Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/4fuu/uv-agent

Experimental coding agent with an ANSI-first terminal TUI and a single run_python action surface.
https://github.com/4fuu/uv-agent

ai-agent ansi-tui coding-agent mcp openai python python-runner terminal-ui tui uv

Last synced: about 14 hours ago
JSON representation

Experimental coding agent with an ANSI-first terminal TUI and a single run_python action surface.

Awesome Lists containing this project

README 

          
# uv-agent



uv-agent tui2 screenshot



[简体中文](README.zh-CN.md)



**Python-native coding agent — one `run_python` boundary. Every external action is

auditable, replayable, and interruptible.**



`uv-agent` channels all model capabilities through a single, well-defined exit: the

model can only touch the outside world via `run_python`. Each call is a complete

Python script executed in a `uv run`-managed isolated environment, using

`uv_agent_runtime` helpers for file editing, command execution, code search, MCP,

sub-agents, images, and more. With only one exit, you can replay any run and see

exactly what happened and why.



The project is still experimental. Public APIs, config fields, and runtime behavior

may change.



## Features



- **Single tool boundary** — no shell, filesystem, browser, or MCP model tools. The

  model writes Python; the managed runtime executes it. Every external action is an

  auditable script.

- **Cache-aware NetGain compaction** — long conversations no longer trigger blind

  compression. A pre-turn lightweight judge round lets the model estimate remaining

  calls and history dependency, then computes the net gain of compaction via an

  economic formula. Compression fires only when cache savings outweigh information

  loss. Recent context is retained verbatim (K tokens) to avoid losing key details.

- **Python managed runtime** — scripts run in a project-shared `uv` environment.

  `uv_agent_runtime` provides helpers for read/write/edit, ripgrep search,

  subprocesses, dependency installation, sub-agents, MCP clients, and more.

  Scripts serve as documentation — no opaque shell commands.

- **Plugin system** — plain Python packages discovered via `uv_agent.plugins` entry

  point. Register runtime helpers, subscribe to events, submit turns from external

  systems. `uvx --with your-plugin uv-agent` and you're set.

- **Self-bootstrapping** — uv-agent is developed using uv-agent. Reading, editing,

  testing, and iterating on the project are done with uv-agent itself.

- **Progressive context disclosure** — skills, MCP servers, and workspace rules are

  not dumped into the prompt all at once. The model receives an index first; full

  content is disclosed only when needed. Removed capabilities are explicitly marked

  to prevent stale-context errors.

- **Goal mode durable memory** — `/goal` creates a per-thread checklist/notes layer

  independent of the chat transcript. After compaction or resume, the model consults

  Goal files rather than relying solely on summarized history.

- **Agent View parallel workspaces** — dispatch bug investigations, implementation

  experiments, and test fixes to isolated Git worktree background sessions. Track

  them all from a single dashboard.

- **Prompt-cache-friendly design** — the system prompt prefix is guaranteed

  byte-identical within an epoch. Compaction requests share the same prefix structure

  as normal calls, maximizing provider-side cache hits. Cache reads are nearly free.



## Cache-Aware Compaction



The cache-aware compaction introduced in v0.16.0 is uv-agent's core optimization for

long-running sessions. Unlike traditional "compress when context hits N%," uv-agent

makes an economic decision before every turn:



1. The model estimates how many more conversation rounds are needed

   (`remaining_calls_bucket`) and how strongly the task depends on history

   (`history_dependency`).

2. It enumerates K retention candidates and evaluates the NetGain for each: future

   cache savings minus compaction call cost, cache invalidation loss, information

   distortion penalty, plus context quality improvement gain.

3. Compaction fires only when the best net gain exceeds a margin-scaled threshold;

   otherwise it skips, avoiding wasted compression for short tasks.



Compaction requests share the exact same prefix structure as normal calls (system

prompt → tools → messages), ensuring provider-side prompt prefix caches stay warm.

Over 90% of input tokens in a typical compaction call are billed at cached rates

(typically 1%–10% of the normal input price).



This design draws on the DP compaction algorithm from

[bash-agent](https://github.com/lloydzhou/bash-agent), with thanks.



## Quick Start



Prerequisites:



- **uv** — https://docs.astral.sh/uv/getting-started/installation/

- **ripgrep** — https://github.com/BurntSushi/ripgrep#installation

- **Git** — needed for normal coding workflows and Worktree mode.



```powershell

# Run the latest published release

uvx uv-agent@latest



# Run from a local checkout

uv run uv-agent



# Single-turn question (no TUI)

uvx uv-agent@latest ask "Summarize the project structure"



# Resume an existing thread

uvx uv-agent@latest ask --thread thr_xxx "Continue where we left off"

```



## Model Configuration



uv-agent ships with no real provider configuration. Configure at least one provider,

model, and level in `~/.uv-agent/config.json` (or project-level

`.uv-agent/config.json`). Keep API keys in environment variables or git-ignored

local config.



Supported API formats:



| `api` value | Format |

| --- | --- |

| `"responses"` | OpenAI Responses API |

| `"chat_completions"` | OpenAI Chat Completions API |

| `"anthropic_messages"` | Anthropic Messages API |



Full configuration example



```json

{

  "providers": {

    "deepseek": {

      "base_url": "https://api.deepseek.com",

      "api_key": "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",

      "timeout_s": 7200,

      "chat_completions": {

        "path": "/chat/completions"

      },

      "message_passthrough": {

        "assistant": [

          "reasoning_content"

        ]

      },

      "reasoning_display": {

        "assistant_message_fields": [

          "reasoning_content"

        ],

        "stream_delta_fields": [

          "reasoning_content"

        ]

      }

    },

    "minimax": {

      "base_url": "https://api.minimaxi.com",

      "api_key": "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",

      "timeout_s": 7200,

      "chat_completions": {

        "path": "/v1/chat/completions"

      },

      "anthropic_messages": {

        "path": "/anthropic/v1/messages"

      }

    }

  },

  "models": {

    "deepseek-v4-flash": {

      "provider": "deepseek",

      "model": "deepseek-v4-flash",

      "api": "chat_completions",

      "supports_images": false,

      "context_window_tokens": 1000000,

      "params": {

        "reasoning_effort": "high"

      }

    },

    "deepseek-v4-pro": {

      "provider": "deepseek",

      "model": "deepseek-v4-pro",

      "api": "chat_completions",

      "supports_images": false,

      "context_window_tokens": 1000000,

      "params": {

        "reasoning_effort": "max"

      }

    },

    "MiniMax-M2.7": {

      "provider": "minimax",

      "model": "MiniMax-M2.7-highspeed",

      "api": "anthropic_messages",

      "supports_images": false,

      "context_window_tokens": 204800

    }

  },

  "levels": {

    "deepseek-flash": {

      "model": "deepseek-v4-flash"

    },

    "deepseek-pro": {

      "model": "deepseek-v4-pro"

    },

    "MiniMax-M2.7": {

      "model": "MiniMax-M2.7"

    }

  },

  "runtime": {

    "default_level": "deepseek-flash",

    "ask_default_level": "deepseek-flash",

    "store_provider_response": false,

    "max_agent_rounds": 1000,

    "compression": {

      "enabled": true,

      "model_level": "deepseek-flash",

      "trigger_ratio": 0.9

    },

    "title_generation": {

      "enabled": true,

      "model_level": "deepseek-flash"

    },

    "branch_name_generation": {

      "enabled": true,

      "model_level": "deepseek-flash",

      "timeout_s": 15.0

    }

  },

  "runner": {

    "default_timeout_s": 7200,

    "max_output_bytes": 1000000,

    "scriptenv_index_url": null

  },

  "pricing": {

    "currency": "RMB",

    "unit": "1M_tokens",

    "models": {

      "deepseek-v4-flash": {

        "input": 1,

        "output": 2,

        "cached_input": 0.02

      },

      "deepseek-v4-pro": {

        "input": 3,

        "output": 6,

        "cached_input": 0.025

      }

    }

  },

  "ui": {

    "completion_notification": {

      "enabled": true

    }

  },

  "plugins": {

    "disabled": [],

    "config": {}

  }

}



```



Use `/config` in the TUI to switch default level, language, and compression

settings. See [configuration](docs/configuration.md) for every option and

[config.example.json](docs/config.example.json) for a standalone example.



## Everyday Workflow



- Type and press `Enter` to send. Use `Ctrl+Enter` / `Ctrl+J` for newlines.

- Type `/` from an empty composer to open the command palette; type to filter.

  `@` for file mentions, `@@` for thread mentions.

- `/level ` to switch models; `/status` to inspect runtime state including

  cache compaction judge details.

- `/goal enable [objective]` for durable task checklists across long sessions.

- Agent View dispatches background tasks to isolated Git worktrees.

- Use `uv-agent tui` for the legacy Textual panels (`/config`, `/models`, etc.).



See [TUI and slash commands](docs/tui.md) for the full list.



## TUI Interfaces



- **tui2** (default, `uv-agent` or `uv-agent tui2`) — lightweight ANSI TUI rendered

  directly in the terminal. Compact status rows, streaming events, Goal/Worktree

  mode, and image attachments.

- **Textual TUI** (`uv-agent tui`) — deprecated, kept for compatibility. The older

  Textual widget-based interface. Screenshot: [docs/t1.png](docs/t1.png).



## Agent View



Agent View is a dashboard for managing multiple background agent sessions. Tasks

run in isolated Git worktrees on auto-generated branches, keeping edits away from

your current checkout. Track status, skim output, continue or discard tasks — all

from one panel.



## Plugins



Plugins are Python packages discovered via the `uv_agent.plugins` entry point. They

run in the uv-agent host process and can register runtime helpers, subscribe to

events, and submit turns from external systems.



```powershell

uvx --with your-uv-agent-plugin uv-agent@latest

```



See [Plugin system](docs/plugins.md) for details.



## Runtime & Context



Every model turn = stable system prompt + on-demand structured context.



- `run_python` is the only external action surface. Scripts execute in a

  project-shared uv environment and import `uv_agent_runtime` helpers. The uv

  environment and working directory are separate; the cwd can change via

  `enter_dir` or Worktree mode.

- Runtime context (helper lists, skills, MCP servers, etc.) uses fingerprinted

  incremental updates — only changed parts are injected, and removed capabilities

  are explicitly marked.

- Workspace rules are disclosed progressively: index first, full AGENTS.md only

  when entering the relevant directory.

- Goal mode provides a durable checklist/notes layer independent of the chat

  transcript, preserving task progress across compaction and resume.

- Checkpoint compaction summarizes the conversation while excluding reloadable

  runtime context. New epochs replay structured context before retained history.



## Documentation



- [Configuration](docs/configuration.md)

- [Full config example](docs/config.example.json)

- [TUI and slash commands](docs/tui.md)

- [Runtime and managed scripts](docs/runtime.md)

- [Plugin system](docs/plugins.md)



## Development



uv-agent is self-bootstrapping — it is developed using uv-agent itself for reading,

editing, testing, and iterating.



```powershell

uv run pytest

```



Local debug state, screenshots, config, and run data belong in `.uv-agent/` and

should stay out of git.



## Acknowledgments



The cache-aware compaction design draws on the DP compaction algorithm and cache

alignment approach from [bash-agent](https://github.com/lloydzhou/bash-agent),

with thanks.



## License



MIT. See [LICENSE](LICENSE).