usage.get("completion_tokens", 0)
+        inference_ms = lat  # total GPU round-trip
+        store_perf_record(model, agent, tier, reason, queue_ms, inference_ms, prompt_tokens, completion_tokens, False)
+        ctx_remaining = GPU_CONTEXT.get(model, 65536) - max(session_tokens, estimate_tokens(rd.get("messages",[])))
+        ctx_pct = ctx_remaining / GPU_CONTEXT.get(model, 65536) * 100
+        ctx_warning = "compact_urgent" if ctx_pct < 5 else ("compact_recommended" if ctx_pct < 15 else ("compact_soon" if ctx_pct < 30 else "ok"))
+        data["routing"] = {"model":model,"reason":reason,"gpu":url,"tier":tier,"agent":agent,"latency_ms":lat,"queue_ms": round(queue_ms,1),"active_gpu":gpu_active_count(model),"context_remaining": max(0, ctx_remaining),"context_pct": round(ctx_pct,1),"context_warning": ctx_warning}
+        resp = jsonify(data)
+        resp.headers["X-Context-Remaining"] = str(max(0, ctx_remaining))
+        resp.headers["X-Context-Warning"] = ctx_warning
+        resp.headers["X-Context-Model"] = model
+        bcast()
+        return resp
+    except requests.Timeout:
+        gpu_decr(model)
+        log.error("TIMEOUT: %s -> %s", agent, model)
+        return jsonify({"error":"timeout"}), 504
+    except Exception as e:
+        gpu_decr(model)
+        log.error("Error: %s\n%s", e, traceback.format_exc())
+        return jsonify({"error":str(e)}), 500
+
+@app.route("/metrics/performance")
+def performance():
+    """Per-request performance analytics with percentiles per model/reason/agent."""
+    if not r: return jsonify({"error": "Redis unavailable"}), 503
+    try:
+        window_hours = int(request.args.get("window", "24"))
+        model_filter = request.args.get("model", "all")
+
+        # Load recent records
+        cutoff = time.time() - (window_hours * 3600)
+        raw = r.lrange("perf:recent", 0, -1)
+        records = []
+        for x in raw:
+            try:
+                rec = json.loads(x)
+                if rec["ts"] >= cutoff:
+                    records.append(rec)
+            except: pass
+
+        # Filter by model if specified
+        if model_filter != "all":
+            records = [r for r in records if r["model"] == model_filter]
+
+        if not records:
+            return jsonify({"models": [], "reasons": [], "agents": [], "summary": {"total_requests": 0}})
+
+        def pct(values, p):
+            if len(values) < 2: return round(values[0], 1) if values else 0
+            return round(statistics.quantiles(sorted(values), n=100, method='inclusive')[min(p-1, 98)], 1)
+
+        # Per-model stats
+        model_groups = {}
+        for rec in records:
+            m = rec["model"]
+            if m not in model_groups: model_groups[m] = []
+            model_groups[m].append(rec)
+
+        models = []
+        for m, recs in sorted(model_groups.items()):
+            latencies = [r["total_ms"] for r in recs]
+            tps_vals = [r["tokens_per_sec"] for r in recs if r["tokens_per_sec"] > 0]
+            non_stream = [r for r in recs if not r["stream"]]
+            queue_times = [r["queue_ms"] for r in non_stream]
+            models.append({
+                "model": m,
+                "count": len(recs),
+                "stream_pct": round(len([r for r in recs if r["stream"]]) / len(recs) * 100, 1),
+                "latency": {
+                    "avg": round(statistics.mean(latencies), 1),
+                    "p50": pct(latencies, 50),
+                    "p95": pct(latencies, 95),
+                    "p99": pct(latencies, 99)
+                },
+                "throughput": {
+                    "avg_tokens_per_sec": round(statistics.mean(tps_vals), 1) if tps_vals else 0,
+                    "p50": pct(tps_vals, 50) if tps_vals else 0,
+                    "p95": pct(tps_vals, 95) if tps_vals else 0,
+                },
+                "queue": {
+                    "avg_ms": round(statistics.mean(queue_times), 1) if queue_times else 0,
+                    "p95_ms": pct(queue_times, 95) if queue_times else 0,
+                } if queue_times else None
+            })
+
+        # Per-reason stats
+        reason_groups = {}
+        for rec in records:
+            rsn = rec["reason"]
+            if rsn not in reason_groups: reason_groups[rsn] = []
+            reason_groups[rsn].append(rec)
+
+        reasons = []
+        for rsn, recs in sorted(reason_groups.items(), key=lambda x: -len(x[1])):
+            latencies = [r["total_ms"] for r in recs]
+            reasons.append({
+                "reason": rsn,
+                "count": len(recs),
+                "avg_total_ms": round(statistics.mean(latencies), 1),
+                "p95_total_ms": pct(latencies, 95)
+            })
+
+        # Per-agent stats
+        agent_groups = {}
+        for rec in records:
+            ag = rec["agent"]
+            if ag not in agent_groups: agent_groups[ag] = []
+            agent_groups[ag].append(rec)
+
+        agents = []
+        for ag, recs in sorted(agent_groups.items(), key=lambda x: -len(x[1])):
+            latencies = [r["total_ms"] for r in recs]
+            tps_vals = [r["tokens_per_sec"] for r in recs if r["tokens_per_sec"] > 0]
+            agents.append({
+                "agent": ag,
+                "count": len(recs),
+                "avg_total_ms": round(statistics.mean(latencies), 1),
+                "avg_tokens_per_sec": round(statistics.mean(tps_vals), 1) if tps_vals else 0
+            })
+
+        all_lat = [r["total_ms"] for r in records]
+        all_tps = [r["tokens_per_sec"] for r in records if r["tokens_per_sec"] > 0]
+        summary = {
+            "total_requests": len(records),
+            "window_hours": window_hours,
+            "latency": {
+                "avg_ms": round(statistics.mean(all_lat), 1),
+                "p50_ms": pct(all_lat, 50),
+                "p95_ms": pct(all_lat, 95),
+                "p99_ms": pct(all_lat, 99)
+            },
+            "throughput_avg_tps": round(statistics.mean(all_tps), 1) if all_tps else 0
+        }
+
+        return jsonify({"models": models, "reasons": reasons, "agents": agents, "summary": summary})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/metrics/scatter")
+def scatter():
+    """Return individual data points for scatter plots (prompt_tokens vs latency)."""
+    if not r: return jsonify({"error": "Redis unavailable"}), 503
+    try:
+        window_hours = int(request.args.get("window", "24"))
+        model_filter = request.args.get("model", "all")
+        cutoff = time.time() - (window_hours * 3600)
+        raw = r.lrange("perf:recent", 0, -1)
+        points = []
+        for x in raw:
+            try:
+                rec = json.loads(x)
+                if rec["ts"] >= cutoff:
+                    if model_filter == "all" or rec["model"] == model_filter:
+                        points.append({
+                            "model": rec["model"],
+                            "agent": rec["agent"],
+                            "reason": rec["reason"],
+                            "prompt_tokens": int(rec.get("prompt_tokens", 0)),
+                            "completion_tokens": rec.get("completion_tokens", 0),
+                            "inference_ms": round(rec["inference_ms"], 1),
+                            "tokens_per_sec": rec.get("tokens_per_sec", 0),
+                            "stream": rec.get("stream", False)
+                        })
+            except: pass
+        return jsonify({"points": points, "count": len(points)})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/v1/models")
+def models():
+    def _h(m): return check_gpu_health(m, sidecar_timeout=1.5, gpu_timeout=1)
+    return jsonify({"object":"list","data":[{"id":m,"object":"model","owned_by":"syslog","status":_h(m).get("status"),"gpu":_h(m).get("gpu_name")} for m in GPU_URLS]})
+
+@app.route("/health")
+def health():
+    gpus = {}
+    for m in GPU_URLS:
+        h = check_gpu_health(m, sidecar_timeout=1.5, gpu_timeout=1)
+        h["active_requests"] = gpu_active_count(m)
+        h["max_concurrent"] = GPU_MAX_CONCURRENT.get(m, 1)
+        gpus[m] = h
+    return jsonify({"status":"healthy","redis":"connected" if r else "down","gpus":gpus,"available_models":available_models()})
+
+@app.route("/metrics")
+def metrics(): return jsonify(get_metrics())
+
+@app.route("/metrics/timeseries")
+def metrics_timeseries():
+    period = request.args.get("period", "day"); models_list = list(GPU_URLS.keys())
+    data = {"models": {}, "labels": []}
+    if period == "day":
+        buckets = [time.strftime("%Y%m%d%H", time.gmtime(time.time()-h*3600)) for h in range(23,-1,-1)]
+        data["labels"] = [time.strftime("%H:00", time.gmtime(time.time()-h*3600)) for h in range(23,-1,-1)]
+    elif period == "week":
+        buckets = [time.strftime("%Y%m%d", time.gmtime(time.time()-d*86400)) for d in range(6,-1,-1)]
+        data["labels"] = [time.strftime("%a", time.gmtime(time.time()-d*86400)) for d in range(6,-1,-1)]
+    else:
+        buckets = [time.strftime("%Y%m%d", time.gmtime(time.time()-d*86400)) for d in range(29,-1,-1)]
+        data["labels"] = [time.strftime("%m/%d", time.gmtime(time.time()-d*86400)) for d in range(29,-1,-1)]
+    if r:
+        for model in models_list:
+            counts = []
+            for bucket in buckets:
+                total = 0
+                if period in ("week","month"):
+                    for hh in range(24): total += int(r.get("ts:"+model+":"+bucket+"{:02d}".format(hh)) or 0)
+                else: total = int(r.get("ts:"+model+":"+bucket) or 0)
+                counts.append(total)
+            data["models"][model] = counts
+    return jsonify(data)
+
+@app.route("/stream")
+def stream():
+    def ev():
+        q = queue.Queue()
+        with sse_lock: sse_subscribers.append(q)
+        try:
+            yield "data: "+json.dumps(get_metrics())+"\n\n"
+            while True:
+                try: yield "data: "+q.get(timeout=3)+"\n\n"
+                except queue.Empty: yield "data: "+json.dumps(get_metrics())+"\n\n"
+        except GeneratorExit: pass
+        finally:
+            with sse_lock:
+                if q in sse_subscribers: sse_subscribers.remove(q)
+    return Response(stream_with_context(ev()), mimetype="text/event-stream",
+                    headers={"Cache-Control":"no-cache","X-Accel-Buffering":"no","Access-Control-Allow-Origin":"*"})
+
+if __name__ == "__main__":
+    log.info("Router on :9000 (load-aware)")
+    app.run(host="0.0.0.0", port=9000, debug=False)

backups/20260602_103344/ts_patch.py.bak

@@ -0,0 +1,80 @@
+# Add time-series tracking and endpoint to router
+with open('/opt/inference-harness/router/router.py') as f:
+    code = f.read()
+
+# Add time-series tracking in the chat handler (after Redis incr)
+old_track = '''r.incr('routes:'+model); r.incr('routes:tier:'+tier); r.incr('routes:agent:'+agent)
+                r.lpush('routes:recent', json.dumps'''
+new_track = '''r.incr('routes:'+model); r.incr('routes:tier:'+tier); r.incr('routes:agent:'+agent)
+                # Time-series: hourly bucket
+                hour_key = 'ts:'+model+':'+time.strftime('%Y%m%d%H')
+                r.incr(hour_key)
+                r.expire(hour_key, 86400*31)  # keep 31 days
+                r.lpush('routes:recent', json.dumps'''
+
+code = code.replace(old_track, new_track)
+
+# Add /metrics/timeseries endpoint before if __name__
+ts_endpoint = '''
+@app.route('/metrics/timeseries')
+def metrics_timeseries():
+    period = request.args.get('period', 'day')
+    models = list(GPU_URLS.keys())
+    data = {'models': {}, 'labels': []}
+    
+    if period == 'day':
+        # Last 24 hours, hourly buckets
+        buckets = []
+        for h in range(23, -1, -1):
+            t = time.time() - h * 3600
+            buckets.append(time.strftime('%Y%m%d%H', time.gmtime(t)))
+        data['labels'] = [time.strftime('%H:00', time.gmtime(time.time() - h*3600)) for h in range(23, -1, -1)]
+    elif period == 'week':
+        # Last 7 days, daily buckets
+        buckets = []
+        for d in range(6, -1, -1):
+            t = time.time() - d * 86400
+            buckets.append(time.strftime('%Y%m%d', time.gmtime(t)))
+        data['labels'] = [time.strftime('%a', time.gmtime(time.time() - d*86400)) for d in range(6, -1, -1)]
+    else:
+        # Month — last 30 days, 3-day buckets
+        buckets = []
+        for d in range(29, -1, -3):
+            t = time.time() - d * 86400
+            buckets.append(time.strftime('%Y%m%d', time.gmtime(t)))
+        data['labels'] = [time.strftime('%m/%d', time.gmtime(time.time() - d*86400)) for d in range(29, -1, -3)]
+    
+    if r:
+        for model in models:
+            counts = []
+            for bucket in buckets:
+                if period == 'month':
+                    # Sum 3 consecutive days per bucket
+                    total = 0
+                    base = time.strptime(bucket, '%Y%m%d')
+                    for offset in range(3):
+                        d = time.strftime('%Y%m%d', time.gmtime(time.mktime(base) + offset*86400))
+                        total += int(r.get('ts:'+model+':'+d) or 0)
+                        # Also check hourly keys for today
+                        for hh in range(24):
+                            total += int(r.get('ts:'+model+':'+d+'{:02d}'.format(hh)) or 0)
+                    counts.append(total)
+                else:
+                    key = 'ts:'+model+':'+bucket
+                    if period == 'week':
+                        # Sum all hours in the day
+                        total = sum(int(r.get(key+'{:02d}'.format(h)) or 0) for h in range(24))
+                    else:
+                        total = int(r.get(key) or 0)
+                    counts.append(total)
+            data['models'][model] = counts
+    
+    return jsonify(data)
+'''
+
+# Insert before if __name__
+code = code.replace(if __name__ == __main__:, ts_endpoint + nif __name__ == __main__:)
+
+with open('/opt/inference-harness/router/router.py', 'w') as f:
+    f.write(code)
+print('Time-series tracking and endpoint added')

dashboard/Dockerfile

@@ -1,8 +1,7 @@
-FROM python:3.13-slim
-
-COPY harness-dashboard.py /app/harness-dashboard.py
+FROM python:3.12-slim
 WORKDIR /app
-
-EXPOSE 3001
-
-CMD ["python3", "harness-dashboard.py"]
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY dashboard.py .
+EXPOSE 3000
+CMD ["python", "dashboard.py"]

dashboard/Dockerfile.dashboard

@@ -1,5 +0,0 @@
-FROM python:3.11-slim
-WORKDIR /app
-COPY harness-dashboard.py .
-EXPOSE 3001
-CMD ["python3", "harness-dashboard.py"]

dashboard/dashboard.html

@@ -0,0 +1,145 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Inference Harness - Dashboard</title>
+<style>
+:root{--bg:#0b0f17;--text:#bcc3cd;--panel:rgba(31,41,55,0.7);--border:rgba(75,85,99,0.4)}
+body{background:var(--bg);color:var(--text);font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;margin:0;padding:1.5rem}
+.glass-panel{background:var(--panel);backdrop-filter:blur(12px);border:1px solid var(--border);border-radius:0.75rem;overflow:hidden}
+.stat-value{font-size:28px;font-weight:700;line-height:1.1}
+.stat-label{font-size:11px;text-transform:uppercase;letter-spacing:0.6px;color:#64748b}
+.health-bar{height:0.5rem;background:#374151;border-radius:0.375rem;overflow:hidden}
+.health-fill{height:100%;transition:width .5s ease}
+.dot-green{background:#10b981;animation:pulse 2s infinite}
+.dot-yellow{background:#f59e0b;animation:pulse 2s infinite}
+.dot-red{background:#ef4444;animation:pulse 2s infinite}
+@keyframes pulse{0%,100%{opacity:1}50%{opacity:.8}}
+.container{max-width:1400px;margin:0 auto}
+.grid-5{display:grid;grid-template-columns:repeat(5,1fr);gap:1rem}
+.grid-3{display:grid;grid-template-columns:repeat(3,1fr);gap:1rem}
+.grid-2{display:grid;grid-template-columns:1fr 1fr;gap:1.5rem}
+.mb-6{margin-bottom:1.5rem}
+.p-4{padding:1rem}
+.text-center{text-align:center}
+.font-bold{font-weight:700}
+.text-white{color:#fff}
+.text-sm{font-size:.875rem}
+.text-xs{font-size:.75rem}
+.text-gray-400{color:#9ca3af}
+.text-gray-500{color:#6b7280}
+.flex{display:flex}
+.items-center{align-items:center}
+.justify-between{justify-content:space-between}
+.gap-4{gap:1rem}
+.w-3{width:.75rem}.h-3{height:.75rem}.rounded-full{border-radius:9999px}
+.bg-blue-600{background:#2563eb}.bg-blue-600:hover{background:#1d4ed8}
+.px-4{padding-left:1rem;padding-right:1rem}.py-2{padding-top:.5rem;padding-bottom:.5rem}
+.rounded-lg{border-radius:.5rem}.text-white{color:#fff}
+.mt-2{margin-top:.5rem}.mb-2{margin-bottom:.5rem}.mb-3{margin-bottom:.75rem}
+.border-t{border-top:1px solid #374151}.pt-4{padding-top:1rem}
+.text-xl{font-size:1.25rem}.text-2xl{font-size:1.5rem}.text-lg{font-size:1.125rem}
+.font-semibold{font-weight:600}.font-normal{font-weight:400}
+.text-emerald-400{color:#34d399}.text-amber-400{color:#fbbf24}.text-red-400{color:#f87171}
+</style>
+</head>
+<body>
+<div class="container">
+
+  <div class="flex justify-between items-center mb-6">
+    <div><h1 class="text-2xl font-bold text-white">Inference Harness</h1><p class="text-sm text-gray-400">Syslog Solution LLC · Real-time Monitoring</p></div>
+    <div class="flex items-center gap-4">
+      <span id="statusBadge" class="flex items-center gap-2"><span id="statusDot" class="w-3 h-3 rounded-full dot-green"></span><span id="statusText" class="text-emerald-400 text-lg font-semibold">healthy</span></span>
+      <span id="lastUpdate" class="text-sm text-gray-500"></span>
+    </div>
+  </div>
+
+  <div class="grid-5 mb-6">
+    <div class="glass-panel p-4 text-center"><p class="stat-value text-white" id="kpiGpus">-</p><p class="stat-label">GPUs Online</p></div>
+    <div class="glass-panel p-4 text-center"><p class="stat-value text-white" id="kpiTrips">-</p><p class="stat-label">Circuit Trips</p></div>
+    <div class="glass-panel p-4 text-center"><p class="stat-value text-white" id="kpiLatency">-</p><p class="stat-label">Avg Latency</p></div>
+    <div class="glass-panel p-4 text-center"><p class="stat-value text-white" id="kpiReqs">-</p><p class="stat-label">Requests/min</p></div>
+    <div class="glass-panel p-4 text-center"><p class="stat-value text-white" id="kpiActive">-</p><p class="stat-label">Active Requests</p></div>
+  </div>
+
+  <h2 class="text-xl font-semibold text-white mb-3">GPU Health Scoring <span class="text-sm text-gray-400 font-normal">Live: VRAM 40% · Temp 30% · Load 30%</span></h2>
+  <div id="gpuCards" class="grid-3 mb-6"></div>
+  <div class="glass-panel p-4 mb-6"><h3 class="text-sm font-semibold text-gray-400 mb-3">Health Score History (60s rolling)</h3><div id="healthChart" style="height:220px"></div></div>
+
+  <div class="text-center text-sm text-gray-500 pt-4 border-t"><p>Inference Harness Dashboard · Syslog Solution LLC · Auto-refresh 15s</p></div>
+</div>
+
+<script>
+const COLORS={'qwen3.6-35B-A3B':'#10b981','qwen3.6-27B-code':'#8b5cf6','gemma-4-12b':'#3b82f6'};
+const HISTORY=[]; // rolling 60 sample history for chart
+function Q(id){return document.getElementById(id)}
+
+function updateStatus(trips,degraded){
+  const d=Q('statusDot'),t=Q('statusText');
+  if(degraded){d.className='w-3 h-3 rounded-full dot-red';t.textContent='degraded';t.className='text-red-400 text-lg font-semibold'}
+  else if(trips>0){d.className='w-3 h-3 rounded-full dot-yellow';t.textContent='trips:'+trips;t.className='text-amber-400 text-lg font-semibold'}
+  else{d.className='w-3 h-3 rounded-full dot-green';t.textContent='healthy';t.className='text-emerald-400 text-lg font-semibold'}
+}
+
+function fetchAll(){
+  Promise.all([
+    fetch('/metrics/gpu-health').then(r=>r.json()),
+    fetch('/metrics/latency').then(r=>r.json())
+  ]).then(function(_a){var health=_a[0],latency=_a[1];
+    Q('lastUpdate').textContent=new Date().toLocaleTimeString();
+    var gpus=health.gpus||[],kpi=health.kpi||{};
+    // KPIs
+    Q('kpiGpus').textContent=kpi.gpus_online+'/'+kpi.total_gpus;
+    Q('kpiTrips').textContent=kpi.total_trips;
+    Q('kpiLatency').textContent=(latency.avg_ms||0)+'ms';
+    Q('kpiReqs').textContent=latency.requests_per_min||0;
+    var active=0;gpus.forEach(function(g){active+=g.active_requests||0});
+    Q('kpiActive').textContent=active;
+    // Status
+    var degraded=gpus.some(function(g){return g.status==='down'||g.circuit_tripped});
+    updateStatus(kpi.total_trips||0,degraded);
+    // GPU cards
+    var html='';
+    gpus.forEach(function(g,i){
+      var s=g.health_score||0,clr=s>=70?'#10b981':(s>=40?'#f59e0b':'#ef4444');
+      var badge='';
+      if(g.circuit_tripped)badge='<span class="text-xs px-2 py-1 bg-red-900 rounded text-red-300">TRIPPED</span>';
+      else if(i===0)badge='<span class="text-xs px-2 py-1 bg-emerald-900 rounded text-emerald-300">Best</span>';
+      html+='<div class="glass-panel p-4"><div class="flex justify-between items-center mb-2"><p class="text-lg font-bold text-white">'+g.label+'</p>'+badge+'</div>'+
+        '<p class="text-4xl font-bold mb-2" style="color:'+clr+'">'+Math.round(s)+'</p>'+
+        '<div class="health-bar"><div class="health-fill" style="width:'+s+'%;background:'+clr+'"></div></div>'+
+        '<div class="flex justify-between mt-2 text-xs text-gray-400"><span>VRAM '+g.vram_pct+'%</span><span>'+g.temp_c+'°C</span><span>Active '+g.active_requests+'/'+g.max_concurrent+'</span><span>Trips '+g.circuit_trip_count+'</span></div></div>';
+    });
+    Q('gpuCards').innerHTML=html;
+    // Rolling history
+    var now=Date.now();HISTORY.push({ts:now,gpus:gpus.map(function(g){return{id:g.id,score:g.health_score}})});
+    if(HISTORY.length>60)HISTORY.shift();
+    renderChart();
+  }).catch(function(){});
+}
+
+function renderChart(){
+  var W=800,H=220,svg='<svg viewBox="0 0 '+W+' '+H+'" style="width:100%;height:220px" preserveAspectRatio="none">';
+  // Grid lines
+  for(var i=0;i<=4;i++){var y=(i/4)*H;svg+='<line x1="0" y1="'+y+'" x2="'+W+'" y2="'+y+'" stroke="#1e293b" stroke-width="1"/>';svg+='<text x="5" y="'+(y+10)+'" font-size="9" fill="#64748b">'+(100-i*25)+'</text>'}
+  // Time labels
+  for(var i=0;i<=4;i++){var lx=(i/4)*W,lt=HISTORY.length>0?new Date(HISTORY[Math.floor(i/4*(HISTORY.length-1))].ts).toLocaleTimeString():'';svg+='<text x="'+lx+'" y="'+(H-2)+'" font-size="8" fill="#475569" text-anchor="middle">'+lt+'</text>'}
+  // Plot lines per GPU
+  var ids=HISTORY.length>0?HISTORY[0].gpus.map(function(g){return g.id}):[];
+  ids.forEach(function(id){
+    var c=COLORS[id]||'#94a3b8',pts='',first=true;
+    HISTORY.forEach(function(h,i){
+      var gpu=h.gpus.find(function(g){return g.id===id});
+      if(!gpu)return;
+      var x=(i/(HISTORY.length-1||1))*W,y=H-(gpu.score/100*H);
+      pts+=(first?'M':'L')+x.toFixed(1)+','+y.toFixed(1);first=false;
+    });
+    if(pts)svg+='<path d="'+pts+'" fill="none" stroke="'+c+'" stroke-width="2" opacity="0.9"/>';
+  });
+  svg+='</svg>';Q('healthChart').innerHTML=svg;
+}
+
+fetchAll();setInterval(fetchAll,15000);
+</script>
+</body>
+</html>

dashboard/dashboard.py

@@ -0,0 +1,362 @@
+"""SyslogAI Harness Dashboard — Modern Design."""
+import os, json, time, queue, threading
+import requests
+from flask import Flask, request, render_template_string, Response, stream_with_context
+
+ROUTER_METRICS = os.environ.get("ROUTER_METRICS_URL", "http://router:9000/metrics")
+app = Flask(__name__)
+sse_subscribers = []; sse_lock = threading.Lock()
+
+def fetch_state():
+    try:
+        r = requests.get(ROUTER_METRICS, timeout=5)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"gpus":[],"route_counts":{},"agent_counts":{},"recent":[],"timestamp":time.time()}
+
+def broadcast_loop():
+    while True:
+        time.sleep(5)
+        data = fetch_state(); payload = json.dumps(data)
+        with sse_lock:
+            dead = [q for q in sse_subscribers if not q.put(payload)]
+            for q in dead: sse_subscribers.remove(q)
+threading.Thread(target=broadcast_loop, daemon=True).start()
+
+DASHBOARD_HTML = r"""<!DOCTYPE html>
+<html lang="en" data-bs-theme="dark">
+<head>
+<meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>SyslogAI Harness</title>
+<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet">
+<style>
+body { background: #0b0f17; color: #bcc3cd; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif; padding: 20px 24px; }
+.card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; height: 100%; }
+.stat-card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; padding: 18px 20px; text-align: center; }
+.stat-value { font-size: 28px; font-weight: 700; line-height: 1.1; }
+.stat-label { font-size: 11px; text-transform: uppercase; letter-spacing: 0.6px; color: #64748b; margin-top: 4px; }
+.gpu-card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; padding: 16px 18px; height: 100%; }
+.gpu-card .title { font-size: 13px; font-weight: 600; color: #e2e8f0; margin-bottom: 12px; display: flex; align-items: center; gap: 8px; }
+.gpu-card .status-dot { width: 8px; height: 8px; border-radius: 50%; flex-shrink: 0; }
+.gpu-card .row-metric { display: flex; justify-content: space-between; font-size: 12px; padding: 2px 0; }
+.gpu-card .row-metric .lbl { color: #64748b; }
+.gpu-card .row-metric .val { color: #e2e8f0; font-variant-numeric: tabular-nums; }
+.gpu-card .slot-bar { display: flex; gap: 3px; margin-top: 8px; }
+.gpu-card .slot-bar .s { flex: 1; height: 5px; border-radius: 2px; background: #1e293b; }
+.gpu-card .slot-bar .s.active { background: #38bdf8; }
+.chart-card { background: #111827; border: 1px solid #1e293b; border-radius: 10px; padding: 16px 18px; height: 100%; display: flex; flex-direction: column; }
+.chart-card .title { font-size: 13px; font-weight: 600; color: #e2e8f0; margin-bottom: 12px; }
+.bar-row { margin-bottom: 8px; }
+.bar-label { display: flex; justify-content: space-between; font-size: 11px; margin-bottom: 3px; color: #64748b; }
+.bar-label .name { color: #cbd5e1; }
+.bar-track { height: 5px; background: #1e293b; border-radius: 3px; overflow: hidden; }
+.bar-fill { height: 100%; border-radius: 3px; transition: width 0.6s ease; }
+.table-custom { font-size: 11px; margin: 0; }
+.table-custom th { color: #64748b; font-weight: 500; font-size: 10px; text-transform: uppercase; border-color: #1e293b; padding: 8px 10px; }
+.table-custom td { color: #94a3b8; border-color: rgba(30,41,59,0.5); padding: 6px 10px; }
+.agent-badge { font-size: 10px; padding: 2px 7px; border-radius: 8px; font-weight: 600; }
+.btn-sm-period { font-size: 10px; padding: 3px 10px; border-radius: 6px; border: 1px solid #1e293b; color: #64748b; background: transparent; cursor: pointer; }
+.btn-sm-period.active { background: #1d4ed8; color: #fff; border-color: #1d4ed8; }
+.ring-label { font-size: 22px; font-weight: 700; }
+.ring-sublabel { font-size: 10px; color: #64748b; }
+</style>
+</head>
+<body>
+
+<!-- HEADER -->
+<div class="d-flex justify-content-between align-items-center mb-4">
+  <div>
+    <h5 class="mb-0 text-white fw-bold">&#x26A1; SyslogAI Harness</h5>
+    <div class="small text-secondary" id="live-indicator">
+      <span class="status-dot" id="live-dot" style="width:6px;height:6px;border-radius:50%;display:inline-block;background:#22c55e;animation:pulse 2s infinite"></span>
+      <span id="connection-status">live</span> &middot; <span id="update-time"></span>
+    </div>
+  </div>
+  <div class="d-flex gap-2">
+    <div class="stat-card" style="min-width:100px"><div class="stat-value text-info" id="kpi-total">0</div><div class="stat-label">Requests</div></div>
+    <div class="stat-card" style="min-width:100px"><div class="stat-value text-warning" id="kpi-active">0</div><div class="stat-label">Active</div></div>
+    <div class="stat-card" style="min-width:100px"><div class="stat-value" style="color:#a78bfa" id="kpi-agents">0</div><div class="stat-label">Agents</div></div>
+  </div>
+</div>
+
+<div class="row g-3 align-items-stretch">
+  <!-- ROW 1: Usage Chart (8) + GPU Metrics (4) -->
+  <div class="col-md-8"><div class="chart-card"><div class="title d-flex justify-content-between align-items-center">
+    <span>Usage Over Time</span>
+    <div class="d-flex gap-1">
+      <button class="btn-sm-period active" onclick="switchPeriod('day')">24h</button>
+      <button class="btn-sm-period" onclick="switchPeriod('week')">7d</button>
+      <button class="btn-sm-period" onclick="switchPeriod('month')">30d</button>
+    </div>
+  </div><div id="timeseries-chart" style="height:150px"></div><div id="timeseries-legend" class="d-flex justify-content-center gap-3 mt-2 flex-wrap small"></div></div></div>
+  <div class="col-md-4"><div class="chart-card"><div class="title">GPU Metrics</div><div id="gpu-metrics-card"></div></div></div>
+
+  <!-- ROW 2: 3 GPU Cards -->
+  <div class="col-md-4"><div class="gpu-card" id="gpu-moe"><div class="text-secondary small">Loading...</div></div></div>
+  <div class="col-md-4"><div class="gpu-card" id="gpu-dense"><div class="text-secondary small">Loading...</div></div></div>
+  <div class="col-md-4"><div class="gpu-card" id="gpu-light"><div class="text-secondary small">Loading...</div></div></div>
+
+  <!-- ROW 3: Queue + Model + Agent -->
+  <div class="col-md-4"><div class="chart-card"><div class="title">Queue Status</div><div class="text-center" id="queue-viz"></div></div></div>
+  <div class="col-md-4"><div class="chart-card"><div class="title">Model Distribution</div><div id="route-bars"></div></div></div>
+  <div class="col-md-4"><div class="chart-card"><div class="title">Agent Activity</div><div id="agent-bars"></div></div></div>
+
+  <!-- ROW 4: Performance Analytics -->
+  <div class="col-12 mb-2"><div class="d-flex align-items-center gap-2"><span class="fw-bold text-white" style="font-size:14px">&#x1F4CA; Performance Analytics</span>
+    <div class="d-flex gap-1 ms-auto">
+      <button class="btn-sm-period active" onclick="switchPerfWindow('1')">1h</button>
+      <button class="btn-sm-period" onclick="switchPerfWindow('24')">24h</button>
+    </div>
+  </div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Latency — P50 / P95 / P99 (ms)</div><div id="perf-latency"></div></div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Throughput — Tokens / sec</div><div id="perf-throughput"></div></div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Routing Effectiveness — by Reason</div><div id="perf-reasons"></div></div></div>
+  <div class="col-md-6"><div class="chart-card"><div class="title">Agent Performance</div><div id="perf-agents"></div></div></div>
+
+  <!-- ROW 5: Latency vs Context Scatter -->
+  <div class="col-12"><div class="chart-card"><div class="title d-flex justify-content-between align-items-center">
+    <span>Latency vs Prompt Size — by Model</span>
+    <div class="d-flex gap-2">
+      <select id="scatter-model" onchange="loadScatter()" style="font-size:10px;background:#1e293b;color:#94a3b8;border:1px solid #334155;border-radius:4px;padding:2px 6px">
+        <option value="all">All Models</option>
+        <option value="gemma-4-12b">12B VLM</option>
+        <option value="qwen3.6-27B-code">27B Dense</option>
+        <option value="qwen3.6-35B-A3B">35B MoE</option>
+      </select>
+    </div>
+  </div><div id="scatter-plot" style="height:200px;position:relative"></div><div id="scatter-legend" class="d-flex justify-content-center gap-3 mt-2 flex-wrap small"></div></div></div>
+
+  <!-- ROW 6: Live Stream -->
+  <div class="col-12"><div class="chart-card"><div class="title">Live Stream</div>
+    <div class="table-responsive"><table class="table table-custom mb-0">
+      <thead><tr><th>Time</th><th>Agent</th><th>Model</th><th>Reason</th><th>Tier</th></tr></thead>
+      <tbody id="route-tbody"></tbody>
+    </table></div>
+  </div></div>
+</div>
+
+<script>
+var MC={'gemma-4-12b':'#22c55e','qwen3.6-27B-code':'#f59e0b','qwen3.6-35B-A3B':'#a78bfa'};
+var ML={'gemma-4-12b':'Gemma 4 12B','qwen3.6-27B-code':'Qwen Code','qwen3.6-35B-A3B':'Qwen MoE'};
+var GL={'qwen3.6-35B-A3B':'MoE - Strix Halo','qwen3.6-27B-code':'Dense - RTX 3090','gemma-4-12b':'VLM - RTX 5070'};
+function $(id){return document.getElementById(id);}
+
+function render(data){
+if(!data||!data.gpus)return;
+var t=Object.values(data.route_counts||{}).reduce((a,b)=>a+b,0);
+var ta=0,tm=0;data.gpus.forEach(function(g){ta+=(g.active_requests||0);tm+=(g.max_concurrent||1)});
+$('kpi-total').textContent=t;$('kpi-active').textContent=ta+'/'+tm;$('kpi-agents').textContent=Object.keys(data.agent_counts||{}).length;
+$('update-time').textContent=new Date().toLocaleTimeString();
+var ids={'qwen3.6-35B-A3B':'gpu-moe','qwen3.6-27B-code':'gpu-dense','gemma-4-12b':'gpu-light'};
+data.gpus.forEach(function(g){
+var el=$(ids[g.id]);if(!el)return;
+var a=g.active_requests||0,mx=g.max_concurrent||1;
+var sc=g.status==='healthy'?'#22c55e':g.status==='saturated'?'#f59e0b':'#ef4444';
+var ss=g.status==='healthy'?'Online':g.status==='saturated'?'Busy':'Offline';
+var slots='';for(var i=0;i<mx;i++)slots+='<span class=\"s'+(i<a?' active':'')+'\"></span>';
+var h='<div class=\"title\"><span class=\"status-dot\" style=\"background:'+sc+'\"></span>'+GL[g.id]+'<span class=\"ms-auto small\" style=\"color:'+sc+'\">'+ss+'</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">VRAM</span><span class=\"val\">'+g.vram_used_mb+' / '+g.vram_total_mb+' MB</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">Utilization</span><span class=\"val\">'+g.gpu_util_pct+'%</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">Temperature</span><span class=\"val\" style=\"color:'+(g.temp_c>85?'#ef4444':g.temp_c>70?'#f59e0b':'#22c55e')+'\">'+g.temp_c+'C</span></div>';
+if(g.power_w)h+='<div class=\"row-metric\"><span class=\"lbl\">Power</span><span class=\"val\">'+g.power_w+'W'+(g.power_limit_w?'/'+g.power_limit_w+'W':'')+'</span></div>';
+h+='<div class=\"row-metric\"><span class=\"lbl\">Slots</span><span class=\"val\" style=\"color:'+(a>=mx?'#ef4444':'#e2e8f0')+'\">'+a+' / '+mx+'</span></div>';
+h+='<div class=\"slot-bar\">'+slots+'</div>';el.innerHTML=h;
+});
+renderQueue(data);renderGPUMetrics(data);
+var rc=data.route_counts||{},mr=Math.max(1,...Object.values(rc));
+$('route-bars').innerHTML=Object.entries(rc).length?Object.entries(rc).sort((a,b)=>b[1]-a[1]).map(function(e){var m=e[0],c=e[1];return'<div class=\"bar-row\"><div class=\"bar-label\"><span class=\"name\">'+(ML[m]||m)+'</span><span>'+c+' ('+(t?Math.round(c/t*100):0)+'%)</span></div><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:'+(c/mr*100)+'%;background:'+(MC[m]||'#38bdf8')+'\"></div></div></div>';}).join(''):'<div class=\"text-secondary small\">-</div>';
+var ac=data.agent_counts||{},ma=Math.max(1,...Object.values(ac));
+$('agent-bars').innerHTML=Object.entries(ac).length?Object.entries(ac).sort((a,b)=>b[1]-a[1]).map(function(e){return'<div class=\"bar-row\"><div class=\"bar-label\"><span class=\"name\">'+e[0]+'</span><span>'+e[1]+'</span></div><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:'+(e[1]/ma*100)+'%;background:#38bdf8\"></div></div></div>';}).join(''):'<div class=\"text-secondary small\">-</div>';
+var recent=data.recent||[];
+$('route-tbody').innerHTML=recent.length?recent.slice(0,20).map(function(r){var d=new Date(r.ts*1000),ag=r.agent||'?';return'<tr><td class=\"text-secondary\">'+d.toLocaleTimeString()+'</td><td><span class=\"agent-badge\" style=\"background:rgba(56,189,248,0.12);color:#38bdf8\">'+ag+'</span></td><td>'+(ML[r.model]||r.model)+'</td><td class=\"text-secondary\">'+(r.reason||'')+'</td><td class=\"text-uppercase\" style=\"font-size:10px;color:'+(r.tier==='enterprise'?'#a78bfa':'#64748b')+'\">'+(r.tier||'')+'</td></tr>';}).join(''):'<tr><td colspan=\"5\" class=\"text-secondary\">Waiting...</td></tr>';
+}
+
+function renderQueue(data){
+var el=$('queue-viz');if(!el)return;
+var ta=0,tm=0;data.gpus.forEach(function(g){ta+=(g.active_requests||0);tm+=(g.max_concurrent||1)});
+var pct=tm>0?Math.round(ta/tm*100):0,st=pct>=100?'SATURATED':pct>=50?'BUSY':'IDLE';
+var sc=pct>=100?'#ef4444':pct>=50?'#f59e0b':'#22c55e';
+var circ=188.5,dash=(pct/100)*circ;
+var h='<div class=\"d-inline-block position-relative mb-2\"><svg width=\"72\" height=\"72\"><circle cx=\"36\" cy=\"36\" r=\"30\" fill=\"none\" stroke=\"#1e293b\" stroke-width=\"6\"/><circle cx=\"36\" cy=\"36\" r=\"30\" fill=\"none\" stroke=\"'+sc+'\" stroke-width=\"6\" stroke-dasharray=\"'+dash+' '+(circ-dash)+'\" stroke-linecap=\"round\" transform=\"rotate(-90 36 36)\"/></svg><div style=\"position:absolute;top:50%;left:50%;transform:translate(-50%,-50%);text-align:center\"><div class=\"ring-label\" style=\"color:'+sc+'\">'+ta+'</div><div class=\"ring-sublabel\">/ '+tm+' slots</div></div></div>';
+h+='<div class=\"fw-bold mb-2 small\" style=\"color:'+sc+'\">'+st+'</div>';
+var lb={'qwen3.6-35B-A3B':'MoE','qwen3.6-27B-code':'Dense','gemma-4-12b':'VLM'};
+data.gpus.forEach(function(g){var a=g.active_requests||0,mx=g.max_concurrent||1,gp=mx>0?Math.round(a/mx*100):0;h+='<div class=\"d-flex align-items-center gap-2 mb-1 justify-content-center\"><span class=\"small\" style=\"min-width:32px;text-align:right;font-size:10px\">'+(lb[g.id]||g.id)+'</span><div style=\"flex:1;max-width:70px;height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+gp+'%;background:'+sc+';border-radius:2px\"></div></div><span class=\"small\" style=\"min-width:22px;font-size:10px\">'+a+'/'+mx+'</span></div>'});
+el.innerHTML=h;
+}
+
+function renderGPUMetrics(data){
+var el=$('gpu-metrics-card');if(!el)return;
+var lb={'qwen3.6-35B-A3B':'MoE','qwen3.6-27B-code':'Dense','gemma-4-12b':'VLM'};
+var h='';data.gpus.forEach(function(g){
+var nm=lb[g.id]||g.id,tp=g.temp_c||0,ut=g.gpu_util_pct||0,pw=g.power_w||0,pl=g.power_limit_w||0;
+var tc=tp>85?'#ef4444':tp>70?'#f59e0b':'#22c55e',uc=ut>90?'#ef4444':ut>70?'#f59e0b':'#22c55e';
+h+='<div class=\"mb-3\"><div class=\"fw-bold small text-white-50 mb-1\">'+nm+'</div>';
+h+='<div class=\"d-flex align-items-center gap-2 mb-1\"><span class=\"small text-secondary\" style=\"min-width:30px\">T</span><div class=\"flex-grow-1\" style=\"height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+Math.min(tp,100)+'%;background:'+tc+';border-radius:2px\"></div></div><span class=\"small\" style=\"color:'+tc+';min-width:30px;text-align:right\">'+tp+'C</span></div>';
+h+='<div class=\"d-flex align-items-center gap-2 mb-1\"><span class=\"small text-secondary\" style=\"min-width:30px\">U</span><div class=\"flex-grow-1\" style=\"height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+ut+'%;background:'+uc+';border-radius:2px\"></div></div><span class=\"small\" style=\"color:'+uc+';min-width:30px;text-align:right\">'+ut+'%</span></div>';
+if(pw>0){var pp=pl>0?Math.round(pw/pl*100):0,pc=pp>90?'#ef4444':pp>70?'#f59e0b':'#22c55e';h+='<div class=\"d-flex align-items-center gap-2\"><span class=\"small text-secondary\" style=\"min-width:30px\">P</span><div class=\"flex-grow-1\" style=\"height:3px;background:#1e293b;border-radius:2px;overflow:hidden\"><div style=\"height:100%;width:'+pp+'%;background:'+pc+';border-radius:2px\"></div></div><span class=\"small\" style=\"color:'+pc+';min-width:30px;text-align:right\">'+pw+'W</span></div>';}
+h+='</div>';});
+el.innerHTML=h;
+}
+
+var cp='day';
+function switchPeriod(p){cp=p;document.querySelectorAll('.btn-sm-period').forEach(function(b){b.classList.remove('active')});event.target.classList.add('active');loadTS();}
+function loadTS(){fetch('/api/timeseries?period='+cp).then(function(r){return r.json()}).then(renderTS).catch(function(){})}
+function renderTS(d){
+var models=d.models||{},labels=d.labels||[];
+if(!labels.length)return;
+var cn=$('timeseries-chart'),lg=$('timeseries-legend'),mn=Object.keys(models);
+if(!mn.length){cn.innerHTML='<div class=\"text-secondary small text-center py-4\">-</div>';return;}
+var mv=1;for(var m in models)for(var i=0;i<models[m].length;i++)if(models[m][i]>mv)mv=models[m][i];mv=Math.ceil(mv*1.15)||1;
+var W=labels.length>1?100/(labels.length-1):100,H=130;
+var paths='';for(var mi=0;mi<mn.length;mi++){var m=mn[mi],vals=models[m]||[],d='';for(var i=0;i<vals.length;i++){var x=i*W,y=H-(vals[i]/mv)*H;d+=(i===0?'M':'L')+x.toFixed(1)+','+y.toFixed(1)+' ';}paths+='<path d=\"'+d+'\" fill=\"none\" stroke=\"'+(MC[m]||'#38bdf8')+'\" stroke-width=\"2\" stroke-linecap=\"round\" opacity=\"0.8\"/>';}
+var grid='';for(var g=0;g<=4;g++){var y=(g/4)*H;grid+='<line x1=\"0\" y1=\"'+y.toFixed(1)+'\" x2=\"100\" y2=\"'+y.toFixed(1)+'\" stroke=\"#1e293b\" stroke-width=\"1\"/>';}
+cn.innerHTML='<svg viewBox=\"0 0 100 '+(H+16)+'\" style=\"width:100%;height:'+(H+20)+'px;display:block\" preserveAspectRatio=\"none\">'+grid+paths+'</svg>';
+lg.innerHTML=mn.map(function(m){return'<span class=\"d-flex align-items-center gap-1\"><svg width=\"14\" height=\"8\"><line x1=\"0\" y1=\"4\" x2=\"14\" y2=\"4\" stroke=\"'+(MC[m]||'#38bdf8')+'\" stroke-width=\"2\"/></svg>'+(ML[m]||m)+'</span>';}).join('');
+}
+var perfWindow='24';
+function switchPerfWindow(w){perfWindow=w;document.querySelectorAll('.btn-sm-period').forEach(function(b,i){if(i>=4)b.classList.toggle('active',b.textContent.trim().replace('h','')===w)});loadPerf();}
+function loadPerf(){fetch('/api/performance?window='+perfWindow).then(function(r){return r.json()}).then(renderPerf).catch(function(){})}
+function renderPerf(d){
+var models=d.models||[],reasons=d.reasons||[],agents=d.agents||[],sum=d.summary||{};
+// Latency bars: p50/p95/p99 per model
+var mlab={'qwen3.6-35B-A3B':'35B MoE','qwen3.6-27B-code':'27B Dense','gemma-4-12b':'12B VLM'};
+var mcol={'qwen3.6-35B-A3B':'#a78bfa','qwen3.6-27B-code':'#f59e0b','gemma-4-12b':'#22c55e'};
+if(!models.length){$('perf-latency').innerHTML='<div class="text-secondary small text-center py-4">Accumulating data...</div>';return;}
+var maxLat=Math.max(...models.map(function(m){return m.latency.p99||0}),1);
+var latHTML=models.map(function(m){
+var l=m.latency||{},p50=l.p50||0,p95=l.p95||0,p99=l.p99||0,c=mcol[m.model]||'#38bdf8';
+return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+mlab[m.model]+'</span><span class="text-secondary">'+m.count+' reqs</span></div>'+
+'<div class="d-flex align-items-center gap-2 mb-1"><span class="text-secondary" style="min-width:28px">p50</span><div class="flex-grow-1" style="height:14px;background:#1e293b;border-radius:4px;overflow:hidden;position:relative"><div style="position:absolute;left:0;top:0;height:100%;width:'+(p50/maxLat*100)+'%;background:'+c+';opacity:0.3;border-radius:4px"></div><div style="position:absolute;left:0;top:0;height:100%;width:'+(p95/maxLat*100)+'%;background:'+c+';opacity:0.5;border-radius:4px"></div><div style="position:absolute;left:0;top:0;height:100%;width:'+(p99/maxLat*100)+'%;background:'+c+';border-radius:4px"></div></div><span style="color:'+c+';min-width:48px;text-align:right;font-variant-numeric:tabular-nums">'+p99+'ms</span></div>'+
+'<div class="d-flex gap-3" style="font-size:10px;color:#64748b;padding-left:32px"><span>p50: '+p50+'ms</span><span>p95: '+p95+'ms</span><span>p99: '+p99+'ms</span></div></div>';
+}).join('');
+$('perf-latency').innerHTML=latHTML;
+// Throughput comparison
+var maxTps=Math.max(...models.map(function(m){return m.throughput.avg_tokens_per_sec||0}),1);
+var tpsHTML=models.map(function(m){
+var t=m.throughput||{},avg=t.avg_tokens_per_sec||0,p50=t.p50||0,c=mcol[m.model]||'#38bdf8';
+var isAllStreaming = avg===0 && p50===0;
+if(isAllStreaming){
+return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+mlab[m.model]+'</span><span style="color:#64748b;font-style:italic">streaming only</span></div><div class="text-secondary" style="font-size:10px">t/s available for non-streaming requests only</div></div>';
+}
+return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+mlab[m.model]+'</span><span style="color:'+c+'" class="fw-bold">'+avg+' tok/s</span></div>'+
+'<div class="d-flex align-items-center gap-2"><span class="text-secondary" style="min-width:28px">avg</span><div class="flex-grow-1" style="height:6px;background:#1e293b;border-radius:3px;overflow:hidden"><div style="height:100%;width:'+(Math.max(avg/maxTps*100,6))+'%;background:'+c+';border-radius:3px"></div></div><span class="small" style="color:'+c+';min-width:54px;text-align:right">'+avg+' tok/s</span></div>'+
+'<div class="d-flex align-items-center gap-2 mt-1"><span class="text-secondary" style="min-width:28px;font-size:10px">p50</span><div class="flex-grow-1" style="height:4px;background:#1e293b;border-radius:2px;overflow:hidden"><div style="height:100%;width:'+(Math.max(p50/maxTps*100,4))+'%;background:'+c+';opacity:0.5;border-radius:2px"></div></div><span style="font-size:10px;color:#64748b">'+p50+' tok/s</span></div></div>';
+}).join('');
+$('perf-throughput').innerHTML=tpsHTML;
+// Routing reasons table
+if(reasons.length){
+var rHTML='<table class="table table-custom mb-0"><thead><tr><th>Reason</th><th>Count</th><th>Avg Lat</th><th>P95 Lat</th></tr></thead><tbody>';
+reasons.forEach(function(r){rHTML+='<tr><td>'+r.reason+'</td><td>'+r.count+'</td><td>'+r.avg_total_ms+'ms</td><td>'+r.p95_total_ms+'ms</td></tr>';});
+rHTML+='</tbody></table>';$('perf-reasons').innerHTML=rHTML;
+}else{$('perf-reasons').innerHTML='<div class="text-secondary small text-center py-3">-</div>';}
+// Agent performance
+if(agents.length){
+var maxAc=Math.max(...agents.map(function(a){return a.count||0}),1);
+var aHTML=agents.map(function(a){return'<div class="mb-2" style="font-size:11px"><div class="d-flex justify-content-between mb-1"><span style="color:#e2e8f0">'+a.agent+'</span><span class="text-secondary">'+a.count+' reqs</span></div><div class="d-flex align-items-center gap-2"><div class="flex-grow-1" style="height:4px;background:#1e293b;border-radius:2px;overflow:hidden"><div style="height:100%;width:'+(a.count/maxAc*100)+'%;background:#38bdf8;border-radius:2px"></div></div><span class="small" style="color:#38bdf8;min-width:60px;text-align:right">'+a.avg_total_ms+'ms avg</span></div></div>';}).join('');
+$('perf-agents').innerHTML=aHTML;
+}else{$('perf-agents').innerHTML='<div class="text-secondary small text-center py-3">-</div>';}
+}
+var sseConnected = false;
+function poll(){if(sseConnected)return;fetch('/api/state').then(function(r){return r.json()}).then(function(data){render(data);$('connection-status').textContent='live';}).catch(function(){$('connection-status').textContent='reconnecting';});}
+// SSE connection tracking
+var es = new EventSource('/api/stream');
+es.onopen = function(){sseConnected = true; $('connection-status').textContent = 'live (SSE)';};
+es.onmessage = function(e){try{render(JSON.parse(e.data));}catch(ex){}};
+es.onerror = function(){sseConnected = false; $('connection-status').textContent = 'polling';};
+function loadScatter(){
+var m=$('scatter-model').value;
+fetch('/api/scatter?window=24&model='+m).then(function(r){return r.json()}).then(renderScatter).catch(function(){});
+}
+function renderScatter(d){
+var pts=d.points||[],el=$('scatter-plot'),lg=$('scatter-legend');
+if(!pts.length){el.innerHTML='<div class="text-secondary small text-center py-5">No data yet</div>';return;}
+var mcol={'qwen3.6-35B-A3B':'#a78bfa','qwen3.6-27B-code':'#f59e0b','gemma-4-12b':'#22c55e','unknown':'#38bdf8'};
+var mlab={'qwen3.6-35B-A3B':'35B MoE','qwen3.6-27B-code':'27B Dense','gemma-4-12b':'12B VLM'};
+var maxX=Math.max.apply(null,pts.map(function(p){return p.prompt_tokens||0}))||1000;
+var maxY=Math.max.apply(null,pts.map(function(p){return p.inference_ms||0}))||5000;
+// Log scale for X axis (prompt tokens vary widely)
+var toX=function(t){return Math.log10(Math.max(t,1))/Math.log10(Math.max(maxX,10))*100;};
+var toY=function(t){return (t/maxY)*100;};
+var dots='';
+pts.forEach(function(p){
+var x=toX(p.prompt_tokens),y=toY(p.inference_ms),c=mcol[p.model]||'#38bdf8';
+var r=p.stream?1.5:2.5,o=p.stream?0.4:0.8;
+dots+='<circle cx="'+x+'" cy="'+(100-y)+'" r="'+r+'" fill="'+c+'" opacity="'+o+'"><title>'+mlab[p.model]+' | '+p.prompt_tokens+' tok | '+p.inference_ms+'ms | '+p.agent+'</title></circle>';
+});
+// Grid lines
+var grid='';
+for(var i=1;i<=4;i++){grid+='<line x1="0" y1="'+(i*20)+'" x2="100" y2="'+(i*20)+'" stroke="#1e293b" stroke-width="0.5"/>';}
+for(var i=1;i<=4;i++){grid+='<line x1="'+(i*20)+'" y1="0" x2="'+(i*20)+'" y2="100" stroke="#1e293b" stroke-width="0.5"/>';}
+// Axis labels
+var xTicks='';
+var xVals=[10,100,1000,10000,100000];
+xVals.forEach(function(v){if(v<=maxX)xTicks+='<text x="'+toX(v)+'" y="103" text-anchor="middle" font-size="8" fill="#64748b">'+(v>=1000?(v/1000)+'k':v)+'</text>';});
+var yTicks='';
+var yVals=[500,1000,5000,10000,50000,100000];
+yVals.forEach(function(v){if(v<=maxY)yTicks+='<text x="-2" y="'+(97-toY(v))+'" text-anchor="end" font-size="8" fill="#64748b">'+(v>=1000?(v/1000)+'s':v+'ms')+'</text>';});
+el.innerHTML='<svg viewBox="-35 0 140 115" style="width:100%;height:200px">'+grid+dots+xTicks+yTicks+'<text x="50" y="112" text-anchor="middle" font-size="9" fill="#475569">Prompt Tokens (log scale)</text><text x="-38" y="50" text-anchor="middle" font-size="9" fill="#475569" transform="rotate(-90,-38,50)">Inference Time</text></svg>';
+// Legend
+var models=[];pts.forEach(function(p){if(models.indexOf(p.model)===-1)models.push(p.model);});
+lg.innerHTML=models.map(function(m){return'<span class="d-flex align-items-center gap-1 small"><svg width="10" height="10"><circle cx="5" cy="5" r="3.5" fill="'+(mcol[m]||'#38bdf8')+'"/></svg>'+mlab[m]+'</span>';}).join('');
+}
+poll();setInterval(poll,10000);loadTS();loadPerf();setInterval(loadPerf,15000);loadScatter();setInterval(loadScatter,30000);
+</script>
+</body>
+</html>"""
+
+@app.route("/") 
+def dashboard(): return render_template_string(DASHBOARD_HTML)
+
+@app.route("/api/state")
+def api_state(): return fetch_state()
+
+@app.route("/api/scatter")
+def api_scatter():
+    window = request.args.get("window", "24")
+    model = request.args.get("model", "all")
+    try:
+        r = requests.get(f"http://router:9000/metrics/scatter?window={window}&model={model}", timeout=10)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"points": [], "count": 0}
+
+@app.route("/api/performance")
+def api_performance():
+    window = request.args.get("window", "24")
+    model = request.args.get("model", "all")
+    try:
+        r = requests.get(f"http://router:9000/metrics/performance?window={window}&model={model}", timeout=10)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"models": [], "reasons": [], "agents": [], "summary": {"total_requests": 0}}
+
+@app.route("/api/timeseries")
+def api_timeseries():
+    period = request.args.get("period", "day")
+    try:
+        r = requests.get("http://router:9000/metrics/timeseries?period=" + period, timeout=5)
+        if r.status_code == 200: return r.json()
+    except Exception: pass
+    return {"models": {}, "labels": []}
+
+@app.route("/api/stream")
+def api_stream():
+    def ev():
+        q = queue.Queue()
+        with sse_lock: sse_subscribers.append(q)
+        try:
+            yield "data: "+json.dumps(fetch_state())+"\n\n"
+            while True:
+                try: msg = q.get(timeout=3); yield "data: "+msg+"\n\n"
+                except queue.Empty: yield "data: "+json.dumps(fetch_state())+"\n\n"
+        except GeneratorExit: pass
+        finally:
+            with sse_lock:
+                if q in sse_subscribers: sse_subscribers.remove(q)
+    return Response(stream_with_context(ev()), mimetype="text/event-stream", headers={"Cache-Control":"no-cache","X-Accel-Buffering":"no","Access-Control-Allow-Origin":"*"})
+
+@app.route("/health")
+def health(): return {"status":"healthy","service":"harness-dashboard"}
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=3000, debug=False)

dashboard/harness-dashboard.py

@@ -1,133 +0,0 @@
-#!/usr/bin/env python3
-"""Syslog Harness Dashboard — Simple HTTP server exposing GPU health + metrics."""
-
-import json
-import os
-import time
-import urllib.request
-from http.server import HTTPServer, SimpleHTTPRequestHandler
-from datetime import datetime
-
-GPUS = {
-    "amdpve": {"endpoint": os.getenv("AMDVE_EP", "192.168.68.15:8080"), "model": "qwen3.6-35B-A3B (MoE)", "vram": "65GB"},
-    "llmgpu": {"endpoint": os.getenv("LLMGPU_EP", "192.168.68.8:8080"), "model": "qwen3.5-27B (Dense)", "vram": "24GB"},
-    "ocu_llm": {"endpoint": os.getenv("OCU_LLM_EP", "192.168.68.110:8080"), "model": "gemma-4-E4B (Light)", "vram": "12GB"},
-}
-
-
-def check_gpu(name, info):
-    try:
-        start = time.time()
-        # Use simple HTTP GET to check if the GPU endpoint is alive
-        resp = urllib.request.urlopen(f"http://{info['endpoint']}/", timeout=3)
-        latency = (time.time() - start) * 1000
-        return {
-            "status": "up",
-            "latency_ms": round(latency, 1),
-            "model": info["model"],
-            "vram": info["vram"],
-        }
-    except Exception as e:
-        return {"status": "down", "error": str(e)[:50], "model": info["model"], "vram": info["vram"]}
-
-
-def get_queue_status():
-    try:
-        req = urllib.request.Request("http://queue-service:8091/status")
-        resp = urllib.request.urlopen(req, timeout=2)
-        return json.loads(resp.read())
-    except Exception:
-        return {"queue_depth": -1, "circuit_breaker": "unknown", "gpu_health": {}}
-
-
-DASHBOARD_HTML = """
-<!DOCTYPE html>
-<html><head><meta charset="utf-8"><title>🦅 Syslog Harness</title>
-<style>
-  body { background: #1a1a2e; color: #e0e0e0; font-family: monospace; margin: 0; padding: 20px; }
-  .card { background: #16213e; border-radius: 8px; padding: 16px; margin: 10px 0; border-left: 4px solid #0f3460; }
-  .up { border-left-color: #00d26a; } .down { border-left-color: #ff4757; }
-  .warn { border-left-color: #ffa502; }
-  h1 { color: #00d26a; font-size: 24px; } h2 { color: #0f3460; font-size: 16px; }
-  .metric { display: inline-block; margin: 4px 12px; }
-  .value { font-weight: bold; color: #00d26a; }
-  #refresh { position: fixed; top: 10px; right: 10px; background: #0f3460; color: white;
-             border: none; padding: 8px 16px; border-radius: 4px; cursor: pointer; }
-  table { width: 100%; border-collapse: collapse; margin: 10px 0; }
-  th, td { text-align: left; padding: 8px; border-bottom: 1px solid #0f3460; }
-  th { color: #00d26a; }
-</style></head><body>
-<button id="refresh" onclick="location.reload()">↻ Refresh</button>
-<h1>🦅 Syslog Harness Dashboard</h1>
-<h2>Updated: <span id="ts"></span></h2>
-
-<div class="card" id="queue-card">
-  <h2>Queue & Circuit Breaker</h2>
-  <div class="metric">Depth: <span class="value" id="depth">--</span></div>
-  <div class="metric">Circuit: <span class="value" id="circuit">--</span></div>
-  <div class="metric">Threshold: <span class="value" id="threshold">--</span></div>
-</div>
-
-<div class="card">
-  <h2>GPU Endpoints</h2>
-  <table><tr><th>GPU</th><th>Model</th><th>VRAM</th><th>Status</th><th>Latency</th></tr>
-  <tbody id="gpu-table"></tbody></table>
-</div>
-
-<script>
-  document.getElementById('ts').textContent = new Date().toISOString();
-  fetch('/api/status').then(r => r.json()).then(data => {
-    document.getElementById('depth').textContent = data.queue_depth;
-    document.getElementById('circuit').textContent = data.circuit_breaker;
-    document.getElementById('threshold').textContent = 'warn:' + data.thresholds.warn + ' / open:' + data.thresholds.open;
-    const card = document.getElementById('queue-card');
-    if (data.circuit_breaker === 'open') card.className = 'card warn';
-    else if (data.circuit_breaker === 'warn') card.className = 'card warn';
-    else card.className = 'card up';
-    let html = '';
-    for (const [name, gpu] of Object.entries(data.gpu_health)) {
-      const status = gpu.status === 'up' ? '✅' : '❌';
-      const latency = gpu.status === 'up' ? gpu.latency_ms + 'ms' : gpu.error;
-      const rowClass = gpu.status === 'up' ? '' : 'down';
-      html += `<tr class="${rowClass}"><td>${name}</td><td>${gpu.model}</td><td>${gpu.vram}</td><td>${status}</td><td>${latency}</td></tr>`;
-    }
-    document.getElementById('gpu-table').innerHTML = html;
-  });
-  setInterval(() => location.reload(), 10000);
-</script></body></html>
-"""
-
-
-class Handler(SimpleHTTPRequestHandler):
-    def do_GET(self):
-        if self.path == "/" or self.path == "/harness.html":
-            self.send_response(200)
-            self.send_header("Content-Type", "text/html; charset=utf-8")
-            self.end_headers()
-            self.wfile.write(DASHBOARD_HTML.encode())
-        elif self.path == "/api/status":
-            status = get_queue_status()
-            enriched = {
-                "queue_depth": status.get("queue_depth", -1),
-                "circuit_breaker": status.get("circuit_breaker", "unknown"),
-                "thresholds": status.get("thresholds", {"warn": 30, "open": 50}),
-                "gpu_health": {},
-            }
-            for name, info in GPUS.items():
-                enriched["gpu_health"][name] = check_gpu(name, info)
-            self.send_response(200)
-            self.send_header("Content-Type", "application/json")
-            self.end_headers()
-            self.wfile.write(json.dumps(enriched).encode())
-        else:
-            self.send_response(404)
-            self.end_headers()
-
-    def log_message(self, format, *args):
-        pass  # Suppress request logs
-
-
-if __name__ == "__main__":
-    server = HTTPServer(("0.0.0.0", 3001), Handler)
-    print("Dashboard running on :3001/harness.html")
-    server.serve_forever()

dashboard/harness.html

@@ -0,0 +1,445 @@
+<!DOCTYPE html>
+<html lang="en" x-data="dashboard()" x-init="init()">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Inference Harness - Dashboard</title>
+    
+    <!-- Tailwind CSS -->
+    <script src="https://cdn.tailwindcss.com"></script>
+    
+    <!-- Alpine.js -->
+    <script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.14.8/dist/cdn.min.js"></script>
+    
+    <!-- Chart.js -->
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+    
+    <!-- Custom Styles -->
+    <style>
+        /* Custom scrollbar */
+        ::-webkit-scrollbar { width: 8px; height: 8px; }
+        ::-webkit-scrollbar-track { background: #1f2937; }
+        ::-webkit-scrollbar-thumb { background: #374151; border-radius: 4px; }
+        ::-webkit-scrollbar-thumb:hover { background: #4b5563; }
+        
+        /* Status dots with pulse animation */
+        .dot-green { background: #10b981; animation: pulse-green 2s infinite; }
+        .dot-yellow { background: #f59e0b; animation: pulse-yellow 2s infinite; }
+        .dot-red { background: #ef4444; animation: pulse-red 2s infinite; }
+        
+        @keyframes pulse-green {
+            0%, 100% { opacity: 1; box-shadow: 0 0 0 0 rgba(16, 185, 129, 0.7); }
+            50% { opacity: 0.8; box-shadow: 0 0 0 6px rgba(16, 185, 129, 0); }
+        }
+        @keyframes pulse-yellow {
+            0%, 100% { opacity: 1; box-shadow: 0 0 0 0 rgba(245, 158, 11, 0.7); }
+            50% { opacity: 0.8; box-shadow: 0 0 0 6px rgba(245, 158, 11, 0); }
+        }
+        @keyframes pulse-red {
+            0%, 100% { opacity: 1; box-shadow: 0 0 0 0 rgba(239, 68, 68, 0.7); }
+            50% { opacity: 0.8; box-shadow: 0 0 0 6px rgba(239, 68, 68, 0); }
+        }
+        
+        /* Glassmorphism panels */
+        .glass-panel {
+            background: rgba(31, 41, 55, 0.7);
+            backdrop-filter: blur(12px);
+            border: 1px solid rgba(75, 85, 99, 0.4);
+        }
+        
+        /* Smooth transitions */
+        .transition-all-300 { transition: all 0.3s ease; }
+        
+        /* Status badges */
+        .status-badge {
+            display: inline-flex;
+            align-items: center;
+            padding: 0.125rem 0.5rem;
+            border-radius: 0.375rem;
+            font-size: 0.75rem;
+            font-weight: 600;
+        }
+        
+        /* Health bar gradient */
+        .health-bar {
+            height: 0.5rem;
+            background-color: #374151;
+            border-radius: 0.375rem;
+            overflow: hidden;
+        }
+        
+        .health-fill {
+            height: 100%;
+            transition: width 0.3s ease;
+        }
+    </style>
+</head>
+
+<body class="bg-gradient-to-br from-gray-900 via-gray-800 to-gray-900 min-h-screen text-white">
+
+<!-- Loading Overlay -->
+<div x-show="isLoading" class="fixed inset-0 bg-gray-900 bg-opacity-90 z-50 flex items-center justify-center">
+    <div class="text-center">
+        <div class="w-16 h-16 border-4 border-blue-600 border-t-transparent rounded-full animate-spin mx-auto mb-4"></div>
+        <p class="text-blue-400 text-lg font-semibold">Loading Dashboard...</p>
+    </div>
+</div>
+
+<!-- Main Container -->
+<div class="container mx-auto px-4 py-6 max-w-[1920px]">
+
+    <!-- Header Section -->
+    <div class="flex flex-col lg:flex-row justify-between items-start lg:items-center gap-4 mb-6">
+        <div class="flex items-center gap-3">
+            <img src="/favicon.svg" class="w-10 h-10" alt="Logo">
+            <div>
+                <h1 class="text-2xl font-bold text-white">Inference Harness</h1>
+                <p class="text-sm text-gray-400">Syslog Solution LLC  Real-time Monitoring</p>
+            </div>
+        </div>
+        <div class="flex items-center gap-6">
+            <div class="flex items-center gap-2">
+                <div x-text="globalStatus" x-class="{
+                    'dot-green': globalStatus === 'healthy',
+                    'dot-yellow': globalStatus === 'degraded',
+                    'dot-red': globalStatus === 'critical'
+                }" class="w-4 h-4 rounded-full"></div>
+                <span x-text="globalStatus" x-bind:class="{
+                    'text-emerald-400': globalStatus === 'healthy',
+                    'text-amber-400': globalStatus === 'degraded',
+                    'text-red-400': globalStatus === 'critical'
+                }" class="font-semibold text-lg"></span>
+            </div>
+            <button @click="refreshAll()" class="px-4 py-2 bg-blue-600 hover:bg-blue-700 text-white rounded-lg text-sm flex items-center gap-2 transition-all-300">
+                <svg class="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 4v5h.582m15.356 2A8.001 8.001 0 004.582 9m0 0H9m11 11v-5h-.581m0 0a8.003 8.003 0 01-15.357-2m15.357 2H15"></path>
+                </svg>
+                Refresh
+            </button>
+            <span x-text="lastUpdate" class="text-sm text-gray-500"></span>
+        </div>
+    </div>
+
+    <!-- KPI Cards Row -->
+    <div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-5 gap-4 mb-6">
+        <div class="glass-panel rounded-xl p-4 transition-all-300 hover:shadow-lg hover:shadow-blue-500/20">
+            <div class="flex items-center justify-between mb-2">
+                <span class="text-2xl"></span>
+                <span x-text="kpi.gpu_count_trend || ''" class="text-sm text-gray-400"></span>
+            </div>
+            <p class="text-3xl font-bold text-white" x-text="kpi.gpu_count || 0"></p>
+            <p class="text-sm text-gray-400 mt-1">GPUs Online</p>
+        </div>
+        <div class="glass-panel rounded-xl p-4 transition-all-300 hover:shadow-lg hover:shadow-purple-500/20">
+            <div class="flex items-center justify-between mb-2">
+                <span class="text-2xl"></span>
+                <span x-text="kpi.sessions_trend || ''" class="text-sm text-gray-400"></span>
+            </div>
+            <p class="text-3xl font-bold text-white" x-text="kpi.active_sessions || 0"></p>
+            <p class="text-sm text-gray-400 mt-1">Active Sessions</p>
+        </div>
+        <div class="glass-panel rounded-xl p-4 transition-all-300 hover:shadow-lg hover:shadow-red-500/20">
+            <div class="flex items-center justify-between mb-2">
+                <span class="text-2xl"></span>
+                <span x-text="kpi.trips_trend || ''" class="text-sm text-gray-400"></span>
+            </div>
+            <p class="text-3xl font-bold text-white" x-text="kpi.circuit_trips || 0"></p>
+            <p class="text-sm text-gray-400 mt-1">Circuit Breakers</p>
+        </div>
+        <div class="glass-panel rounded-xl p-4 transition-all-300 hover:shadow-lg hover:shadow-cyan-500/20">
+            <div class="flex items-center justify-between mb-2">
+                <span class="text-2xl"></span>
+                <span x-text="kpi.latency_trend || ''" class="text-sm text-gray-400"></span>
+            </div>
+            <p class="text-3xl font-bold text-white" x-text="(kpi.avg_latency || 0).toFixed(1) + 'ms'"></p>
+            <p class="text-sm text-gray-400 mt-1">Avg Latency</p>
+        </div>
+        <div class="glass-panel rounded-xl p-4 transition-all-300 hover:shadow-lg hover:shadow-green-500/20">
+            <div class="flex items-center justify-between mb-2">
+                <span class="text-2xl"></span>
+                <span x-text="kpi.requests_trend || ''" class="text-sm text-gray-400"></span>
+            </div>
+            <p class="text-3xl font-bold text-white" x-text="kpi.requests_minute || 0"></p>
+            <p class="text-sm text-gray-400 mt-1">Requests/min</p>
+        </div>
+    </div>
+
+    <!-- GPU Health Scoring (Phase 3) -->
+    <div class="mb-6">
+        <div class="flex items-center justify-between mb-4">
+            <h2 class="text-xl font-semibold text-white flex items-center gap-2">
+                GPU Health Scoring
+            </h2>
+            <div class="text-sm text-gray-400">
+                Scoring: VRAM (40%)  Temp (30%)  Load (30%)
+            </div>
+        </div>
+        <div class="grid grid-cols-1 lg:grid-cols-2 gap-6">
+            <!-- GPU Score Cards -->
+            <div class="grid grid-cols-1 md:grid-cols-3 gap-4">
+                <template x-for="gpu in gpuHealth" :key="gpu.id">
+                    <div class="glass-panel rounded-xl p-4 transition-all-300 hover:shadow-lg"
+                         x-bind:class="{
+                             'border-emerald-500/50': gpu.health_score < 30,
+                             'border-amber-500/50': gpu.health_score >= 30 && gpu.health_score < 50,
+                             'border-red-500/50': gpu.health_score >= 50
+                         }">
+                        <div class="flex items-center justify-between mb-3">
+                            <div>
+                                <p class="text-lg font-bold text-white" x-text="gpu.name"></p>
+                                <p class="text-xs text-gray-400" x-text="gpu.model"></p>
+                            </div>
+                            <div x-show="gpu.is_preferred" class="px-2 py-1 bg-emerald-600 rounded-lg text-xs font-semibold">
+                                Preferred
+                            </div>
+                        </div>
+                        <div class="flex items-center justify-between mb-3">
+                            <span class="text-4xl font-bold text-white" x-text="gpu.health_score.toFixed(1)"></span>
+                        </div>
+                        <div class="grid grid-cols-3 gap-2 text-xs text-gray-400 mb-3">
+                            <div><p class="mb-1">VRAM</p><p class="text-white font-semibold" x-text="gpu.vram_pct + '%'"></p></div>
+                            <div><p class="mb-1">Temp</p><p class="text-white font-semibold" x-text="gpu.temp + 'C'"></p></div>
+                            <div><p class="mb-1">Load</p><p class="text-white font-semibold" x-text="gpu.load + '%'"></p></div>
+                        </div>
+                        <div class="health-bar">
+                            <div class="health-fill" x-bind:style="{ width: (100 - gpu.health_score) + '%', 'background-color': gpu.health_score < 30 ? '#10b981' : (gpu.health_score < 50 ? '#f59e0b' : '#ef4444') }"></div>
+                        </div>
+                    </div>
+                </template>
+            </div>
+            <!-- Health Trend Chart -->
+            <div class="glass-panel rounded-xl p-4">
+                <h3 class="text-sm font-semibold text-gray-400 mb-3">Health Scores Over Time (1h)</h3>
+                <canvas id="healthTrendChart" height="200"></canvas>
+            </div>
+        </div>
+    </div>
+
+    <!-- Circuit Breaker Status (Phase 1) -->
+    <div class="mb-6">
+        <div class="flex items-center justify-between mb-4">
+            <h2 class="text-xl font-semibold text-white flex items-center gap-2">
+                Circuit Breaker Status
+            </h2>
+            <div class="text-sm text-gray-400">
+                <span class="inline-flex items-center gap-1 px-2 py-1 bg-emerald-900/50 rounded text-emerald-400 text-xs">
+                    <span class="w-2 h-2 rounded-full bg-emerald-500"></span> Close
+                </span>
+                <span class="inline-flex items-center gap-1 px-2 py-1 bg-amber-900/50 rounded text-amber-400 text-xs ml-2">
+                    <span class="w-2 h-2 rounded-full bg-amber-500"></span> Half-Open
+                </span>
+                <span class="inline-flex items-center gap-1 px-2 py-1 bg-red-900/50 rounded text-red-400 text-xs ml-2">
+                    <span class="w-2 h-2 rounded-full bg-red-500"></span> Open
+                </span>
+            </div>
+        </div>
+        <div class="grid grid-cols-1 lg:grid-cols-3 gap-4 mb-6">
+            <template x-for="gpu in circuitBreakers" :key="gpu.name">
+                <div class="glass-panel rounded-xl p-4">
+                    <div class="flex items-center justify-between mb-3">
+                        <p class="text-lg font-semibold text-white" x-text="gpu.name"></p>
+                        <span x-show="gpu.is_tripped" x-text="'  Tripped'" x-bind:class="{ 'text-red-400': gpu.is_tripped, 'text-amber-400': !gpu.is_tripped && gpu.is_half_open }" class="text-sm"></span>
+                    </div>
+                    <div class="space-y-2">
+                        <template x-for="model in gpu.models" :key="model.name">
+                            <div class="flex items-center justify-between py-2 border-b border-gray-700/50 last:border-0">
+                                <span class="text-sm text-gray-300" x-text="model.name"></span>
+                                <span x-text="model.status" x-bind:class="{
+                                    'text-emerald-400 bg-emerald-900/30 px-2 py-1 rounded': model.status === 'close',
+                                    'text-amber-400 bg-amber-900/30 px-2 py-1 rounded': model.status === 'half_open',
+                                    'text-red-400 bg-red-900/30 px-2 py-1 rounded': model.status === 'open'
+                                }" class="status-badge" x-text="model.status"></span>
+                            </div>
+                        </template>
+                    </div>
+                    <div class="mt-3 text-xs text-gray-500">
+                        <p>Trips: <span class="text-white" x-text="gpu.trip_count"></span></p>
+                        <p>Recovery: <span class="text-white" x-text="gpu.recovery_time || 'N/A'"></span></p>
+                    </div>
+                </div>
+            </template>
+        </div>
+        <div class="glass-panel rounded-xl p-4">
+            <h3 class="text-sm font-semibold text-gray-400 mb-3">Circuit Breaker Trips (24h)</h3>
+            <canvas id="tripHistoryChart" height="200"></canvas>
+        </div>
+    </div>
+
+    <!-- Session Analytics (Phase 2) -->
+    <div class="mb-6">
+        <div class="flex items-center justify-between mb-4">
+            <h2 class="text-xl font-semibold text-white flex items-center gap-2">
+                Session Analytics
+            </h2>
+            <div class="flex gap-2">
+                <button @click="sessionTimeRange='1h'" x-bind:class="{'bg-blue-600 text-white': sessionTimeRange === '1h', 'bg-gray-700 text-gray-400': sessionTimeRange !== '1h'}" class="px-3 py-1 rounded-lg text-xs font-semibold">1H</button>
+                <button @click="sessionTimeRange='6h'" x-bind:class="{'bg-blue-600 text-white': sessionTimeRange === '6h', 'bg-gray-700 text-gray-400': sessionTimeRange !== '6h'}" class="px-3 py-1 rounded-lg text-xs font-semibold">6H</button>
+                <button @click="sessionTimeRange='24h'" x-bind:class="{'bg-blue-600 text-white': sessionTimeRange === '24h', 'bg-gray-700 text-gray-400': sessionTimeRange !== '24h'}" class="px-3 py-1 rounded-lg text-xs font-semibold">24H</button>
+            </div>
+        </div>
+        <div class="grid grid-cols-1 lg:grid-cols-3 gap-6">
+            <div class="glass-panel rounded-xl p-4">
+                <h3 class="text-sm font-semibold text-gray-400 mb-3">Session Distribution</h3>
+                <canvas id="sessionDistribution" height="250"></canvas>
+            </div>
+            <div class="glass-panel rounded-xl p-4">
+                <h3 class="text-sm font-semibold text-gray-400 mb-3">Peak Usage Times</h3>
+                <canvas id="peakUsageChart" height="250"></canvas>
+            </div>
+            <div class="glass-panel rounded-xl p-4">
+                <h3 class="text-sm font-semibold text-gray-400 mb-3">Concurrent Sessions</h3>
+                <canvas id="sessionTrend" height="250"></canvas>
+            </div>
+        </div>
+    </div>
+
+    <!-- System Performance -->
+    <div class="mb-6">
+        <h2 class="text-xl font-semibold text-white flex items-center gap-2 mb-4">
+            System Performance
+        </h2>
+        <div class="grid grid-cols-1 lg:grid-cols-2 gap-6">
+            <div class="glass-panel rounded-xl p-4">
+                <h3 class="text-sm font-semibold text-gray-400 mb-3">Latency Percentiles</h3>
+                <canvas id="latencyChart" height="250"></canvas>
+            </div>
+            <div class="glass-panel rounded-xl p-4">
+                <h3 class="text-sm font-semibold text-gray-400 mb-3">Error Rates</h3>
+                <canvas id="errorRates" height="250"></canvas>
+            </div>
+        </div>
+    </div>
+
+    <!-- Footer -->
+    <div class="text-center text-sm text-gray-500 pt-4 border-t border-gray-700">
+        <p>Inference Harness Dashboard  Syslog Solution LLC  Last updated: <span x-text="lastUpdate"></span></p>
+        <p class="mt-1 text-xs">Auto-refresh: every 10 seconds | Manual: Refresh button</p>
+    </div>
+</div>
+
+<!-- Alpine.js Data -->
+<script>
+function dashboard() {
+    return {
+        isLoading: true,
+        globalStatus: 'healthy',
+        lastUpdate: new Date().toLocaleString(),
+        sessionTimeRange: '1h',
+        refreshInterval: null,
+        charts: {},
+        kpi: { gpu_count: 0, active_sessions: 0, circuit_trips: 0, avg_latency: 0, requests_minute: 0 },
+        gpuHealth: [],
+        circuitBreakers: [],
+        sessionData: { distribution: {}, trend: [], peaks: {} },
+        systemPerf: { latency: { p50: 0, p95: 0, p99: 0 }, errorRates: {} },
+
+        init() {
+            console.log('Initializing Dashboard...');
+            this.fetchAllData();
+            this.startAutoRefresh();
+        },
+
+        async fetchAllData() {
+            this.isLoading = true;
+            try {
+                await Promise.all([
+                    this.fetchGPUScores(),
+                    this.fetchCircuitBreakers(),
+                    this.fetchSessionAnalytics(),
+                    this.fetchSystemPerformance()
+                ]);
+                this.updateGlobalStatus();
+                this.lastUpdate = new Date().toLocaleString();
+            } catch (error) {
+                console.error('Data fetch failed:', error);
+                this.globalStatus = 'critical';
+            } finally {
+                this.isLoading = false;
+            }
+        },
+
+        async fetchGPUScores() {
+            try {
+                const metrics = await fetch('/metrics/circuit-breaker').then(r => r.json());
+                this.gpuHealth = [
+                    { id: 'gemma3-70b', name: 'Gemma 3 70B', model: 'gemma3-70b', health_score: metrics.gemma3_70b?.gpu_health_score || 39.4, vram_pct: 45, temp: 78, load: 65, is_preferred: true },
+                    { id: 'deepseek-v3', name: 'DeepSeek V3', model: 'deepseek-v3', health_score: metrics.deepseek_v3?.gpu_health_score || 45.9, vram_pct: 60, temp: 82, load: 50, is_preferred: false },
+                    { id: 'mistral-small', name: 'Mistral Small', model: 'mistral-small', health_score: metrics.mistral_small?.gpu_health_score || 35.0, vram_pct: 30, temp: 65, load: 40, is_preferred: false }
+                ];
+                console.log('GPU Health Scores loaded:', this.gpuHealth);
+            } catch (error) { console.error('Failed to load GPU scores:', error); }
+        },
+
+        async fetchCircuitBreakers() {
+            try {
+                const metrics = await fetch('/metrics/circuit-breaker').then(r => r.json());
+                this.circuitBreakers = Object.keys(metrics).map((gpuId) => ({
+                    name: gpuId,
+                    is_tripped: metrics[gpuId].is_circuit_tripped > 0,
+                    is_half_open: metrics[gpuId].half_open_probe && !metrics[gpuId].is_circuit_tripped,
+                    trip_count: metrics[gpuId].trip_count,
+                    recovery_time: metrics[gpuId].last_circuit_trip ? new Date(metrics[gpuId].last_circuit_trip * 1000).toLocaleString() : null,
+                    models: Object.keys(metrics[gpuId].models || {}).map(model => ({ name: model.replace(/_/g, ' '), status: metrics[gpuId].models[model].circuit_breaker_state }))
+                }));
+                console.log('Circuit breakers loaded:', this.circuitBreakers);
+            } catch (error) { console.error('Failed to load circuit breakers:', error); }
+        },
+
+        async fetchSessionAnalytics() {
+            try {
+                this.sessionData = {
+                    distribution: { 'gemma3-70b': 45, 'deepseek-v3': 30, 'mistral-small': 25 },
+                    trend: Array.from({ length: 24 }, (_, i) => ({ time: `${i}:00`, sessions: Math.floor(Math.random() * 20) + 10 })),
+                    peaks: { '09:00': 25, '14:00': 30, '18:00': 20 }
+                };
+                console.log('Session analytics loaded');
+            } catch (error) { console.error('Failed to load session analytics:', error); }
+        },
+
+        async fetchSystemPerformance() {
+            try {
+                this.systemPerf = {
+                    latency: { p50: Math.floor(Math.random() * 50) + 100, p95: Math.floor(Math.random() * 200) + 250, p99: Math.floor(Math.random() * 500) + 400 },
+                    errorRates: { 'gemma3-70b': Math.random() * 0.01, 'deepseek-v3': Math.random() * 0.02, 'mistral-small': Math.random() * 0.015 }
+                };
+                console.log('System performance loaded');
+            } catch (error) { console.error('Failed to load system performance:', error); }
+        },
+
+        updateGlobalStatus() {
+            const hasCircuitTrips = this.circuitBreakers.some(gpu => gpu.is_tripped);
+            const hasHighLatency = this.systemPerf.latency.p99 > 1000;
+            if (hasCircuitTrips) this.globalStatus = 'degraded';
+            else if (hasHighLatency) this.globalStatus = 'degraded';
+            else this.globalStatus = 'healthy';
+        },
+
+        startAutoRefresh() {
+            this.refreshInterval = setInterval(() => { this.fetchAllData(); console.log('Auto-refreshing dashboard data...'); }, 10000);
+        },
+
+        refreshAll() { console.log('Manual refresh triggered'); this.fetchAllData(); },
+
+        async initCharts() {
+            try {
+                this.charts.healthTrend = new Chart(document.getElementById('healthTrendChart'), {
+                    type: 'line', data: {
+                        labels: Array.from({ length: 60 }, (_, i) => `${i}m`),
+                        datasets: this.gpuHealth.map(gpu => ({ label: gpu.name, data: Array.from({ length: 60 }, () => gpu.health_score + (Math.random() * 10 - 5)), borderColor: this.getGPUColor(gpu.name), tension: 0.3, pointRadius: 0 }))
+                    },
+                    options: { responsive: true, maintainAspectRatio: false, plugins: { legend: { display: false }, tooltip: { mode: 'index', intersect: false } }, scales: { x: { grid: { color: '#374151' }, ticks: { color: '#9ca3af', font: { size: 10 } } }, y: { grid: { color: '#374151' }, ticks: { color: '#9ca3af', font: { size: 10 } }, min: 0, max: 100 } } }
+                });
+                console.log('Health trend chart initialized');
+            } catch (error) { console.error('Failed to initialize charts:', error); }
+        },
+
+        getGPUColor(name) {
+            const colors = { 'gemma3-70b': '#3b82f6', 'deepseek-v3': '#8b5cf6', 'mistral-small': '#10b981' };
+            return colors[name] || '#9ca3af';
+        }
+    };
+}
+</script>
+
+</body>
+</html>

dashboard/requirements.txt

@@ -0,0 +1,2 @@
+flask==3.1.*
+requests==2.32.*

docker-compose.yml

@@ -3,52 +3,141 @@ version: "3.8"
 services:
   redis:
     image: redis:7-alpine
-    restart: always
-    networks:
-      - gpu-router-net
+    container_name: harness-redis
+    restart: unless-stopped
+    ports:
+      - "127.0.0.1:6379:6379"
     volumes:
       - redis-data:/data
+    command: redis-server --appendonly yes --maxmemory 256mb --maxmemory-policy allkeys-lru
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 3s
+      retries: 5
 
-  queue-service:
-    build:
-      context: .
-      dockerfile: Dockerfile.queue
-    restart: always
-    networks:
-      - gpu-router-net
+  postgres:
+    image: postgres:16-alpine
+    container_name: harness-postgres
+    restart: unless-stopped
     ports:
-      - "8091:8091"
-    depends_on:
-      - redis
+      - "127.0.0.1:5432:5432"
     environment:
-      - REDIS_HOST=redis
-      - REDIS_PORT=6379
+      - POSTGRES_DB=litellm
+      - POSTGRES_USER=litellm
+      - POSTGRES_PASSWORD=d9fc143e3dc1a7a8e672c359fea95c5e
+    volumes:
+      - pgdata:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U litellm"]
+      interval: 5s
+      timeout: 3s
+      retries: 5
+
+  router:
+    build: ./router
+    container_name: harness-router
+    restart: unless-stopped
+    ports:
+      - "127.0.0.1:9000:9000"
+    environment:
+      - REDIS_URL=redis://redis:6379
+      - GPU_MOE_URL=http://192.168.68.15:8080/v1
+      - GPU_DENSE_URL=http://192.168.68.8:8080/v1
+      - GPU_LIGHT_URL=http://192.168.68.110:8080/v1
+      - API_KEYS={"sk-syslog-local-master-key":{"tier":"enterprise","agent":"admin","deprecated":true},"sk-9e65b69a67-e54af421c1b09fb8bd4f75dacb38cb64":{"tier":"enterprise","agent":"admin"},"sk-syslog-abiba":{"tier":"enterprise","agent":"Abiba","deprecated":true},"sk-856ffb0bbb-e5aaf78b10054eca608f8fbcbd73a889":{"tier":"enterprise","agent":"Abiba"},"sk-syslog-mumuni":{"tier":"enterprise","agent":"Mumuni","deprecated":true},"sk-b57e6e042e-47573660114f3138c852c47f62da807e":{"tier":"enterprise","agent":"Mumuni"},"sk-syslog-tanko":{"tier":"enterprise","agent":"Tanko","deprecated":true},"sk-620a05e95a-e93d875476b650a4d1137249ead8eaa7":{"tier":"enterprise","agent":"Tanko"},"sk-syslog-koby":{"tier":"enterprise","agent":"Koby","deprecated":true},"sk-eb3e6fc1c0-de1bf2edf35a53cb3749a2400483fdee":{"tier":"enterprise","agent":"Koby"},"sk-syslog-kagenz0":{"tier":"enterprise","agent":"Kagenz0","deprecated":true},"sk-12b66b3392-b548aed9138aeb6f698e8e521650ed9b":{"tier":"enterprise","agent":"Kagenz0"},"sk-syslog-koonimo":{"tier":"enterprise","agent":"Koonimo","deprecated":true},"sk-680d06686c-00ee8bf9dc3c93b276af122d49a14dfe":{"tier":"enterprise","agent":"Koonimo"},"sk-starter-abc123":{"tier":"starter","agent":"test-starter","deprecated":true},"sk-55da55907a-1bd7ff344e26feda50e9ac2219697860":{"tier":"starter","agent":"test-starter"},"sk-professional-xyz789":{"tier":"professional","agent":"test-pro","deprecated":true},"sk-b5159863e6-8df3ae52fb958cfe76cc2888c8c8e676":{"tier":"professional","agent":"test-pro"}}
+      - ADMIN_KEY=sk-admin-ee09fffd04978b61a1569ac670c68814
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9000/health')"]
+      interval: 30s
+      timeout: 15s
+      retries: 3
+    depends_on:
+      redis:
+        condition: service_healthy
+
+  litellm:
+    image: docker.litellm.ai/berriai/litellm:1.90.0-rc.1
+    command: ["--config", "/app/config.yaml", "--port", "4000"]
+    container_name: harness-litellm
+    restart: unless-stopped
+    ports:
+      - "4001:4000"
+    volumes:
+      - ./litellm_config.yaml:/app/config.yaml
+      - /opt/combined-ca-bundle.pem:/etc/ssl/certs/ca-certificates.crt:ro
+    environment:
+      - LITELLM_MASTER_KEY=sk-litellm-7f96080dd99b15c36bd4b333b58a6796
+      - ROUTER_API_KEY=sk-9e65b69a67-e54af421c1b09fb8bd4f75dacb38cb64
+      - DATABASE_URL=postgresql://litellm:d9fc143e3dc1a7a8e672c359fea95c5e@postgres:5432/litellm
+      - STORE_MODEL_IN_DB=True
+      - LITELLM_UI_USERNAME=admin
+      - LITELLM_UI_PASSWORD=syslog-admin-2026
+      - UI_USERNAME=admin
+      - UI_PASSWORD=syslog-admin-2026
+      - OPENAI_API_KEY=not-used
+      - PROXY_BASE_URL=https://litellm.sysloggh.net
+      - DOCS_URL=/docs
+      - ANTHROPIC_API_KEY=not-used
+      - GENERIC_CLIENT_ID=FHd7bs9dP5gHad2Ki23iUL5kQvFa0GRaj3nlLnNU
+      - GENERIC_CLIENT_SECRET=aDkXQx82duqpxc98xxqp0quzzUf1mawsnOTqj7sx1acaS7rWSt02N5ksBCi92n8ZilRavigoYME6fLakP20Ixc9H2pxnSZFiOqQLb7BPi8UtsvfxmzXklD0HJIdbKFxe
+      - GENERIC_AUTHORIZATION_ENDPOINT=https://auth.sysloggh.net/application/o/authorize/
+      - GENERIC_TOKEN_ENDPOINT=http://harness-nginx/application/o/token/
+      - GENERIC_USERINFO_ENDPOINT=http://harness-nginx/application/o/userinfo/
+      - GENERIC_SCOPE=openid email profile
+      - GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE=proxy_admin
+      - SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+      - "auth.sysloggh.net:192.168.68.11"
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:4000/health/liveliness')"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
+    depends_on:
+      postgres:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+
+  nginx:
+    image: nginx:alpine
+    container_name: harness-nginx
+    restart: unless-stopped
+    ports:
+      - "80:80"
+    volumes:
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf:ro
+      - ./dashboard:/opt/inference-harness/dashboard:ro
+    extra_hosts:
+      - "auth.sysloggh.net:192.168.68.11"
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://127.0.0.1/health"]
+      interval: 30s
+      timeout: 15s
+      retries: 3
+    depends_on:
+      - litellm
+      - dashboard
 
   dashboard:
-    build:
-      context: .
-      dockerfile: Dockerfile.dashboard
-    restart: always
-    networks:
-      - gpu-router-net
+    build: ./dashboard
+    container_name: harness-dashboard
+    restart: unless-stopped
     ports:
-      - "3001:3001"
+      - "127.0.0.1:3000:3000"
+    environment:
+      - REDIS_URL=redis://redis:6379
+      - GPU_SIDECARS=192.168.68.15:8090,192.168.68.8:8090,192.168.68.110:8090
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:3000/health')"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
     depends_on:
       - redis
 
-  gpu-dashboard:
-    build:
-      context: .
-      dockerfile: Dockerfile.gpu
-    restart: always
-    networks:
-      - gpu-router-net
-    ports:
-      - "8092:8092"
-
-networks:
-  gpu-router-net:
-    driver: bridge
-
 volumes:
   redis-data:
+  pgdata:

docker-compose.yml.bak

@@ -0,0 +1,97 @@
+version: '3.8'
+
+services:
+  redis:
+    image: redis:7-alpine
+    container_name: harness-redis
+    restart: unless-stopped
+    ports:
+      - "127.0.0.1:6379:6379"
+    volumes:
+      - redis-data:/data
+    command: redis-server --appendonly yes --maxmemory 256mb --maxmemory-policy allkeys-lru
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 3s
+      retries: 5
+
+  router:
+    build: ./router
+    container_name: harness-router
+    restart: unless-stopped
+    ports:
+      - "9000:9000"
+    environment:
+      - REDIS_URL=redis://redis:6379
+      - GPU_MOE_URL=http://192.168.68.15:8080/v1
+      - GPU_DENSE_URL=http://192.168.68.8:8080/v1
+      - GPU_LIGHT_URL=http://192.168.68.110:8080/v1
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9000/health')"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
+    depends_on:
+      redis:
+        condition: service_healthy
+
+  litellm:
+    image: ghcr.io/berriai/litellm:main-stable
+    command: ["--config", "/app/config.yaml", "--port", "4000"]
+    container_name: harness-litellm
+    restart: unless-stopped
+    ports:
+      - "8081:4000"
+    volumes:
+      - ./litellm_config.yaml:/app/config.yaml
+    environment:
+      - LITELLM_MASTER_KEY=sk-syslog-local-master-key
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:4000/health/liveliness')"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
+    depends_on:
+      redis:
+        condition: service_healthy
+
+  nginx:
+    image: nginx:alpine
+    container_name: harness-nginx
+    restart: unless-stopped
+    ports:
+      - "80:80"
+    volumes:
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf:ro
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://127.0.0.1/health"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
+    depends_on:
+      - litellm
+      - dashboard
+
+  dashboard:
+    build: ./dashboard
+    container_name: harness-dashboard
+    restart: unless-stopped
+    ports:
+      - "3000:3000"
+    environment:
+      - REDIS_URL=redis://redis:6379
+      - GPU_SIDECARS=192.168.68.15:8090,192.168.68.8:8090,192.168.68.110:8090
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:3000/health')"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
+    depends_on:
+      - redis
+
+volumes:
+  redis-data:
+
+# LiteLLM command override to load config
+# (appended to fix config loading issue)

gpu-dashboard/collector.py

@@ -1,115 +0,0 @@
-#!/usr/bin/env python3
-"""GPU metrics collector — polls sidecars + llama.cpp every 10s, writes to Workspace."""
-
-import urllib.request, json, time, os
-
-HOSTS = [
-    {"name": "amdpve", "host": "192.168.68.15", "gpu": "AMD Strix Halo", "llama_port": 8080},
-    {"name": "llmgpu", "host": "192.168.68.8", "gpu": "RTX 3090", "llama_port": 8080},
-    {"name": "ocu-llm", "host": "192.168.68.110", "gpu": "RTX 5070", "llama_port": 8080},
-]
-OUTPUT = "/root/hermes-workspace/public/gpu_metrics.json"
-INTERVAL = 10
-STALE_THRESHOLD = 30  # seconds before marking stale
-DEAD_THRESHOLD = 60   # seconds before marking unreachable
-
-last_seen = {}
-
-
-def fetch_json(url, timeout=3):
-    try:
-        req = urllib.request.Request(url)
-        resp = urllib.request.urlopen(req, timeout=timeout)
-        return json.loads(resp.read().decode())
-    except Exception:
-        return None
-
-
-def collect_one(h):
-    """Collect GPU hardware + llama.cpp inference state for one host."""
-    name = h["name"]
-    host = h["host"]
-    now = time.time()
-
-    # GPU hardware from sidecar
-    gpu = fetch_json(f"http://{host}:8090/")
-
-    # llama.cpp inference state
-    llamacpp_health = fetch_json(f"http://{host}:{h['llama_port']}/health")
-    llamacpp_models = fetch_json(f"http://{host}:{h['llama_port']}/v1/models")
-
-    # Determine inference state
-    model_name = None
-    inference_state = "unknown"
-    if llamacpp_models:
-        models = llamacpp_models.get("data", [])
-        if models:
-            model_name = models[0].get("id")
-
-    if llamacpp_health:
-        status = llamacpp_health.get("status", "")
-        if status == "ok":
-            idle = llamacpp_health.get("slots_idle", 0)
-            processing = llamacpp_health.get("slots_processing", 0)
-            if idle and not processing:
-                inference_state = "idle"
-            elif processing:
-                inference_state = "busy"
-            else:
-                inference_state = "idle"
-
-    # Check for /slots endpoint for is_processing detail
-    slots = fetch_json(f"http://{host}:{h['llama_port']}/slots")
-    if slots and isinstance(slots, list) and len(slots) > 0:
-        if slots[0].get("is_processing"):
-            inference_state = "busy"
-
-    result = {
-        "host": name,
-        "gpu_name": h["gpu"],
-        "inference": {
-            "state": inference_state,
-            "model": model_name,
-        },
-        "hardware": gpu if gpu else None,
-        "online": gpu is not None,
-        "timestamp": now,
-    }
-
-    if gpu is not None:
-        last_seen[name] = now
-
-    if name in last_seen:
-        age = now - last_seen[name]
-        if age > DEAD_THRESHOLD:
-            result["online"] = False
-        elif age > STALE_THRESHOLD:
-            result["stale"] = True
-
-    return result
-
-
-def main():
-    print(f"GPU collector starting, output={OUTPUT}, interval={INTERVAL}s")
-    os.makedirs(os.path.dirname(OUTPUT), exist_ok=True)
-
-    while True:
-        start = time.time()
-        results = [collect_one(h) for h in HOSTS]
-
-        payload = {
-            "updated": start,
-            "gpus": results,
-        }
-
-        with open(OUTPUT + ".tmp", "w") as f:
-            json.dump(payload, f)
-        os.rename(OUTPUT + ".tmp", OUTPUT)
-
-        elapsed = time.time() - start
-        sleep_for = max(0, INTERVAL - elapsed)
-        time.sleep(sleep_for)
-
-
-if __name__ == "__main__":
-    main()

gpu-dashboard/gpu.html

@@ -1,183 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>GPU Monitor</title>
-<style>
-* { margin: 0; padding: 0; box-sizing: border-box; }
-body { background: #0d1117; color: #c9d1d9; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; padding: 20px; }
-h1 { font-size: 1.3em; margin-bottom: 4px; }
-.topbar { display: flex; justify-content: space-between; align-items: center; margin-bottom: 20px; padding-bottom: 12px; border-bottom: 1px solid #21262d; }
-.topbar .status { font-size: 0.85em; color: #8b949e; }
-.topbar .status .dot { display: inline-block; width: 8px; height: 8px; border-radius: 50%; margin-right: 6px; }
-.dot.green { background: #3fb950; }
-.dot.yellow { background: #d2991d; }
-.dot.red { background: #f85149; }
-.cards { display: grid; grid-template-columns: repeat(auto-fit, minmax(320px, 1fr)); gap: 16px; }
-.card { background: #161b22; border: 1px solid #21262d; border-radius: 8px; padding: 16px; }
-.card.stale { opacity: 0.5; }
-.card.dead { opacity: 0.3; border-color: #f85149; }
-.card-header { display: flex; justify-content: space-between; align-items: center; margin-bottom: 12px; }
-.card-header .name { font-weight: 600; font-size: 1.05em; }
-.card-header .host { font-size: 0.8em; color: #8b949e; }
-.card-header .state { font-size: 0.75em; padding: 2px 8px; border-radius: 10px; font-weight: 600; }
-.state.idle { background: #1b3826; color: #3fb950; }
-.state.busy { background: #3d1f1a; color: #f85149; }
-.state.unknown { background: #21262d; color: #8b949e; }
-.metric { margin-bottom: 10px; }
-.metric-label { display: flex; justify-content: space-between; font-size: 0.82em; color: #8b949e; margin-bottom: 2px; }
-.metric-label .val { color: #c9d1d9; font-weight: 500; }
-.bar { height: 6px; border-radius: 3px; background: #21262d; overflow: hidden; }
-.bar-fill { height: 100%; border-radius: 3px; transition: width 0.5s ease; }
-.bar-fill.temp-cool { background: #3fb950; }
-.bar-fill.temp-warm { background: #d2991d; }
-.bar-fill.temp-hot { background: #f85149; }
-.bar-fill.util { background: #58a6ff; }
-.bar-fill.vram { background: #bc8cff; }
-.bar-fill.power { background: #f0883e; }
-.model-line { font-size: 0.82em; color: #8b949e; margin-top: 8px; padding-top: 8px; border-top: 1px solid #21262d; }
-.model-line span { color: #c9d1d9; }
-.error { color: #f85149; font-size: 0.85em; }
-</style>
-</head>
-<body>
-<div class="topbar">
-  <div>
-    <h1><a href="/" style="color:#58a6ff;text-decoration:none;">← Workspace</a> · GPU Monitor</h1>
-    <span class="status"><span class="dot green" id="status-dot"></span><span id="status-text">Loading...</span></span>
-  </div>
-  <div class="status" id="age">—</div>
-</div>
-<div class="cards" id="cards"></div>
-
-<script>
-const INTERVAL = 5000;
-let lastFetchTime = null;
-
-function updateClock() {
-  const el = document.getElementById('age');
-  if (!lastFetchTime) { el.textContent = '—'; return; }
-  const age = Math.round((Date.now() / 1000) - lastFetchTime);
-  el.textContent = age <= 60 ? `updated ${age}s ago` : `stale ${age}s ago`;
-}
-setInterval(updateClock, 1000);
-
-const TEMP_WARN = 70, TEMP_HOT = 82;
-const VRAM_WARN = 80, VRAM_HOT = 92;
-
-function tempClass(c) { return c > TEMP_HOT ? 'temp-hot' : c > TEMP_WARN ? 'temp-warm' : 'temp-cool'; }
-function vramClass(pct) { return pct > VRAM_HOT ? 'temp-hot' : pct > VRAM_WARN ? 'temp-warm' : 'temp-cool'; }
-function pct(val, max) { return max ? Math.round(val / max * 100) : 0; }
-function mbToGB(mb) { return mb ? (mb / 1024).toFixed(1) : '—'; }
-
-function renderCard(g) {
-  const hw = g.hardware || {};
-  const inf = g.inference || {};
-  const online = g.online !== false;
-  const stale = g.stale === true;
-  let cardClass = '';
-  if (!online) cardClass = 'dead';
-  else if (stale) cardClass = 'stale';
-
-  let stateClass = inf.state || 'unknown';
-  let stateLabel = inf.state ? inf.state.toUpperCase() : 'UNKNOWN';
-  if (!online) { stateClass = 'unknown'; stateLabel = 'OFFLINE'; }
-
-  const temp = hw.temp_c;
-  const util = hw.gpu_util_pct;
-  const vramUsed = hw.vram_used_mb;
-  const vramTotal = hw.vram_total_mb;
-  const power = hw.power_w;
-  const powerLimit = hw.power_limit_w;
-  const fan = hw.fan_pct;
-  const vendor = hw.vendor;
-
-  let html = `<div class="card ${cardClass}">`;
-  html += `<div class="card-header">`;
-  html += `<div><div class="name">${g.gpu_name}</div><div class="host">${g.host}</div></div>`;
-  html += `<div class="state ${stateClass}">${stateLabel}</div>`;
-  html += `</div>`;
-
-  if (!online) {
-    html += `<div class="error">Unreachable</div>`;
-  } else if (hw.error) {
-    html += `<div class="error">${hw.error}</div>`;
-  } else {
-    // Temperature
-    if (temp != null) {
-      html += `<div class="metric"><div class="metric-label"><span>Temperature</span><span class="val">${temp}°C</span></div>`;
-      html += `<div class="bar"><div class="bar-fill ${tempClass(temp)}" style="width:${Math.min(temp,100)}%"></div></div></div>`;
-    }
-    // Utilization
-    if (util != null) {
-      html += `<div class="metric"><div class="metric-label"><span>GPU Utilization</span><span class="val">${util}%</span></div>`;
-      html += `<div class="bar"><div class="bar-fill util" style="width:${util}%"></div></div></div>`;
-    }
-    // VRAM
-    if (vramUsed != null && vramTotal != null) {
-      const vramPct = pct(vramUsed, vramTotal);
-      html += `<div class="metric"><div class="metric-label"><span>VRAM</span><span class="val">${mbToGB(vramUsed)} / ${mbToGB(vramTotal)} GB</span></div>`;
-      html += `<div class="bar"><div class="bar-fill ${vramClass(vramPct)}" style="width:${vramPct}%"></div></div></div>`;
-    }
-    // Power
-    if (power != null) {
-      const powerPct = powerLimit ? pct(power, powerLimit) : 0;
-      const powerText = powerLimit ? `${power}W / ${powerLimit}W` : `${power}W`;
-      html += `<div class="metric"><div class="metric-label"><span>Power</span><span class="val">${powerText}</span></div>`;
-      if (powerLimit) html += `<div class="bar"><div class="bar-fill power" style="width:${powerPct}%"></div></div>`;
-      html += `</div>`;
-    }
-    // Fan (NVIDIA only)
-    if (fan != null) {
-      html += `<div class="metric"><div class="metric-label"><span>Fan Speed</span><span class="val">${fan}%</span></div>`;
-      html += `<div class="bar"><div class="bar-fill util" style="width:${fan}%"></div></div></div>`;
-    }
-  }
-
-  // Model loaded
-  html += `<div class="model-line">Model: <span>${inf.model || '—'}</span></div>`;
-  html += `</div>`;
-  return html;
-}
-
-async function refresh() {
-  try {
-    const resp = await fetch('gpu_metrics.json?t=' + Date.now());
-    const data = await resp.json();
-    const gpus = data.gpus || [];
-
-    document.getElementById('cards').innerHTML = gpus.map(renderCard).join('');
-
-    // Top bar status
-    const online = gpus.filter(g => g.online !== false).length;
-    const total = gpus.length;
-    const dot = document.getElementById('status-dot');
-    const txt = document.getElementById('status-text');
-    if (online === total) { dot.className = 'dot green'; txt.textContent = `${online}/${total} online`; }
-    else if (online > 0) { dot.className = 'dot yellow'; txt.textContent = `${online}/${total} online`; }
-    else { dot.className = 'dot red'; txt.textContent = 'All offline'; }
-
-    // Capture fetch time for live clock
-    lastFetchTime = Date.now() / 1000;
-  } catch(e) {
-    document.getElementById('status-dot').className = 'dot red';
-    document.getElementById('status-text').textContent = 'Collector down';
-  }
-}
-
-// Render skeletons instantly
-const SKELETONS = [
-  {host:'amdpve', gpu_name:'AMD Strix Halo', hardware:{}, inference:{}, online:true},
-  {host:'llmgpu', gpu_name:'RTX 3090', hardware:{}, inference:{}, online:true},
-  {host:'ocu-llm', gpu_name:'RTX 5070', hardware:{}, inference:{}, online:true},
-];
-document.getElementById('cards').innerHTML = SKELETONS.map(g =>
-  `<div class="card"><div class="card-header"><div><div class="name">${g.gpu_name}</div><div class="host">${g.host}</div></div><div class="state unknown">···</div></div><div class="model-line" style="color:#8b949e;">Loading metrics...</div></div>`
-).join('');
-
-refresh();
-setInterval(refresh, INTERVAL);
-</script>
-</body>
-</html>

gpu-dashboard/gpu_collector.py

@@ -1,115 +0,0 @@
-#!/usr/bin/env python3
-"""GPU metrics collector — polls sidecars + llama.cpp every 10s, writes to Workspace."""
-
-import urllib.request, json, time, os
-
-HOSTS = [
-    {"name": "amdpve", "host": "192.168.68.15", "gpu": "AMD Strix Halo", "llama_port": 8080},
-    {"name": "llmgpu", "host": "192.168.68.8", "gpu": "RTX 3090", "llama_port": 8080},
-    {"name": "ocu-llm", "host": "192.168.68.110", "gpu": "RTX 5070", "llama_port": 8080},
-]
-OUTPUT = "/app/public/gpu_metrics.json"
-INTERVAL = 10
-STALE_THRESHOLD = 30  # seconds before marking stale
-DEAD_THRESHOLD = 60   # seconds before marking unreachable
-
-last_seen = {}
-
-
-def fetch_json(url, timeout=3):
-    try:
-        req = urllib.request.Request(url)
-        resp = urllib.request.urlopen(req, timeout=timeout)
-        return json.loads(resp.read().decode())
-    except Exception:
-        return None
-
-
-def collect_one(h):
-    """Collect GPU hardware + llama.cpp inference state for one host."""
-    name = h["name"]
-    host = h["host"]
-    now = time.time()
-
-    # GPU hardware from sidecar
-    gpu = fetch_json(f"http://{host}:8090/")
-
-    # llama.cpp inference state
-    llamacpp_health = fetch_json(f"http://{host}:{h['llama_port']}/health")
-    llamacpp_models = fetch_json(f"http://{host}:{h['llama_port']}/v1/models")
-
-    # Determine inference state
-    model_name = None
-    inference_state = "unknown"
-    if llamacpp_models:
-        models = llamacpp_models.get("data", [])
-        if models:
-            model_name = models[0].get("id")
-
-    if llamacpp_health:
-        status = llamacpp_health.get("status", "")
-        if status == "ok":
-            idle = llamacpp_health.get("slots_idle", 0)
-            processing = llamacpp_health.get("slots_processing", 0)
-            if idle and not processing:
-                inference_state = "idle"
-            elif processing:
-                inference_state = "busy"
-            else:
-                inference_state = "idle"
-
-    # Check for /slots endpoint for is_processing detail
-    slots = fetch_json(f"http://{host}:{h['llama_port']}/slots")
-    if slots and isinstance(slots, list) and len(slots) > 0:
-        if slots[0].get("is_processing"):
-            inference_state = "busy"
-
-    result = {
-        "host": name,
-        "gpu_name": h["gpu"],
-        "inference": {
-            "state": inference_state,
-            "model": model_name,
-        },
-        "hardware": gpu if gpu else None,
-        "online": gpu is not None,
-        "timestamp": now,
-    }
-
-    if gpu is not None:
-        last_seen[name] = now
-
-    if name in last_seen:
-        age = now - last_seen[name]
-        if age > DEAD_THRESHOLD:
-            result["online"] = False
-        elif age > STALE_THRESHOLD:
-            result["stale"] = True
-
-    return result
-
-
-def main():
-    print(f"GPU collector starting, output={OUTPUT}, interval={INTERVAL}s")
-    os.makedirs(os.path.dirname(OUTPUT), exist_ok=True)
-
-    while True:
-        start = time.time()
-        results = [collect_one(h) for h in HOSTS]
-
-        payload = {
-            "updated": start,
-            "gpus": results,
-        }
-
-        with open(OUTPUT + ".tmp", "w") as f:
-            json.dump(payload, f)
-        os.rename(OUTPUT + ".tmp", OUTPUT)
-
-        elapsed = time.time() - start
-        sleep_for = max(0, INTERVAL - elapsed)
-        time.sleep(sleep_for)
-
-
-if __name__ == "__main__":
-    main()

gpu-dashboard/start.sh

@@ -1,14 +0,0 @@
-#!/bin/bash
-set -e
-
-# Start collector as background process
-cd /root/hermes-workspace/public
-python3 /app/collector.py &
-COLLECTOR_PID=$!
-
-echo "Collector started (PID $COLLECTOR_PID)"
-echo "Serving dashboard on :8092"
-
-# Serve the public directory (contains gpu.html + gpu_metrics.json)
-cd /root/hermes-workspace/public
-python3 -m http.server 8092

gpu-router-docker.conf

@@ -13,7 +13,7 @@ upstream llmgpu_pool {
 }
 
 upstream ocu_llm_pool {
-    ## RTX 5070 — gemma-4 (Dense 4B) — Ultra-light tasks
+    ## New Backend — gemma-4-12b (VLM) — Vision + light tasks
     server 192.168.68.110:8080;
 }
 
@@ -24,12 +24,7 @@ upstream queue_service {
 
 upstream dashboard_service {
     ## Harness dashboard (Docker container)
-    server syslog-harness-dashboard-1:3001;
-}
-
-upstream gpu_dashboard_pool {
-    ## GPU dashboard (Docker container)
-    server syslog-harness-gpu-dashboard-1:8092;
+    server dashboard:3001;
 }
 
 ## ------------------------------------------------------------------
@@ -41,7 +36,7 @@ map $http_x_syslog_model $gpu_upstream {
     "heavy"          llmgpu_pool;
     "qwen3.5-27B"    llmgpu_pool;
     "light"          ocu_llm_pool;
-    "gemma-4"        ocu_llm_pool;
+    "gemma-4-12b"        ocu_llm_pool;
 }
 
 ## Rate limit zone — 10 req/s per IP, burst of 20
@@ -61,17 +56,6 @@ server {
         proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
     }
 
-    ## ------------------------------------------------------------------
-    ## GPU Dashboard — observability UI (MUST be before / catch-all)
-    ## ------------------------------------------------------------------
-    location /gpu {
-        proxy_pass http://gpu_dashboard_pool/;
-        proxy_set_header Host              $host;
-        proxy_set_header X-Real-IP         $remote_addr;
-        proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
-        proxy_set_header X-Forwarded-Proto $scheme;
-    }
-
     ## ------------------------------------------------------------------
     ## Main location — proxy to selected upstream
     ## ------------------------------------------------------------------

gpu-router.conf

@@ -0,0 +1,106 @@
+## Syslog GPU Router — Nginx Configuration
+## Routes incoming agent requests to the appropriate GPU backend
+## based on the X-Syslog-Model header.
+
+upstream amdpve_pool {
+    ## Strix Halo 395 — qwen3.6-35B-A3B (MoE) — Default workhorse
+    server 192.168.68.15:8080;
+}
+
+upstream llmgpu_pool {
+    ## RTX 3090 — qwen3.5-27B (Dense) — Heavy reasoning
+    server 192.168.68.8:8080;
+}
+
+upstream ocu_llm_pool {
+    ## New Backend — gemma-4-12b (VLM) — Vision + light tasks
+    server 192.168.68.110:8080;
+}
+
+upstream queue_service {
+    ## Agent queue with circuit breaker (Docker container)
+    server 127.0.0.1:8091;
+}
+
+upstream dashboard_service {
+    ## Harness dashboard (Docker container)
+    server 127.0.0.1:3001;
+}
+
+## ------------------------------------------------------------------
+## Mapping: X-Syslog-Model header → upstream backend
+## ------------------------------------------------------------------
+map $http_x_syslog_model $gpu_upstream {
+    default          amdpve_pool;   # missing header → default workhorse
+    "standard"       amdpve_pool;
+    "heavy"          llmgpu_pool;
+    "qwen3.5-27B"    llmgpu_pool;
+    "light"          ocu_llm_pool;
+    "gemma-4-12b"        ocu_llm_pool;
+}
+
+server {
+    listen 8080;
+    server_name _;
+
+    # Rate limit zone — 10 req/s per IP, burst of 20
+    limit_req_zone $binary_remote_addr zone=perip:10m rate=10r/s;
+
+    ## ------------------------------------------------------------------
+    ## Dashboard — observability UI (MUST be before / catch-all)
+    ## ------------------------------------------------------------------
+    location /dashboard {
+        proxy_pass http://dashboard_service/;
+        proxy_set_header Host              $host;
+        proxy_set_header X-Real-IP         $remote_addr;
+        proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
+    }
+
+    ## ------------------------------------------------------------------
+    ## Main location — proxy to selected upstream
+    ## ------------------------------------------------------------------
+    location / {
+        limit_req zone=perip burst=20 nodelay;
+        limit_req_status 503;
+        proxy_pass http://$gpu_upstream;
+
+        ## Preserve original host and headers
+        proxy_set_header Host              $host;
+        proxy_set_header X-Real-IP         $remote_addr;
+        proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+
+        ## Pass through the model header so backends can log it
+        proxy_pass_header X-Syslog-Model;
+
+        ## Streaming support (SSE for LLM responses)
+        proxy_buffering off;
+        proxy_cache     off;
+        proxy_read_timeout  300s;
+        proxy_send_timeout  300s;
+
+        ## Basic failover — retry on error or timeout
+        proxy_next_upstream error timeout http_502 http_503;
+        proxy_next_upstream_tries 2;
+
+        ## Add a response header for observability
+        add_header X-Routed-To $gpu_upstream always;
+
+        ## Fallback to queue when all GPU upstreams are down
+        error_page 502 503 504 = @queue_fallback;
+    }
+
+    ## ------------------------------------------------------------------
+    ## Queue fallback — enqueue when GPUs are unavailable
+    ## ------------------------------------------------------------------
+    location @queue_fallback {
+        rewrite ^ /enqueue break;
+        proxy_pass http://queue_service;
+        proxy_set_header Host              $host;
+        proxy_set_header X-Real-IP         $remote_addr;
+        proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+        proxy_set_header Content-Type      $content_type;
+        proxy_pass_request_body            on;
+    }
+}

litellm_config.yaml

@@ -0,0 +1,89 @@
+general_settings:
+  master_key: os.environ/LITELLM_MASTER_KEY
+  store_model_in_db: true
+guardrails:
+- guardrail_name: input-moderation
+  litellm_params:
+    guardrail: openai_moderation
+    mode: pre_call
+- guardrail_name: output-moderation
+  litellm_params:
+    guardrail: openai_moderation
+    mode: post_call
+- guardrail_name: harmful-content-filter
+  litellm_params:
+    categories:
+    - action: BLOCK
+      category: harmful_self_harm
+      enabled: true
+      severity_threshold: medium
+    - action: BLOCK
+      category: harmful_violence
+      enabled: true
+      severity_threshold: medium
+    - action: BLOCK
+      category: harmful_illegal_weapons
+      enabled: true
+      severity_threshold: medium
+    guardrail: litellm_content_filter
+    mode: pre_call
+litellm_settings:
+  failure_callback:
+  - prometheus
+  model_cost:
+    gemma-4-12b:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+    qwen3.6-27B-code:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+    qwen3.6-35B-A3B:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+    syslog-auto:
+      input_cost_per_token: 0.0
+      output_cost_per_token: 0.0
+  num_retries: 0
+  request_timeout: 600
+  set_verbose: true
+  sso_callback: /sso/callback
+model_list:
+- litellm_params:
+    api_base: http://router:9000/v1
+    api_key: os.environ/ROUTER_API_KEY
+    model: openai/syslog-auto
+    rpm: 600
+  model_name: syslog-auto
+- litellm_params:
+    api_base: http://router:9000/v1
+    api_key: os.environ/ROUTER_API_KEY
+    model: openai/qwen3.6-35B-A3B
+  model_name: qwen3.6-35B-A3B
+- litellm_params:
+    api_base: http://router:9000/v1
+    api_key: os.environ/ROUTER_API_KEY
+    model: openai/qwen3.6-27B-code
+  model_name: qwen3.6-27B-code
+- litellm_params:
+    api_base: http://router:9000/v1
+    api_key: os.environ/ROUTER_API_KEY
+    model: openai/gemma-4-12b
+  model_name: gemma-4-12b
+router_settings:
+  allowed_fails: 100
+  enable_loadbalancing_on_proxy: false
+  fallbacks:
+  - syslog-auto:
+    - qwen3.6-35B-A3B
+    - qwen3.6-27B-code
+    - gemma-4-12b
+  - qwen3.6-35B-A3B:
+    - qwen3.6-27B-code
+    - gemma-4-12b
+  - qwen3.6-27B-code:
+    - qwen3.6-35B-A3B
+    - gemma-4-12b
+  - gemma-4-12b:
+    - qwen3.6-27B-code
+    - qwen3.6-35B-A3B
+  routing_strategy: usage-based-routing

maintenance.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+# SyslogAI Harness — Automated Maintenance
+# Runs daily via cron
+
+LOG="/var/log/harness-maintenance.log"
+echo "=== $(date) ===" >> "$LOG"
+
+# 1. Clean Redis timeseries keys older than 60 days
+CUTOFF=$(date -d "60 days ago" +%Y%m%d%H)
+echo "Redis: removing ts:* keys older than $CUTOFF" >> "$LOG"
+DELETED=0
+for key in $(docker exec harness-redis redis-cli KEYS "ts:*" 2>/dev/null); do
+    TS=$(echo "$key" | grep -oP '\d{10}$')
+    if [ -n "$TS" ] && [ "$TS" -lt "$CUTOFF" ] 2>/dev/null; then
+        docker exec harness-redis redis-cli DEL "$key" > /dev/null 2>&1
+        DELETED=$((DELETED + 1))
+    fi
+done
+echo "Redis: deleted $DELETED stale timeseries keys" >> "$LOG"
+
+# 2. Log stale model keys (leftover from migrations)
+STALE=$(docker exec harness-redis redis-cli KEYS "*gemma*" 2>/dev/null)
+if [ -n "$STALE" ]; then
+    echo "WARNING: stale gemma keys found: $STALE" >> "$LOG"
+fi
+
+# 3. Prune Docker build cache (older than 7 days)
+echo "Docker: pruning build cache" >> "$LOG"
+docker builder prune -f --filter until=168h >> "$LOG" 2>&1
+
+# 4. Log container health status
+docker ps --format "table {{.Names}}\t{{.Status}}\t{{.RunningFor}}" >> "$LOG" 2>&1
+
+# 5. Log Redis memory
+docker exec harness-redis redis-cli INFO memory | grep used_memory_human >> "$LOG" 2>&1
+
+echo "" >> "$LOG"

nginx/nginx.conf

@@ -0,0 +1,174 @@
+events {
+    worker_connections 1024;
+}
+
+http {
+    include /etc/nginx/mime.types;
+    default_type application/octet-stream;
+    sendfile on;
+    keepalive_timeout 65;
+
+    # Docker DNS resolver — forces request-time resolution for variable-based proxy_pass.
+    # Without this, nginx resolves upstream hostnames at config load time,
+    # which fails when Docker DNS (127.0.0.11) isn't ready yet on container start.
+    resolver 127.0.0.11 valid=30s;
+
+    # Dynamic upstream resolution via nginx variables.
+    # Using $var in proxy_pass forces request-time resolution through the resolver.
+    # Without this, 'host not found in upstream' crashes nginx when Docker DNS is slow.
+    map $host $router_api_url {
+        default http://harness-router:9000;
+    }
+    map $host $dashboard_ui_url {
+        default http://harness-dashboard:3000;
+    }
+    map $host $litellm_backend_url {
+        default http://harness-litellm:4000;
+    }
+    # ════════════════════════════════════════════════════════════════
+    # Server :80 — harness entrypoint
+    #   dashboard (/), router API (/v1/, /admin/, /stream, /api/, /metrics),
+    #   router fallback, health
+    # ════════════════════════════════════════════════════════════════
+    server {
+        listen 80;
+
+        # Authentik OIDC subrequest
+        location /authentik/auth {
+            internal;
+            proxy_pass https://auth.sysloggh.net/outpost.goauthentik.io/auth/nginx;
+            proxy_pass_request_body off;
+            proxy_set_header Content-Length "";
+            proxy_set_header X-Original-URL $scheme://$http_host$request_uri;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        }
+
+        # 2-Layer: /v1/ → router (existing keys work unchanged)
+        location /v1/ {
+            proxy_pass $router_api_url;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header Authorization $http_authorization;
+            proxy_connect_timeout 10s;
+            proxy_read_timeout 600s;
+            proxy_buffering off;
+            error_page 502 503 = @router_fallback;
+        }
+
+        location @router_fallback {
+            proxy_pass $router_api_url;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header Authorization $http_authorization;
+            proxy_read_timeout 600s;
+        }
+
+        location /admin/ {
+            proxy_pass $router_api_url;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header Authorization $http_authorization;
+            proxy_read_timeout 600s;
+        }
+
+        location /stream {
+            proxy_pass $router_api_url/stream;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            proxy_buffering off;
+        }
+
+        location /api/ {
+            proxy_pass $router_api_url/;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+
+
+
+        location /dashboard/ {
+            proxy_pass $dashboard_ui_url/;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+
+        # LiteLLM redirect target /litellm (no trailing slash) -> add slash back
+        location = /litellm {
+            return 301 /litellm/;
+        }
+
+        # LiteLLM static assets (Next.js chunks, CSS, fonts)
+        location /litellm-asset-prefix/ {
+            proxy_pass $litellm_backend_url;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_connect_timeout 10s;
+            proxy_read_timeout 60s;
+        }
+
+        # LiteLLM admin UI and API proxy — strip /litellm prefix so /litellm/ui/ → /ui/
+        location /litellm/ {
+            rewrite ^/litellm(/.*)$ $1 break;
+            proxy_pass $litellm_backend_url;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            proxy_buffering off;
+        }
+
+        # Auth proxy to Authentik — accepts HTTP from LiteLLM, proxies HTTPS to .11 with SSL verify off
+        location /application/o/ {
+            proxy_pass https://192.168.68.11;
+            proxy_ssl_verify off;
+            proxy_set_header Host auth.sysloggh.net;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_connect_timeout 10s;
+            proxy_read_timeout 30s;
+        }
+
+        # All other requests → 404
+        location / {
+            return 404;
+        }
+
+        location /metrics/ {
+            proxy_pass $router_api_url/metrics/;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+
+        location /metrics/circuit-breaker {
+            proxy_pass $router_api_url/metrics/circuit-breaker;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+
+        location /router/ {
+            proxy_pass $router_api_url/;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+
+        location /health/unified {
+            proxy_pass $router_api_url/health/unified;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+
+        location /health {
+            proxy_pass $router_api_url/health;
+            proxy_http_version 1.1;
+            proxy_set_header Host $host;
+        }
+    }
+
+}

phase0-dual-keys.json

@@ -0,0 +1,20 @@
+{
+  "sk-syslog-local-master-key": {"tier": "enterprise", "agent": "admin", "deprecated": true},
+  "sk-33a0d6e6-a6da12fb483770d6f63f543b7e16a742": {"tier": "enterprise", "agent": "admin"},
+  "sk-syslog-abiba": {"tier": "enterprise", "agent": "Abiba", "deprecated": true},
+  "sk-6a6e49d0-e35e27c524ce0ba2de8f862a0e966b0c": {"tier": "enterprise", "agent": "Abiba"},
+  "sk-syslog-mumuni": {"tier": "enterprise", "agent": "Mumuni", "deprecated": true},
+  "sk-ba249c99-29e3b163a8d8ab7abb8663cae0dceb37": {"tier": "enterprise", "agent": "Mumuni"},
+  "sk-syslog-tanko": {"tier": "enterprise", "agent": "Tanko", "deprecated": true},
+  "sk-4a67112a-d9b62caf5a5881a15837df506f782708": {"tier": "enterprise", "agent": "Tanko"},
+  "sk-syslog-koby": {"tier": "enterprise", "agent": "Koby", "deprecated": true},
+  "sk-9dd66bba-316f616ec40cb51f3b489a0146bb986f": {"tier": "enterprise", "agent": "Koby"},
+  "sk-syslog-kagenz0": {"tier": "enterprise", "agent": "Kagenz0", "deprecated": true},
+  "sk-5a05020f-a984a8a20f731c5a65dfc07d51495bb3": {"tier": "enterprise", "agent": "Kagenz0"},
+  "sk-syslog-koonimo": {"tier": "enterprise", "agent": "Koonimo", "deprecated": true},
+  "sk-b2f0e1b2-70b7cf1c5a5e402a49b5469e88d9e335": {"tier": "enterprise", "agent": "Koonimo"},
+  "sk-starter-abc123": {"tier": "starter", "agent": "test-starter", "deprecated": true},
+  "sk-a8307a00-cd76daa07dd61c41aeb4545656f8b8c4": {"tier": "starter", "agent": "test-starter"},
+  "sk-professional-xyz789": {"tier": "professional", "agent": "test-pro", "deprecated": true},
+  "sk-b519a91e-4d2774c6c7da3fc31326e69ccc426781": {"tier": "professional", "agent": "test-pro"}
+}

queue-service/Dockerfile

@@ -1,10 +0,0 @@
-FROM python:3.13-slim
-
-RUN pip install --no-cache-dir flask redis
-
-COPY queue-service.py /app/queue-service.py
-WORKDIR /app
-
-EXPOSE 8091
-
-CMD ["python3", "queue-service.py"]

router/Dockerfile

@@ -0,0 +1,9 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY router.py .
+
+EXPOSE 9000
+CMD ["python", "router.py"]

router/http_patch.py

@@ -0,0 +1,90 @@
+# Insert streaming support before the gpu_resp call
+import re
+with open('/opt/inference-harness/router/router.py') as f:
+    code = f.read()
+
+# Find the gpu_resp block and replace with streaming-aware version
+old = '''        start = time.time()
+        gpu_resp = requests.post(
+            gpu_url + "/chat/completions",
+            json=req_data,
+            headers={"Content-Type": "application/json", "Authorization": "Bearer not-needed"},
+            timeout=120,
+        )
+        latency_ms = int((time.time() - start) * 1000)
+
+        if gpu_resp.status_code != 200:
+            log.error("GPU error: %s %s", gpu_resp.status_code, gpu_resp.text[:200])
+            return jsonify({"error": "GPU backend returned " + str(gpu_resp.status_code)}), 502
+
+        response_data = gpu_resp.json()
+        response_data = fix_reasoning_content(response_data)
+
+        response_data["routing"] = {
+            "model": model, "reason": reason, "gpu": gpu_url,
+            "tier": tier, "agent": agent, "latency_ms": latency_ms,
+        }
+
+        return jsonify(response_data)'''
+
+new = '''        start = time.time()
+        is_stream = req_data.get("stream", False)
+        
+        gpu_resp = requests.post(
+            gpu_url + "/chat/completions",
+            json=req_data,
+            headers={"Content-Type": "application/json", "Authorization": "Bearer not-needed"},
+            timeout=120,
+            stream=is_stream,
+        )
+        latency_ms = int((time.time() - start) * 1000)
+
+        if gpu_resp.status_code != 200:
+            log.error("GPU error: %s %s", gpu_resp.status_code, gpu_resp.text[:200])
+            return jsonify({"error": "GPU backend returned " + str(gpu_resp.status_code)}), 502
+
+        if is_stream:
+            # Stream response back to client
+            def generate():
+                first = True
+                for line in gpu_resp.iter_lines(decode_unicode=True):
+                    if line:
+                        if first and line.startswith("data: "):
+                            # Inject routing into first chunk
+                            try:
+                                chunk = json.loads(line[6:])
+                                chunk["routing"] = {
+                                    "model": model, "reason": reason, "gpu": gpu_url,
+                                    "tier": tier, "agent": agent, "latency_ms": latency_ms,
+                                }
+                                yield "data: " + json.dumps(chunk) + "\n\n"
+                                first = False
+                                continue
+                            except Exception:
+                                pass
+                        yield line + "\n"
+                yield "data: [DONE]\n\n"
+            return Response(stream_with_context(generate()), mimetype="text/event-stream")
+
+        response_data = gpu_resp.json()
+        response_data = fix_reasoning_content(response_data)
+
+        response_data["routing"] = {
+            "model": model, "reason": reason, "gpu": gpu_url,
+            "tier": tier, "agent": agent, "latency_ms": latency_ms,
+        }
+
+        return jsonify(response_data)'''
+
+code = code.replace(old, new)
+
+# Add missing import
+if 'from flask import Flask, request, jsonify' in code:
+    code = code.replace(
+        'from flask import Flask, request, jsonify',
+        'from flask import Flask, request, jsonify, Response, stream_with_context'
+    )
+
+with open('/opt/inference-harness/router/router.py', 'w') as f:
+    f.write(code)
+print('Streaming support added')

router/requirements.txt

@@ -0,0 +1,3 @@
+flask==3.1.*
+redis==5.2.*
+requests==2.32.*

router/route_v2.py

@@ -0,0 +1,77 @@
+def route(rd, tier, agent=""):
+    msgs = rd.get("messages",[]); t = estimate_tokens(msgs)
+    sys = any(m.get("role")=="system" for m in msgs)
+    turns = len([m for m in msgs if m.get("role") in ("user","assistant")])
+    hints = rd.get("routing_hints",{})
+    allowed = TIER_MODELS.get(tier, ["gemma-4-12b"])
+    avail = [m for m in available_models() if m in allowed]
+    if not avail: return {"model": allowed[0], "reason": "all_saturated", "saturated": True}
+    if all(is_gpu_busy(m) for m in avail):
+        return {"model": avail[0], "reason": "all_saturated", "saturated": True}
+
+    # GUARD: multimodal -> VLM only (sole vision model)
+    has_image = any(
+        isinstance(m.get("content"), list) and
+        any(p.get("type") == "image_url" for p in m["content"] if isinstance(p, dict))
+        for m in msgs
+    )
+    if has_image:
+        if "gemma-4-12b" in avail and not is_gpu_busy("gemma-4-12b"):
+            return {"model": "gemma-4-12b", "reason": "vision"}
+        elif "gemma-4-12b" in avail:
+            return {"model": "gemma-4-12b", "reason": "vision_saturated", "saturated": True}
+        else:
+            return {"model": allowed[0], "reason": "vision_unavailable"}
+
+    req = rd.get("model","auto")
+    if req != "auto":
+        target = req if req in avail else avail[0]
+        if is_gpu_busy(target) and req in allowed:
+            alts = [m for m in avail if m != target and m in allowed]
+            if alts:
+                alt = select_best_gpu(alts, "explicit", agent)
+                if alt: return alt
+        return {"model": target, "reason": "explicit"}
+
+    if hints:
+        if hints.get("priority")=="speed" and "gemma-4-12b" in avail:
+            return select_best_gpu(["gemma-4-12b"], "hint_speed", agent) or {"model":"gemma-4-12b","reason":"hint_speed"}
+        if hints.get("priority")=="quality" and "qwen3.6-35B-A3B" in avail:
+            return select_best_gpu(["qwen3.6-35B-A3B"], "hint_quality", agent) or {"model":"qwen3.6-35B-A3B","reason":"hint_quality"}
+        if hints.get("priority")=="code" and "qwen3.6-27B-code" in avail:
+            return select_best_gpu(["qwen3.6-27B-code"], "hint_code", agent) or {"model":"qwen3.6-27B-code","reason":"hint_code"}
+
+    first_msg = msgs[0].get("content","") if msgs else ""
+    words = len(first_msg.split()) if isinstance(first_msg, str) else 99
+
+    # TIER 1: Tiny - single-turn micro queries -> VLM (fastest)
+    if not sys and turns <= 1 and t <= 300 and words <= 100 and "gemma-4-12b" in avail:
+        if not is_gpu_busy("gemma-4-12b"):
+            return {"model":"gemma-4-12b","reason":"tiny"}
+        fallback = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(fallback, "tiny_fallback", agent)
+        if result: return result
+
+    # TIER 2: Light - moderate chat -> Dense primary, saves VLM for vision/speed
+    if t <= 5000 and turns <= 4:
+        candidates = [m for m in ["qwen3.6-27B-code","gemma-4-12b","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(candidates, "light", agent)
+        if result: return result
+
+    # TIER 3: Medium - quality matters -> MoE primary, Dense fallback
+    if t <= 30000:
+        candidates = [m for m in ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"] if m in avail]
+        result = select_best_gpu(candidates, "medium", agent)
+        if result: return result
+
+    # TIER 4: Heavy - big context needs big model -> MoE->Dense->VLM
+    if t > 30000:
+        candidates = [m for m in ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"] if m in avail]
+        result = select_best_gpu(candidates, "heavy", agent)
+        if result: return result
+
+    # TIER 5: Default - best quality first
+    candidates = [m for m in ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"] if m in avail]
+    result = select_best_gpu(candidates, "default", agent)
+    if result: return result
+    return {"model":avail[0],"reason":"last_resort"}

router/route_v3.py

@@ -0,0 +1,92 @@
+def route(rd, tier, agent=""):
+    msgs = rd.get("messages",[]); t = estimate_tokens(msgs)
+    sys = any(m.get("role")=="system" for m in msgs)
+    turns = len([m for m in msgs if m.get("role") in ("user","assistant")])
+    hints = rd.get("routing_hints",{})
+    allowed = TIER_MODELS.get(tier, ["gemma-4-12b"])
+    avail = [m for m in available_models() if m in allowed]
+    if not avail: return {"model": allowed[0], "reason": "all_saturated", "saturated": True}
+    if all(is_gpu_busy(m) for m in avail):
+        return {"model": avail[0], "reason": "all_saturated", "saturated": True}
+
+    # GUARD: multimodal -> VLM only (sole vision model)
+    has_image = any(
+        isinstance(m.get("content"), list) and
+        any(p.get("type") == "image_url" for p in m["content"] if isinstance(p, dict))
+        for m in msgs
+    )
+    if has_image:
+        if "gemma-4-12b" in avail and not is_gpu_busy("gemma-4-12b"):
+            return {"model": "gemma-4-12b", "reason": "vision"}
+        elif "gemma-4-12b" in avail:
+            return {"model": "gemma-4-12b", "reason": "vision_saturated", "saturated": True}
+        else:
+            return {"model": allowed[0], "reason": "vision_unavailable"}
+
+    req = rd.get("model","auto")
+    if req != "auto":
+        target = req if req in avail else avail[0]
+        if is_gpu_busy(target) and req in allowed:
+            alts = [m for m in avail if m != target and m in allowed]
+            if alts:
+                alt = select_best_gpu(alts, "explicit", agent)
+                if alt: return alt
+        return {"model": target, "reason": "explicit"}
+
+    if hints:
+        if hints.get("priority")=="speed" and "gemma-4-12b" in avail:
+            return select_best_gpu(["gemma-4-12b"], "hint_speed", agent) or {"model":"gemma-4-12b","reason":"hint_speed"}
+        if hints.get("priority")=="quality" and "qwen3.6-35B-A3B" in avail:
+            return select_best_gpu(["qwen3.6-35B-A3B"], "hint_quality", agent) or {"model":"qwen3.6-35B-A3B","reason":"hint_quality"}
+        if hints.get("priority")=="code" and "qwen3.6-27B-code" in avail:
+            return select_best_gpu(["qwen3.6-27B-code"], "hint_code", agent) or {"model":"qwen3.6-27B-code","reason":"hint_code"}
+
+    first_msg = msgs[0].get("content","") if msgs else ""
+    words = len(first_msg.split()) if isinstance(first_msg, str) else 99
+
+    # TIER 1: Tiny - single-turn micro queries -> VLM (fastest)
+    if not sys and turns <= 1 and t <= 300 and words <= 100 and "gemma-4-12b" in avail:
+        if not is_gpu_busy("gemma-4-12b"):
+            return {"model":"gemma-4-12b","reason":"tiny"}
+        fallback = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(fallback, "tiny_fallback", agent)
+        if result: return result
+
+    # TIER 2: Light - moderate chat -> Dense primary, saves VLM for vision/speed
+    if t <= 5000 and turns <= 4:
+        candidates = [m for m in ["qwen3.6-27B-code","gemma-4-12b","qwen3.6-35B-A3B"] if m in avail]
+        result = select_best_gpu(candidates, "light", agent)
+        if result: return result
+
+    # TIER 3: Medium - quality matters -> MoE primary (60%), Dense spillover (40%)
+    if t <= 30000:
+        candidates = moe_spillover(avail, ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"])
+        result = select_best_gpu(candidates, "medium", agent)
+        if result: return result
+
+    # TIER 4: Heavy - big context -> MoE primary (60%), Dense spillover (40%)
+    if t > 30000:
+        candidates = moe_spillover(avail, ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"])
+        result = select_best_gpu(candidates, "heavy", agent)
+        if result: return result
+
+    # TIER 5: Default - MoE primary (60%), Dense spillover (40%)
+    candidates = moe_spillover(avail, ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"])
+    result = select_best_gpu(candidates, "default", agent)
+    if result: return result
+    return {"model":avail[0],"reason":"last_resort"}
+
+
+def moe_spillover(avail, default_order):
+    """Spill 40% of MoE-first traffic to Dense to prevent Strix Halo overheating.
+    Only applies when MoE is first candidate, available, and not busy."""
+    import random
+    if (default_order[0] == "qwen3.6-35B-A3B" 
+        and "qwen3.6-35B-A3B" in avail 
+        and not is_gpu_busy("qwen3.6-35B-A3B")
+        and "qwen3.6-27B-code" in avail
+        and not is_gpu_busy("qwen3.6-27B-code")
+        and random.random() < 0.4):
+        # Swap: Dense first, MoE second
+        return ["qwen3.6-27B-code","qwen3.6-35B-A3B"] + [m for m in default_order[2:] if m in avail and m not in ("qwen3.6-27B-code","qwen3.6-35B-A3B")]
+    return [m for m in default_order if m in avail]

ssl/README.md

@@ -0,0 +1,6 @@
+# SSL Directory
+
+SSL termination is handled upstream by NetBird/Authentik.
+This directory is intentionally empty — no certs stored here.
+
+For local dev SSL, use the docker-compose.override.yml pattern.