flyteplugins-agents-deepagents

Run Deep Agents — LangChain's agent harness with built-in planning, a virtual filesystem, and subagents — durably on Flyte.

pip install flyteplugins-agents-deepagents

You keep writing Deep Agents code; Flyte is the durable runtime underneath:

• Tools are Flyte tasks. Stack @tool on @env.task and each tool call the agent (or any of its subagents) makes runs as a durable child action — its own container/resources, retries, and caching.
• Model turns are replayable. On the builder path (or by wrapping your own model in DurableChatModel), every model turn is recorded via flyte.trace, so a crashed/retried run replays completed turns instead of re-calling (and re-billing) the model.
• Memory spans runs. run_agent(..., memory_key=...) persists the conversation and the agent's virtual filesystem to a durable keyed store, so a later run with the same key picks up both.

import flyte
from flyteplugins.agents.deepagents import run_agent, tool

env = flyte.TaskEnvironment("deep-agent")

@tool
@env.task(cache="auto", retries=3)
async def search_web(query: str) -> str:
    """Search the web for a query."""
    ...

@env.task(report=True, retries=3)
async def research_agent(question: str) -> str:
    return await run_agent(
        question,
        tools=[search_web],
        instructions="You are an expert researcher.",
        model="anthropic:claude-sonnet-4-6",
        subagents=[{
            "name": "critic",
            "description": "Critiques draft answers.",
            "system_prompt": "You are a ruthless critic.",
        }],
    )

To bring your own agent, build it with create_deep_agent (attaching @tool-wrapped tasks natively) and pass it as run_agent(agent=...); wrap the model in DurableChatModel(inner=...) to keep durable model turns on that path. See examples/ for the full patterns.