AgentTakt
io.github.ryoohshima由 GitHub 账号 ryoohshima 发布v0.2.2更新于 Oct 24, 2026
Review, edit, and approve AI agent task plans in a ComfyUI-style visual node editor in your terminal
概览
AgentTakt 让 AI 代理提交任务计划,你在终端界面中以节点图形式审阅、编辑并批准后,计划才会被执行。
- 可以做什么
- 接收代理提交的计划,并在另一个终端窗口中渲染为节点图,包含节点、依赖边和按类型着色。用鼠标和键盘编辑图:移动、新增、删除节点,绘制依赖边,修改参数。批准或拒绝计划,并将编辑后的计划 JSON 返回给代理执行。仅展示计划而不阻塞代理等待批准。校验图结构(包括环检测),并把错误返回给代理自行修正。
- 适用场景
- 当你希望在 AI 编码代理执行多步计划前设置人工确认环节,或希望以可视化图而非文本方式查看和调整代理计划时使用。
- 运行要求
- 以 Python 包形式通过 stdio 在本地运行(需 Python 3.10+,通常用 uvx 启动)。需要两个进程:由代理客户端启动的 MCP 服务器,以及你在单独终端中启动的 TUI。需要支持鼠标上报的终端模拟器。客户端中 request_approval 的工具超时需设得足够长,例如 30 分钟。
安装
在 SourceWeft 中
- 打开 控制台中的 AgentTakt,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
[PyPI - Version] [PyPI - Python Version] [License: MIT]
[AgentTakt demo: drag nodes, draw a dependency edge, approve]
AgentTakt is an MCP (Model Context Protocol) server and TUI tool. When an AI agent (an "Executor" such as Claude Code) sends a task execution plan over MCP, AgentTakt renders it as a node graph in your terminal. You review it with mouse and keyboard — move, add, and delete nodes, draw dependency edges, edit parameters — then approve, and the edited plan JSON is returned to the Executor for execution.
Features
- Terminal-native — no web UI; everything runs inside your terminal
- Visual node editor — rounded nodes, dependency edges, and per-type coloring, powered by Textual
- Mouse-first editing — drag nodes to move them, draw edges between ports (rubber band), click to select and delete
- Safe approval loop — cycle detection (DAG guarantee) and other validations at the entry point, returning errors the agent can self-correct
Requirements
- Python 3.10+ (recommended: uv)
- A terminal emulator with mouse reporting (iTerm2, WezTerm, kitty, Ghostty, ...)
Installation
If you have uv, no installation is needed. uvx agenttakt fetches and runs AgentTakt on demand, and the .mcp.json example below starts the MCP server the same way.
If you don't have uv, install AgentTakt once:
Installing is also handy for everyday use even with uv — you start the TUI by hand, so plain agenttakt beats typing uvx agenttakt each time:
Agent Plugins
This repository is an Agent Plugins 1.0.0 package. In a compatible client, load the repository root as the plugin directory:
The plugin requires uvx on PATH and runs the published agenttakt package
from PyPI. Its uv cache is stored under the client-managed PLUGIN_DATA
directory. Start the TUI separately with uvx agenttakt before using the tools.
For request_approval, configure a sufficiently long tool timeout in your
client (for example, 30 minutes); the portable MCP format has no timeout field.
The development .mcp.json is a separate client-native configuration that runs
the local checkout. server.json supplies metadata for the MCP Registry.
Quick Start
AgentTakt runs as two processes: the MCP server, which Claude Code starts for you, and the TUI, which you start yourself in a separate terminal. The TUI is what displays the plan, so start it before asking the Executor for approval.
Running the TUI in the same session as Claude Code does not work. A stdio MCP server has its standard input and output reserved for protocol traffic, so the same process cannot also drive a full-screen terminal UI. That is why the two halves are separate processes talking over a Unix domain socket.
1. Start the TUI (in its own terminal)
An idle screen appears, waiting for plans from the Executor. Leave this terminal open. If no TUI is running when the Executor calls request_approval, the call fails with:
AgentTakt editor is not running. Ask the user to run "agenttakt" in a separate terminal, then call request_approval again.
On startup the TUI checks PyPI in the background and shows a notification when a newer version is available. Set AGENTTAKT_NO_UPDATE_CHECK=1 to disable the check.
2. Register the MCP server with the Executor (Claude Code)
Add the following to your project's .mcp.json:
[!IMPORTANT] Setting
timeout(milliseconds) explicitly is required. Therequest_approvaltool blocks until the human finishes reviewing. MCP progress notifications do not extend client-side timeouts, so the default would cut the request off before approval. The example above sets 30 minutes (1800000). This does not apply toshow_plan, which returns as soon as the TUI receives the plan.
3. Request approval from the Executor
When the Executor calls the MCP tool request_approval(plan, summary), the plan appears in the TUI as a node graph. Once the human edits and approves (or rejects) it, the result is returned as:
See docs/schema.md for the plan JSON format and what to write in each node.
Display-only plans (show_plan)
show_plan(plan, summary) shows a plan in the TUI without waiting for approval — it returns {"status": "displayed"} as soon as the editor receives it. Use it when you just want visibility into what the agent is planning, in any mode (not only plan mode). The plan opens with a [view-only] header; closing it sends nothing back to the Executor.
Agents call request_approval naturally when the host is in plan mode, but they will not volunteer plans outside it. To encourage that, add an instruction like this to your project's CLAUDE.md (or equivalent agent instructions):
Note: a [view-only] plan occupies the editor until dismissed; a later request_approval waits in the queue behind it.
Debug mode (try it without MCP)
Loads a plan from a file, opens the editor, and writes the approval result to --out.
Key Bindings
Mouse: drag a node to move it; drag from a node's output port (●, right edge) and release on another node to create an edge.
Edges are drawn as braille Bezier-like curves by default. If they render poorly in your environment, switch to rounded orthogonal lines with --edges orthogonal.
Documentation
- Plan JSON schema — data model, node fields, what to write in
type/data, and validation rules - Changelog — release notes for each version
License
来源:README.md,提交 a36a2bd
工具
0版本历史
2- v0.2.2最新Sep 23, 2026
- v0.2.1Sep 16, 2026
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