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name: litellm description: "Use for LiteLLM Python SDK, AI Gateway proxy, model routing, provider endpoint mapping, MCP/A2A agent tooling, pass-through routes, guardrails, virtual keys, spend tracking, and troubleshooting across OpenAI-compatible LLM providers." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

LiteLLM

Use this skill when a task involves LiteLLM as a Python SDK or AI Gateway. LiteLLM provides OpenAI-format calls across many providers, a proxy server for centralized model access, routing and fallback policy, provider endpoint translation, and agent-tool integrations such as MCP and A2A.

Start Here

1. If the user asks for direct Python calls, imports, streaming, embeddings, structured outputs, callbacks, caching, token/cost checks, or SDK errors, use sub-skills/sdk-core/SKILL.md.
2. If the user asks to run or configure the AI Gateway proxy, write YAML config, manage keys/teams/spend, configure guardrails, or check proxy health, use sub-skills/proxy-server/SKILL.md.
3. If the user asks about Router, model groups, fallbacks, retries, cooldowns, routing strategies, health-check routing, tags, aliases, or budget-aware routing, use sub-skills/routing/SKILL.md.
4. If the user asks which provider prefix, endpoint helper, OpenAI-compatible route, pass-through target, or endpoint family to use, use sub-skills/providers-and-endpoints/SKILL.md.
5. If the user asks about MCP, A2A, Claude Code/Cursor agent gateway setup, tool approval, MCP auth/OAuth, or agent routes, use sub-skills/agent-tools/SKILL.md.

Installation Patterns

• SDK-only usage: pip install litellm or uv add litellm.
• Proxy usage: pip install 'litellm[proxy]', uv tool install 'litellm[proxy]', or the project’s supported container/deployment path.
• Thin proxy client CLI usage: install the cli extra when a lightweight local client is enough.
• Optional workflows may require extras such as caching, semantic-router, mlflow, google, proxy-runtime, extra_proxy, mcp, or a2a-sdk; install only the extras required by the selected workflow.

Minimal Checks

Run the bundled checker before debugging deeper issues:

python scripts/check_litellm_environment.py
python scripts/check_litellm_environment.py --check-proxy-cli

For live provider calls, confirm the provider key and model prefix first. Prefer mock/import/signature checks when the task is only code migration or configuration review.

Shared References

• Read references/model-and-config-primer.md for model naming, credential, parameter, and YAML conventions shared across SDK, proxy, router, and endpoint workflows.
• Read references/troubleshooting.md for cross-cutting install, import, credentials, network, optional dependency, and source-of-truth checks.
• Read references/repo-provenance.md before deciding whether this skill is current for a LiteLLM checkout or should be refreshed.

Routing Boundaries

• SDK calls and endpoint selection overlap: use sdk-core for how to call LiteLLM and providers-and-endpoints for which endpoint/provider route is valid.
• Proxy config and routing overlap: use proxy-server for server/auth/admin operations and routing for model-group selection, fallback policy, and load-balancing semantics.
• Agent tools and proxy config overlap: use agent-tools for MCP/A2A protocol details and proxy-server for the underlying server, key, and deployment setup.
• Provider pass-through and agent routes overlap: use providers-and-endpoints for HTTP pass-through mechanics and agent-tools when the route exposes tools or agents to an IDE/agent client.

Safe Verification Defaults

• Do not run live provider calls unless the user explicitly wants real API traffic and provides credentials.
• Do not start long-running proxy, database, benchmark, load, or deployment workflows unless the task requires them.
• Use bundled scripts for offline validation first: SDK smoke checks, proxy health checks, router config inspection, pass-through mock targets, and MCP tool-shape validation.