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chore: add guidance for code agents
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AGENTS.md
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# AGENTS.md
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This file is for guiding code agents (like Claude Code, GitHub Copilot, OpenAI Codex, etc.) to work in LangBot project.
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## Project Overview
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LangBot is a open-source LLM native instant messaging bot development platform, aiming to provide an out-of-the-box IM robot development experience, with Agent, RAG, MCP and other LLM application functions, supporting global instant messaging platforms, and providing rich API interfaces, supporting custom development.
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LangBot has a comprehensive frontend, all operations can be performed through the frontend. The project splited into these major parts:
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- `./pkg`: The core python package of the project backend.
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- `./pkg/platform`: The platform module of the project, containing the logic of message platform adapters, bot managers, message session managers, etc.
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- `./pkg/provider`: The provider module of the project, containing the logic of LLM providers, tool providers, etc.
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- `./pkg/pipeline`: The pipeline module of the project, containing the logic of pipelines, stages, query pool, etc.
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- `./pkg/api`: The api module of the project, containing the http api controllers and services.
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- `./pkg/plugin`: LangBot bridge for connecting with plugin system.
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- `./libs`: Some SDKs we previously developed for the project, such as `qq_official_api`, `wecom_api`, etc.
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- `./templates`: Templates of config files, components, etc.
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- `./web`: Frontend codebase, built with Next.js + **shadcn** + **Tailwind CSS**.
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- `./docker`: docker-compose deployment files.
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## Backend Development
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We use `uv` to manage dependencies.
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```bash
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pip install uv
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uv sync --dev
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```
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Start the backend and run the project in development mode.
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```bash
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uv run main.py
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```
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Then you can access the project at `http://127.0.0.1:5300`.
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## Frontend Development
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We use `pnpm` to manage dependencies.
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```bash
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cd web
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cp .env.example .env
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pnpm install
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pnpm dev
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```
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Then you can access the project at `http://127.0.0.1:3000`.
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## Plugin System Architecture
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LangBot is composed of various internal components such as Large Language Model tools, commands, messaging platform adapters, LLM requesters, and more. To meet extensibility and flexibility requirements, we have implemented a production-grade plugin system.
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Each plugin runs in an independent process, managed uniformly by the Plugin Runtime. It has two operating modes: `stdio` and `websocket`. When LangBot is started directly by users (not running in a container), it uses `stdio` mode, which is common for personal users or lightweight environments. When LangBot runs in a container, it uses `websocket` mode, designed specifically for production environments.
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Plugin Runtime automatically starts each installed plugin and interacts through stdio. In plugin development scenarios, developers can use the lbp command-line tool to start plugins and connect to the running Runtime via WebSocket for debugging.
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> Plugin SDK, CLI, Runtime, and entities definitions shared between LangBot and plugins are contained in the [`langbot-plugin-sdk`](https://github.com/langbot-app/langbot-plugin-sdk) repository.
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## Some Development Tips and Standards
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- LangBot is a global project, any comments in code should be in English, and user experience should be considered in all aspects.
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- Thus you should consider the i18n support in all aspects.
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- LangBot is widely adopted in both toC and toB scenarios, so you should consider the compatibility and security in all aspects.
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## Some Principles
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- Keep it simple, stupid.
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- Entities should not be multiplied unnecessarily
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- 八荣八耻
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以瞎猜接口为耻,以认真查询为荣。
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以模糊执行为耻,以寻求确认为荣。
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以臆想业务为耻,以人类确认为荣。
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以创造接口为耻,以复用现有为荣。
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以跳过验证为耻,以主动测试为荣。
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以破坏架构为耻,以遵循规范为荣。
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以假装理解为耻,以诚实无知为荣。
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以盲目修改为耻,以谨慎重构为荣。
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