Build AI agents that
don't lose their work.
JamJet is the durable runtime for AI agents — with checkpoint replay,
full execution traces, and runtime-enforced reliability.
Write Python. Run with Rust reliability.
Your agents crash. Ours recover.
Every step is checkpointed as it happens. When a worker dies mid-run, the scheduler reclaims the lease and resumes exactly where it left off.
$ jamjet run research-pipeline.yaml ▸ Starting execution exec_7f3a... ▸ [Plan] ✓ completed 420ms ▸ [Research] ✓ completed 1.2s ▸ [Analyze] ✗ worker crashed ▸ Lease expired · reclaiming... ▸ [Analyze] ✓ resumed 890ms ▸ [Review] ✓ completed 650ms ▸ [Synthesize] ✓ completed 1.1s ▸ Execution complete · 5/5 nodes · 0 events lost
The Analyze step fails unexpectedly.
The scheduler detects the failure and reclaims work.
No rerun of completed steps. Picks up exactly where it stopped.
All 5 nodes complete. Full execution integrity preserved.
Completed steps stay completed. A crash on step 6 doesn't rerun steps 1–5.
External calls aren't re-executed. Emails aren't re-sent. Charges aren't re-applied.
Tokens already spent stay spent. Resume costs only what remains.
Replay the full execution from checkpoints. See exactly what happened, when.
Production outcomes,
not feature lists
Recover from crashes without rerunning everything
Completed steps are never repeated. JamJet resumes from the exact point of failure instead of forcing a full rerun.
Pause safely for human approval
Approval gates suspend durably and survive restarts. Long-running workflows stay reliable even when humans are in the loop.
Replay any execution to debug exactly what happened
Replay a run from checkpoints, inspect traces, and see per-node cost and latency. No guessing from logs.
Connect tools and external agents with MCP + A2A
Use built-in client and server support for both protocols. Connect to tools, delegate to agents, and keep every call inside a durable execution model.
Enforce cost and iteration limits in the runtime
Cost caps, token budgets, and iteration limits are enforced by the runtime, not left to application discipline.
Evaluate agents like software, not vibes
Run evals as workflow nodes with judge-based, assertion-based, latency, and cost scoring. Fail CI on regressions.
Deploy with enterprise controls when needed
Tenant isolation, PII redaction, OAuth delegation, mTLS federation, and retention controls — enforced at the runtime layer.
Python you know.
Durability you don't
have to build.
Every @task is checkpointed. Every workflow survives crashes. Human approval gates pause durably. Cost limits are enforced by the runtime.
from jamjet import task, workflow, approval @task(model="claude-sonnet-4-6", max_cost=0.50) async def analyze(data: dict) -> Report: """Analyze data — checkpointed, cost-capped.""" @workflow async def pipeline(raw: dict): report = await analyze(raw) # crash-safe await approval(report) # durable pause return await publish(report) # resumes here
Coming from LangGraph?
Typed state, workflow steps, routing, and graph-like structure — without relearning how to think.
No optional checkpoint plumbing. Every step is durable when you run on the JamJet runtime.
Typed schemas catch state and interface problems before they spread.
Budgets and iteration caps live in the scheduler, not in scattered guard code.
At a glance
Also migrating from:
CrewAI → OpenAI Agents SDK →Built for your role
Ship agents that survive production
Use your Python mental model, but get durable execution, replayable traces, and runtime-enforced safety. Start with @task, scale to full workflows without rewrites.
Standardize agent infrastructure
One runtime for reliability, observability, eval, and governance. Tenant isolation, PII redaction, OAuth delegation, and mTLS — all enforced at the Rust layer.
Enterprise controls →Run reproducible experiments
The same runtime properties that make agents reliable in production make experiments reproducible in research. ExperimentGrid, checkpoint replay, publication-ready export.
Explore the research toolkit →Reliable agents for production.
Reproducible agents for research.
Same runtime.
Sweep 6 strategies across models and seeds in one command. Parallel execution with durable checkpoints across every condition.
Replay the exact failed run. Fork with modified inputs for ablations. No need to rebuild infrastructure or re-run completed conditions.
Paper-ready LaTeX tables with mean ± std, CSV, and JSON. From experiment to results without custom scripts or manual formatting.
Enterprise-grade when you need it
All enforced at the Rust runtime layer, not by convention.
Security & enterprise docs →