Progi

by zseta

231 downloads
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GitHub

About

MCP-native workflow engine for your AI harness. 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.

Details

Author
zseta
Downloads
231
Categories
AI, Automation, Project Management, Productivity, Other

- Structured workflows with per-step playbooks
- Two-pass authoring from plain-language descriptions
- Persistent storage in a local SQLite database
- Live monitoring web UI for task progress
- Step advancement with output submission
- Playbook updating without re-authoring the entire workflow

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Progi
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Describe your workflow in plain language to Progi. It runs a two-pass authoring process to create a structured skeleton and per-step playbooks, then persists everything with save_workflow. Run tasks with create_task, then start_or_continue_task to step through the workflow, and submit_output to advance. Monitor progress via the web UI (default at 127.0.0.1:8000) or disable it with PROGI_NO_WEB=1. Configure database path using the PROGI_DB_PATH environment variable.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

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.

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 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. ---

Tools

Work loop

| Tool | Description | |---|---| | 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) | | submit_output | Mark the current step complete, store its output, and advance to the next step (or mark done) |

Workflow authoring

| Tool | Description | |---|---| | 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 | | update_playbook | Replace the playbook content for a step | 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

| Variable | Default | Purpose | |---|---|---| | 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 | > Use an absolute path for PROGI_DB_PATH If you want to start Monitoring on a different port: ``json { "mcpServers": { "progi": { "command": "uvx", "args": ["progi"], "env": { "PROGI_WEB_PORT": "8080" } } } } ``
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