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Progi is an MCP-native workflow engine for your AI harness

2 stars PythonOthers Updated Jul 29, 2026
claudecopilotmcpmcp-serverprogiworkflowworkflow-engineprogi-mcp

Documentation

Progi - MCP-native Workflow Engine

Progi teaches your agent how you like to get things done. So you can do your best work without re-explaining your process or losing context between sessions.

License: MIT
PyPI
MCP

Get started

Add Progi to your MCP client config (GH Copilot / Cursor / Claude Code / etc):

json
{
  "mcpServers": {
    "progi": {
      "command": "uvx",
      "args": ["progi"]
    }
  }
}

Progi Monitoring starts automatically at `http://127.0.0.1:8000`.

If you want to start Monitoring on a different port:

json
{
  "mcpServers": {
    "progi": {
      "command": "uvx",
      "args": ["progi"],
      "env": {
        "PROGI_WEB_PORT": "8080"
      }
    }
  }
}

How it works

1. Describe your workflow

*"Hey Progi, help me create workflow for creating integrations, reviewing code, and publishing PRs."*

Describe your process in plain language. You can be detailed or just provide a rough idea. Progi stores it as a structured workflow with per-step playbooks.

2. Run tasks, stay in the loop

*"Hey Progi, start a new task, we need to review a new docs PR in the repo."*

Your agent loads the workflow, works through each step using your playbooks, and loops you in at critical checkpoints to review output.

3. Monitor progress

Progi Monitoring gives you a live view of every running and completed task — status, progress, and the full output history across all your workflows.

4. Optimize as you go

Tweak playbooks in Progi Monitoring between runs. Because workflows live in a database and survive context resets, every future task picks up your changes automatically — your process gets sharper with each iteration.


MCP Tools

Work loop

ToolDescription
`create_task`Create a new task under a given workflow (status `todo`); returns a preview of its first step
`list_tasks`List tasks, optionally filtered by status and/or workflow
`start_or_continue_task`Main work-loop entry point — starts or resumes a task and returns the current step's playbook, input data, and output spec
`update_progress_notes`Overwrite a task's progress notes (mid-step save point)
`finish_step`Mark the current step complete, store its output, and advance to the next step (or mark done)

Workflow authoring

ToolDescription
`get_process_skeleton_prompt`Return the Pass 1 system prompt for turning a plain-language description into a structured workflow skeleton
`get_playbook_authoring_prompt`Return the Pass 2 system prompt for authoring a step's playbook (injects workflow context)
`save_workflow`Persist a new workflow, its steps, and playbooks
`list_workflows`Return all workflows with their ordered steps

Authoring is two passes: Pass 1 turns a plain-language description into a structured skeleton; Pass 2 authors each step's playbook. `save_workflow` persists both.


Configuration

VariableDefaultPurpose
`PROGI_DB_PATH`OS data dir (`platformdirs`)SQLite file location
`PROGI_WEB_HOST``127.0.0.1`Web UI bind host
`PROGI_WEB_PORT``8000`Web UI port
`PROGI_NO_WEB``0`Set to `1` to disable the web UI

Run modes: `uvx progi` (MCP + web UI), `uvx progi --no-web` (MCP only), `uvx progi-web` (web UI only).

> Use an absolute path for `PROGI_DB_PATH`

Frequently asked questions

What is progi?

progi is Progi is an MCP-native workflow engine for your AI harness

How do I install progi?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is progi open source?

Yes — it is hosted on GitHub at https://github.com/zseta/progi and has 2 stars.

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