9.9K stars! A large-scale native instant messaging robot platform. This open source project makes AI conversations smarter!
Explore the new heights of AI conversation technology, this open source project makes instant messaging smarter.
Core content:
1. Features and advantages of the large-model native instant messaging robot platform
2. Detailed introduction to the plug-in ecosystem and enterprise-level function matrix
3. Technical architecture analysis and practical application scenario cases
"High stability, plug-in support, multi-modal - large model native instant messaging robot platform"
Project Highlights
? Enterprise-level conversation solution : integrates mainstream big models and supports multi-platform instant messaging
? Plug-in extension architecture : 50+ plug-in ecosystems meet various business scenarios
? Production-level stability : a complete testing system ensures 7x24 hours of stable operation
? Out-of-the-box : Baota/Docker/source code three deployment methods optional
Core Function Panorama
? Plugin ecosystem
Supports event monitoring, Agent, process control and other extension methods, and has implemented:
Intelligent work order system Knowledge Base Q&A Multi-turn dialogue management Third-party service connection
# Example: Creating a weather query plugin
from langbot.plugins import BasePlugin
class WeatherPlugin (BasePlugin) :
triggers = [ "weather" ]
async def handle_message (self, message) :
city = message.content.split()[ -1 ]
# Call the weather API to get data
return f" {city} Today is sunny, 25℃～30℃"
?️ Enterprise-level feature matrix
? Multiple model support
Seamless connection:
OpenAI GPT series Moonshot Anthropic Claude Deep adaptation of OneAPI
? Efficient deployment solution
# Docker one-click deployment
docker run -d --name langbot \
-e OPENAI_API_KEY=your_key \
-p 8000:8000 \
rockchinq/langbot:latest
Technical architecture analysis
Practical application scenarios
Case 1: Intelligent Customer Service System
Through the combination of plug-ins:
Automatic ticket classification Accurate search of knowledge base Complex problems transferred to manual Session Recording Analysis
Case 2: AI content review
# Sensitive word filtering plugin example
class ContentFilter (BasePlugin) :
async def before_send (self, message) :
if contains_sensitive_words(message.content):
return self.block( "Contains prohibited content" )
return message
Comparison of similar projects
| LangBot | ||||
Core advantages :
Domestic adaptation is better Lower threshold for plug-in development Smarter context management The community ecology is more active
Developer Ecosystem
? Complete Chinese documentation system ? Rich deployment tutorials ? Active developer community ? The growing plugin market
Project Summary
As a new generation of conversational robot platform, LangBot has unique value in the following scenarios:
Enterprises need to quickly build intelligent customer service systems Developers want to flexibly expand AI capabilities Service providers who need to connect to multiple large models A production environment that values system stability
Similar projects recommended:
Botpress : A conversation management platform for enterprises Rasa : Open Source Machine Learning Conversational System Microsoft Bot Framework : Enterprise-level robot development framework
