Agentspan

If you're deploying AI agents that need to survive process crashes — whether from OOM, deployments, or spot-instance terminations — Agentspan is the only open-source tool that gives you durable execution with a Pythonic API. The crash + resume demo on their site is compelling: your process dies, the agent keeps running on the server, and you reconnect from any machine to pick up where you left off. We'd reach for this when building long-running data pipelines, batch processing jobs, or any agent workflow where human approval steps could take hours or days. The HITL decorator (@tool(approval_required=True)) pauses execution indefinitely and resumes cleanly from Slack, a web portal, or code — no timeouts, no lost state. Where it bites: this is not a no-code tool. You write Python. It's also not fully managed — you either self-host (Docker/Helm) or pay for Orkes Cloud. If you don't need crash resilience, simpler frameworks like LangChain or direct LLM calls suffice. Closest alternative is Temporal's Python SDK, which also provides durable execution but requires learning a different programming model (activities, workflows). Agentspan feels more natural: you write regular agents and get durability for free. In practice, the testing helpers (mock_run, expect) are a standout — they let you test agent logic deterministically in CI without an LLM or server. That alone reduces a lot of production risk. One caveat: the project is relatively new, so community and documentation are still growing. But the core (Conductor) is battle-tested. If you need reliable agents now, it's worth the investment.