llmbroker
Turn a crowd of free, rate-limited LLMs into one reliable model — no premium subscription, no single point of failure. No LangChain, no heavy deps.
pip install llmbroker
llmbroker preset freetier > llms.toml # ready-made pool of free models
llmbroker env llms.toml > .env # which API keys to get, and where
llms = llmbroker.Broker("llms.toml")
reply = llms.ask("Explain decorators in one sentence")
print(reply.text) # groq rate-limited? gemini answers instead
Fill in whichever keys are easy — models without keys just stay inactive.
| Automatic failover | llms.ask(...) — next model answers when one is down |
| Chat, tools & agents | llms.chat(messages, tools=...), run_tool_loop(...) |
| Async-first | AsyncBroker — same engine, for FastAPI / agents / workers |
| Scale out | Broker("postgresql://…") — sqlite / Postgres / MongoDB, calling code unchanged |
| Self-regulating pool | reply.record_quality(0.3) — weak models sink per task kind; rate later with llms.record_quality(...) |
| Pluggable secrets | env vars, DB, AWS, Vault, or your own backend |
| Multi-user mode | per-user API keys on top of one shared pool |
Development
Do not forget to run . ./activate.sh. It needs uv installed.
Use pre-commit hooks for code quality:
pre-commit install
Install invoke preferably with uv tool:
uv tool install invoke
For a list of available scripts run invoke --list; for details on one, invoke <script> --help.
The bundled freetier preset drifts as providers change their free tiers; refresh
it with invoke catalog-refresh, which prints the maintenance runbook
(presets/freetier-refresh-prompt.md).
Reports:
Created with cookiecutter using template
1 maintainer
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