SyslogSolution/syslog-harness
7
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
492a4fe68bd2507d2d3dfc154976a221a850551a
syslog-harness/LITELLM-MIGRATION-PLAN.md
T

845 lines
31 KiB
Markdown
Raw Blame History

# 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 (40/30/30)
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` |
### 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):
```\nharness-litellm | ghcr.io/berriai/litellm:main-stable | 127.0.0.1:80814000
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 keytier) | 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: orgteamuser 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** | | Multi-provider: OpenAIAzureTogether | External model resilience |
| **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 40/30/30 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. 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
- Estimate tokens, detect system msg
- Count turns, check routing_hints
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:
# All three GPUs exposed as a single virtual "syslog-router" model
# LiteLLM passes through to our router, which handles actual GPU selection
- model_name: syslog-auto # Default auto-routing
litellm_params:
model: openai/syslog-auto # Using OpenAI-compatible format
api_base: http://router:9000/v1
api_key: os.environ/ROUTER_API_KEY
rpm: 600 # Cap total RPM across all GPUs
# Individual GPU pass-through (for explicit model requests)
- 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
# Note: All local GPU routing is handled by custom router
enable_loadbalancing_on_proxy: false # Disable LiteLLM's internal LB
allowed_fails: 0 # Set to 0 router's circuit breaker is authoritative
# Cost tracking: map model names to per-token pricing
# These are passed through from our router's X-Usage-Tokens header
```
### 4.4 Router Modifications (Engine Room Updates)
To accommodate LiteLLM, the `router-fixed.py` requires the following updates:
1. **New header passthrough**: Forward `X-LiteLLM-*` headers to GPU (transparent already works)
2. **New endpoint for health passthrough**: `GET /v1/models` already works
3. **Disable own key management**: Remove `/admin/keys/*` endpoints (migrate to LiteLLM UI)
4. **Keep ALL routing logic**: No changes to `route()`, `select_best_gpu()`, `check_gpu_health()`, slot management, etc.
5. **Add LiteLLM-compatible response**: Return `X-Usage-Tokens` header so LiteLLM can track token costs
```python
# ADD to router-fixed.py chat() response:
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 (30% weight):
```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 (more headroom, cooler, less power-constrained, less loaded)
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
# On CT 116 host (syslog-api)
echo "192.168.68.11 auth.sysloggh.net" >> /etc/hosts
```
Add to docker-compose.yml (see 7):
```yaml
extra_hosts:
- "auth.sysloggh.net:192.168.68.11"
```
2. **Deploy Postgres container** alongside existing services:
```bash
cd /opt/litellm
# Add postgres to docker-compose.yml
docker compose up -d postgres
```
*Note: Use a dedicated volume for `pgdata` to ensure LiteLLM database persistence.*
3. **Replace LiteLLM config** with production config.yaml (see 4.3)
- All 4 models `http://router:9000/v1`
- Add guardrails (pre-call, post-call, content filter)
- Set `num_retries: 0` (router handles retry)
- Set `request_timeout: 600`
4. **Deploy custom_sso.py** for Authentik OIDC integration
- Mount to LiteLLM container volume
- Reference in config.yaml: `custom_ui_sso_sign_in_handler: custom_sso.custom_ui_sso_sign_in_handler`
5. **Restart LiteLLM container** with new config
```bash
docker compose restart litellm
```
6. **Verify internal routing** LiteLLM Router pass-through works:
```bash
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"}]}'
```
**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 (watch `/metrics`)
### 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.**
```\nAgent LiteLLM (:4000) Router (:9000) GPU
(new, testing) (existing, unchanged)
Agent can also directly hit :9000 as fallback (unchanged)
```
**Tasks:**
1. **Create virtual keys for test agents** via LiteLLM UI or API:
```bash
curl -X POST http://127.0.0.1:4000/key/generate \
-H "Authorization: Bearer *** \
-H "Content-Type: application/json" \
-d '{"models":["syslog-auto"],"metadata":{"agent":"test"}}'
```
- Mirror existing API_KEYS in LiteLLM's key store
- Set per-key budgets (test with $100 cap)
2. **Verify pass-through works**
```bash
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"}]}'
```
3. **Run 24-hour shadow**: Both :4000 and :9000 active, agents use :9000
- Monitor LiteLLM spend logs vs router metrics confirm parity
- Verify GPU health metrics unaffected
- Check guardrails not generating false positives
**Zero-Downtime Guarantee:** Router :9000 remains the primary agent endpoint. LiteLLM :4000 is tested in parallel with no impact on production traffic.
### Phase 2: Cutover (Week 2) Gradual Agent Migration, Zero Cumulative Downtime
**Goal:** Move agents one-by-one to LiteLLM endpoint. Each agent is migrated individually other agents unaffected.
**Tasks:**
1. **Migrate API keys to LiteLLM virtual keys:**
- Create virtual key per agent in LiteLLM UI
- Set model access: `syslog-auto` (default), plus individual GPU models
- Set per-agent budget limits
- Create teams:
- "Core Agents" (Abiba, Mumuni, Tanko) enterprise tier
- "Dev Agents" (Kagenz0, Koby, Koonimo) professional tier
2. **Update agent configs one at a time:**
- Change `OPENAI_API_BASE` from `http://192.168.68.116:9000/v1` `http://192.168.68.116:4000/v1`
- Replace agent API keys with LiteLLM virtual keys
- Test each agent individually verify routing works
- **Per-agent downtime: <2 minutes**
3. **Migrate admin functions:**
- Key creation/revocation LiteLLM UI
- Rate limit management LiteLLM per-key RPM + router hardware RPM (dual enforcement)
- Deprecated key tracking LiteLLM UI key list
4. **Enable SSO via Authentik + custom_sso.py:**
- Authentik OAuth2 provider configured
- NGINX auth_request for /ui/* paths
- Custom UI SSO sign-in handler reads x-authentik-* headers
- Users authenticate with existing Authentik credentials
5. **Keep router :9000 accessible** as emergency fallback for 48 hours
- NGINX configured with router_fallback for 502 errors
- Agents can revert by changing `OPENAI_API_BASE` back to :9000
**Zero-Downtime Guarantee:** Each agent has <2 minutes downtime during config update. Router :9000 stays online throughout. Fallback path available for instant rollback.
### Phase 3: Production Hardening (Week 3+) Optimize & Scale
**Goal:** Lock down, optimize, monitor. Prepare for client-facing services.
**Tasks:**
1. **Remove deprecated router endpoints** (only after all agents migrated):
- Drop `/admin/keys/*` fully migrated to LiteLLM UI
- Drop Phase 0 dual-key logic (LiteLLM handles key rotation)
- Simplify `API_KEYS` to single `ROUTER_API_KEY`
2. **Add LiteLLM observability:**
- Prometheus metrics export
- Slack/email budget alerts via webhook Hermes
- Daily spend report webhook
3. **Enable LiteLLM caching** (Redis, shared with router):
```yaml
router_settings:
redis_host: os.environ/REDIS_HOST
redis_port: 6379
cache: true
cache_ttl: 3600
```
4. **Add external model fallbacks** for client-facing services:
- Add Anthropic Claude as fallback for code-heavy requests
- Add OpenAI GPT-4o as fallback for reasoning overflow
- LiteLLM's native fallback chains handle this cleanly
5. **Router slim-down:** Extract GPU health metrics to dedicated /health only
- Keep: routing, slots, health checks, performance recording
- Remove: key management, dual-key logic, admin endpoints
6. **Multi-tenancy setup** for client-facing inference services:
- Organization Team User hierarchy per client
- Per-client budget limits and guardrail policies
- Self-service onboarding via Authentik SSO LiteLLM UI
**Zero-Downtime Guarantee:** All changes are additive new features added while existing routing continues uninterrupted. Router remains the GPU intelligence layer throughout.
---
---
## 6. Nginx Configuration (with Authentik OIDC Forward Auth)
The existing nginx config routes `/admin/` router :9000. This MUST change:
```nginx
# OLD (remove)
# location /admin/ {
# proxy_pass http://127.0.0.1:9000/admin/;
# }
# === Authentik auth subrequest endpoint ===
location /authentik/auth {
internal;
# Proxy to Authentik's outpost on acerpve (192.168.68.11)
# Uses internal /etc/hosts resolution: auth.sysloggh.net 192.168.68.11
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 (for OIDC redirect flow) ===
location /sso/callback {
proxy_pass http://127.0.0.1:4000/sso/callback;
proxy_set_header Host $host;
}
# === API endpoint Bearer token auth (no Authentik) ===
location /v1/ {
# Primary: LiteLLM gateway
proxy_pass http://127.0.0.1:4000/v1/;
proxy_set_header Host $host;
proxy_read_timeout 600s;
# Fallback: direct router (if LiteLLM down)
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 (needs master_key, not Authentik) ===
location /key/ {
proxy_pass http://127.0.0.1:4000/key/;
proxy_set_header Host $host;
proxy_set_header Authorization $http_authorization;
}
# Keep router metrics accessible (not behind LiteLLM)
location /router/ {
proxy_pass http://127.0.0.1:9000/;
}
# Health check combines both layers
location /health {
proxy_pass http://127.0.0.1:4000/health;
}
```
---
---
## 7. Docker Compose (`docker-compose.yml` on CT 116)
**Deployment host:** CT 116 `syslog-api` (192.168.68.116) on minipve. All services co-located.
```yaml
services:
# Layer 1: LiteLLM Gateway (Policy & Admin)
litellm:
image: ghcr.io/berriai/litellm:main-stable
network_mode: "host"
extra_hosts:
- "auth.sysloggh.net:192.168.68.11"
volumes:
- ./config.yaml:/app/config.yaml:ro
- ./custom_sso.py:/app/custom_sso.py:ro
environment:
- LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY}
- DATABASE_URL=postgresql://litellm:***@localhost:5432/litellm
- STORE_MODEL_IN_DB=True
- ROUTER_API_KEY=${ROUTER_API_KEY}
- OPENAI_API_KEY=*** # Optional: external fallback
- ANTHROPIC_API_KEY=${ANTH...KEY} # Optional: external fallback
- PROXY_BASE_URL=https://litellm.sysloggh.net
command:
- --config
- /app/config.yaml
- --port
- "4000"
depends_on:
postgres:
condition: service_healthy
restart: unless-stopped
# Database for LiteLLM
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 also needs auth resolution
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
# Layer 2: Custom Router (Intelligence & Hardware)
# Already deployed separately not in this compose file
# The router is managed by the existing harness deployment on CT 116
volumes:
pgdata:
```
---
---
## 8. Risk Mitigation
| Risk | Mitigation |
|------|------------|
| LiteLLM adds latency overhead | Shadow mode measures: <50ms extra is acceptable for admin features. LiteLLM is a thin proxy. |
| LiteLLM down = all agents down | NGINX fallback to router :9000 direct (see 6). Agents can also be configured with dual endpoints. |
| Key sync drift (LiteLLM keys router keys) | Single-source: LiteLLM is key authority. Router uses one `ROUTER_API_KEY` from LiteLLM's perspective. Agent keys live in LiteLLM only. |
| Spend tracking inaccurate for local GPUs | Configure `model_cost` per GPU with $0 rate (self-hosted). Optionally track "internal cost" via custom pricing. |
| Double rate limiting (LiteLLM + Router) | Keep both intentionally: LiteLLM for per-user soft caps, Router for hardware protection. Non-overlapping concerns. |
| PostgreSQL failure | LiteLLM can run with SQLite fallback, but UI features degrade. Postgres is the recommended path. |
| Router custom logic becomes a black box to LiteLLM | Acceptable trade-off. LiteLLM sees router as opaque OpenAI endpoint. GPU-level routing decisions are router's domain. |
---
---
## 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 | Google/GitHub/Microsoft OIDC |
| Budget enforcement | None | Automatic: key suspended at $limit |
| GPU routing intelligence | Full (unchanged) | Full (unchanged) |
| GPU health monitoring | Full (unchanged) | Full (unchanged) |
---
---
## 10. Migration Commands (Quick Reference) CT 116
**Target:** CT 116 `syslog-api` (192.168.68.116), minipve. All services co-located.
```bash
# On CT 116 (SSH via minipve: pct exec 116 bash):
# === Phase 0: Infrastructure Prep ===
# 0. DNS split-horizon fix
echo "192.168.68.11 auth.sysloggh.net" >> /etc/hosts
getent hosts auth.sysloggh.net # Verify 192.168.68.11
# 1. Navigate to harness deployment directory
cd /opt/litellm
# 2. Deploy Postgres (LiteLLM DB)
docker compose up -d postgres
# 3. Replace LiteLLM config with production config.yaml (see 4.3)
# - All models http://router:9000/v1
# - Add guardrails (pre-call, post-call, content filter)
# - Set num_retries: 0 (router handles retry)
# - Set request_timeout: 600
# 4. Deploy custom_sso.py
# - Mount to LiteLLM container volume
# - Reference in config.yaml: custom_ui_sso_sign_in_handler: custom_sso.custom_ui_sso_sign_in_handler
# 5. Restart LiteLLM container
docker compose restart litellm
# 6. Verify internal routing
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
**Branching Strategy:**
- `main` production-ready code
- `feature/litellm-migration` current development branch
- `deploy/phase-0`, `deploy/phase-1`, etc. deployment-specific branches
**Conventional Commits:**
```
<type>(<scope>): <description>
- feat(plan): update LiteLLM migration plan for CT 116 deployment with Authentik OIDC + zero-downtime strategy
- fix(router): handle None temp_c/vram_pct in gpu_health_score
- chore(docker): add postgres volume for LiteLLM database persistence
- docs: LiteLLM migration plan two-layer architecture with model identity gap analysis
```
**Deployment Flow:**
1. Commit changes to `feature/litellm-migration`
2. Open PR to `main`
3. Review with `git diff --stat`
4. Merge to `main` after approval
5. Run deployment scripts in order: Phase 0 Phase 1 Phase 2 Phase 3
---
## 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
**Verification:**
- Check LiteLLM `/health` endpoint
- Verify GPU metrics via router `/metrics`
- Test each agent individually
- Confirm no 502/503 errors in logs
---
## Appendix A: Model Identity Gap Analysis
| Model | GPU | VRAM | Context | Status |
|-------|-----|------|---------|--------|
| qwen3.6-35B-A3B | MoE/Strix | 65GB | 262K | Healthy |
| qwen3.6-27B-code | Dense/RTX3090 | 24GB | 262K | Healthy |
| gemma-4-12b | VLM/RTX 5070 | 12GB | 262K | Healthy |
**Notes:**
- All three GPUs are operational and available for LiteLLM routing
- GPU health scoring (40/30/30) prevents routing to unhealthy GPUs
- Router handles all GPU-level routing decisions LiteLLM sees them as opaque endpoints
---
## Appendix B: LiteLLM Virtual Key Migration
**Current API Keys LiteLLM Virtual Keys:**
| 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`
- Circuit breaker metrics `http://127.0.0.1:9000/metrics/circuit-breaker`
**Alerts:**
- GPU health score > 70 alert to Hermes
- Circuit breaker trip alert to Hermes
- LiteLLM spend > $100/day alert to Hermes
- LiteLLM latency > 1000ms alert to Hermes
Reference in New Issue View Git Blame Copy Permalink