jamjet-labs/jamjet
by Various
The open-source safety layer for AI agents — block unsafe tool calls, require approval, enforce budgets, audit, replay.
MCP
jamjet-labs/jamjet
Added 1 June 2026
Overview
An open-source safety layer for AI agents. It blocks unsafe tool calls, requires approval, enforces budgets, and provides audit and replay capabilities.
Best for
Best for
Teams building AI agents that need safety and audit controls without vendor lock-in
Use cases
- Enforcing spending limits on agent tool usage
- Requiring human approval for sensitive tool calls
- Auditing and replaying agent actions for compliance
How to use
Install
pip install jamjet Tested with
Claude Desktop, Claude Code, Cursor, ChatGPT, OpenAI Agents SDK, LangChain, CrewAI, ADK
Example client config
# ~/.jamjet/policy.yaml\nversion: 1\nrules:\n - { match: "*delete*", action: block }\n - { match: "payments.*", action: require_approval }\n - { match: "shell.exec", action: block } Notes
An open-source safety layer for AI agents. It blocks unsafe tool calls, requires approval, enforces budgets, and provides audit and replay capabilities.
13 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Enforcing spending limits on agent tool usage
- Requiring human approval for sensitive tool calls
- Auditing and replaying agent actions for compliance
Pros
- Open-source and self-hostable, no vendor lock-in
- Built in Rust for performance and reliability
- Provides multiple safety controls in one tool: approval, budgets, audit, replay
Cons
- Early stage project with small community (13 stars)
- Adds latency and overhead to agent workflows
- Requires integration effort with existing agent frameworks
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Open-source and self-hostable, no vendor lock-in
- Built in Rust for performance and reliability
- Provides multiple safety controls in one tool: approval, budgets, audit, replay
Cons
- Early stage project with small community (13 stars)
- Adds latency and overhead to agent workflows
- Requires integration effort with existing agent frameworks
Pairs with
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