Skip to content

README_EN.md

Latest commit

 

History

History
278 lines (204 loc) · 14.6 KB

README_EN.md

File metadata and controls

278 lines (204 loc) · 14.6 KB
CountBot Logo

CountBot

Open-source AI agent framework and runtime hub for Chinese users

Connects LLMs, IM channels, workflows, and external tools so AI can actually execute work

中文 | English

GitHub stars Python License

What Is CountBot

CountBot is a lightweight AI agent framework that better fits Chinese users' habits, and also a runtime hub built for local deployment and long-running operation.

It connects roles, teams, workflows, tool invocation, memory, LLMs, IM channels, and local workspaces so AI can run continuously, collaborate across entry points, and execute real tasks.

You can think of CountBot as both a framework and a hub:

  • Upward, it connects to 100+ LLM providers and different model strategies
  • Outward, it connects to Web, WeChat, Lark, DingTalk, Telegram, Discord, QQ, Weibo, and more
  • Inward, it organizes roles, teams, workflows, memory, and security boundaries
  • At the execution layer, it connects files, Shell, Web, screenshots, file transfer, and external professional tools such as Claude Code, Codex, and OpenCode

In one sentence: CountBot is an AI agent framework and runtime hub that connects models, channels, teams, and tools.

CountBot was born from natural language. Its vision is not to require more people to learn complex configuration and programming before they can use AI, but to let ordinary users interact with AI directly through natural language to retrieve information, generate content, break down tasks, call tools, orchestrate workflows, and even build their own assistants, team collaboration flows, and automation systems.

Latest Updates

  • v0.9.0

    • Added MCP client module with multi-server connections, health checks, and tool discovery. Disabled by default, designed for advanced users with personalized needs
    • Added Wiki knowledge base module with BM25 full-text search, batch retrieval, relevance filtering, and LRU cache
    • New MCP and Wiki management panels with component modularization and i18n support
    • New WebSocket status broadcast module for real-time MCP connection state sync to frontend
    • Improved context management and heartbeat mechanism, simplified greeting logic to reduce LLM calls
    • Refined tool registration and initialization flow for better startup consistency
    • Added API Key rotation with automatic failover and multi-key support
    • Improved send_media tool compatibility for web mode, supports image preview and file download in Web UI
    • Optimized model preset interface and updated build artifacts
    • Release notes: https://654321.ai/docs/releases/v0.9.0

  • v0.8.3

  • v0.8.2

    • Added API Key rotation and failover capability with automatic key switching on 401/429 errors
    • Improved send_media tool web-mode compatibility
    • Release notes: https://654321.ai/docs/releases/v0.8.2

  • v0.8.1

    • Fixed DeepSeek multi-turn reasoning_content loss issue
    • Added knowledge base module with BM25 search
    • Updated documentation and build artifacts
    • Release notes: https://654321.ai/docs/releases/v0.8.1

  • v0.8.0

    • Addressed multiple issues and user-reported problems, fixing a batch of known bugs in high-frequency usage scenarios
    • Fully optimized the frontend interface and interaction experience, improving overall usability and operation flow
    • Continued optimizing the end-to-end response pipeline to improve the accuracy, timeliness, and overall stability of AI replies
    • Refactored conversation context maintenance with short-context summary caching, overflow history summarization, and full-session memory persistence
    • Strengthened multi-channel reliability, especially for WeChat, WeCom, and Lark message handling, deduplication, and streaming delivery control
    • Optimized heartbeat and scheduled execution logic to avoid counting messages before successful delivery and to support schedule refresh after config changes
    • Improved reasoning switches and runtime config resolution with provider-specific thinking field mapping and persona merge rules
    • Unified bind host and port environment variables and added documentation for COUNTBOT_HOST / COUNTBOT_PORT
    • Release notes: https://654321.ai/docs/releases/v0.8.0

  • v0.7.0

    • Addressed multiple issue reports, fixed a batch of known bugs, and improved overall stability
    • Added file upload and processing flows to the WEBUI, improved Mermaid rendering, and made chart presentation more user-friendly
    • Optimized frontend pages and interaction flows so everyday operations around skills, configuration, and tools feel smoother
    • Optimized the tool invocation chain and context organization, significantly reducing token usage compared with previous versions
    • Added a model thinking control switch so you can adjust reasoning intensity by scenario and get faster perceived responses
    • Added find-skills with full Tencent Cloud SkillsHub integration, enabling conversational search, install, enable, disable, and delete for skills
    • Added ima-knowledge-base and ima-notes with full IMA knowledge-base and note capabilities, including search, upload, web import, reading, creation, and append
    • Release notes: https://654321.ai/docs/releases/v0.7.0

  • v0.6.0

    • Added WeChat ClawBot integration with support for binding multiple accounts
    • Added external coding tool integration for Claude, Codex, and OpenCode, which can either be called by the LLM as tools or act as agents connected to IM channels
    • Added the secure first-time remote initialization entry /setup/<random>
    • Added REMOTE_SETUP_SECRET_TTL_MINUTES so the validity period of the remote setup entry can be controlled
    • Strengthened remote authentication boundaries, covering /api/* and /ws/chat
    • Release notes: https://654321.ai/docs/releases/v0.6.0

  • v0.5.0

    • Agent Teams became a first-class capability, supporting multi-role collaboration, context handoff, and team-level orchestration
    • The configuration system evolved from session-level configuration to role-level, team-level, and multi-bot coordination
    • The channel system was further strengthened to support more enterprise and social entry points
    • Frontend panels for chat, configuration, skills, teams, and tools were upgraded as a whole
    • Release notes: https://654321.ai/docs/releases/v0.5.0

  • v0.4.0

    • Introduced session-level configuration so different sessions can use different models and prompts
    • Expanded channel support for Lark, WeCom, Weibo, Xiaozhi AI, and more
    • Improved multi-agent collaboration and added usage entry points such as /help
    • Release notes: https://654321.ai/docs/releases/v0.4.0

  • v0.3.0

    • Multi-agent collaboration modes (pipeline / graph / council) were first systematized
    • Scheduled tasks, skill configuration, and workspace management were significantly enhanced
    • Release notes: https://654321.ai/docs/releases/v0.3.0

  • v0.2.0 · 2026-02-26

    • Focused on bug fixes, interaction improvements, and cleanup of the build workflow
    • Pushed CountBot from "basically runnable" toward "stable and ready to iterate"

  • Official Open Source Release · 2026-02-21

    • CountBot was open-sourced for the first time, including the base framework, startup scripts, agent core, channel system, tool system, skill system, and foundational frontend capabilities
    • Public iteration has continued from that day onward

Why CountBot

CountBot starts from the broader trend of software being driven by natural language, and aims to let AI truly land in real tasks and everyday work.

Software development and software usage are changing:

  1. Interaction between people and software is moving from complex commands and code toward natural language.
  2. Tools and systems are moving from standardized products toward a future where more people can define and create personalized tools.

The core challenge for AI agents is no longer just model capability alone. It is how to combine LLMs, tool invocation, multi-channel access, permission boundaries, and long-running operation into a system that can actually be deployed and maintained.

OpenClaw has already validated a viable path for local execution and autonomous agents. On top of that, CountBot focuses on filling key gaps in Chinese scenario adaptation, lightweight deployment, usability expansion, security control, and governance.

CountBot is not merely a conversational AI application. It is open infrastructure for real tasks: deployable, extensible, and governable, so ordinary users can use natural language to drive AI, organize tools, connect channels, and actually land business and daily workflows.

Core Capabilities

Module Description
Agent Loop ReAct reasoning, tool invocation, result feedback, and iterative control
Agent Teams Three collaboration modes: pipeline, graph, and council
Multi-bot channel matrix One workspace can serve multiple channels, multiple bots, and multiple business entry points
Configuration layers Global defaults, roles, teams, and runtime session configuration working together
Tools and skills Files, Shell, Web, screenshots, memory, workflows, media delivery, and skill extensions
External execution tool integration Connect external professional tools such as Claude Code, Codex, and OpenCode into one unified runtime
Memory and sessions Long-term memory, summaries, context injection, and session isolation
Cron and Heartbeat Scheduled tasks, proactive reminders, and long-running background operation
Security and workspace Local control, path restrictions, audit logs, timeouts, and remote authentication boundaries
Multi-model access Compatible with major domestic and international models, with team-level model override support

Use Cases

  • Breaking down complex tasks into multiple roles that collaborate through natural-language instructions
  • Running your own AI assistant, AI team, or automation workflow locally or in a private environment
  • Serving multiple entry points at the same time, including Web, WeCom, Lark, DingTalk, Telegram, and Discord
  • Unifying tool invocation, file operations, message delivery, and scheduled tasks in one runtime
  • Bringing LLMs, message channels, tool usage, and team collaboration into a single operating hub
  • Going beyond a simple AI assistant to build a sustainable, long-running agent system

Quick Start

Option 1: Start From Source

git clone https://github.com/countbot-ai/CountBot.git
cd CountBot

# Default installation
pip install -r requirements.txt

# If you are in mainland China, you can use the Aliyun mirror
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/

python start_app.py

After startup, CountBot opens at http://127.0.0.1:8000 by default.

You can override the default bind host and port with environment variables. Priority is COUNTBOT_HOST / COUNTBOT_PORT > defaults.

$env:COUNTBOT_HOST = '0.0.0.0'
$env:COUNTBOT_PORT = '8001'
python start_app.py
set COUNTBOT_HOST=0.0.0.0
set COUNTBOT_PORT=8001
python start_app.py

If GitHub access is limited, you can switch to Gitee:

git clone https://gitee.com/countbot-ai/CountBot.git

Option 2: Use the Desktop Build

Documentation

Document Description Link
Quick Start Installation, configuration, and startup https://654321.ai/docs/getting-started/quick-start-guide
Configuration Manual Full configuration reference https://654321.ai/docs/getting-started/configuration-manual
Deployment and Operations Startup, deployment, and troubleshooting https://654321.ai/docs/advanced/deployment
Remote Access Guide Remote initialization, authentication, and troubleshooting https://654321.ai/docs/advanced/remote-access
Authentication Password setup and access boundaries https://654321.ai/docs/advanced/auth
API Reference REST API and WebSocket https://654321.ai/docs/api-reference
Release Notes Version history and release changes https://654321.ai/docs/releases/v0.8.0

For the full documentation site, see: https://654321.ai/docs

Development and Contribution

Local Development

uvicorn backend.app:app --reload --host 0.0.0.0 --port 8000

Community

  • QQ group: 1028356423
  • Discussion topics: CountBot usage, issue feedback, secondary development, and scenario co-creation

Issue Reporting

License and Acknowledgements

License

MIT License

Project Inspiration

  • OpenClaw
  • NanoBot
  • ZeroClaw
  • anthropics/skills

Technical Thanks

Thanks to FastAPI, Vue.js, SQLAlchemy, Pydantic, LiteLLM, and other open-source projects.

An AI agent runtime hub that connects models, channels, teams, and tools

Official Website · GitHub · Gitee · Full Documentation