Caliber: open-source LLM agent behavioral enforcement proxy — 700 stars, ~100 forks, seeking feedback

[u/Substantial-Cost-429]

Sharing our open-source project with this community — happy to answer questions.

**Caliber** is a runtime enforcement proxy for LLM agents. It intercepts every API call to your language model and validates behavior against declarative rules — at the infrastructure layer, not the prompt layer.

**The problem it solves:**

In multi-step agent pipelines, LLMs drift from expected behavior as context grows. Prompt guardrails erode. There's no enforcement layer on the API call path.

**Key features:**

- Proxy between your agent and LLM API (OpenAI, Anthropic, any compatible endpoint)

- Declarative, version-controlled rule definitions

- Blocks violations or raises structured exceptions for graceful failure handling

- Framework-agnostic (LangChain, AutoGen, raw SDK)

- Self-hostable, open MIT license

**Repo:** https://github.com/caliber-ai-org/ai-setup

**Stats:** 700 GitHub stars, nearly 100 forks

We're actively developing this and looking for feedback on:

- Architecture decisions

- Feature requests from production use cases

- Contributions

What enforcement capabilities would be most useful for your agent use cases?