mcpField
P
Progi

Progi

Local
github.com/zsetaAI & Agents2Updated 18w ago

About Progi

Progi is an MCP-native workflow engine for agents, storing structured workflows and step-by-step playbooks so tasks can continue without repeatedly restating a process. It provides task controls and a monitoring UI for tracking progress and revising playbooks.

Key features

  1. 01Create workflows from plain-language descriptions
  2. 02Store step-specific playbooks
  3. 03Create and resume workflow tasks
  4. 04Track task status and output history
  5. 05Revise playbooks between runs

Use cases

  1. 01Guide an agent through recurring code reviews
  2. 02Resume a task after a context reset
  3. 03Monitor progress across active workflows
  4. 04Refine task procedures between runs

MCP support

Progi runs as an MCP server and exposes tools for workflow authoring, task creation, resumption, progress updates, and completion. Its MCP configuration can optionally run a web UI for monitoring.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"progi": {
"command": "uvx",
"args": [
"progi"
],
"env": {
"PROGI_DB_PATH": "<PROGI_DB_PATH>"
}
}
}
}
Run commanduvx progi
Environment variables to set
PROGI_DB_PATHCustom path for the Progi SQLite database file

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiprogi0.3.2

Tools

10

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
namestringYes
descriptionstringNo
workflow_idintegerYes

Details

From the project README

Progi - MCP-native Workflow Engine

Progi

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):

{
  "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:

{
  "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_taskCreate a new task under a given workflow (status todo); returns a preview of its first step
list_tasksList tasks, optionally filtered by status and/or workflow
start_or_continue_taskMain work-loop entry point — starts or resumes a task and returns the current step's playbook, input data, and output spec
update_progress_notesOverwrite a task's progress notes (mid-step save point)
finish_stepMark the current step complete, store its output, and advance to the next step (or mark done)
Workflow authoring
ToolDescription
get_process_skeleton_promptReturn the Pass 1 system prompt for turning a plain-language description into a structured workflow skeleton
get_playbook_authoring_promptReturn the Pass 2 system prompt for authoring a step's playbook (injects workflow context)
save_workflowPersist a new workflow, its steps, and playbooks
list_workflowsReturn 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_PATHOS data dir (platformdirs)SQLite file location
PROGI_WEB_HOST127.0.0.1Web UI bind host
PROGI_WEB_PORT8000Web UI port
PROGI_NO_WEB0Set 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

Comments

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