We've been building Caliber to solve AI agent configuration management and released our full setup as open source. The response has been great — 888 GitHub stars and approaching 100 forks.
Repo: https://github.com/caliber-ai-org/ai-setup
The problem: every team integrating LLMs/AI agents ends up rebuilding the same config infrastructure — API key management, model selection logic, fallback chains, rate limiting configs. There's no standard.
We tried to build that standard and open-source it. Key things in the repo:
- Structured config schemas for AI agents
- Multi-model fallback configuration
- Environment isolation patterns
- Observability and health check hooks
We'd love feedback from the community:
- What AI agent config challenges aren't covered here?
- What features would make this genuinely useful for your projects?
- Any integrations (LangChain, AutoGPT, etc.) you'd want to see?
This is a community project — PRs and feature requests are very welcome.
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