qd-evolve 0.1.14

Multi-agent AI framework with A2A protocol support, group chat, persistent memory, and extensible tool system

QD-Evolve

中文版

Multi-agent AI framework with A2A protocol support, group chat, persistent memory, and extensible tool system.

• DESIGN.md — design philosophy, invariants, architecture, implementation

Installation

Prerequisites

• Python 3.13+ — download
• Mosquitto v5 broker (MQTT/GChat mode only) — download

Verify Python is ready:

Windows

python --version
# Python 3.13.x  ← must be 3.13 or newer

If the command is not found:

1. Re-run the Python installer
2. Check "Add python.exe to PATH" at the bottom of the first screen
3. Or search "Manage app execution aliases" in Windows Settings and turn off the Python alias that opens the Store

macOS / Linux

python3 --version
# Python 3.13.x  ← must be 3.13 or newer

Step 1 — Create a project folder

mkdir my-agent
cd my-agent

Step 2 — Create and activate a virtual environment

Windows

python -m venv .venv
.venv\Scripts\activate

macOS / Linux

python3 -m venv .venv
source .venv/bin/activate

Step 3 — Install qd-evolve

pip install qd-evolve --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

# Optional extras
pip install qd-evolve[memory] --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu   # Embeddings & conversation memory
pip install qd-evolve[boat] --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu     # BOAT bridge

If you use uv instead of pip, the extra index is configured automatically — just run uv add qd-evolve.

Step 4 — Initialize your project

qd-evolve init

This copies default tools, skills, and config templates into your project folder:

my-agent/
├── .venv/                 # Virtual environment
├── tools/                 # User tools — add/delete freely
│   ├── bridge/            #   Bridge connectors (OAT, MCP)
│   ├── cli/               #   CLI tool wrappers
│   ├── func/              #   Python function tools
│   └── mcp/               #   MCP server configs
├── skills/                # Skills — add/delete freely
│   ├── baidu-search/
│   ├── register-cli/
│   ├── search-tools/
│   └── ...
├── config.minimal.json    # Minimal config — copy to config.json
└── config.json.example    # Full config reference

Running init again is safe: existing files are never overwritten. New default files from package updates are added.

Step 5 — Configure

copy config.minimal.json config.json    # Windows
# cp config.minimal.json config.json     # macOS / Linux

Edit config.json and set your API key. Minimal setup for single-agent chat:

{
  "env_vars": {
    "PYTHONIOENCODING": "utf-8",
    "PYTHONUTF8": "1",
    "SERPER_API_KEY": "YOUR_SERPER_API_KEY"
  },
  "default_provider": "deepseek",
  "default_model": "deepseek-v4-pro",
  "providers": [
    {
      "name": "deepseek",
      "api_key": "YOUR_DEEPSEEK_API_KEY",
      "base_url": "https://api.deepseek.com",
      "api": "openai-completions",
      "models": [
        { "name": "deepseek-v4-pro", "reasoning": true, "context_window": 1000000, "max_tokens": 131072 }
      ]
    }
  ],
  "agents_config": {
    "agents": [
      {
        "name": "default",
        "toolbox": {
          "tools": {
            "load_func": "preload",
            "load_skill": "preload",
            "load_cli": "preload"
          }
        }
      }
    ]
  }
}

API keys you'll need:

• Provider key (DeepSeek, OpenAI, Anthropic, etc.) — required for chat
• Serper key — optional, for web search. Free tier at serper.dev. Without it the agent may open browser windows to search.

See Configuration below for full multi-agent, MQTT, and toolbox setup.

Verify

qd-evolve chat --agent default

Troubleshooting

pip install fails with CMake / nmake errors

This means pip is trying to build llama-cpp-python from source. Make sure you included --extra-index-url:

pip install qd-evolve --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

Browser windows open when the agent searches the web

The agent has a search tool that needs a Serper API key (SERPER_API_KEY in env_vars). Without it, the agent may fall back to the browser MCP tool which opens visible browser windows. Get a free key at serper.dev and add it to config.json.

'python' is not recognized or python: command not found

Python is not installed or not on your PATH. See the verification steps in Prerequisites above.

Install from source

git clone https://github.com/juzcn/qd-evolve
cd qd-evolve

# Install dependencies
uv sync

# Optional: BOAT bridge extras
uv sync --extra boat

Source installs have tools/ and skills/ already in the project root — no init needed.

Quick Start

# Single-agent chat
qd-evolve chat --agent default

# Multi-agent A2A chat over HTTP
qd-evolve a2a-http

# Run an agent as standalone A2A HTTP server
qd-evolve a2a-http serve --agent <name>

# Multi-agent in-process chat (all agents loaded locally, no network)
qd-evolve a2a-inproc

# Multi-agent MQTT chat (requires Mosquitto v5 broker)
qd-evolve a2a-mqtt

# Run an agent as MQTT-accessible server
qd-evolve a2a-mqtt serve --agent <name>

# Group chat — WeChat-style multi-agent group (requires Mosquitto v5 broker)
# Supports: AI agents, terminal human agents, WeChat human agents
qd-evolve gchat --agent <name>

# Manage tool enable/disable/preload
qd-evolve toolbox --agent <name>

# Browse and search conversation memories
qd-evolve memory --agent <name>

Four Systems

System	Entry	Transport	Use Case
Chat	qd-evolve chat --agent <name>	In-process only	Single-agent, no network
A2A Inproc	qd-evolve a2a-inproc	In-process only	Multi-agent in-process
A2A	qd-evolve a2a-http	HTTP + in-proc	Multi-agent over HTTP/SSE
MQTT	qd-evolve a2a-mqtt	MQTT v5 + in-proc	Multi-agent over MQTT
GChat	qd-evolve gchat	MQTT v5 (group topics)	WeChat-style group chat

Each system is fully independent — no protocol fallback between them. See DESIGN.md for the full architecture.

Configuration

All configuration via config.json. No CLI config commands, no .env files.

Provider & Model

{
  "default_provider": "openai",
  "default_model": "gpt-4o",
  "providers": [
    {
      "name": "openai",
      "api_key": "sk-...",
      "base_url": "https://api.openai.com/v1",
      "api": "openai-completions",
      "models": [
        { "name": "gpt-4o", "context_window": 128000, "max_tokens": 4096 }
      ]
    },
    {
      "name": "anthropic",
      "api_key": "sk-ant-...",
      "api": "anthropic",
      "models": [
        { "name": "claude-sonnet-4-6", "context_window": 200000, "max_tokens": 8192 }
      ]
    },
    {
      "name": "deepseek",
      "api_key": "...",
      "base_url": "https://api.deepseek.com",
      "api": "openai-completions",
      "models": [
        { "name": "deepseek-v4-pro", "reasoning": true, "context_window": 1000000, "max_tokens": 131072 }
      ]
    }
  ]
}

Three API types: openai-completions, openai-response, anthropic. Set at provider level via api field. Streaming is global (stream field). Reasoning/thinking is per-model (reasoning: true).

Multi-Agent

{
  "agents_config": {
    "chat_agent": "planner",
    "agents": [
      {
        "name": "planner",
        "description": "Plans and delegates tasks",
        "provider": "openai",
        "model": "gpt-4o",
        "memory_db": "planner.db",
        "server": { "host": "127.0.0.1", "port": 8001 },
        "toolbox": { "tools": {} }
      },
      {
        "name": "human",
        "description": "Human for approvals",
        "provider": "human",
        "server": { "host": "127.0.0.1", "port": 8002 }
      },
      {
        "name": "wechat_user",
        "description": "Human via WeChat iLink",
        "provider": "wechat-human",
        "server": { "host": "127.0.0.1", "port": 8003 }
      }
    ]
  }
}

Per-agent provider/model with global fallback. provider: "human" for terminal human agents, "wechat-human" for WeChat iLink bridge. Each agent has its own memory DB, server config, and toolbox state. WeChat human agents persist their session token via the wechat_session field.

MQTT Broker

{
  "agents_config": {
    "mqtt_broker": {
      "host": "127.0.0.1",
      "port": 1883
    }
  }
}

Requires external Mosquitto v5 broker.

Toolbox

Tool enable/disable/preload per agent, managed via qd-evolve toolbox --agent <name> (Textual TUI) or by editing config.json directly.

{
  "agents_config": {
    "agents": [{
      "name": "planner",
      "toolbox": {
        "tools": { "run_shell": "preload", "web_search": "enabled" },
        "mcp_servers": { "filesystem": "disabled" },
        "bridge": { "oat:boat": "enabled", "oat:coat": "disabled" },
        "cli": { "git": "preload" },
        "skills": { "code-review": "preload" }
      }
    }]
  }
}

Three states: enabled (on-demand schema), preload (schema at startup), disabled (invisible to agent).

Runtime Features

Slash Commands

Command	Description
/models	Switch provider/model
/agents	List discovered agents
/tools	List available tools
/skills	List available skills
/cli	List registered CLI tools
/status	Show runtime status (loaded tools, skills, CLI)
/memory	List saved memories
/reset	Reset conversation history
/help	Show all available commands
/quit	Exit

Heartbeat

Agent-managed idle detection. When no activity for heartbeat_idle_seconds, sends a heartbeat prompt to the LLM. If LLM responds with ".", stays silent. Set 0 to disable. Mode-specific templates selected automatically.

Task Cancellation

Cooperative cancellation for sub-agents and A2A tasks. cancel_sub_task(task_id) and cancel_task(task_id) signal the running agent to stop at the next safe checkpoint — after the current LLM call or tool execution. No threads are killed; the agent unwinds gracefully and pushes a "cancelled" result.

Replay Mode

--replay <file> feeds pre-recorded inputs for automated testing. --output <file> captures output.

Token Stats

Per-turn and cumulative input/output tokens with context window usage percentage.

A2A Protocol

Full A2A v1.0 implementation:

• Agent discovery: /.well-known/agent.json
• Methods: message/send, message/stream, tasks/get, tasks/cancel, tasks/resubscribe, tasks/pushNotification, agent/getExtendedAgentCard
• Task lifecycle: submitted → working → completed / failed / canceled / input_required
• SSE streaming: message/stream returns StreamResponse events
• Push notifications: webhook callbacks on task completion

Group Chat

WeChat-style multi-agent group via MQTT. All configured agents form a single group.

• AI agents: Background loop processes @mentions, runs agent in parallel, publishes responses
• Terminal human agents (provider: "human"): Interactive prompt — type messages, see group activity
• WeChat human agents (provider: "wechat-human"): Bidirectional WeChat iLink bridge — long-poll for incoming WeChat messages, forward group responses back to WeChat. QR login on startup, session persisted to config.json
• @all mentions everyone; specific @agent_name directs to one agent

Project Layout

qd-evolve/
├── qd_evolve/       # Main package (agent, core, tools, bridge, utils, _templates)
├── tools/           # User tools (func, cli, mcp, bridge)
├── skills/          # Skills (SKILL.md files)
├── templates/       # User Jinja2 template overrides
├── tests/           # pytest suite (~930 tests)
├── config.json      # All configuration
├── memory.db        # Conversation memory (SQLite + sqlite-vec)
└── pyproject.toml   # Dependencies and build config

See DESIGN.md for architecture and full module map.

Requirements

• Python 3.13+
• External Mosquitto v5 broker (MQTT/GChat mode only)
• API keys for configured providers (DeepSeek, OpenAI, Anthropic, etc.)

License

MIT