---
title: Build durable agents with Restate and Pydantic AI
description: >-
  Restate now integrates with Pydantic AI, adding durable execution, stateful
  sessions, human-in-the-loop, and multi-agent orchestration in a few lines of
  code.
date: '2026-05-12'
authors:
  - Giselle van Dongen
categories:
  - Pydantic AI
  - Integrations
  - Durable Execution
canonical: 'https://pydantic.dev/articles/restate-durable-execution-pydanticai'
---
> ## Documentation Index
> Fetch the complete documentation index at: https://pydantic.dev/llms.txt
> Use this file to discover all available pages before exploring further.

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**warning**: agent query parameters (`goal` and `organization`) not included.

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---


> Markdown version of [Build durable agents with Restate and Pydantic AI](https://pydantic.dev/articles/restate-durable-execution-pydanticai) — the canonical HTML page.
>
> By Giselle van Dongen · 2026-05-12 · Pydantic AI, Integrations, Durable Execution
>
> Related: [Would you deploy an agent you don't control?](https://pydantic.dev/articles/pydantic-ai-vs-claude-agent-sdk.md) · [Running Pydantic AI agents on Jev, and seeing what they decided](https://pydantic.dev/articles/jev-pydantic-ai-live-view.md)
>
> All articles: [/articles.md](https://pydantic.dev/articles.md) · Site index: [/llms.txt](https://pydantic.dev/llms.txt)

---

# Build durable agents with Restate and Pydantic AI

*This is a guest post by [Giselle van Dongen](https://github.com/gvdongen).*

We're excited to announce that [Restate](https://restate.dev/) is now integrating with [Pydantic AI](https://pydantic.dev/pydantic-ai). Restate gives agents lightweight, flexible durable execution to make them resilient to failures, persist state across executions, and pause/resume when needed. This integration lets you add that to your Pydantic AI agents with just a few lines of code.

## What Restate adds to your Pydantic AI agents

Pydantic AI gives you a type-safe, Pythonic way to define agents with structured outputs, tools, and a unified model provider interface. Restate adds the ingredients they need to work well in production:

- **Durable execution**: retries and recovery, with every step journaled so progress isn't lost or duplicated
- **Durable sessions**: stateful entities keyed by user or conversation, with built-in state and concurrency control
- **Human-in-the-loop**: pause for approvals that take minutes or months, surviving crashes in between
- **Multi-agent orchestration**: durable RPC, fan-out, and timeouts across agents, tools, and services
- **Task control**: cancel, kill, roll back, or restart executions. One at a time or in bulk, via UI or API

Restate is open source and is easy to self-host (single binary, no extra databases, no separate worker processes).

## Getting started

Install the SDK alongside Pydantic AI:

```bash
uv init .
uv add restate_sdk pydantic-ai
```

Run a Restate server:

```bash
docker run -p 8080:8080 -p 9070:9070 docker.restate.dev/restatedev/restate:latest
```

Now create your first durable agent:

```python
import restate
from pydantic_ai import Agent, RunContext
from restate.ext.pydantic import RestateAgent, restate_context

weather_agent = Agent(
    "openai:gpt-5.2",
    system_prompt="You are a helpful agent that provides weather updates.",
)

@weather_agent.tool()
async def get_weather(_run_ctx: RunContext[None], city: str) -> dict:
    """Get the current weather for a given city."""
    return await restate_context().run_typed("fetch weather", fetch_weather_api, city=city)

restate_agent = RestateAgent(weather_agent)
agent_service = restate.Service("WeatherAgent")

@agent_service.handler()
async def run(_ctx: restate.Context, message: str) -> str:
    result = await restate_agent.run(message)
    return result.output

app = restate.app([agent_service])
```

Your agent is now resilient to failures, with every step logged and recoverable:

- Failed LLM calls will be retried.
- Failed calls to the weather API will be retried.
- If the service crashes, Restate remembers all the steps it did in a journal, and will resume the execution on another process by replaying the journal.

But this is just the start.

## Building blocks for reliable agents

### Durable sessions with concurrency control

Implement stateful agents keyed by user or session ID, with Restate Virtual Objects. Message history is persisted in Restate's durable K/V store and automatically restored on each request:

```python
assistant = Agent(
    "openai:gpt-5.2",
    system_prompt="You are a helpful assistant.",
)
restate_assistant = RestateAgent(assistant)

chat = VirtualObject("Chat")

@chat.handler()
async def message(ctx: ObjectContext, req: ChatMessage) -> str:
    # Load message history from Restate's durable key-value store
    history = await ctx.get("messages", serde=MessageSerde())

    result = await restate_assistant.run(req.message, message_history=history)

    # Store updated history back in Restate state
    ctx.set("messages", result.all_messages(), serde=MessageSerde())
    return result.output
```

The conversation state lives in Restate, queryable through the UI, with automatic concurrency control to prevent race conditions when users send multiple messages.

### Human approvals as durable promises

Real-world agents need human oversight. Durable promises make this easy: your agent pauses execution, waits for human input, and resumes automatically, even if the service crashes while waiting:

```python
@agent.tool
async def human_approval(_run_ctx: RunContext[None], claim: InsuranceClaim) -> str:
    """Ask for human approval for high-value claims."""

    # Create a durable promise
    approval_id, approval_promise = restate_context().awakeable(type_hint=str)

    # Notify the moderator (persisted)
    await restate_context().run_typed(
        "Request review", request_human_review, claim=claim, awakeable_id=approval_id
    )

    # Wait for review (survives crashes)
    return await approval_promise
```

Add timeouts to prevent workflows from waiting indefinitely. Restate persists both the timeout and the approval promise, maintaining the correct remaining time through restarts:

```python
# Wait for human approval for at most 3 hours to reach our SLA
match await restate.select(
    approval=approval_promise,
    timeout=restate_context().sleep(timedelta(hours=3)),
):
    case ["approval", approved]:
        return "Approved" if approved else "Rejected"
    case _:
        return "Approval timed out, evaluate with AI"
```

### Multi-agent orchestration

Pydantic AI lets you define specialized agents and route between them. With Restate, all routing decisions are durable: if any agent in the chain fails, the entire workflow is recovered from Restate's journal:

```python
intake_agent = Agent(
    "openai:gpt-5.2",
    system_prompt="Route insurance claims to the appropriate specialist.",
)

medical_agent = Agent(
    "openai:gpt-5.2",
    system_prompt="Review medical claims for coverage and necessity.",
)
restate_medical_agent = RestateAgent(medical_agent)

@intake_agent.tool
async def consult_medical_specialist(_run_ctx: RunContext[None], claim: InsuranceClaim) -> str:
    """Route to the medical specialist for medical insurance claims."""
    result = await restate_medical_agent.run(claim.model_dump_json())
    return result.output

# Add other agents here
```

You can also call agents deployed as separate services using durable RPC, or fan out to multiple agents in parallel using `restate.gather()`:

```python
@agent.tool
async def check_fraud(_run_ctx: RunContext[None], claim: InsuranceClaim) -> str:
    """Analyze the probability of fraud."""
    # Durable service call to a remote agent; persisted and retried by Restate
    return await restate_context().service_call(run_fraud_agent, claim)
```

## Start building with Pydantic AI and Restate
Pydantic AI gives you a type-safe, Pythonic way to define agents, tools, and structured outputs. Restate handles what they need to run in production: durable execution, sessions, human approvals, and orchestration, all from a single binary, no extra database or worker queue.

Try it out now:
- Pydantic AI durable execution [docs](https://pydantic.dev/docs/ai/integrations/durable_execution/restate/).
- Follow the [Restate + Pydantic AI quickstart](https://docs.restate.dev/ai-quickstart).
- Read the [getting started guides](https://docs.restate.dev/ai/patterns/durable-agents) on sessions, human-in-the-loop, and multi-agent patterns.
- Browse the [code examples](https://github.com/restatedev/ai-examples/tree/main/pydantic-ai) on GitHub.