Litelm: LiteLLM Without the Bloat

litellm's routing + translation in ~2,900 lines and 2 dependencies (openai , httpx ). litellm routes LLM calls across providers and translates between message formats. That core is buried under 100k+ LOC of proxy servers, caching layers, cost tracking, and dozens of features most users never touch. litelm extracts just the call path — model routing, message translation, streaming, tool use, embeddings — and nothing else. No Router class, no proxy, no caching. pip install litelm # openai + httpx pip install litelm[anthropic] # + anthropic SDK pip install litelm[bedrock] # + boto3 pip install litelm[all] # everything import litelm # Basic completion response = litelm.completion("openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}]) print(response.choices[0].message.content) # Streaming for chunk in litelm.completion("groq/llama-3.1-70b-versatile", messages=[...], stream=True): print(chunk.choices[0].delta.content or "", end="") # Embeddings response = litelm.embedding("openai/text-embedding-3-small", input=["hello world"]) Every function has an async variant: acompletion , aembedding , aresponses , atext_completion . The API mirrors litellm — same function names, same arguments, same response types. If you're using litellm today, switching is s/litellm/litelm/ in your imports. | litellm | litelm | | |---|---|---| Model routing (provider/model → right endpoint) | ✓ | ✓ | | Message translation (Anthropic, Bedrock, Cloudflare, Mistral) | ✓ | ✓ | Streaming + stream_chunk_builder | ✓ | ✓ | | Tool use (function calling) | ✓ | ✓ | | Embeddings | ✓ | ✓ | | Text completions | ✓ | ✓ | | OpenAI Responses API | ✓ | ✓ | | Mock responses | ✓ | ✓ | | Router (load balancing, fallbacks) | ✓ | ✗ | | Proxy server | ✓ | ✗ | | Caching / budgeting / cost tracking | ✓ | ✗ | | Token counting | ✓ | ✗ | | Image gen, audio, OCR, fine-tuning | ✓ | ✗ | | Agents, guardrails, scheduler | ✓ | ✗ | Routes to 19 providers via "provider/model-name" syntax. Any OpenAI-compatible endpoint works via api_base . | Provider | Env Var | Handler | Verified | |---|---|---|---| | OpenAI | OPENAI_API_KEY | OpenAI SDK | Yes | | Anthropic | ANTHROPIC_API_KEY | Custom | Yes | | Groq | GROQ_API_KEY | OpenAI-compat | Yes | | Mistral | MISTRAL_API_KEY | Custom | Yes | | xAI | XAI_API_KEY | OpenAI-compat | Yes | | OpenRouter | OPENROUTER_API_KEY | OpenAI-compat | Yes | | Azure | AZURE_API_KEY | OpenAI SDK (Azure) | Yes | | Bedrock | AWS_ACCESS_KEY_ID | Custom | No | | Cloudflare | CLOUDFLARE_API_TOKEN | Custom | No | | Together | TOGETHERAI_API_KEY | OpenAI-compat | No | | Fireworks | FIREWORKS_API_KEY | OpenAI-compat | No | | DeepSeek | DEEPSEEK_API_KEY | OpenAI-compat | No | | Perplexity | PERPLEXITYAI_API_KEY | OpenAI-compat | No | | DeepInfra | DEEPINFRA_API_TOKEN | OpenAI-compat | No | | Gemini | GEMINI_API_KEY | OpenAI-compat | No | | Cohere | COHERE_API_KEY | OpenAI-compat | No | | Ollama | — | OpenAI-compat | No | | vLLM | — | OpenAI-compat | No | | LM Studio | — | OpenAI-compat | No | Set the environment variable for your provider: export OPENAI_API_KEY=sk-... export ANTHROPIC_API_KEY=sk-ant-... Or pass directly: litelm.completion("openai/gpt-4o", messages=[...], api_key="sk-...") litelm.completion("openai/gpt-4o", messages=[...], api_base="http://localhost:8000/v1") All provider errors are mapped to litelm's exception hierarchy: from litelm import ContextWindowExceededError, RateLimitError, AuthenticationError try: response = litelm.completion("openai/gpt-4o", messages=messages) except ContextWindowExceededError: # prompt too long — truncate and retry pass except RateLimitError: # back off pass except AuthenticationError: # bad API key pass tools = [{"type": "function", "function": { "name": "get_weather", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, }}] response = litelm.completion( "openai/gpt-4o", messages=[{"role": "user", "content": "Weather in Paris?"}], tools=tools, tool_choice="required", ) tool_call = response.choices[0].message.tool_calls[0] print(tool_call.function.name, tool_call.function.arguments) Any OpenAI-compatible server works via api_base : # vLLM litelm.completion("openai/my-model", messages=[...], api_base="http://localhost:8000/v1") # Ollama litelm.completion("ollama/llama3", messages=[...], api_base="http://localhost:11434/v1") # LM Studio litelm.completion("openai/local-model", messages=[...], api_base="http://localhost:1234/v1") litelm is human-directed, AI-assisted software. Much of the code was written with Claude Code using Claude Opus 4.6/4.7. Code written from 2026-05-14 onward is written through Pi using GPT-5.5. Compatibility claims are based on tests and maintainer review, not AI authorship. Maintainer attestation, 2026-09-11: LiteLLM's routing/formatting changes were reviewed from 649eb2d through 9a715df2 . The audit triaged 360 core-path commits, inspected upstream tests for potentially relevant behavior, and fixed the resulting compatibility gaps test-first. Local scoped tests: 262 passed, 55 skipped ; all 45 available-provider live tests and all 10 DSPy smoke tests also passed with the current dependency lock. This attests litelm's declared routing/formatting/DSPy surface only, not full litellm compatibility. Alpha. 262 own tests passing. The current scoped LiteLLM 9a715df2 baseline has 75 passing ported tests and no remaining actionable assertion/runtime failures. DSPy drop-in verified — all 7 execution paths proven live (Predict, CoT, typed signatures, streaming, embeddings, tool use, multi-output). uv run --extra all pytest tests/ -x --ignore=tests/ported --timeout=10 # 262 non-live tests bash scripts/ported_contract.sh # 49 fast upstream contract tests uv run --extra all pytest tests/test_live.py -m live --timeout=30 # 45 live provider tests uv run pytest tests/test_dspy_smoke.py -m live --timeout=60 # 10 DSPy integration tests Live tests require API keys in .env.test . Skipped by default; run with -m live .