litellm-huawei-maas-proxy

name

LiteLLM-Huawei-MaaS-Proxy

description

Deploy, configure, validate, troubleshoot, or extend an OpenAI-compatible API proxy backed by PostgreSQL, Prometheus, and Grafana, routing through Huawei ModelArts MaaS (ap-southeast-1) with multi-key load balancing. TRIGGER when the task involves LiteLLM proxy deployment, Docker Compose stack with litellm_config.yaml, Huawei MaaS model routing, virtual key or budget management, Prometheus/Grafana observability for LLM traffic, custom_callbacks.py TTFT/TPOT/ITL metrics, multi-key load balancing, an optional self-hosted SearXNG search MCP (`web_search`/`fetch_url`) as an alternative to Exa, wiring a `claude-glm` (claude-code-router) client through the proxy, or any reference to `LITELLM_MASTER_KEY`, `HUAWEI_MAAS_API_KEY`, `MCP_TOKEN`, or `docker compose` with this stack.

LiteLLM Huawei MaaS Proxy

Deploy an OpenAI-compatible API proxy backed by PostgreSQL, Prometheus, and Grafana, routing through Huawei ModelArts MaaS (ap-southeast-1) with multi-key load balancing.

This repo ships runtime stack files for deterministic clone-and-run deployment. The file templates below serve as reference for understanding each file or building from scratch when git is not available.

When to Use

Situation	Route
Deploy the full stack from scratch	Follow Deployment Workflow
Add or modify a model in the proxy	Follow Adding a new model
Troubleshoot a broken deployment	Follow Repair Playbook, then Common failure modes
Validate an existing deployment	Follow Validation Sequence
Manage virtual keys, budgets, or teams	Follow Virtual key management
Extend observability (custom metrics, dashboards)	Read Metrics and Grafana Dashboard sections
Backup, restore, or reset data	Follow Operations
Add self-hosted search, or wire a claude-glm client	Follow Optional: SearXNG Search MCP and claude-glm Client

When NOT to use:

• Direct MaaS API calls without proxy (no spend tracking, no rate limiting)
• Non-Huawei LLM providers (this stack is MaaS-specific)
• Multi-host / Kubernetes deployment (this is a single-host Docker Compose stack)

Required Inputs

Confirm before making changes:

• Huawei MaaS API key — from ModelArts MaaS console. Mandatory main key.
• Additional MaaS API keys (optional) — for load balancing. Each extra key multiplies effective RPM/TPM per model.
• Huawei MaaS API base — https://api-ap-southeast-1.modelarts-maas.com/openai/v1 (ap-southeast-1 / CN-Hong Kong). Do not swap regions without re-validating models and quotas.
• Explicit MaaS model IDs to expose (e.g. glm-5.1, deepseek-v4-flash). Verify in MaaS console — do not guess. If the user only gives one model, prefer explicit routing for that model instead of adding all five.
• LiteLLM listen port — default 4000. Override in docker-compose.yml ports section if colliding with an existing service.
• Prometheus listen port — default 9090.
• Grafana listen port — default 3000.
• Prometheus retention — default 15d. Adjust for disk capacity.
• Whether virtual keys already exist — if yes, LITELLM_SALT_KEY is immutable and cannot be changed.
• Docker 20.10+ with Compose V2 on the target host.

All of these are collected by ./scripts/init_env.sh (interactive, --auto, or --ci mode). The user can always choose manual .env editing instead.

Core Rules

• Never commit .env, real API keys, virtual keys, or bearer tokens. Secrets live in .env (gitignored) with 0600 permissions.
• Never change LITELLM_SALT_KEY after virtual keys exist. Recovery requires docker compose down -v and fresh start.
• Model names are case-sensitive. Must match MaaS console exactly.
• MaaS is region-locked to ap-southeast-1.
• LiteLLM config is generated by scripts/generate_config.sh. Do not edit litellm_config.yaml directly — it will be overwritten. Edit .env and re-run generate_config.sh.
• LiteLLM config is read-only at startup. Changes require docker compose restart litellm.
• Every model must have non-zero input_cost_per_token and output_cost_per_token for budget enforcement to work.
• Keep master key admin-only. Mint child virtual keys per team/service/environment.
• Make proxy the only egress path for MaaS traffic so budgets, rate limits, and spend logs stay centralized.
• STORE_MODEL_IN_DB: True — DB models take precedence over config file models.
• drop_params: True — unsupported parameters silently dropped rather than causing errors.
• TTFT and ITL custom metrics are streaming-only.
• Search injection is LiteLLM-side. custom_callbacks.py detects search intent and calls Exa when EXA_API_KEY is available.
• Image routing is LiteLLM-side. custom_callbacks.py detects image_url / image content blocks and rewrites the request to vision-openrouter.
• Claude Code search should strip local WebSearch/WebFetch in CCR. Pair this proxy with the claude-code-maas-direct-router CCR bridge so GLM is not asked to emit fragile search tool JSON.
• With N MaaS API keys, each model has N deployments. LiteLLM load-balances across them. Effective RPM/TPM = per-key × N.
• HUAWEI_MAAS_API_KEY is mandatory. Extra keys are optional. HUAWEI_MAAS_API_KEY_COUNT and HUAWEI_MAAS_API_KEY_N are set by init_env.sh.

Multi-Key Load Balancing

The proxy supports multiple MaaS API keys for increased throughput and resilience:

Concept	Detail
Main key	HUAWEI_MAAS_API_KEY — mandatory, always required
Extra keys	Optional, collected by init_env.sh or set via HUAWEI_MAAS_EXTRA_API_KEYS (CI mode)
Internal vars	HUAWEI_MAAS_API_KEY_COUNT=N, HUAWEI_MAAS_API_KEY_0 through _N-1 — set by init_env.sh
Config generation	scripts/generate_config.sh reads .env and generates litellm_config.yaml with N deployments per model
Routing strategy	simple-shuffle (default) — round-robin with random shuffle. Alternatives: least-busy, latency-based-routing
Cooldown	cooldown_time: 30, allowed_fails: 3 — failed deployments temporarily removed from rotation
Effective capacity	RPM/TPM per model = per-key × N keys
Backward compatible	Single key (N=1) = identical behavior to before

Adding/removing MaaS API keys

Add a key:

1. Add HUAWEI_MAAS_API_KEY_N=<key> to .env (next index)
2. Increment HUAWEI_MAAS_API_KEY_COUNT
3. Run scripts/generate_config.sh
4. Run docker compose restart litellm

Remove a key:

1. Remove the HUAWEI_MAAS_API_KEY_N line from .env
2. Re-number remaining keys to be contiguous (_0, _1, ...)
3. Update HUAWEI_MAAS_API_KEY_COUNT
4. Run scripts/generate_config.sh
5. Run docker compose restart litellm

Change routing strategy:

./scripts/generate_config.sh --routing-strategy=least-busy
docker compose restart litellm

Architecture

Client → LiteLLM (:4000) → Huawei MaaS (ap-southeast-1)
               │               │
               │          ┌────┴────┐
               │          │ N API   │  (N = HUAWEI_MAAS_API_KEY_COUNT)
               │          │ keys    │  LiteLLM load-balances across N deployments
               │          └────────┘
               ├── PostgreSQL (:5432)  — keys, usage, spend
               ├── Prometheus (:9090)  — /metrics scrape every 15s
               └── Grafana   (:3000)  — pre-built dashboard

Startup chain: PostgreSQL (pg_isready) → LiteLLM (/health/liveliness) → Prometheus (scrape) → Grafana.

Request flow: Client → LiteLLM:4000 → (router selects deployment) → Huawei MaaS. LiteLLM logs usage/spend to PostgreSQL, exposes /metrics for Prometheus, returns response.

With N MaaS API keys, each model has N deployments. LiteLLM's router distributes requests across deployments using the configured routing_strategy (default: simple-shuffle). Total effective RPM/TPM = per-key × N.

Multi-Key Load Balancing

The proxy supports multiple MaaS API keys for load balancing and increased throughput:

Environment variables

Variable	Set by	Description
HUAWEI_MAAS_API_KEY	Manual / init_env.sh	Main MaaS API key (mandatory)
HUAWEI_MAAS_API_KEY_COUNT	init_env.sh	Total number of keys (1 + extra)
HUAWEI_MAAS_API_KEY_0	init_env.sh	Indexed key 0 (same as main key)
HUAWEI_MAAS_API_KEY_N	init_env.sh	Indexed keys 1, 2, 3...
HUAWEI_MAAS_EXTRA_API_KEYS	Manual (CI mode)	Comma-separated extra keys

Config generation

• litellm_config.yaml is generated by scripts/generate_config.sh from litellm_config.yaml.example
• The generated file is gitignored — never edit it directly
• With N keys, each model has N deployments (e.g., glm-5.1 → glm-5.1--maas-key-0, glm-5.1--maas-key-1, ...)
• Total effective RPM/TPM = per-key × N

Router settings

Setting	Value	Purpose
routing_strategy	simple-shuffle	Random selection across healthy deployments
cooldown_time	30	Seconds to remove failed deployment from rotation
allowed_fails	3	Failures before cooldown triggers

Backward compatibility

Single key = identical behavior to before. No changes required for existing single-key deployments.

Codebase

.
├── README.md                                       human-facing overview
├── SKILL.md                                        agent-facing workflow (this file)
├── docker-compose.yml                              4-service orchestrator (references assets/config/)
├── assets/config/
│   ├── litellm_config.yaml.example                model catalog example (tracked in git)
│   ├── litellm_config.yaml                         generated config (gitignored)
│   ├── custom_callbacks.py                         TTFT/TPOT/ITL Prometheus histograms
│   ├── rolling_budget_hook.py                       three-tier rolling-window budget (key/user/team)
│   ├── prometheus.yml                              15s scrape → litellm:4000
│   ├── .env.example                                environment template
│   └── grafana/
│       └── provisioning/
│           ├── datasources/prometheus.yml           auto-linked Prometheus datasource
│           └── dashboards/
│               ├── dashboards.yml                   file-based provider, 30s refresh
│               └── litellm_overview.json            pre-built overview dashboard
├── references/
│   ├── architecture.md                              topology, services, volumes, environment
│   ├── metrics-and-dashboards.md                    PromQL, custom metrics, Grafana panel config
│   ├── operations.md                                health checks, backup, restart, usage, endpoints
│   └── troubleshooting.md                           repair playbook, failure modes, common mistakes
├── patches/                                         patched litellm source mounted over the image
│   ├── proxy_server.py                              Responses-API streaming adaptation
│   ├── utils.py                                     async chunk wrapper tolerance
│   └── README.md                                    what changed + how to refresh per image tag
├── adapter/                                         optional Anthropic-format adapter (profile: adapter)
│   ├── server.js                                    Anthropic Messages → LiteLLM /v1/chat/completions
│   └── start.sh / stop.sh                           run outside Docker
├── demo/                                            rolling-budget live demo (mock model + scripts)
├── scripts/
│   ├── init_env.sh                                  interactive .env setup (manual or agent-guided)
│   ├── generate_config.sh                           generates litellm_config.yaml from .env
│   ├── bootstrap_finops_team.py                     create a team + scoped virtual key
│   └── validate_e2e.sh                              12-step end-to-end validation
├── .env                                             actual secrets (gitignored)
└── .gitignore                                       .env and litellm_config.yaml

File-by-file reference

File	Role	Key details
docker-compose.yml	Service orchestration	YAML anchor, 4 services with healthcheck chain, named volumes, mounts from ./assets/config/
assets/config/litellm_config.yaml.example	Model catalog example	openai/ prefix + MaaS endpoint, tpm/rpm per model, per-token pricing, tracked in git
assets/config/litellm_config.yaml	Generated config	Created by generate_config.sh, gitignored, N deployments per model
assets/config/custom_callbacks.py	Custom callback	PrometheusTTFTTPOTITL(CustomLogger), metrics, Exa search injection, image routing, Responses tool repair, invalid reasoning_effort stripping
assets/config/rolling_budget_hook.py	Pre-call budget hook	Three-tier rolling-window budget from LiteLLM_SpendLogs, 429 on overage, BUDGET_TIER_* env, fail-open on DB error
patches/proxy_server.py, patches/utils.py	Image source patches	Mounted over editable + site-packages copies; adapt Responses-API streaming. Re-derive per image tag (see patches/README.md)
adapter/server.js	Optional adapter	Anthropic Messages → LiteLLM /v1/chat/completions; --profile adapter, port 4010
assets/config/prometheus.yml	Scrape config	Single job litellm at 15s interval
assets/config/grafana/provisioning/datasources/prometheus.yml	Datasource	Prometheus type, proxy access, http://prometheus:9090
assets/config/grafana/provisioning/dashboards/dashboards.yml	Dashboard provider	File-based, org 1, 30s update interval
assets/config/grafana/provisioning/dashboards/litellm_overview.json	Pre-built dashboard	UID litellm-overview, 10s auto-refresh, template variables: model, datasource, includes Deployment Load Balancing row
scripts/generate_config.sh	Config generator	Reads .env, generates litellm_config.yaml from template, creates N deployments per model

Docker Compose Services

Service	Image	Container name	Port	Healthcheck	Depends on
litellm	ghcr.io/berriai/litellm:v1.83.14-stable.patch.3	litellm_proxy	4000:4000	GET /health/liveliness every 30s, 10s timeout, 3 retries, 40s start period	db (healthy)
db	postgres:16-alpine	litellm_pg_db	(internal 5432)	pg_isready every 5s, 5s timeout, 10 retries	—
prometheus	prom/prometheus:v3.3.1	litellm_prometheus	9090:9090	GET /-/healthy every 15s, 5s timeout, 3 retries, 10s start period	litellm (healthy)
grafana	grafana/grafana:11.5.2	litellm_grafana	3000:3000	GET /api/health every 15s, 5s timeout, 3 retries, 15s start period	prometheus (healthy)

Volume mounts

Service	Host path	Container path	Mode
litellm	./assets/config/litellm_config.yaml	/app/config.yaml	ro (generated file)
litellm	./assets/config/custom_callbacks.py	/app/custom_callbacks.py	ro
litellm	./assets/config/rolling_budget_hook.py	/app/rolling_budget_hook.py	ro
litellm	./patches/proxy_server.py	/app/litellm/proxy/proxy_server.py (+ site-packages copy)	ro
litellm	./patches/utils.py	/app/litellm/proxy/utils.py (+ site-packages copy)	ro
db	postgres_data volume	/var/lib/postgresql/data	rw
prometheus	./assets/config/prometheus.yml	/etc/prometheus/prometheus.yml	ro
prometheus	prometheus_data volume	/prometheus	rw
grafana	./assets/config/grafana/provisioning	/etc/grafana/provisioning	ro
grafana	grafana_data volume	/var/lib/grafana	rw

Named volumes

Volume name	Survives down?	Removed by
litellm_postgres_data	Yes	docker compose down -v
litellm_prometheus_data	Yes	docker compose down -v
litellm_grafana_data	Yes	docker compose down -v

LiteLLM container environment

Set via env_file: .env plus explicit environment:

Variable	Source	Value
DATABASE_URL	docker-compose	postgresql://llmproxy:${DB_PASSWORD}@db:5432/litellm
STORE_MODEL_IN_DB	docker-compose	True
BUDGET_TIER_KEY / _USER / _TEAM	docker-compose / .env	Rolling-window budget tiers, <n><s|m|h|d>:<usd>; default unlimited
LITELLM_MASTER_KEY	.env	Admin key, must start with sk-
LITELLM_SALT_KEY	.env	Key encryption salt
HUAWEI_MAAS_API_KEY	.env	Main Huawei MaaS API key
HUAWEI_MAAS_API_KEY_N	.env	Indexed MaaS API keys (0, 1, 2...)
HUAWEI_MAAS_API_KEY_COUNT	.env	Number of MaaS API keys
HUAWEI_MAAS_API_BASE	.env	https://api-ap-southeast-1.modelarts-maas.com/openai/v1

LiteLLM command: --config=/app/config.yaml

Deployment Workflow

Follow in order. Do not skip validation steps.

0. Preflight

docker --version          # expect 20.10+
docker compose version    # expect v2

1. Install from monorepo

MONOREPO="https://github.com/binrogithub/1-3-Cloud-Adoption-Skills.git"
TEMP_DIR="/home/1-3-Cloud-Adoption-Skills"
LITELLM_DIR="/home/LiteLLM-Huawei-MaaS-Proxy"

git clone --depth 1 "$MONOREPO" "$TEMP_DIR"
cp -r "$TEMP_DIR/AI/AI-Coding/LiteLLM-Huawei-MaaS-Proxy" "$LITELLM_DIR"
rm -rf "$TEMP_DIR"
cd "$LITELLM_DIR"

2. Configure .env

Two paths — choose one:

Guided (recommended for agents and first-time deployers):

./scripts/init_env.sh              # interactive — prompt for each secret, ask for number of extra MaaS keys
./scripts/init_env.sh --auto       # agent mode — auto-generate all, prompt for MaaS API key(s)
./scripts/init_env.sh --ci         # CI mode — all from env vars (including HUAWEI_MAAS_EXTRA_API_KEYS)

The script writes .env with 0600 permissions, validates required values, and refuses to proceed if HUAWEI_MAAS_API_KEY is missing or placeholder. After collecting keys, it sets HUAWEI_MAAS_API_KEY_COUNT and indexed HUAWEI_MAAS_API_KEY_N vars.

Manual (full control over every value):

cp assets/config/.env.example .env
$EDITOR .env                       # fill all secrets and HUAWEI_MAAS_API_KEY(s)
chmod 600 .env

Or generate secrets individually:

python3 -c "import secrets; print('sk-' + secrets.token_urlsafe(32))"   # MASTER_KEY
python3 -c "import secrets; print(secrets.token_urlsafe(32))"            # SALT_KEY, passwords

2b. Generate config

After .env is configured:

./scripts/generate_config.sh       # generates litellm_config.yaml from template and .env

This reads HUAWEI_MAAS_API_KEY_COUNT and creates N deployments per model.

3. Pre-deploy validation

Before starting the stack, verify .env is complete:

source .env
for VAR in LITELLM_MASTER_KEY LITELLM_SALT_KEY DB_PASSWORD HUAWEI_MAAS_API_KEY; do
  VAL="${!VAR:-}"
  [ -z "$VAL" ] && echo "MISSING: $VAR" || echo "OK: $VAR (len=${#VAL})"
done

If any variable is missing or contains a placeholder, halt and fix before proceeding. The stack will fail at runtime with incomplete secrets.

4. Start the stack

docker compose up -d

5. Wait for healthy services

docker compose ps

All four services must show healthy or running. LiteLLM has a 40s start period.

6. Validate direct MaaS connectivity

curl -s https://api-ap-southeast-1.modelarts-maas.com/openai/v1/models \
  -H "Authorization: Bearer $HUAWEI_MAAS_API_KEY" | jq '.data[].id'

Expect a list of model IDs. If 403, key is wrong or expired.

7. Validate LiteLLM health

curl -s http://localhost:4000/health/liveliness
curl -s http://localhost:4000/health \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" | jq '.healthy_count, .unhealthy_count'

Expect unhealthy_count: 0.

8. Validate proxied chat completion

curl -s http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "glm-5.1", "messages": [{"role": "user", "content": "Reply with OK only."}]}' | jq '.choices[0].message.content'

9. Validate streaming

curl -s http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": "Count to 3."}], "stream": true}' | head -5

Expect SSE chunks (data: {...}).

10. Validate Prometheus metrics

curl -s http://localhost:4000/metrics | grep -c "litellm_"
curl -s http://localhost:9090/api/v1/targets | jq '.data.activeTargets[] | {job: .labels.job, health: .health}'

Expect metric count > 0 and Prometheus target health = up.

11. Validate Grafana dashboard

curl -s -o /dev/null -w "%{http_code}\n" http://localhost:3000

Expect 200.

12. Validate virtual key minting

curl -s -X POST http://localhost:4000/key/generate \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"models": ["glm-5.1"], "max_budget": 1.0, "duration": "1d"}' | jq '.key'

Expect a virtual key starting with sk-.

Validation Sequence

For an existing deployment, run in order:

1. docker compose ps — all services healthy
2. curl http://localhost:4000/health/liveliness — LiteLLM process up
3. curl http://localhost:4000/health -H "Authorization: Bearer $LITELLM_MASTER_KEY" — upstream reachable per model
4. Chat completion with master key on glm-5.1 — sync path
5. Streaming completion — SSE path
6. /key/generate — mints a virtual key
7. Chat completion with virtual key — multi-user path and budget hooks
8. /metrics | grep -c litellm_ — metrics flowing
9. Prometheus targets — scraping
10. http://localhost:3000 — Grafana reachable

Environment Reference

Variable	Required	Default	Description
LITELLM_MASTER_KEY	Yes	—	Admin key, must start with sk-
LITELLM_SALT_KEY	Yes	—	Key encryption salt — immutable after first virtual key
DB_PASSWORD	Yes	—	PostgreSQL password for llmproxy user
HUAWEI_MAAS_API_KEY	Yes	—	Main MaaS API key from ModelArts console (CN-Hong Kong)
HUAWEI_MAAS_API_BASE	Yes	—	https://api-ap-southeast-1.modelarts-maas.com/openai/v1
HUAWEI_MAAS_API_KEY_COUNT	Auto	1	Number of MaaS API keys (set by init_env.sh)
HUAWEI_MAAS_API_KEY_N	Auto	—	Indexed keys (0, 1, 2...). Set by init_env.sh.
HUAWEI_MAAS_EXTRA_API_KEYS	No	—	Comma-separated extra keys for CI mode
PROMETHEUS_RETENTION	No	15d	Prometheus TSDB retention period
GRAFANA_PASSWORD	No	admin	Grafana admin password

Endpoints

Service	URL	Auth
LiteLLM API	http://localhost:4000	Authorization: Bearer <key>
LiteLLM Admin UI	http://localhost:4000/ui	Login with LITELLM_MASTER_KEY
Prometheus	http://localhost:9090	None
Grafana	http://localhost:3000	admin / GRAFANA_PASSWORD

LiteLLM API routes

Route	Method	Description
/v1/chat/completions	POST	OpenAI-compatible chat completions
/v1/models	GET	List available models
/health/liveliness	GET	Liveness probe (used by healthcheck)
/health	GET	Per-model health (auth required)
/metrics	GET	Prometheus metrics endpoint
/key/generate	POST	Generate scoped virtual key
/key/info	POST	Get key info
/key/update	POST	Update key settings
/key/delete	POST	Delete a key
/model/info	GET	Model details including pricing (auth required)
/ui	GET	Admin UI

Models

Name	in / out	RPM	TPM	Cost (in/out per token)
glm-5.1	192K / 128K	30	500K	$1.078 / $3.774 × 10⁻⁶
glm-5	192K / 64K	30	500K	$0.809 / $2.965 × 10⁻⁶
deepseek-v4-pro	1M / 128K	3	30K	$1.617 / $3.235 × 10⁻⁶
deepseek-v4-flash	1M / 128K	3	30K	$0.135 / $0.270 × 10⁻⁶
deepseek-v3.2	128K / 32K	700	500K	$0.270 / $0.404 × 10⁻⁶

Model configuration structure

Single key (backward compatible):

- model_name: <public-name>
  litellm_params:
    model: openai/<maas-model-name>
    api_base: os.environ/HUAWEI_MAAS_API_BASE
    api_key: os.environ/HUAWEI_MAAS_API_KEY
    tpm: <tokens-per-minute>
    rpm: <requests-per-minute>
  model_info:
    max_tokens: <total>
    max_input_tokens: <input>
    max_output_tokens: <output>
    input_cost_per_token: <price>
    output_cost_per_token: <price>

Multi-key (N keys → N deployments per model):

With N MaaS API keys, generate_config.sh creates N deployments per model:

- model_name: <public-name>
  litellm_params:
    model: openai/<maas-model-name>
    api_base: os.environ/HUAWEI_MAAS_API_BASE
    api_key: os.environ/HUAWEI_MAAS_API_KEY_0
    tpm: <tokens-per-minute>
    rpm: <requests-per-minute>
  model_info: { ... }

- model_name: <public-name>
  litellm_params:
    model: openai/<maas-model-name>
    api_base: os.environ/HUAWEI_MAAS_API_BASE
    api_key: os.environ/HUAWEI_MAAS_API_KEY_1
    tpm: <tokens-per-minute>
    rpm: <requests-per-minute>
  model_info: { ... }

# ... one entry per key

LiteLLM's router load-balances across all deployments for the same model_name.

Adding a new model

1. Find model name and rate/price info in ModelArts MaaS console
2. Add entry to model_list in assets/config/litellm_config.yaml.example following the structure above
3. Ensure model_name matches MaaS exactly (case-sensitive)
4. Set tpm/rpm from MaaS console quotas
5. Set non-zero input_cost_per_token and output_cost_per_token (per-token, not per-1K)
6. Regenerate config: ./scripts/generate_config.sh
7. Restart: docker compose restart litellm
8. Verify: curl -s http://localhost:4000/v1/models -H "Authorization: Bearer $LITELLM_MASTER_KEY" | jq '.data[].id'
9. Confirm pricing: curl -s http://localhost:4000/model/info -H "Authorization: Bearer $LITELLM_MASTER_KEY" | jq '.data[] | {model: .model_name, input_cost: .input_cost_per_token, output_cost: .output_cost_per_token}'

Adding/removing MaaS API keys

To add a key:

1. Add the new key to .env as HUAWEI_MAAS_API_KEY_N (next index)
2. Increment HUAWEI_MAAS_API_KEY_COUNT
3. Regenerate config: ./scripts/generate_config.sh
4. Restart: docker compose restart litellm
5. Verify deployment count: curl -s http://localhost:4000/model/info -H "Authorization: Bearer $LITELLM_MASTER_KEY" | jq '[.data[].model_name] | length'

To remove a key:

1. Remove the key from .env
2. Decrement HUAWEI_MAAS_API_KEY_COUNT
3. Re-index remaining keys if needed (keys must be 0, 1, 2... contiguous)
4. Regenerate config: ./scripts/generate_config.sh
5. Restart: docker compose restart litellm

To rotate an expired key:

1. Replace the expired key value in .env
2. Regenerate config: ./scripts/generate_config.sh
3. Restart: docker compose restart litellm

Proxy settings

Configured in assets/config/litellm_config.yaml under litellm_settings:

Setting	Value	Meaning
num_retries	3	Retry failed calls 3 times within same deployment
request_timeout	600	Raise TimeoutError after 600s (full request latency; matches LiteLLM default)
stream_timeout	60	Raise TimeoutError after 60s waiting for first token (TTFT only)
drop_params	True	Drop unsupported params instead of erroring
set_verbose	False	Suppress debug logging
callbacks	["prometheus", "custom_callbacks.my_prometheus_logger"]	Built-in Prometheus + custom TTFT/TPOT/ITL

Under general_settings:

Setting	Value	Meaning
database_connection_pool_limit	10	Max DB connections
database_connection_timeout	60	DB connection timeout in seconds

Usage

Chat completion

curl -s http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "glm-5.1", "messages": [{"role": "user", "content": "Hello!"}]}'

Streaming

curl -s http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": "Count to 5."}], "stream": true}'

Thinking mode (DeepSeek)

curl -s http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "deepseek-v4-pro", "messages": [{"role": "user", "content": "Solve step by step."}], "extra_body": {"thinking": {"type": "enabled"}}}'

Python SDK

from openai import OpenAI

client = OpenAI(base_url="http://localhost:4000/v1", api_key="sk-...")
response = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Virtual key management

curl -s -X POST http://localhost:4000/key/generate \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"models": ["glm-5.1", "deepseek-v4-flash"], "max_budget": 10.0, "duration": "30d"}'

curl -s -X POST http://localhost:4000/key/info \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"key": "sk-..."}'

curl -s -X POST http://localhost:4000/key/update \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"key": "sk-...", "max_budget": 50.0}'

curl -s -X POST http://localhost:4000/key/delete \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"keys": ["sk-..."]}'

Metrics

Built-in LiteLLM metrics (on /metrics)

Metric	Type	Description
litellm_proxy_total_requests_metric	counter	Total requests
litellm_request_total_latency_metric	histogram	End-to-end latency
litellm_llm_api_latency_metric	histogram	Upstream API latency only
litellm_spend_metric	counter	Cumulative spend (USD)
litellm_input_tokens_metric	counter	Input tokens
litellm_output_tokens_metric	counter	Output tokens
litellm_deployment_state	gauge	0=healthy, 1=partial, 2=outage

Deployment-level metrics (multi-key)

Metric	Type	Labels	Description
litellm_deployment_success_responses_total	counter	litellm_model_name	Successful responses per deployment
litellm_deployment_failure_responses_total	counter	litellm_model_name, exception_status, exception_class	Failed responses per deployment
litellm_deployment_cooled_down	gauge	litellm_model_name	1 if deployment is in cooldown
litellm_deployment_latency_per_output_token	histogram	litellm_model_name	Latency per output token by deployment

Label note: Deployment metrics use litellm_model_name (includes deployment suffix like glm-5.1--maas-key-0), not model.

Custom metrics (custom_callbacks.py)

Metric	Type	When	Bucket range
litellm_custom_ttft_seconds	histogram	Streaming only	0.01s → 30s
litellm_custom_tpot_seconds	histogram	Always	0.001s → 5s
litellm_custom_itl_seconds	histogram	Streaming only	0.001s → 5s

All custom metrics labeled: model, model_group, api_provider.

Custom callback internals

PrometheusTTFTTPOTITL(CustomLogger):

• TTFT = completion_start_time - api_call_start_time (streaming only, when > 0)
• TPOT = (end_time - start_time) / output_tokens (always, when output_tokens > 0)
• ITL = (end_time - completion_start_time) / (output_tokens - 1) (streaming only, when output_tokens > 1)

Useful PromQL

rate(litellm_proxy_total_requests_metric[5m]) * 60
histogram_quantile(0.99, rate(litellm_request_total_latency_metric_bucket[5m]))
rate(litellm_spend_metric[1d])
rate(litellm_input_tokens_metric[5m])*60 + rate(litellm_output_tokens_metric[5m])*60
litellm_deployment_state == 2
histogram_quantile(0.95, rate(litellm_custom_ttft_seconds_bucket[5m]))
rate(litellm_custom_tpot_seconds_sum[5m]) / rate(litellm_custom_tpot_seconds_count[5m])

Grafana Dashboard

Pre-built dashboard (litellm_overview.json):

• Auto-refresh: 10s
• Default time range: Last 1 hour
• Template variables: model (multi-select), datasource (Prometheus selector)
• Panel sections: Request Rates, Latency Percentiles, Spend, Token Rates, Deployment State, Custom TTFT/TPOT/ITL, Deployment Load Balancing

Deployment Load Balancing panels (multi-key)

When multiple MaaS API keys are configured, the dashboard includes:

Panel	Description
Deployments Per Model	Shows N deployments per model (bar chart)
Request Distribution	Per-deployment request counts (pie/bar)
Cooldown Events	Deployments temporarily removed from rotation (time series)
Per-Deployment Latency	Latency breakdown by deployment (heatmap or time series)
Deployment Health	Current health status per deployment (stat panel)

Access at http://localhost:3000, login with admin / GRAFANA_PASSWORD.

Operations

Health checks

docker compose ps
curl -s http://localhost:4000/health/liveliness
curl -s http://localhost:4000/health \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" | jq '.healthy_count, .unhealthy_count'
curl -s http://localhost:9090/api/v1/targets | jq '.data.activeTargets[] | {job: .labels.job, health: .health}'
curl -s https://api-ap-southeast-1.modelarts-maas.com/openai/v1/models \
  -H "Authorization: Bearer $HUAWEI_MAAS_API_KEY"

Backup & restore

docker compose exec db pg_dump -U llmproxy litellm > backup_$(date +%Y%m%d).sql
cat backup_20260516.sql | docker compose exec -T db psql -U llmproxy litellm

Restart & reset

docker compose restart litellm
docker compose down && docker compose up -d
docker compose down -v && docker compose up -d

Troubleshooting commands

docker compose logs litellm
docker compose logs -f litellm
docker compose logs db
docker compose exec db pg_isready -d litellm -U llmproxy
docker compose logs prometheus
docker compose logs grafana
docker volume ls | grep litellm
docker compose exec litellm env | grep -E '^(LITELLM|DB_|HUAWEI|STORE_)'

Repair Playbook

1. Inspect state — docker compose ps and docker compose logs litellm --tail 50
2. Inspect config — read assets/config/litellm_config.yaml and .env before editing
3. Confirm environment — verify .env contains real MaaS key (not placeholder)
4. Check DB — docker compose exec db pg_isready -d litellm -U llmproxy
5. Check LiteLLM health — curl -s http://localhost:4000/health -H "Authorization: Bearer $LITELLM_MASTER_KEY"
6. Fix the issue — see Common failure modes below
7. Restart if config changed — docker compose restart litellm
8. Re-validate — run scripts/validate_e2e.sh or Validation Sequence

Common failure modes

Symptom	Cause	Fix
litellm keeps restarting	DB not ready or wrong DB_PASSWORD	Check docker compose logs db, verify .env
401 from /v1/chat/completions	Wrong or missing API key	Verify Authorization: Bearer sk-... header
404 model not found	Model name mismatch	Names are case-sensitive, must match MaaS console
No metrics in Prometheus	LiteLLM healthcheck failing	Check docker compose ps, ensure litellm healthy
LITELLM_SALT_KEY error	Salt changed after keys created	Use original salt; if lost, docker compose down -v
MaaS 403	Wrong region or expired key	Verify key in ModelArts console, region must be ap-southeast-1
Callback import error	custom_callbacks.py not mounted	Check volume mount in docker-compose.yml
unhealthy_count > 0 in /health	Upstream model unreachable	Check MaaS key, model ID, region; do not add wildcards
Budget not consumed	Zero input_cost_per_token / output_cost_per_token	Set non-zero pricing; verify with /model/info
Prometheus target down	LiteLLM not healthy or not started	Check healthcheck chain: db → litellm → prometheus
Grafana shows no data	Prometheus not scraping or wrong datasource	Check targets; verify datasource URL is http://prometheus:9090
Virtual key 403	Key expired, over budget, or model not in allow-list	Check key with /key/info
Intermittent TimeoutError	request_timeout too low for LLM calls (applies to full request latency, not just TTFT)	Increase request_timeout (default 600s); add stream_timeout for tighter TTFT deadline

Sanitization Rules

• Never write real secrets into committed files. Use .env (gitignored) with 0600 permissions.
• In output or documentation, use placeholders: sk-<master-key>, <maas-api-key>, <db-password>.
• In configuration demos, read secrets from env vars (os.environ/...) or $VAR_NAME placeholders.
• Mask discovered keys as <prefix>...<suffix> (len=N) or ***redacted***.
• LiteLLM may print api_key values in startup logs. Scan after troubleshooting: docker compose logs litellm 2>&1 | grep -i 'api_key\|sk-'; set set_verbose: False if keys appear.

Common Mistakes

Mistake	Why it's wrong	Correct approach
Committing .env to git	Leaks all secrets	.env is gitignored; never git add .env
Changing LITELLM_SALT_KEY after creating virtual keys	All existing keys unreadable	Keep original salt; if lost, full reset
Giving clients the raw HUAWEI_MAAS_API_KEY	Bypasses spend tracking, rate limiting, audit	Mint virtual keys via /key/generate
Using per-1K-token pricing in model_info	LiteLLM expects per-token pricing	Use input_cost_per_token (e.g. 0.000001078, not 0.001078)
Adding a model with zero pricing	Budgets don't consume spend	Set non-zero input_cost_per_token and output_cost_per_token
Guessing model names	MaaS model IDs are case-sensitive	Verify exact name in MaaS console
Editing config without restarting	Config is read at startup only	docker compose restart litellm after changes
Running docker compose down expecting data loss	Volumes survive down	Use docker compose down -v to destroy data
Checking /health/liveliness instead of /health for model status	Liveliness only checks process	Use /health with auth for model-level diagnostics
Setting request_timeout too low (e.g. 10s)	LLM calls routinely exceed 10s end-to-end, causing intermittent TimeoutErrors	Use request_timeout: 600 (default); use stream_timeout for tighter TTFT control

Reference: File Templates

Templates for reference — to understand each file's structure or build from scratch when git is not available. When cloning this repo, the actual files take precedence.

.gitignore

.env

.env.example

# ── Proxy Auth ───────────────────────────────────
LITELLM_MASTER_KEY="sk-change-me"
LITELLM_SALT_KEY="change-me-to-a-long-random-string"

# ── Database ─────────────────────────────────────
DB_PASSWORD="change-me-to-a-strong-password"

# ── Huawei MaaS ──────────────────────────────────
HUAWEI_MAAS_API_KEY="change-me-to-your-maas-api-key"
HUAWEI_MAAS_API_KEY_0="change-me-to-your-maas-api-key"
HUAWEI_MAAS_API_BASE="https://api-ap-southeast-1.modelarts-maas.com/openai/v1"

# ── Prometheus ───────────────────────────────────
PROMETHEUS_RETENTION="15d"

# ── Grafana ──────────────────────────────────────
GRAFANA_PASSWORD="change-me-to-a-strong-password"

docker-compose.yml

x-default: &default
  restart: unless-stopped
  logging:
    driver: "json-file"
    options:
      max-size: "10m"
      max-file: "3"

services:
  litellm:
    <<: *default
    container_name: litellm_proxy
    image: ghcr.io/berriai/litellm:v1.83.14-stable.patch.3
    ports:
      - "4000:4000"
    volumes:
      - ./assets/config/litellm_config.yaml:/app/config.yaml:ro
      - ./assets/config/custom_callbacks.py:/app/custom_callbacks.py:ro
    environment:
      DATABASE_URL: "postgresql://llmproxy:${DB_PASSWORD}@db:5432/litellm"
      STORE_MODEL_IN_DB: "True"
    env_file:
      - .env
    depends_on:
      db:
        condition: service_healthy
    healthcheck:
      test: ["CMD-SHELL", "python3 -c \"import urllib.request; urllib.request.urlopen('http://localhost:4000/health/liveliness')\""]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    command:
      - "--config=/app/config.yaml"

  db:
    <<: *default
    image: postgres:16-alpine
    container_name: litellm_pg_db
    environment:
      POSTGRES_DB: litellm
      POSTGRES_USER: llmproxy
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -d litellm -U llmproxy"]
      interval: 5s
      timeout: 5s
      retries: 10

  prometheus:
    <<: *default
    image: prom/prometheus:v3.3.1
    container_name: litellm_prometheus
    volumes:
      - ./assets/config/prometheus.yml:/etc/prometheus/prometheus.yml:ro
      - prometheus_data:/prometheus
    ports:
      - "9090:9090"
    command:
      - "--config.file=/etc/prometheus/prometheus.yml"
      - "--storage.tsdb.path=/prometheus"
      - "--storage.tsdb.retention.time=${PROMETHEUS_RETENTION:-15d}"
    depends_on:
      litellm:
        condition: service_healthy
    healthcheck:
      test: ["CMD-SHELL", "wget --spider -q http://localhost:9090/-/healthy || exit 1"]
      interval: 15s
      timeout: 5s
      retries: 3
      start_period: 10s

  grafana:
    <<: *default
    image: grafana/grafana:11.5.2
    container_name: litellm_grafana
    ports:
      - "3000:3000"
    environment:
      GF_SECURITY_ADMIN_PASSWORD: ${GRAFANA_PASSWORD:-admin}
      GF_USERS_ALLOW_SIGN_UP: "false"
    volumes:
      - ./assets/config/grafana/provisioning:/etc/grafana/provisioning:ro
      - grafana_data:/var/lib/grafana
    depends_on:
      prometheus:
        condition: service_healthy
    healthcheck:
      test: ["CMD-SHELL", "wget --spider -q http://localhost:3000/api/health || exit 1"]
      interval: 15s
      timeout: 5s
      retries: 3
      start_period: 15s

volumes:
  postgres_data:
    name: litellm_postgres_data
  prometheus_data:
    name: litellm_prometheus_data
  grafana_data:
    name: litellm_grafana_data

litellm_config.yaml

model_list:

  # ───────── Huawei MaaS Models ───────────

  - model_name: glm-5.1
    litellm_params:
      model: openai/glm-5.1
      api_base: os.environ/HUAWEI_MAAS_API_BASE
      api_key: os.environ/HUAWEI_MAAS_API_KEY
      tpm: 500000
      rpm: 30
    model_info:
      max_tokens: 198000
      max_input_tokens: 192000
      max_output_tokens: 128000
      input_cost_per_token: 0.000001078
      output_cost_per_token: 0.000003774

  - model_name: glm-5
    litellm_params:
      model: openai/glm-5
      api_base: os.environ/HUAWEI_MAAS_API_BASE
      api_key: os.environ/HUAWEI_MAAS_API_KEY
      tpm: 500000
      rpm: 30
    model_info:
      max_tokens: 198000
      max_input_tokens: 192000
      max_output_tokens: 64000
      input_cost_per_token: 0.000000809
      output_cost_per_token: 0.000002965

  - model_name: deepseek-v4-pro
    litellm_params:
      model: openai/deepseek-v4-pro
      api_base: os.environ/HUAWEI_MAAS_API_BASE
      api_key: os.environ/HUAWEI_MAAS_API_KEY
      tpm: 30000
      rpm: 3
    model_info:
      max_tokens: 1000000
      max_input_tokens: 1000000
      max_output_tokens: 128000
      input_cost_per_token: 0.000001617
      output_cost_per_token: 0.000003235

  - model_name: deepseek-v4-flash
    litellm_params:
      model: openai/deepseek-v4-flash
      api_base: os.environ/HUAWEI_MAAS_API_BASE
      api_key: os.environ/HUAWEI_MAAS_API_KEY
      tpm: 30000
      rpm: 3
    model_info:
      max_tokens: 1000000
      max_input_tokens: 1000000
      max_output_tokens: 128000
      input_cost_per_token: 0.000000135
      output_cost_per_token: 0.00000027

  - model_name: deepseek-v3.2
    litellm_params:
      model: openai/deepseek-v3.2
      api_base: os.environ/HUAWEI_MAAS_API_BASE
      api_key: os.environ/HUAWEI_MAAS_API_KEY
      tpm: 500000
      rpm: 700
    model_info:
      max_tokens: 160000
      max_input_tokens: 128000
      max_output_tokens: 32000
      input_cost_per_token: 0.00000027
      output_cost_per_token: 0.000000404


litellm_settings:
  num_retries: 3
  request_timeout: 600
  stream_timeout: 60
  drop_params: True
  set_verbose: False
  callbacks:
    - "prometheus"
    - custom_callbacks.my_prometheus_logger
  ui_theme_config:
    logo_url: "https://upload.wikimedia.org/wikipedia/en/thumb/0/04/Huawei_Standard_logo.svg/3840px-Huawei_Standard_logo.svg.png"
    favicon_url: "https://upload.wikimedia.org/wikipedia/en/thumb/0/04/Huawei_Standard_logo.svg/3840px-Huawei_Standard_logo.svg.png"

router_settings:
  routing_strategy: simple-shuffle
  num_retries: 3
  cooldown_time: 30
  allowed_fails: 3

general_settings:
  database_connection_pool_limit: 10
  database_connection_timeout: 60

custom_callbacks.py

"""
Custom LiteLLM callback that emits TTFT, TPOT, and ITL as Prometheus histograms.

TTFT  = completion_start_time - api_call_start_time  (streaming only)
TPOT  = total_latency / output_tokens
ITL   = (end_time - completion_start_time) / max(output_tokens - 1, 1)  (streaming only)
"""

from datetime import datetime
from litellm.integrations.custom_logger import CustomLogger
from prometheus_client import Histogram


def _to_timestamp(val):
    """Convert datetime or numeric to a float unix timestamp."""
    if val is None:
        return None
    if isinstance(val, (int, float)):
        return float(val)
    if hasattr(val, "timestamp"):
        return val.timestamp()
    return None


class PrometheusTTFTTPOTITL(CustomLogger):
    """Custom callback that emits TTFT, TPOT, and ITL as Prometheus histograms."""

    def __init__(self):
        super().__init__()

        self.ttft = Histogram(
            "litellm_custom_ttft_seconds",
            "Time to first token in seconds (streaming only)",
            labelnames=["model", "model_group", "api_provider"],
            buckets=(0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0),
        )

        self.tpot = Histogram(
            "litellm_custom_tpot_seconds",
            "Time per output token in seconds",
            labelnames=["model", "model_group", "api_provider"],
            buckets=(0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 5.0),
        )

        self.itl = Histogram(
            "litellm_custom_itl_seconds",
            "Inter-token latency in seconds (average between successive tokens, streaming only)",
            labelnames=["model", "model_group", "api_provider"],
            buckets=(0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 5.0),
        )

    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        try:
            stream = kwargs.get("stream", False)
            completion_start_time = kwargs.get("completion_start_time")
            api_call_start_time = kwargs.get("api_call_start_time")

            slo = kwargs.get("standard_logging_object") or {}
            model = slo.get("model") or kwargs.get("model", "unknown")
            model_group = slo.get("model_group") or model
            api_provider = slo.get("custom_llm_provider") or "unknown"

            labels = {"model": model, "model_group": model_group, "api_provider": api_provider}

            output_tokens = 0
            if response_obj is not None:
                usage = None
                if hasattr(response_obj, "get"):
                    usage = response_obj.get("usage")
                elif hasattr(response_obj, "usage"):
                    usage = response_obj.usage
                if usage is not None:
                    if isinstance(usage, dict):
                        output_tokens = usage.get("completion_tokens", 0) or 0
                    elif hasattr(usage, "completion_tokens"):
                        output_tokens = usage.completion_tokens or 0

            start_ts = _to_timestamp(start_time)
            end_ts = _to_timestamp(end_time)
            api_start_ts = _to_timestamp(api_call_start_time)
            comp_start_ts = _to_timestamp(completion_start_time)

            if stream and api_start_ts and comp_start_ts:
                ttft_seconds = comp_start_ts - api_start_ts
                if ttft_seconds > 0:
                    self.ttft.labels(**labels).observe(ttft_seconds)

            if output_tokens > 0 and start_ts and end_ts:
                total_latency = end_ts - start_ts
                tpot_seconds = total_latency / output_tokens
                self.tpot.labels(**labels).observe(tpot_seconds)

                if stream and comp_start_ts:
                    streaming_duration = end_ts - comp_start_ts
                    if streaming_duration > 0 and output_tokens > 1:
                        itl_seconds = streaming_duration / (output_tokens - 1)
                        self.itl.labels(**labels).observe(itl_seconds)

        except Exception as e:
            print(f"[PrometheusTTFTTPOTITL] Error: {e}")


my_prometheus_logger = PrometheusTTFTTPOTITL()

prometheus.yml

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: "litellm"
    static_configs:
      - targets: ["litellm:4000"]

grafana/provisioning/datasources/prometheus.yml

apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    editable: false

grafana/provisioning/dashboards/dashboards.yml

apiVersion: 1

providers:
  - name: LiteLLM
    orgId: 1
    folder: ""
    type: file
    disableDeletion: false
    updateIntervalSeconds: 30
    options:
      path: /etc/grafana/provisioning/dashboards
      foldersFromFilesStructure: false

The pre-built litellm_overview.json dashboard is at assets/config/grafana/provisioning/dashboards/litellm_overview.json.

Optional: SearXNG Search MCP and claude-glm Client

Two opt-in capabilities are bundled (folded in from the former single-ECS gateway skill). Both are optional — the core proxy does not require them.

Self-hosted SearXNG search MCP (alternative to Exa injection). The search Docker Compose profile adds searxng (private meta-search, internal only) and searxng-mcp (bearer-authenticated FastMCP HTTP server on :8788, tools web_search and fetch_url).

sed "s/@@SEARXNG_SECRET@@/$(openssl rand -hex 32)/" \
  assets/config/searxng/settings.yml.example > assets/config/searxng/settings.yml
echo "MCP_TOKEN=$(openssl rand -hex 16)" >> .env
docker compose --profile search up -d      # core `up -d` does NOT start these

Rules:

• SearXNG is internal-only (expose: 8080); the bearer-auth MCP on :8788 is the public face. For a remote host, CIDR-lock :8788 (and :4000) to client /32s — never 0.0.0.0/0.
• search.formats must include json in settings.yml or the MCP gets HTML and tool calls fail.
• MCP_TOKEN is required when the search profile is used. The compose file keeps it soft (${MCP_TOKEN:-}) so the core stack still parses without it, but the MCP server refuses to start unauthenticated — if MCP_TOKEN is empty the searxng-mcp container exits immediately (check docker compose logs searxng-mcp).

claude-glm client wiring. Route Claude Code through claude-code-router (ccr) to this proxy with CLAUDE_CONFIG_DIR isolation so the user's plain claude stays on Anthropic. Mint a per-client virtual key with scripts/bootstrap_finops_team.py, then follow references/claude-glm-and-search-onboarding.md for ccr config, wrapper install, and registering the SearXNG MCP into the isolated client only. Pre-deploy assets: assets/config/claude-code-router.config.json.example and assets/config/claude-glm-wrapper.sh.example.

Bundled Resources

• references/architecture.md — topology, services, volumes
• references/metrics-and-dashboards.md — PromQL, custom metrics, Grafana
• references/operations.md — health checks, backup, restart, usage
• references/troubleshooting.md — repair playbook, failure modes
• references/claude-glm-and-search-onboarding.md — optional SearXNG search MCP + claude-glm client onboarding
• scripts/init_env.sh — interactive .env setup (manual, agent-guided, or CI)
• scripts/generate_config.sh — generates litellm_config.yaml from .env
• scripts/validate_e2e.sh — 12-step end-to-end validation
• scripts/bootstrap_finops_team.py — create a LiteLLM team + scoped virtual key (FinOps onboarding)
• assets/config/searxng_mcp_server.py — FastMCP HTTP server wrapping SearXNG (optional search profile)
• assets/config/searxng/settings.yml.example — SearXNG settings template (JSON enabled)
• assets/config/claude-code-router.config.json.example — ccr provider config for claude-glm
• assets/config/claude-glm-wrapper.sh.example — claude-glm wrapper with isolation + context headroom

Output Expectations

On completion, leave behind:

• docker compose ps with all four services healthy
• .env populated with real secrets, chmod 600, no placeholders
• Validated: direct MaaS request, proxied request, streaming, metrics, Grafana, virtual key
• Operator note listing: endpoints, file paths, master key location, MaaS region, virtual keys created

Verification Exit Criteria

• .env exists with all required variables set (no placeholders)
• .env has 0600 permissions
• HUAWEI_MAAS_API_KEY_COUNT is set and ≥ 1
• All HUAWEI_MAAS_API_KEY_N vars (0 through COUNT-1) are set and not placeholders
• litellm_config.yaml is generated (not the template)
• litellm_config.yaml has 5 × N model_list entries (N = HUAWEI_MAAS_API_KEY_COUNT)
• router_settings.routing_strategy is set
• docker compose ps shows all 4 services healthy
• curl http://localhost:4000/health/liveliness returns 200
• /health with master key returns unhealthy_count: 0
• Chat completion with master key on glm-5.1 succeeds
• Streaming completion succeeds
• /metrics returns LiteLLM metrics (count > 0)
• Prometheus target litellm is up
• Grafana returns 200 on port 3000
• Virtual key generation succeeds
• No real secrets appear in git diff
• HUAWEI_MAAS_API_KEY_COUNT matches number of keys in .env
• Each model has N deployments (N = HUAWEI_MAAS_API_KEY_COUNT)
• litellm_config.yaml is generated (not manually edited)
• Each MaaS API key tested individually succeeds