stitchflow-local-agent 0.3.16.3

Local Stitchflow daemon for browser automation tasks and data sync

stitchflow-local-agent

Local daemons that automate SaaS admin tasks for a Stitchflow workspace:

• Action agent — executes user removals and role changes in ChatGPT and Calendly.
• Data sync — exports user CSVs from ChatGPT and Calendly, then uploads them to Stitchflow.

Both run headlessly via agent-browser (Playwright) using a shared persistent browser profile for session management.

Install

pip install "git+https://github.com/Stitchflow-website-webapps/stitchflow-local-agent.git"

Two CLI commands become available:

CLI	Purpose
stitchflow-agent	Action execution — user removal, role changes
stitchflow-sync	Daily data sync — app CSV export and Stitchflow upload

Quick start

# 1. First-time setup — installs agent-browser, opens browsers for login
stitchflow-agent setup
stitchflow-sync setup

# 2. Single run
stitchflow-agent run
stitchflow-sync run

# 3. Foreground daemon commands (for local testing)
stitchflow-agent daemon
stitchflow-sync daemon

# 4. Check health
stitchflow-agent status
stitchflow-sync status

Package structure

stitchflow_local_agent/
  common/                  shared config, browser helpers, daemon, logging, installer
  actions/
    api_client.py          Stitchflow task API client
    dispatcher.py          task routing by (integration, action)
    chatgpt/               ChatGPT: remove_user
    calendly/              Calendly: remove_user, change_role
  sync/
    stitchflow_upload.py   generic CSV → Stitchflow UI upload
    calendly/              Calendly user export (UI-based)
    chatgpt/               ChatGPT user export (API via browser session)
  agent_cli.py             stitchflow-agent entrypoint
  sync_cli.py              stitchflow-sync entrypoint

---

Action agent (stitchflow-agent)

Polls the Stitchflow API for pending tasks, claims them, and executes each one through browser automation.

stitchflow-agent setup    # auto-install agent-browser + Chromium, configure env, open login
stitchflow-agent login    # re-authenticate (auto-installs if missing)
stitchflow-agent run      # process all pending tasks once
stitchflow-agent daemon   # poll continuously (foreground; launchd/task manages background)
stitchflow-agent status   # show config, daemon PID, last task results

Supported actions

Integration	Action	Description
ChatGPT	remove_user	Remove a team member by email from the ChatGPT admin panel
Calendly	remove_user	Remove a user from the Calendly organization
Calendly	change_role	Change a user's role (Admin ↔ User)

Extending

Register a new handler in actions/dispatcher.py:

HANDLERS[("app_name", "action_name")] = handler_function

---

Data sync (stitchflow-sync)

Exports user CSVs from connected apps and uploads each to the matching Stitchflow connection. All configured apps are synced in a single cycle.

stitchflow-sync setup     # open Calendly, ChatGPT, and Stitchflow for login
stitchflow-sync run       # run one full sync cycle
stitchflow-sync daemon    # local foreground loop (use launchd/task scheduler for production background runs)
stitchflow-sync status    # show config, logs, daemon status, recent CSVs

Supported sync sources

App	Export method	Notes
Calendly	UI automation — click Export → Active → download ZIP	CSV extracted from ZIP automatically
ChatGPT	API via browser session — JS fetches /api/auth/session, paginates the users API	Account ID auto-discovered from session

Sync cycle

Each cycle runs sequentially: Calendly export → upload → ChatGPT export → upload → close browser.

Calendly

1. Opens Calendly admin → clicks Export → Active → downloads ZIP → extracts CSV.
2. Opens Stitchflow → Connections → searches "Calendly" → uploads CSV → polls until "Connected".

ChatGPT

1. Opens ChatGPT → executes JS that fetches the auth token, auto-discovers the workspace account ID from the session, and paginates the users API.
2. Converts the returned JSON to CSV in Python, saves to ~/.stitchflow/data-sync/chatgpt/.
3. Opens Stitchflow → Connections → searches "ChatGPT" → uploads CSV → polls until "Connected".

Audit logs

JSONL logs under ~/.stitchflow/logs/:

File	Records
calendly-export.jsonl	Each Calendly export — timestamp, CSV path, file size (success) or error (failure)
chatgpt-export.jsonl	Each ChatGPT export — timestamp, CSV path, file size, user count (success) or error (failure)
data-sync-audit.jsonl	Each Stitchflow upload per app — timestamp, CSV path (success) or error (failure)
data-sync.log	Full sync cycle run log

Extending

Add a new sync source in three steps:

1. Create sync/<app>/export.py with an async def export_<app>_users() -> str | None function.
2. The upload side (sync/stitchflow_upload.py) is already generic — pass any app_name.
3. Wire the export into sync_cli.py's run_sync() via _sync_app().

---

Environment variables

Variable	Required	Default	Description
STITCHFLOW_API_URL	Yes	http://localhost:3030	Stitchflow API base URL
STITCHFLOW_WORKSPACE_ID	Yes	—	Stitchflow workspace ID
STITCHFLOW_API_KEY	No	—	API key (prefer keyring storage)
AGENT_BROWSER_PROFILE	No	~/.agent-browser-profile	Persistent Chromium profile path
CHATGPT_ACCOUNT_ID	No	auto-discovered	Override for ChatGPT workspace ID (normally detected from session)
STITCHFLOW_AGENT_POLL_INTERVAL_SECONDS	No	60	Action daemon poll interval
STITCHFLOW_AGENT_POLL_MAX_IDLE_SECONDS	No	900	Action daemon max idle backoff
STITCHFLOW_AGENT_POLL_MAX_ERROR_SECONDS	No	300	Action daemon max error backoff
STITCHFLOW_SYNC_INTERVAL_SECONDS	No	86400	Sync daemon interval (24 h)

All config is persisted to ~/.stitchflow/agent.env during setup.

Daemon scheduling (recommended for production)

See DAEMON_SETUP.md for macOS launchd and Windows Task Scheduler installation guides covering both daemons.

Security

• API keys — stored in the OS credential manager (macOS Keychain / Windows DPAPI) via keyring. Falls back to ~/.stitchflow/agent.env if keyring is unavailable.
• Browser sessions — maintained in a persistent profile at ~/.agent-browser-profile. No credentials are stored in code or config files.
• Action proof artifacts — before.png, after.png, and audit.json per task under ~/.stitchflow/artifacts/.
• Audit trail — rolling log at ~/.stitchflow/logs/audit.log.

Dependencies

Package	Purpose
Python 3.11 - 3.13	Runtime
httpx	Async HTTP client for the Stitchflow API
keyring	OS credential manager integration
agent-browser	Playwright-based browser automation CLI (installed via npm)

stitchflow-agent setup / stitchflow-sync setup auto-installs agent-browser via npm install -g agent-browser (or brew install agent-browser on macOS), then runs agent-browser install to download Chromium.

Building and releasing

All Python source files are compiled to native extensions via Cython before distribution. Published wheels contain only compiled binaries — no .py or .c source files.

Release workflow

1. Bump the version in pyproject.toml.
2. Commit the version bump, then create the matching tag from that same commit: git commit -am "Release v<version>" && git tag v<version> && git push && git push --tags.
3. GitHub Actions builds compiled wheels for macOS (ARM + Intel), Linux, and Windows across Python 3.11–3.13.
4. A verification step checks that no source files leaked into any wheel.
5. The workflow validates that GITHUB_REF matches [project].version before publishing.
6. Wheels are published to PyPI automatically.

Local testing

# Install build deps
pip install "cython>=3.0" setuptools wheel

# Compile extensions in-place (for development testing)
python setup.py build_ext --inplace

# Build a wheel for your current platform
pip wheel . --no-deps -w dist/

# Verify no source files in the wheel
unzip -l dist/*.whl | grep -E '\.(py|c)$'
# (should return no results)

CI configuration

The build pipeline is defined in .github/workflows/build-wheels.yml. It requires a PYPI_API_TOKEN secret configured in the GitHub repository settings under an environment named pypi.

Further reading

• ARCHITECTURE.md — internals: dispatcher, browser layer, sync pipeline, error handling, file layout.
• DAEMON_SETUP.md — launchd / Task Scheduler install guides.