# 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