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Langbot

An open-source Python framework for building agentic instant-messaging bots, with model integrations, knowledge-base orchestration, plugins, and deployment options.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Langbot — OpenViST project profile in Workflow Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use LangBot when a bot must operate across multiple supported messaging platforms or connect messaging users to compatible models, agents, knowledge sources, and plugins.

Who it is for

  • Teams building AI bots for several messaging channels
  • Developers creating knowledge-backed messaging assistants
  • Organizations extending bots with plugins or workflow integrations

Documented uses

  • Connect LLMs or agent platforms to messaging channels
  • Build bots that converse and execute tasks
  • Deliver knowledge-base question answering through supported messaging services
  • Configure and monitor multiple bot pipelines
  • Deploy organization-specific agent bots
Before you choose

Requirements & limits

The project is publicly available under the Apache-2.0 license. Documented access and deployment paths include LangBot Cloud, local launch, Docker Compose, and several cloud or server deployment options.

  • Channel coverage matters: documented support includes Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK, Satori, Email, and Matrix.
  • The project is Python-based and documents Python 3.10–3.13 in its quick-start repository excerpt.
  • Teams can choose between a browser management panel, local launch, Docker Compose, or other documented deployment routes.
  • The plugin system and knowledge-base capabilities are documented, but implementation details for a specific plugin or knowledge base are not provided here.
  • The framework integrates with a broad set of LLM and agent platforms, including OpenAI, Anthropic, DeepSeek, Dify, n8n, Langflow, Coze, Gemini, GLM, and Ollama.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Launch LangBot with Docker Compose

Start the LangBot service from its public repository using the documented Docker Compose path.

Before you start

  • Git and Docker Compose are required by the documented command sequence.

Steps

  1. Clone the LangBot repository.

  2. Change into the repository's docker directory.

  3. Run Docker Compose with the all profile enabled: docker compose --profile all up -d.

Expected outcome

The documented Docker Compose startup sequence is executed for a LangBot deployment.

Guide limitations

  • The supplied instructions do not describe configuration, channel setup, model setup, or verification after startup.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
langbot-app/LangBot · Official homepage title · retained in its source language
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