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Faramesh – open-source runtime enforcement for AI agents

Faramesh – open-source runtime enforcement for AI agents

by brian_r_hall·Mar 18, 2026·1 point·0 comments

AI Analysis

●●SolidBig BrainSolve My Problem

Code-based rules beat AI monitoring AI for actual enforcement guarantees.

Strengths
  • Three verdict system (permit/deny/defer) handles real-world edge cases gracefully
  • Never fails open — errors block actions by default, critical for safety
  • Works with five major agent frameworks without SDK changes
Weaknesses
  • Cloud platform not yet available, limits team collaboration features
  • AI agent governance space heating up with well-funded competitors
Category
Target Audience

Developers building production AI agents with LangChain, CrewAI, or AutoGen

Similar To

Credo AI · Arize Phoenix · LangSmith

Post Description

I'm Brian, co-founder of Faramesh. My co-founder Amjad and I were writing a research paper on AI agents last year, and the deeper we got into how agents actually execute in production the more obvious it became that there's no real solution for execution control. Agents can be sandboxed, they can have network policies, but nothing sits at the action layer and evaluates decisions before they run against an actual policy.

So we built Faramesh.

It intercepts tool calls before they execute, evaluates them against a declarative policy, blocks or approves, and logs everything. Works with LangChain, CrewAI, AutoGen, MCP, LangGraph. Open source, no signup. You can clone it and have it running against your agent in a few minutes.

The OpenShell announcement from NVIDIA this week is a good reference point for where Faramesh fits. OpenShell handles what the agent can reach. Faramesh handles what the agent is allowed to do once it gets there. Different layers.

Would love feedback from anyone running agents in production, especially where you've hit cases where access controls weren't enough.

github.com/faramesh-labs/faramesh -- faramesh.dev

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