@zseta
基本信息
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.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"progi": {
"command": "uvx",
"args": [
"progi"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
Progi is an MCP-native workflow engine that teaches your AI agent your preferred processes. It stores structured workflows with per-step playbooks in a local SQLite database, survives context resets, and provides a monitoring web UI. It is intended for developers who want their agent to follow consistent, reusable procedures across sessions.
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.
Progi stores workflows as structured steps with playbooks in a persistent database. Unlike plain prompts, these workflows survive context resets, and each future task automatically picks up the latest playbook changes.
Set the `PROGI_DB_PATH` environment variable to an absolute path (defaults to the OS data directory). The web UI binds to `127.0.0.1:8000` by default; you can change the port with `PROGI_WEB_PORT` or disable it entirely with `PROGI_NO_WEB=1`.
All data is stored in a local SQLite database whose location is controlled by `PROGI_DB_PATH`. No external cloud or server is required.
Progi exposes an MCP‑compliant tool interface over stdio (when invoked via the `uvx` command) plus a web UI for monitoring. It does not provide its own network transport or authentication layer beyond the MCP protocol.
Pass 1 converts a plain-language description into a structured workflow skeleton using `get_process_skeleton_prompt`. Pass 2 authors each step’s detailed playbook using `get_playbook_authoring_prompt`. The result is persisted with `save_workflow`.
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