langbot-app/LangBot

LangBot

Production-grade platform for building agentic IM bots - 生产级多平台智能机器人开发平台/ Agent、知识库编排、插件系统 / Bots for Discord / Slack / LINE / Telegram / WeChat(企业微信, 企微智能机器人, 公众号) / 飞书 / 钉钉 / QQ / Matrix e.g. Integrated with ChatGPT(GPT), DeepSeek, Dify, n8n, Langflow, Coze, Claude, Gemini, GLM, Ollama, SiliconFlow, Moonshot, openclaw / hermes agent, deerflow

Repository Homepage

44/100RAG Agents

Stars16,550

Forks1,465

LanguagePython

LicenseApache-2.0

Usage guide

LangBot is an open-source project around agent, coze, deepseek with 16,550 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.

Repository license: Apache-2.0Commercial use permitted, review additional terms

Key features

Implemented mainly in Python, useful for judging integration effort in a similar stack.
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.

Best for

Evaluating LangBot for Python AI workflows.
Comparing a GitHub project with 16,550 stars and current repository activity.

Pros

LangBot has visible GitHub traction with 16,550 stars. Topics: agent, coze, deepseek.
The project provides an external homepage for deeper evaluation.

Cons

Production fit still depends on documentation depth, issue activity, and release cadence.
License review should confirm the Apache-2.0 terms fit your use case.

Production readiness

LangBot should be validated with its README, release history, open issues, and integration requirements before production use.

License risk

Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.

LangBot architecture preview

LangBot's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI / Claude / Gemini / Ollama, Runtime context, GitHub / Slack / Discord / Telegram / WeChat, and returns Grounded answers / search results.

Entry

Web / product entry

Users start from a web UI, hosted product surface, or browser-based workflow.

https://langbot.app

Runtime

Agent orchestration runtime

The orchestration layer plans tasks, calls tools, manages context, and decides the next action.

agent workflow

Runtime dependencies

Model

OpenAI / Claude / Gemini / Ollama

Model calls are likely routed through OpenAI, Claude, Gemini, Ollama based on README and topic signals.

OpenAI, Claude, Gemini, Ollama

Context

Runtime context

Runtime state, user input, repository files, or configuration provide context for each task.

context signal

Tools

GitHub / Slack / Discord / Telegram / WeChat

Tool adapters let the runtime act outside the model through GitHub / Slack / Discord / Telegram / WeChat.

GitHub, Slack, Discord, Telegram, WeChat

Output

Grounded answers / search results

The final result is an answer or ranked result grounded in retrieved context.

answer output

Install tutorial

Before you install

Python runtime and an isolated virtual environment
Docker Engine with enough disk space for images and volumes
A clean working directory for the first test run

Step 1

Check the runtime environment

LangBot has Docker in the setup path. Confirm Docker Engine works and reserve enough disk space for images and volumes.

Step 2

Get the project files

Start from the official repository or package so the first run matches the documented behavior.

terminal

$ git clone https://github.com/langbot-app/LangBot

Step 3

Install or build dependencies

Run the next setup command detected from the project documentation.

terminal

$ docker compose --profile all up -d

Adoption guidance and sources

Practical use cases

Agent workflow prototype

Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.

Knowledge-base assistant

Use it for document-grounded AI workflows where retrieval quality matters.

Production-grade platform for building agentic IM bots - 生产级多平台智能机器人开发

This is one of the documented reasons to evaluate LangBot before choosing a stack.

Focus area: agent

This is one of the documented reasons to evaluate LangBot before choosing a stack.

RAG project comparison

Compare LangBot with similar projects before committing to a stack.

Before adopting

Complete one clean-environment verification using the official LangBot setup path.
Review repository license, model weights, external services, and dependency terms for your use case.
Check recent commits, release cadence, issue response, and documentation depth.
Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.

Configuration notes

Check exposed ports, mounted volumes, and environment variables before running the container in a shared environment.

Sources checked

These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.

langbot-app/LangBot GitHub repository

Troubleshooting

If Docker startup fails, check port conflicts, image pull permissions, and volume paths first.
If installation fails, first confirm the command is being run from the README-specified directory.
If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
If output looks wrong, return to the smallest documented LangBot example before adding complex data.
For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.

What is LangBot?

LangBot is an open-source rag project. Production-grade platform for building agentic IM bots - 生产级多平台智能机器人开发平台/ Agent、知识库编排、插件系统 / Bots for Discord / Slack / LINE / Telegram / WeChat(企业微信, 企微智能机器人, 公众号) / 飞书 / 钉钉 / QQ / Matrix e.g. Integrated with ChatGPT(GPT), DeepSeek, Dify, n8n, Langflow, Coze, Claude, Gemini, GLM, Ollama, SiliconFlow, Moonshot, openclaw / hermes agent, deerflow

How do I install LangBot?

Start with the official README. The first detected setup step is: git clone https://github.com/langbot-app/LangBot.

Is LangBot beginner-friendly?

If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.

Can LangBot be used commercially?

GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.

Does LangBot need a GPU?

GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.

How should I decide whether to adopt LangBot?

Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.