We built an open-source proxy that enforces LLM agent rules at the API layer - 700 GitHub stars

Reddit r/artificial / 4/26/2026

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The post argues that prompt-based guardrails for LLM agents are unreliable, especially as context grows or agents perform multi-step chains.
It introduces “Caliber,” an open-source, provider-agnostic proxy that reads rule definitions from plain Markdown and enforces them at the API layer for every call.
The authors report strong early community traction, citing 700 GitHub stars and nearly 100 forks.
The project invites feedback, feature requests, and contributions from people building AI agents.
The core idea is to shift safety/control logic from the prompt into an external enforcement layer to improve consistency.

Cross-posting here because this problem affects everyone building with AI agents.

Prompt-based guardrails fail. The model follows your system prompt in a demo, then ignores rules when context gets big or the agent chains multiple steps.

We built Caliber - an open-source proxy that reads your rules from plain markdown and enforces them at the API layer, not in the prompt. Every call. Provider-agnostic.

Just hit 700 GitHub stars ⭐ and nearly 100 forks - the reception from devs building with AI has been amazing.

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

Would love:

- Feedback on the approach

- Feature requests from people building AI agents

- Anyone who wants to contribute to the project

Building this open-source for the community.

submitted by /u/Substantial-Cost-429
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