bulkllm 0.5.2

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bulkllm

Enhancements over vanilla LiteLLM

bulkllm builds on top of litellm and adds a few extras:

• Automatic model registration. The package knows how to fetch the list of models from OpenAI, Anthropic, Gemini and OpenRouter and registers them with LiteLLM. Results are cached on disk so they can be reused offline.
• Centralised rate limiting. A RateLimiter implementation enforces RPM, TPM, input and output token limits per model (or regex group) and works with both async and sync code.
• Retry‑aware completion wrappers. Thin wrappers around litellm.completion/acompletion integrate Tenacity retries, rate limiting and usage tracking.
• Usage tracking with statistics. Per‑model usage is tracked in memory with histograms, percentiles and cost calculations via the UsageTracker and UsageStat helpers.
• Predefined LLM configurations. A large catalogue of model presets with cost information and convenient selection helpers is included.

Development

Always run make checku before committing.

Quick Commands

• make init create the environment and install dependencies
• make help see available commands
• make autoformat format code
• make autoformat-unsafe format code - including 'unsafe' fixes
• make lint run linter
• make typecheck run type checker
• make test run tests
• make coverage run tests with coverage report
• make check run all checks (format, lint, typecheck, test)
• make checku run all checks (format-unsafe, lint, typecheck, test)

Code Conventions

Testing

• Use pytest (no test classes).
• Always set match= in pytest.raises.
• Prefer monkeypatch over other mocks.
• Mirror the source-tree layout in tests/.
• Always run make checku after making changes.

Exceptions

• Catch only specific exceptions—never blanket except: blocks.
• Don’t raise bare Exception.

Python

• Manage env/deps with uv (uv add|remove, uv run -- …).
• No logging config or side-effects at import time.
• Keep interfaces (CLI, web, etc.) thin; put logic elsewhere.
• Use typer for CLI interfaces, fastapi for web interfaces, and pydantic for data models.