Run any AI agent framework on a durable workflow runtime

AGNT5 does not require you to throw away an existing agent framework. This cookbook shows how to keep LangGraph, OpenAI Agents SDK, Vercel AI SDK, or a plain Python tool loop while adding durable execution, replay, and traces underneath.

Scenario

Your team already has an agent that calls tools and returns a structured result. It works locally, but production failures are hard to recover from. You want the agent call to run inside a durable workflow without rewriting the agent.

What you build

A wrapper function around existing agent logic.
A workflow that checkpoints the framework result.
Trace metadata for model calls, tool calls, and outputs.
A recovery path after worker or API failures.
A migration path that keeps framework choice flexible.

Wrap the existing agent

Treat the existing agent as a step implementation.

@function
async def run_existing_agent(request: AgentRequest) -> AgentResult:
    result = await existing_agent.invoke(
        input=request.prompt,
        metadata={"customer_id": request.customer_id},
    )
    return AgentResult.model_validate(result)

Then orchestrate it with AGNT5.

@workflow
async def durable_agent_run(ctx: WorkflowContext, request: AgentRequest) -> AgentResult:
    prepared = await ctx.step(prepare_request, request)
    result = await ctx.step(run_existing_agent, prepared)
    return await ctx.step(record_agent_result_once, request.request_id, result)

The agent framework remains inside run_existing_agent. AGNT5 owns the durable step boundary around it.

Trace integration

At minimum, include:

framework name and version,
model name,
tool names,
final structured output,
external side-effect receipts.

If the framework exposes callback hooks, map those events into AGNT5 trace metadata. If it does not, record the final input and output at the step boundary.

Recovery model

On replay, AGNT5 returns the journaled AgentResult instead of calling the framework again. On retry after a failed attempt, the wrapper runs again with the same input. Any side effects inside the framework still need idempotency keys.

Production checks

The framework call only runs inside a step.
The step result is structured and serializable.
Tool side effects use stable idempotency keys.
The trace links framework events back to the AGNT5 run ID.
Replay does not call the model again for completed steps.