jamjet 0.8.7

JamJet — open-source safety layer for AI agents. Block unsafe tool calls, require approval, enforce budgets, audit, replay.

JamJet

Write the safety policy once. Run it everywhere your agents can act.

JamJet sits underneath your Python agent — Claude Code, OpenAI Agents SDK, MCP clients, LangChain, CrewAI, ADK, 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 where tool calls need control.

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See it in 60 seconds

pip install jamjet
jamjet demo unsafe-tool-call

No API key. No Docker. No cloud account. The model is mocked; the enforcement path is real. Three more demos run the same way:

jamjet demo approval        # pause-for-approval flow
jamjet demo budget-cap      # $0.05 cost cap
jamjet demo mcp-tool-policy # MCP-shaped policy

The rest of this README is the Python authoring guide — workflows, durability shims, MCP/A2A integration, and the hosted control plane. For the full positioning + cross-language adapters (Claude Code hook, MCP shim, OpenAI guardrail, TypeScript SDK, CLI), see the main repo README.

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What you get in the Python package

• jamjet demo <scenario> — runnable enforcement demos with no setup
• Policy as code — YAML beside your workflow OR jamjet.cloud.policy(...) in Python
• @durable — exactly-once execution across crashes/restarts for any side-effecting tool, with shims for LangChain, CrewAI, ADK, OpenAI Agents SDK, Anthropic Agent SDK
• jamjet.cloud — optional two-line install for the hosted control plane (free tier: 5K traces/mo, single project)
• jamjet.integrations.openai_guardrail — drop-in guardrail for the OpenAI Agents SDK that runs the same policy.yaml
• MCP client + A2A — connect to MCP servers and delegate to external agents from a workflow; the runtime carries trace context across both protocols

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Quick Start

Requirements: Python 3.11+

# Install the CLI (runtime binary included)
pip install jamjet

# Option A — scaffold a new project
jamjet init my-agent-project
cd my-agent-project

# Option B — add JamJet to an existing project (works like git init)
cd my-existing-project
jamjet init

# Start local dev runtime (SQLite, embedded — no server setup needed)
jamjet dev

In another terminal:

# Run the example workflow
jamjet run examples/basic_tool_flow

Hello World — Python

from jamjet import Workflow, tool
from pydantic import BaseModel

class SearchResult(BaseModel):
    summary: str
    sources: list[str]

@tool
async def web_search(query: str) -> SearchResult:
    # your implementation here
    ...

workflow = Workflow("research")

@workflow.state
class ResearchState(BaseModel):
    question: str
    result: SearchResult | None = None

@workflow.step
async def search(state: ResearchState) -> ResearchState:
    result = await web_search(query=state.question)
    return state.model_copy(update={"result": result})

@workflow.step
async def summarize(state: ResearchState) -> ResearchState:
    # model call here
    ...

Hello World — YAML

# workflow.yaml
workflow:
  id: research
  version: 0.1.0
  state_schema: schemas.ResearchState
  start: search

nodes:
  search:
    type: tool
    tool_ref: tools.web_search
    input:
      query: "{{ state.question }}"
    output_schema: schemas.SearchResult
    next: summarize

  summarize:
    type: model
    model: default_chat
    prompt: prompts/summarize.md
    output_schema: schemas.Summary
    next: end

jamjet validate workflow.yaml
jamjet run workflow.yaml --input '{"question": "What is JamJet?"}'

Connecting to an MCP Server

# agents.yaml
agents:
  researcher:
    model: default_chat
    mcp:
      servers:
        github:
          transport: http_sse
          url: https://mcp.github.com/v1
          auth:
            type: bearer
            token_env: GITHUB_TOKEN

# workflow.yaml (node using MCP tool)
nodes:
  search_github:
    type: mcp_tool
    server: github
    tool: search_code
    input:
      query: "{{ state.search_query }}"
    output_schema: schemas.SearchResults
    retry_policy: io_default

Delegating to an External Agent via A2A

nodes:
  code_review:
    type: a2a_task
    remote_agent: partner_reviewer   # defined in agents.yaml
    skill: security_review
    input:
      code: "{{ state.generated_code }}"
    stream: true
    timeout: 300s
    on_input_required: human_review

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Durability across frameworks

Wrap any side-effecting tool with @durable to get exactly-once execution across crashes, restarts, and replays — regardless of which agent framework you're using.

from jamjet import durable

@durable
def charge_card(amount: float) -> dict:
    return stripe.charges.create(amount=amount)

Pair it with a durable_run() context manager — one shim per framework:

Framework	Import
LangChain	from jamjet.langchain import durable_run
CrewAI	from jamjet.crewai import durable_run
Google ADK	from jamjet.adk import durable_run
Anthropic Agent SDK	from jamjet.anthropic_agent import durable_run
OpenAI Agents SDK	from jamjet.openai_agents import durable_run

Example with LangChain:

from langchain.agents import AgentExecutor
from jamjet import durable, durable_run

@durable
def charge_card(amount): ...  # your real tool

executor = AgentExecutor(...)

# Use a stable execution_id that survives process restarts.
# (Persist this id in your job queue / DB / wherever you start agent runs.)
AGENT_RUN_ID = "booking-agent-run-abc123"

with durable_run(AGENT_RUN_ID):
    executor.invoke({"input": "book a flight to Tokyo"})
    # If the process crashes mid-`charge_card` and restarts under the same
    # AGENT_RUN_ID, the cached result is returned on replay — Stripe is
    # never called twice.

For framework-native run-identity (where you'd otherwise want with durable_run(executor):), see the per-shim docs in jamjet/<framework>/__init__.py — note that most frameworks expose run_id only per-invocation via callbacks, so threading a stable identity across crash boundaries is the user's responsibility.

See the per-module guide at jamjet/durable/README.md.

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JamJet Cloud — Hosted Governance (jamjet.cloud)

Starting in 0.6.0, the jamjet package includes a jamjet.cloud submodule for the hosted control plane. Two-line install:

import jamjet.cloud as jamjet
from openai import OpenAI

jamjet.configure(api_key="jj_xxxxxxxxxxxx", project="my-agent")

# Every OpenAI / Anthropic call is now captured automatically.
resp = OpenAI().chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "hello"}],
)

Open app.jamjet.dev/dashboard/traces — the call appears within ~5 seconds with model, tokens, cost, and duration.

Optional governance primitives:

jamjet.policy("block", "payments.*")              # filter tools by name
jamjet.budget(max_cost_usd=5.00)                   # cap spend; raises BudgetExceeded
approval = jamjet.require_approval(action="charge_card", context={...})
@jamjet.trace                                       # decorate any function
def lookup_customer(...): ...

Install with the LLM SDK you use:

pip install jamjet[openai]      # auto-instrument OpenAI
pip install jamjet[anthropic]   # or Anthropic
pip install jamjet[cloud-all]   # both

Full guide: Cloud Quickstart · Sign up free

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Architecture

┌──────────────────────────────────────────────────────────┐
│                     Authoring Layer                       │
│              Python SDK  |  YAML  |  CLI                  │
├──────────────────────────────────────────────────────────┤
│                 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      │  Protocol Adapter Framework  │
├────────────────────────────┴─────────────────────────────┤
│                      Runtime Services                      │
│  Model Adapters  |  Tool Execution  |  Memory/Retrieval   │
│  Policy Engine   |  Observability   |  Secret Management  │
├──────────────────────────────────────────────────────────┤
│                    Control Plane / APIs                    │
│        REST / gRPC  |  Agent Registry  |  Admin           │
├──────────────────────────────────────────────────────────┤
│                         Storage                           │
│           Postgres (production)  |  SQLite (local)        │
└──────────────────────────────────────────────────────────┘

Read more: Architecture Overview

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Documentation

Start here

Guide	Description
Quickstart	Get a workflow running in 10 minutes
Core Concepts	Agents, workflows, nodes, state, durability
Workflow Authoring	YAML and Python authoring guide
Python SDK	Full SDK reference
YAML Reference	Complete YAML spec

Connect to other systems

Guide	Description
MCP Integration	Connect to MCP servers, expose tools
A2A Integration	Delegate to and serve external agents
Agent Model	Agent Cards, lifecycle, autonomy levels
Human-in-the-Loop	Approval nodes, state editing, audit
Observability	Traces, replay, cost attribution, OTel GenAI
Deployment	Production deployment guide

Advanced & enterprise

Guide	Description
Security	Auth, secrets, RBAC, OAuth delegated agent auth
WASI Sandboxing	Sandboxed tool execution via Wasmtime
Eval Node	Evaluation as a workflow primitive
ANP Discovery	Decentralized agent discovery via DID

Architecture deep-dives

Document	Description
Architecture Overview	Full system architecture
Execution Model	State machine, node types, IR
State & Durability	Event sourcing, snapshotting, recovery
Agent Model	Agent-native runtime design
MCP Architecture	MCP client/server internals
A2A Architecture	A2A protocol internals
Protocol Adapters	Extensible protocol layer

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Contributing

We welcome contributions of all kinds — bug reports, feature requests, documentation, and code.

• Read CONTRIBUTING.md to get started
• Check open issues for good first issue tags
• RFCs for major changes: see docs/rfcs/
• Architecture decisions: see docs/adr/

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Community

• GitHub Discussions: github.com/jamjet-labs/jamjet/discussions

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License

Apache 2.0 — see LICENSE.