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Progi

Created By
Attila Toth4 days ago
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.
Content

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

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)
submit_outputMark 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
update_playbookReplace 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

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

Use an absolute path for PROGI_DB_PATH

If you want to start Monitoring on a different port:

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

Server Config

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