agent-framework/python/samples/04-hosting/af-hosting/local_responses_workflow

local_responses_workflow — workflow target with structured intake + checkpoints

A Workflow (intake → writer → legal reviewer → formatter) hosted behind both the Responses API and the Invocations API, with the host configured to persist per-conversation checkpoints. Mirrors ../../foundry-hosted-agents/responses/04_workflows/ but uses the agent-framework-hosting stack instead of the Foundry-Hosted-Agents runtime, and adds a structured intake step (SloganBrief with topic / style / audience fields) at the front of the workflow.

What's interesting

• AgentFrameworkHost(target=workflow, …) — the host detects a Workflow target and dispatches to workflow.run(...) (no Agent.create_session(...)).
• Two channels are mounted side-by-side (ResponsesChannel at /responses, InvocationsChannel at /invocations). Both share the same brief_hook that adapts the channel-native input into the workflow start executor's typed input — Responses delivers a list[Message], Invocations delivers a str, but the hook normalises both to text and produces a SloganBrief.
• The hook parses the inbound text as JSON ({"topic": ..., "style": ..., "audience": ...}); if parsing fails it uses the whole text as topic with defaults.
• The workflow's first executor (BriefIntakeExecutor) accepts SloganBrief directly — that's what gets sent into workflow.run(...) by the host.
• checkpoint_location=storage/checkpoints/ — the host scopes a FileCheckpointStorage per conversation (Responses keys it on previous_response_id / conversation_id; Invocations keys it on session_id) and restores from the latest checkpoint at the start of every turn before applying the new input. Without an isolation key the host skips checkpointing for that request.
• No HistoryProvider — the workflow owns its own state via the checkpoint store.

Run

export FOUNDRY_PROJECT_ENDPOINT=https://<your-project>.services.ai.azure.com
export FOUNDRY_MODEL=gpt-5.4-nano
az login

uv sync
uv run hypercorn app:app --bind 0.0.0.0:8000

Single-process for quick iteration:

uv run python app.py

Call locally

Two clients are provided next to app.py:

• call_server.py — Python client using the OpenAI SDK (Responses API only).
• call_server.rest — raw REST examples for both the Responses and Invocations endpoints (open in VS Code with the REST Client extension or any compatible HTTP-file runner).

uv sync --group dev

# Structured brief via the OpenAI SDK (Responses API):
uv run python call_server.py \
    '{"topic": "electric SUV", "style": "playful", "audience": "young families"}'

# Plain topic (style/audience default to "modern" / "general"):
uv run python call_server.py "electric SUV"

# Continue an existing conversation by its `response.id`:
uv run python call_server.py --previous-response-id <response-id> \
    '{"topic": "electric SUV", "style": "retro", "audience": "boomers"}'

After a few turns, inspect storage/checkpoints/<isolation_key>/ — each conversation has its own subdirectory of checkpoint files written by the host.

This sample is local-only — no Dockerfile, no Foundry packaging. For a Foundry-Hosted-Agents-compatible packaging see ../foundry_hosted_agent.