mcp-agent can execute workflows on the built-in asyncio executor or on Temporal. 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.
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.
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. |
Enable the Temporal engine
Switch the execution engine and point at a Temporal cluster (the examples assume temporal server start-dev):
execution_engine: temporal
temporal:
host: "localhost"
port: 7233
namespace: "default"
task_queue: "mcp-agent"
max_concurrent_activities: 10
temporal server start-dev
# Web UI: http://localhost:8233 | gRPC: localhost:7233
@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:
# 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())
# 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)
@app.async_tool so clients invoke a single tool call while Temporal handles retries and state:
# 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()
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 demonstrates the flow:
# 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}")
mcp-agent workflows resume):
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"},
)
app.context.executor.signal_bus, so you can prototype locally and switch to Temporal when you need durability.
The Temporal server example also shows how durable workflows call nested MCP servers and trigger MCP 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.
Add optional top-level overrides to preload custom workflow tasks and refine retry behaviour:
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
types in non_retryable_error_types prevents Temporal from re-running an activity when the failure is not recoverable (see the Temporal failure reference). 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). 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) 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 – side-by-side asyncio vs. Temporal workflows (basic, router, parallel, evaluator-optimizer) plus a detailed README walking through setup.
- Temporal MCP server – exposes durable workflows as MCP tools, demonstrates
workflows-resume, and includes a client script for pause/resume flows.
- Temporal tracing example – shows the same code running with Jaeger exports once you flip the
execution_engine.
Example projects
