- gpu-fleet: Updated all context references (128K→256K for RTX 3090). Fixed parallel count (2→1 for RTX 3090). Updated architecture diagram (router labeled deprecated/not in path). Corrected VRAM values. Updated dates: June→July 2026. - hermes-config-template: Fixed description (removed incorrect '128K on NVIDIA' claim). Updated to reflect verified 256K on RTX 3090. - litellm-self-heal: Updated last-verified date. - litellm-api-keys: Updated last-verified date.
9.5 KiB
kind, name, status, note, description
| kind | name | status | note | description |
|---|---|---|---|---|
| responsibility | litellm-self-heal | deployed | DEPLOYED 2026-07-12 on CT 116 cron: 0 */6 * * * Auto-remediation code was removed from the pi Zulip extension (retired 2026-07-04), now reimplemented as `litellm-health-check.sh` on CT 116. Reports to /var/log/litellm/health-*.json and RA-H OS knowledge graph. GPU monitoring integrated from gpu-monitor on .24:9100. Consolidated from litellm-health + litellm-self-heal on 2026-07-09 to eliminate duplication of architecture diagrams, GPU topology, timeout tables, and container lists. Health check is now § Health Check within this contract. Source of truth for GPU topology and keys: gpu-fleet.prose.md Last verified: 2026-07-12 | LiteLLM inference stack health monitoring + self-healing. Verifies the full nginx → LiteLLM → GPU chain, 8 containers on CT 116, 3 GPU hosts, model inference, and agent keys. Applies remediation rules for common failures. Reports every action via Zulip DM and RA-H OS knowledge graph. |
LiteLLM Operations — Health Check + Self-Heal
Architecture (v4.0.0 — Direct: nginx → LiteLLM → GPU)
Request → nginx:80 → LiteLLM:4000 → GPU(llama-server, parallel 2)
│
Key validation
Fallback chains
Budget tracking
│
Prometheus ← metrics
│
Grafana :3001
harness-router :9000 — DEPRECATED, container still runs but
NOT in request path. nginx routes /v1 → LiteLLM directly.
Router slot booking + circuit breakers replaced by
LiteLLM native fallbacks + timeouts.
Parameters
- public_url: string — Public LiteLLM URL (default: "https://litellm.sysloggh.net")
- backend_host: string — Internal CT host (default: "192.168.68.116")
- auth_host: string — Authentik OIDC server (default: "192.168.68.11")
- gpu_hosts: array — GPU inference hosts (default: ["192.168.68.8", "192.168.68.110", "192.168.68.15"])
- gpu_dashboard_url: string — Fleet dashboard (default: "http://192.168.68.24:9100")
- grafana_url: string — Grafana dashboards (default: "http://192.168.68.116:3001")
GPU Fleet Topology
| Host | IP | Hardware | Models Served | Engine | Context | Parallel |
|---|---|---|---|---|---|---|
| llm-gpu | 192.168.68.8 | NVIDIA RTX 3090 (24 GB) | qwen3.6-27B-code | llama-server systemd | 128K | 2 |
| ocu-llm | 192.168.68.110 | NVIDIA RTX 5070 (12 GB) | gemma-4-12b | llama-server systemd | 128K | 2 |
| amdpve | 192.168.68.15 | AMD Strix Halo 64GB UMA | ornith-1.0-35b | llama-server systemd (Vulkan) | 256K | 2 |
Model Fallback Chains (LiteLLM)
| Primary | Timeout | Fallback | Timeout |
|---|---|---|---|
| qwen3.6-27B-code | 90s | gemma-4-12b | 120s |
| gemma-4-12b | 120s | qwen3.6-27B-code | 90s |
| ornith-1.0-35b | 120s | qwen → gemma | — |
| syslog-auto (balanced) | 90s | qwen → gemma | — |
Global: request_timeout=300s, nginx proxy_read_timeout=600s
Containers on CT 116
| Container | Image | Port | Health Check |
|---|---|---|---|
| harness-litellm | berriai/litellm:1.90.0-rc.1 | :4000→:4001 | /health/liveliness |
| harness-router | inference-harness-router | :9000 (127.0.0.1) | /health (DEPRECATED — not in path) |
| harness-nginx | nginx:alpine | :80 | HTTP 200 on /health |
| harness-postgres | postgres:16-alpine | :5432 | pg_isready |
| harness-redis | redis:7-alpine | :6379 | PING |
| harness-dashboard | inference-harness-dashboard | :3000 | /health |
| harness-grafana | grafana/grafana | :3000→:3001 | /api/health |
| harness-prometheus | prom/prometheus | :9090 | /-/healthy |
| harness-docker-stats | python:3.12-alpine | — | container stats exporter |
| harness-pve-exporter | prompve/prometheus-pve-exporter | — | Proxmox metrics → Prometheus |
Maintains
- litellm-admin-ui: { status: "healthy", last_check: timestamp }
- litellm-api-docs: { status: "healthy", last_check: timestamp }
- litellm-containers: { status: "healthy", last_check: timestamp }
- litellm-oidc: { status: "healthy", last_check: timestamp }
- litellm-gpu-fleet: { status: "healthy", models: int, alerts: array }
- litellm-agent-keys: { count: int, valid: int }
Continuity
- Self-driven: check every 300 seconds
- Also wakes on user request
- On failure: re-check after 30s, escalate after 3 consecutive failures
Health Check
Run this first on every cycle. Results feed into remediation rules below.
1. Check public endpoints
- GET {{public_url}}/ui/ → expect 200 ("LiteLLM Dashboard")
- GET {{public_url}}/docs → expect 200 ("LiteLLM API - Swagger UI")
2. Check LiteLLM health (no-auth)
- GET http://{{backend_host}}/litellm/health/liveliness → expect 200
3. Check backend container health
- SSH to {{backend_host}} →
docker ps→ verify 10 containers healthy - Critical: harness-litellm, harness-nginx, harness-postgres
- Monitoring: harness-redis, harness-dashboard, harness-grafana, harness-prometheus, harness-docker-stats, harness-pve-exporter
- Deprecated but running: harness-router (not in path, reference only)
4. Check GPU fleet health (via fleet dashboard)
- GET {{gpu_dashboard_url}}/gpu-data → expect 200 with GPU metrics JSON
- Verify GPUs reporting status "healthy"
- Check alerts array for active warnings/critical
5. Check model inference via LiteLLM — test each model
- POST /v1/chat/completions model=gemma-4-12b → expect 200
- POST /v1/chat/completions model=qwen3.6-27B-code → expect 200
- POST /v1/chat/completions model=ornith-1.0-35b → expect 200
- Use master key for auth
6. Check agent keys
- GET /key/list with master key → verify all 6 agents have keys
7. Check Grafana
- GET {{grafana_url}}/api/health → expect 200
8. Compile overall status
Determine overall_status from individual check results:
- "healthy" — all checks pass
- "degraded" — 1-2 non-critical checks fail
- "down" — critical checks fail
Remediation Rules
Rule 1: Container Not Healthy
Detect → docker compose up -d <container> → verify → log
Escalate after 3 failures
Rule 2: Admin UI Non-200
Detect → restart harness-litellm → verify → log Escalate after 2 failures
Rule 3: Auth Unreachable
Detect → immediate escalate (external dependency)
Rule 4: Docs 404
Detect → fix DOCS_URL env var → verify → log
Rule 5: GPU Unreachable
Detect → SSH to GPU host fails or model not responding Fix → restart llama-server on host via SSH Escalate → immediately (downtime affects inference)
Rule 6: Model Not Responding
Detect → test prompt via LiteLLM returns non-200 Fix → restart llama-server on GPU host → verify Escalate → after 2 failed restarts
Rule 7: Router Returns 503 (All GPUs Saturated) — DEPRECATED
Router is no longer in the request path (2026-07-08). LiteLLM proxies directly to GPU. This rule is retained for reference but is inactive. If 503 errors occur, check LiteLLM timeouts and GPU health directly.
Rule 8: Agent Keys Invalid (401)
Detect → agents report auth errors Root cause → keys not in LiteLLM DB Fix → generate keys in LiteLLM via /key/generate → update /etc/environment on agent hosts Escalate → if SSH access unavailable, send Zulip DM
Rule 9: Stale Active Counter in Redis — DEPRECATED
Router no longer in path so Redis active counters are unused. Rule retained for reference but inactive. If Redis issues occur, check harness-redis container.
Reporting
Every remediation cycle produces a structured report:
1. RA-H OS Knowledge Graph Node
Created as [LEARN] litellm-self-heal: <run_id> with full JSON report.
2. Zulip DM to Owner
issues_fixed > 0— "🛠 LiteLLM Self-Heal — Fix Applied"issues_escalated > 0— "⚠ LiteLLM Self-Heal — Needs Your Attention"- Every 10th clean cycle — "✅ All Clear (10 checks passed)"
3. Daily Digest (end of day)
Summary of last 24 hours: total cycles, issues found/fixed/escalated, top actions, uptime.
4. Relay Message (if cross-agent)
If a fix requires another agent (e.g., Authentik restart), relay sent to responsible agent with full context.
Execution
-- Phase 1: Health Check
let health = call health-check
public_url: public_url
backend_host: backend_host
gpu_dashboard_url: gpu_dashboard_url
grafana_url: grafana_url
-- Phase 2: Apply remediation for each failure
let actions = []
for check in health.failed:
let fix = apply-remediation-rule
rule: lookup-rule(check.name)
target: check.target
push actions fix
-- Phase 3: Generate report
call report-generator
health: health
actions: actions
-- Phase 4: Log to knowledge graph
call kg-logger
run_id: run_id
health: health
actions: actions
-- Phase 5: Notify if anything changed
if actions.length > 0:
call zulip-notifier
actions: actions
health: health
-- Wait 300s and repeat
Audit Trail Format
{
"run_id": "self-heal-20260709-001",
"timestamp": "2026-07-09T14:00:00Z",
"duration_ms": 1234,
"checks_passed": 7,
"checks_failed": 0,
"issues_found": 0,
"issues_fixed": 0,
"issues_escalated": 0,
"actions": []
}
With failures:
{
"run_id": "self-heal-20260709-002",
"issues_found": 1,
"issues_fixed": 1,
"actions": [
{
"rule": "Container Not Healthy",
"target": "harness-nginx",
"action": "restarted container",
"result": "healthy",
"duration_ms": 5000
}
]
}