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deepagent-temporal

Temporal integration for Deep Agents — durable execution for AI agent workflows.

If your Deep Agent process crashes mid-task, all progress is lost. Sub-agents are ephemeral. Human-in-the-loop approval blocks a running process. deepagent-temporal solves these problems by running your Deep Agent as a Temporal Workflow.

Experimental

This project is experimental. Use at your own risk.

Key features

Feature Without Temporal With deepagent-temporal
Durable execution Process crash = lost progress Survives crashes, restarts, deployments
Sub-agents In-process, ephemeral Independent Child Workflows
Worker affinity N/A Sticky task queues keep file ops on same machine
Human-in-the-loop Blocks a live process Zero-resource wait via Temporal Signals
Token streaming In-process only Callback capture + optional Redis Streams for real-time delivery

3-line migration

# Connect to Temporal and wrap your agent
client = await Client.connect("localhost:7233")
temporal_agent = TemporalDeepAgent(agent, client, task_queue="coding-agents")

# Same API — now with durable execution
result = await temporal_agent.ainvoke(
    {"messages": [HumanMessage(content="Fix the bug in main.py")]},
    config={"configurable": {"thread_id": "task-123"}},
)

The ainvoke, astream, get_state, and resume APIs are identical to vanilla Deep Agents.

Next steps