> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mcp-agent.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Durable Agents (Temporal)

> Run long-lived MCP workflows with Temporal pause/resume and human approvals

mcp-agent can execute workflows on the built-in asyncio executor or on [Temporal](https://temporal.io/). Temporal adds durable state, automatic retries, and first-class pause/resume semantics for long-running MCP tools. The best part: **switching is just a config change**—set `execution_engine: temporal` and your existing workflows, tools, and agents keep working.

<Tip>
  Outside of configuration (and starting a Temporal worker), you rarely need to touch your code. The same `@app.workflow`, `@app.workflow_run`, `@app.async_tool`, `Agent`, and AugmentedLLM APIs behave identically with Temporal behind the scenes.
</Tip>

## When to choose Temporal

| Reach for Temporal when…                                                     | Asyncio alone is enough when…                                |
| ---------------------------------------------------------------------------- | ------------------------------------------------------------ |
| Workflows must survive restarts, deploys, or worker crashes.                 | Runs are short-lived and you can re-trigger them on failure. |
| Human approvals, scheduled delays, or days-long research loops are in scope. | The agent answers a single request synchronously.            |
| You need history, querying, and signal support from the Temporal UI or CLI.  | You only need to fan out a few tasks inside one process.     |

Temporal also unlocks adaptive throttling, workflow versioning, and seamless integration with mcp-agent Cloud.

## Enable the Temporal engine

Switch the execution engine and point at a Temporal cluster (the examples assume `temporal server start-dev`):

```yaml theme={null}
execution_engine: temporal

temporal:
  host: "localhost"
  port: 7233
  namespace: "default"
  task_queue: "mcp-agent"
  max_concurrent_activities: 10
```

Start a local server for development:

```bash theme={null}
temporal server start-dev
# Web UI: http://localhost:8233  |  gRPC: localhost:7233
```

The [configuration reference](/reference/configuration#temporalsettings) documents TLS, API keys, automatic retries, and metadata headers when you deploy to production.

Temporal relies on a replay model: the deterministic parts of your workflow (the code you wrote under `@app.workflow_run`) are re-executed after a crash, while non-deterministic work—LLM calls, MCP tool calls, HTTP requests—is automatically offloaded to Temporal activities by the executor. mcp-agent handles that split for you; you keep writing straightforward async Python.

## Run a worker

Workers poll Temporal for workflow/activity tasks. The helper `create_temporal_worker_for_app` wires your `MCPApp` into a worker loop:

```python theme={null}
# examples/temporal/run_worker.py
import asyncio
import logging

import workflows  # noqa: F401  # registers @app.workflow classes
from main import app
from mcp_agent.executor.temporal import create_temporal_worker_for_app

logging.basicConfig(level=logging.INFO)

async def main():
    async with create_temporal_worker_for_app(app) as worker:
        await worker.run()

if __name__ == "__main__":
    asyncio.run(main())
```

Keep this process running while you start workflows or expose durable tools.

## Launch workflows (or tools) durably

The executor API is unchanged—Temporal persists the state machine behind the scenes:

```python theme={null}
# examples/temporal/basic.py
async with app.run() as agent_app:
    executor = agent_app.executor  # TemporalExecutor
    handle = await executor.start_workflow(
        "SimpleWorkflow",
        "Print the first 2 paragraphs of https://modelcontextprotocol.io/introduction",
    )
    result = await handle.result()
    print(result)
```

You can also expose a Temporal run as an MCP tool. The orchestrator example uses `@app.async_tool` so clients invoke a single tool call while Temporal handles retries and state:

```python theme={null}
# examples/temporal/orchestrator.py (excerpt)
@app.async_tool(name="OrchestratorWorkflow")
async def run_orchestrator(task: str, app_ctx: AppContext | None = None) -> str:
    context = app_ctx or app.context
    orchestrator = Orchestrator(
        llm_factory=OpenAIAugmentedLLM,
        available_agents=[finder, writer, proofreader, fact_checker, style_enforcer],
        plan_type="full",
        context=context,
    )
    return await orchestrator.generate_str(task)

async with app.run() as orchestrator_app:
    executor = orchestrator_app.executor
    handle = await executor.start_workflow("OrchestratorWorkflow", task)
    report = await handle.result()
```

This pattern is ideal for “long-running tool” buttons in MCP clients: the tool call returns immediately with a run identifier and you can stream progress or resume later.

## Human approvals, pause, and resume

Temporal signals map directly to `executor.wait_for_signal` and `executor.signal_workflow`. The pause/resume workflow shipped in [`examples/mcp_agent_server/temporal`](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/mcp_agent_server/temporal) demonstrates the flow:

```python theme={null}
# PauseResumeWorkflow (excerpt)
print(f"Workflow paused. workflow_id={self.id} run_id={self.run_id}")
try:
    await app.context.executor.wait_for_signal(
        signal_name="resume",
        workflow_id=self.id,
        run_id=self.run_id,
        timeout_seconds=60,
    )
except TimeoutError:
    raise ApplicationError("Timed out waiting for resume signal", type="SignalTimeout", non_retryable=True)

return WorkflowResult(value=f"Workflow resumed! {message}")
```

Resume it from another process, the Temporal UI, or mcp-agent Cloud (`mcp-agent workflows resume`):

```python theme={null}
async with app.run() as agent_app:
    executor = agent_app.executor
    await executor.signal_workflow(
        workflow_name="PauseResumeWorkflow",
        workflow_id="pause-resume-123",
        signal_name="resume",
        payload={"approved_by": "alex"},
    )
```

The same helper works on the asyncio executor via `app.context.executor.signal_bus`, so you can prototype locally and switch to Temporal when you need durability.

### Nested tools and elicitation

The Temporal server example also shows how durable workflows call nested MCP servers and trigger [MCP elicitation](https://modelcontextprotocol.io/specification/2025-06-18/client/elicitation) when a human response is required. Activities such as `call_nested_elicitation` log progress via `app.app.logger` so the request trace and Temporal history stay aligned.

## Configure workflow-task modules and retry policies

Add optional top-level overrides to preload custom workflow tasks and refine retry behaviour:

```yaml theme={null}
execution_engine: temporal

workflow_task_modules:
  - my_project.temporal_tasks  # importable module path

workflow_task_retry_policies:
  my_project.temporal_tasks.generate_summary:
    maximum_attempts: 1
  mcp_agent.workflows.llm.augmented_llm_openai.OpenAICompletionTasks.request_completion_task:
    maximum_attempts: 2
    non_retryable_error_types:
      - AuthenticationError
      - PermissionDeniedError
      - BadRequestError
      - NotFoundError
      - UnprocessableEntityError
  custom_tasks.*:
    initial_interval: 1.5   # seconds (number, string, or timedelta)
    backoff_coefficient: 1.2
  *:
    maximum_attempts: 3
```

* `workflow_task_modules` entries are standard Python import paths; they are imported before the worker begins polling so `@workflow_task` functions register globally.
* `workflow_task_retry_policies` accepts exact activity names, module or class suffixes (`prefix.suffix`), trailing wildcards like `custom_tasks.*`, or the global `*`. The most specific match wins.
* Retry intervals accept seconds (`1.5`), strings (`"2"`), or `timedelta` objects.
* Marking error `type`s in `non_retryable_error_types` prevents Temporal from re-running an activity when the failure is not recoverable (see the [Temporal failure reference](https://docs.temporal.io/references/failures#application-failure)). For provider SDKs, useful values include:
  * OpenAI/Azure OpenAI: `AuthenticationError`, `PermissionDeniedError`, `BadRequestError`, `NotFoundError`, `UnprocessableEntityError`.
  * Anthropic: `AuthenticationError`, `PermissionDeniedError`, `BadRequestError`, `NotFoundError`, `UnprocessableEntityError`.
  * Azure AI Inference: `HttpResponseError` (raised with non-retryable status codes such as 400/401/403/404/422).
  * Google GenAI: `InvalidArgument`, `FailedPrecondition`, `PermissionDenied`, `NotFound`, `Unauthenticated`.
* mcp-agent raises `WorkflowApplicationError` (wrapping Temporal's `ApplicationError` when available) for known non-retryable provider failures, so these policies work even if you run without the Temporal extra installed.
* Inspect an activity’s fully-qualified name via `func.execution_metadata["activity_name"]` or through the Temporal UI history when adding a mapping.
* Temporal matches `non_retryable_error_types` using the exception class name string you supply (see the [RetryPolicy reference](https://docs.temporal.io/references/sdk-apis/python/temporalio.common/#temporalio-common-RetryPolicy)). Use the narrowest names possible—overly generic entries such as `NotFoundError` can suppress legitimate retries if a workflow expects to handle that condition and try again.

With these pieces in place you can gradually introduce durability: start on asyncio, flip the config once you need retries/pause/resume, then iterate on policies and module preloading as your workflow surface grows.

## Operating durable agents

* **Temporal Web UI** ([http://localhost:8233](http://localhost:8233)) lets you inspect history, replay workflow code, and emit signals.
* **Workflow handles** expose `describe()`, `query()`, and `list()` helpers for custom dashboards or integrations.
* **Observability**: enable OpenTelemetry (`otel.enabled: true`) to stream spans + logs while Temporal provides event history.
* **Deployment**: mcp-agent Cloud uses the same configuration. Once deployed, Cloud exposes CLI commands (`mcp-agent workflows list`, `resume`, `cancel`) that call the same signal/query APIs shown above.

## Deeper dives

* [Temporal example suite](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/temporal) – side-by-side asyncio vs. Temporal workflows (basic, router, parallel, evaluator-optimizer) plus a detailed [README](https://github.com/lastmile-ai/mcp-agent/blob/main/examples/temporal/README.md) walking through setup.
* [Temporal MCP server](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/mcp_agent_server/temporal) – exposes durable workflows as MCP tools, demonstrates `workflows-resume`, and includes a client script for pause/resume flows.
* [Temporal tracing example](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/tracing/temporal) – shows the same code running with Jaeger exports once you flip the `execution_engine`.

## Example projects

* [examples/temporal](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/temporal) – basic workflow, evaluator-optimizer, router, and orchestrator patterns on Temporal.
* [examples/mcp\_agent\_server/temporal](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/mcp_agent_server/temporal) – MCP server with durable human approvals, nested servers, and elicitation.
* [examples/oauth/pre\_authorize](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/oauth/pre_authorize) – demonstrates pre-authorised credentials for background Temporal workflows.