prose-contracts/litellm-self-heal.prose.md

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. Script: `/opt/inference-harness/scripts/litellm-health-check.sh` on CT 116 (cron `0 */6 * * *`). 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 (/home/llmuser/llama-wrapper.sh, -c 262144 --parallel 2 --ngl 99)	256K	2
ocu-llm	192.168.68.110	NVIDIA RTX 5070 (12 GB)	gemma-4-12b	llama-server systemd (/home/llmuser/llama-wrapper.sh, --ctx-size 262144 --parallel 2, IQ4_NL + MTP draft)	256K	2
amdpve	192.168.68.15	AMD Strix Halo 64GB UMA	qwen3.6-35B-udq4 (LiteLLM alias: strix-moe)	llama-server systemd (Vulkan)	256K	2

Verified on ground 2026-07-16 via curl /v1/models on each host + llama-wrapper.sh. The AMD host's underlying model is qwen3.6-35B-udq4; LiteLLM exposes it under two model_names: qwen3.6-35B-udq4 and strix-moe (rpm 40). The legacy name ornith-1.0-35b does NOT exist in LiteLLM and must not be referenced.

LiteLLM Model Surface (ground truth — /opt/inference-harness/litellm_config.yaml on CT 116)

model_names served: qwen3.6-27B-code, gemma-4-12b, qwen3.6-35B-udq4, strix-moe, gpu-dense, gpu-light, syslog-auto.

• syslog-auto is a weighted router model: qwen3.6-27B-code (0.55, rpm 500) + qwen3.6-35B-udq4 (0.30, rpm 60) + gemma-4-12b (0.15, rpm 200).
• gpu-dense / gpu-light are high-rpm aliases (rpm 500) onto qwen3.6-27B-code / gemma-4-12b respectively.
• Key scoping: agent keys are restricted to ['syslog-auto','qwen3.6-27B-code','gemma-4-12b','strix-moe','gpu-dense','gpu-light']. As of 2026-07-16 the baggy/koby/mumuni/abiba-pi keys ALSO include qwen3.6-35B-udq4; abiba-pi additionally includes deepseek-v4-pro (cloud fallback). kagenz0/koonimo/pi-agents-unified have the standard 6 only. Agents should still use the stable alias strix-moe (not the raw qwen3.6-35B-udq4) so model swaps don't break them.
• NEVER use litellm_proxy_master_key (the sk-litellm-... master key) for inference. It is for admin endpoints only (/key/list, /key/generate, /key/info). All inference — agent traffic, health-check model tests, monitor scripts — uses agent-specific keys. The health-check script's model tests use a dedicated monitor agent key stored at /etc/litellm-monitor.env on CT 116 (root-only, chmod 600); /key/list is the only call that legitimately uses $MASTER_KEY.

Model Fallback Chains (LiteLLM)

Primary	Timeout	Fallback	Timeout
qwen3.6-27B-code	300s	gemma-4-12b	120s
gemma-4-12b	120s	qwen3.6-27B-code	300s
qwen3.6-35B-udq4 / strix-moe	300s	qwen → gemma	—
syslog-auto (balanced)	300s	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

Script Operations (synced 2026-07-16)

• Health-check script (/opt/inference-harness/scripts/litellm-health-check.sh on CT 116): gpu-fleet check fails only on critical alerts (warnings are informational). Tests strix-moe (not ornith-1.0-35b).
• GPU monitor (/root/scripts/gpu-monitor-server.py on pi .24): runs as systemd unit gpu-monitor.service (was bare & process). gpu_count includes Strix Halo (was 2, now 3). VRAM alert thresholds: warning 93%, critical 97% (raised from 90/95 — 256K context steady-state is ~96% on RTX 3090, not a fault).
• Agent key monitor (/root/scripts/agent-health-check.py on pi .24, cron */10): reads each agent's live LITELLM_API_KEY from its gateway process env via SSH — never hardcodes keys (hardcoded keys rot on rotation and caused 9×401/30min). Fleet roster: abiba, tanko, mumuni, koby, koonimo (legacy tdunna/baggy removed — never existed).
• Stale keys cleaned: daily-infra-report.py SYNTHETIC_API_KEY was stale (sk-U_ydi3B → 401); now reads LITELLM_MASTER_KEY from env. Deprecated scripts (router-original.py, router-phase0-backup.py, apply-fixes.py) still reference sk-syslog-local-master-key but do not actively poll LiteLLM.

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=strix-moe → 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
    }
  ]
}