Skip to main content

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

Project description

QD-Evolve

PyPI version 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+
  • Mosquitto v5 broker (MQTT/GChat mode only) — download

Install from PyPI

pip install qd-evolve

# Or with uv
uv add qd-evolve

# Optional: BOAT bridge extras
pip install qd-evolve[boat]

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

Configuration

Create a config.json in your working directory. Minimal setup for single-agent chat:

{
  "env_vars": {
    "PYTHONIOENCODING": "utf-8",
    "PYTHONUTF8": "1"
  },
  "default_provider": "deepseek",
  "default_model": "deepseek-v4-pro",
  "providers": [
    {
      "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 }
      ]
    }
  ],
  "agents_config": {
    "agents": [
      {
        "name": "default",
        "toolbox": {
          "tools": {
            "load_func": "preload",
            "load_skill": "preload",
            "load_cli": "preload"
          }
        }
      }
    ]
  }
}

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

Verify

qd-evolve chat --agent default

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 Switch agent
/tools List tools
/load <tool> Load tool schema
/memory List saved memories with full content
/compress Force context compression
/clear Clear conversation
/help Show help
/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.

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, utils, _templates)
├── tools/           # User tools (func, cli, mcp, bridge)
├── skills/          # Skills (SKILL.md files)
├── templates/       # User Jinja2 template overrides
├── tests/           # pytest suite
├── 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+
  • uv for dependency management
  • External Mosquitto v5 broker (MQTT/GChat mode only)
  • API keys for configured providers

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

qd_evolve-0.1.6.tar.gz (444.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

qd_evolve-0.1.6-py3-none-any.whl (151.2 kB view details)

Uploaded Python 3

File details

Details for the file qd_evolve-0.1.6.tar.gz.

File metadata

  • Download URL: qd_evolve-0.1.6.tar.gz
  • Upload date:
  • Size: 444.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.11

File hashes

Hashes for qd_evolve-0.1.6.tar.gz
Algorithm Hash digest
SHA256 1d186dbdcc2cf5bbd9b7172de168094a1a3c28d734d7fc76088b9708dc3b6d64
MD5 5ddb4525f24372f98fc9f5593fb9c445
BLAKE2b-256 26f55cd9a687cf428a6e2207e3949fbc3dfee284936b8f0f2a50fb14c1283d97

See more details on using hashes here.

File details

Details for the file qd_evolve-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: qd_evolve-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 151.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.11

File hashes

Hashes for qd_evolve-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 5aa6c17d7b8f44163cfeb81dbbeb772038727fc7665f7a976f7f2c156bc61d30
MD5 52b1e5f52317d78f8905d02f20c82b32
BLAKE2b-256 caf1fb10ead484994fcc6d75824841ada1f27189c6654c8a521cb9e3f2c63a94

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page