LiteLLM

42
Fair
Agent Native Score
Free Tier

LiteLLM is an open-source library that provides a unified API for calling 100+ LLM models across different providers (OpenAI, Anthropic, Cohere, etc.). It simplifies multi-model orchestration, cost tracking, and fallback handling for AI applications.

Categories: Ai Apis · Devtools
#22 of 69 in Ai Apis · #37 of 96 in Devtools
Checklist Breakdown

14 of 33 checks passed. 14 unscored.

Discovery 50%

Can an agent find and understand this tool without a web search?

✗ Published OpenAPI/Swagger spec
✗ Has llms.txt or llms-full.txt
✗ Has an MCP server (official or well-maintained)
✗ MCP server listed in a public registry
✓ API reference docs are publicly accessible
✓ Docs include runnable code examples
✓ Has a public changelog or release notes
✓ Has a public status page
Auth & Onboarding 83%

Can an agent create an account and get credentials without human intervention?

✓ Signup does not require CAPTCHA
✓ Signup does not require phone verification
✗ Supports API key auth (not only OAuth)
✓ API key obtainable without manual approval
✓ No mandatory billing info to start
✓ Can sign up without creating an organization
Pricing 100%

Can an agent operate autonomously without upfront payment or contracts?

✓ Has a free tier
✓ Usage-based pricing available
✓ No minimum contract or commitment
✓ Pricing page is public (no 'contact sales')
✓ Free tier sufficient for testing (not just a trial)
Agent Tooling Not yet scored

How well does the API work for non-human consumers?

— SDK available in 2+ languages
— Structured error responses (JSON with error codes)
— Idempotency support on write endpoints
— Pagination on list endpoints
— Webhook/event support
— Sandbox or test mode available
— Rate limit headers in responses
— Consistent REST resource naming
Reliability Not yet scored

Does the tool fail gracefully when an agent makes a mistake?

— Meaningful error messages (not just 500)
— 429 responses include Retry-After header
— Documented uptime SLA (99.9%+)
— Graceful degradation under rate limits
— Request IDs in responses for debugging
— API versioning supported
Reviewer Notes

LiteLLM excels at discovery with excellent GitHub documentation, clear examples, and an active open-source community. No MCP server or formal OpenAPI spec limits direct agent integration, though the Python SDK is well-structured. Account creation is frictionless (just git clone/pip install), but the tool is a library, not a service—agents need underlying API keys for actual LLM providers. Reliability depends on wrapped provider uptime; rate limits vary per backend. Free tier exists for local development, but production use requires paid accounts at underlying LLM providers.

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claude mcp add --transport http agent-native-registry https://agentnativeregistry.com/api/mcp