Faramesh

open-source runtime enforcement for AI agents

Open SourceOtherAPI, Self-hosted (Docker/Kubernetes compatible)

What is Faramesh?

Faramesh is an open-source runtime enforcement platform designed to add a critical control layer for AI agents operating in production environments. It functions as an execution control plane that sits between AI agents and their actions, implementing policy enforcement, approval workflows, and thorough audit trails. This addresses a key challenge in AI deployment: ensuring that autonomous agents operate safely within defined boundaries while maintaining visibility and accountability. Faramesh is particularly relevant for organizations deploying AI agents for critical business functions where uncontrolled agent behaviour could result in compliance violations, financial loss, or security incidents. By providing guardrails at runtime rather than just at training time, Faramesh enables teams to deploy more sophisticated AI agents with confidence, knowing that dangerous or policy-violating actions can be intercepted, flagged for approval, or blocked entirely.

Key Features

Runtime Policy Enforcement

Define and enforce policies that control what actions AI agents can execute, preventing unauthorized or risky operations

Approval Workflows

Implement multi-step approval processes for sensitive agent actions, requiring human review before execution

thorough Audit Trails

Maintain detailed logs of all agent actions, decisions, and enforcement events for compliance and debugging

Agent Execution Control

Fine-grained control over agent behaviour at runtime without requiring model retraining

Open-Source Architecture

Community-driven development with transparent codebase for security auditing and customization

Pros & Cons

Advantages

Enables safer deployment of AI agents in production by adding oversight mechanisms between decision and action
Open-source nature provides transparency, allowing security audits and custom modifications for specific organizational needs
Addresses regulatory and compliance requirements through audit trails and approval workflows
No retraining required, enforcement is applied at the execution level, making it compatible with existing AI systems
Reduces risk of costly AI agent mistakes or policy violations in mission-critical applications

Limitations

As an open-source tool, may require technical expertise to implement, configure, and maintain compared to commercial alternatives
Approval workflow requirements could introduce latency into agent operations, potentially slowing time-to-action in urgent scenarios
Community support may be more limited than commercial solutions, depending on adoption and contributor base

Use Cases

Financial services firms deploying AI agents for trading, approvals, or customer interactions where regulatory compliance is critical

Enterprise automation where AI agents handle sensitive operations like access provisioning, data modifications, or contract execution

Healthcare systems using AI agents for diagnostic assistance or administrative tasks where audit trails and approval workflows are legally required

E-commerce platforms implementing AI agents for refunds, disputes, or inventory decisions with built-in human oversight

Security operations centers using AI agents for incident response with mandatory approval gates for destructive actions

Pricing

Open SourceFree

Full access to Faramesh runtime enforcement platform, community support, self-hosted deployment

Quick Info

Website: faramesh.dev
Pricing: Open Source
Platforms: API, Self-hosted (Docker/Kubernetes compatible)
Categories: Other
Launched: Mar 2026

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