jamjet-labs/jamjet

The open-source safety layer for AI agents.

jamjet.dev · Quickstart · Docs · Examples · Blog · Discord

JamJet sits underneath your agent — LangChain, CrewAI, ADK, MCP servers, custom code — and enforces what prompts cannot:

• 🛡️ Block unsafe tool calls at runtime (database deletes, payments, file writes)
• ✋ Pause for human approval on risky actions, durably
• 💸 Cap cost per agent, per run, per project
• 📒 Record an audit trail that survives a regulator's review
• ⏪ Replay or resume crashed runs from the last checkpoint

Keep your agent framework. Add JamJet when tool calls need control.

            Your Agent / Framework
   (LangChain · CrewAI · ADK · custom · MCP client)
                     │
                     ▼
  ┌───────────────────────────────────────────────┐
  │            JamJet Safety Layer                │
  │   policy · approval · budget · audit · replay  │
  └───────────────────────────────────────────────┘
                     │
                     ▼
        Tools · MCP servers · APIs · DBs · Agents

Prompts are not a security boundary. The runtime is.

→ Read When AI Deletes the Database for why this is a runtime architecture problem, not a model problem.

60-second safety demo

pip install jamjet

Drop a policy beside your agent code. The runtime intercepts any matching tool call before it leaves the agent's process — blocked_tools are refused outright, require_approval_for pauses execution durably and waits for an out-of-band decision (crashes don't lose the approval; execution resumes when it arrives).

# workflow.yaml
policy:
  blocked_tools:
    - "*delete*"
    - "payments.refund"
  require_approval_for:
    - "database.*"
    - "payment.transfer"
    - "user.suspend"

Python, with the hosted control plane:

import jamjet
jamjet.cloud.configure(api_key="jj_...", project="my-agent")
jamjet.cloud.policy("block", "*delete*")
jamjet.cloud.policy("require_approval", "database.*")
# Every OpenAI / Anthropic call in this process is now policy-gated.

→ Runnable approval workflow in examples/hitl-approval · Cloud Quickstart

Use JamJet when your agent can…

• call MCP servers or arbitrary tools
• write to a database
• send emails or Slack messages
• trigger payments or external API calls
• access customer data or PII
• run for minutes/hours and needs to survive crashes
• spend real model budget at scale
• delegate to other agents

What JamJet adds

Works with your stack — not a replacement

JamJet does not replace LangChain, LangGraph, CrewAI, Google ADK, Spring AI, or your custom agent code. Use those to build agent behavior. Use JamJet to control what happens at runtime.

Community-built integrations for LangChain, LlamaIndex, CrewAI, AutoGen, Pydantic-AI, DSPy, Spring AI, and LangChain4j live in jamjet-labs/jamjet-examples/integrations. Want to build the official integration for your framework? Claim a slot — first 10 merged contributors get JamJet swag.

Examples

→ All 19 examples · Community integrations · Build your own

Sub-products

Engram — the JamJet ecosystem's memory layer for agents. Where JamJet provides durable execution (process can crash and resume), Engram provides durable memory (facts persist across runs and version cleanly via supersede()). Temporal knowledge graph, hybrid retrieval, conflict detection. Ships as a Rust crate (also bundled into the Rust runtime above), an MCP server (Docker · GHCR), a standalone Python library (github.com/jamjet-labs/engram, 71% on LongMemEval-S), a Python client for the MCP server, and a Spring AI ChatMemoryRepository. Comparison with Mem0/Zep → java-ai-memory.dev.

JamJet Java Runtime — embeds durable execution directly in your JVM, no Docker or sidecar, 8.9× faster than calling out to one. Works with Spring AI, LangChain4j, and Google ADK. → Launch post.

Architecture

┌──────────────────────────────────────────────────────────┐
│                     Authoring Layer                       │
│     Python SDK  |  Java SDK  |  Go SDK (planned)  |  YAML  │
├──────────────────────────────────────────────────────────┤
│                 Compilation / Validation                   │
│           Graph IR  |  Schema  |  Policy lint             │
├────────────────────────────┬─────────────────────────────┤
│      Rust Runtime Core     │      Protocol Layer          │
│  Scheduler  |  State SM    │  MCP Client  |  MCP Server   │
│  Event log  |  Snapshots   │  A2A Client  |  A2A Server   │
│  Workers    |  Timers      │                              │
├────────────────────────────┴─────────────────────────────┤
│                    Enterprise Services                     │
│  Policy  |  Audit  |  PII Redaction  |  OAuth  |  mTLS     │
├──────────────────────────────────────────────────────────┤
│                      Runtime Services                      │
│  Model Adapters  |  Tool Execution  |  Engram Memory      │
├──────────────────────────────────────────────────────────┤
│                         Storage                           │
│           Postgres (production)  |  SQLite (local)        │
└──────────────────────────────────────────────────────────┘

"Engram Memory" here is the in-process distribution bundled with the Rust runtime. Engram also ships standalone — see Sub-products.

Roadmap

Documentation

Full docs at jamjet.dev

Quickstart · Concepts · Python SDK · Java SDK · YAML Workflows · REST API · MCP · A2A · Eval · Enterprise · Observability · CLI · Deployment

Contributing

Contributions welcome — see CONTRIBUTING.md.

Looking for a starter task?

• Build a framework integration — 8 slots open, first 10 contributors get JamJet swag
• Browse good first issues
• Join the conversation in Discord

Community

GitHub Discussions · Issues · Discord

License

Apache 2.0 — see LICENSE.

Hosted control plane available at app.jamjet.dev — traces, approval queue, audit retention, team projects. Optional. The runtime, both SDKs, and Engram are Apache-2.0 with no usage limits.

⭐ Star JamJet if you believe agents need a runtime safety layer

Built by Sunil Prakash · © 2026 JamJet Labs · jamjet.dev · Apache 2.0