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LMForge - End-to-End LLMOps Platform for Multi-Model Agents

calendar_todayAdded Jan 25, 2026
categoryAgent & Tooling
codeOpen Source
PythonVue.jsWorkflow AutomationDockerKnowledge BaseMulti-Agent SystemLangChainRAGAI AgentsWeb ApplicationFlaskAgent & ToolingAutomation, Workflow & RPAKnowledge Management, Retrieval & RAGEnterprise Applications & Office

LMForge is a comprehensive AI Agent development platform supporting multiple models (OpenAI/DeepSeek/Wenxin/Tongyi), knowledge base management, workflow automation, and enterprise-grade security. Built with Flask + Vue3 + LangChain, featuring one-click Docker deployment.

One-Minute Overview#

LMForge is a full-stack LLMOps platform designed specifically for developing multi-model AI Agents. It supports multiple mainstream large language models, provides knowledge base management, workflow automation features, and includes enterprise-grade security. The platform is built with Flask + Vue3 + LangChain technology stack, offering an intuitive user interface with Docker one-click deployment support.

Core Value: A one-stop solution for AI Agent development, deployment, and management without complex configuration to quickly build enterprise-grade applications.

Quick Start#

Installation Difficulty: Medium - Requires Docker environment, database configuration, and multiple API key setup

# Clone repository
git clone https://github.com/Haohao-end/LMForge-End-to-End-LLMOps-Platform-for-Multi-Model-Agents.git
cd llmops/docker

# Configure environment
nano .env  # Fill with your actual credentials

# Launch services
docker compose up -d --build

Is this suitable for my scenario?

  • Enterprise AI Application Development: Ideal for enterprises needing to integrate multiple LLMs and build complex workflows
  • Knowledge-Enhanced AI: Perfect for scenarios where specialized expertise needs to be incorporated into AI systems
  • Simple Personal Projects: Too complex for applications requiring only a single model
  • Resource-Constrained Environments: Requires 8GB+ RAM, not suitable for small servers

Core Capabilities#

1. Multi-Model Support - Unlocking AI Diversity#

  • Supports OpenAI, DeepSeek, Wenxin, Tongyi, and other mainstream models
  • Allows configuration of multiple models with flexible switching based on needs Actual Value: Avoids vendor lock-in, enables selection of optimal models for specific needs, reducing costs while increasing application flexibility

2. Knowledge Base Management - Enhancing AI Professional Capabilities#

  • Supports custom knowledge bases with enterprise document imports
  • Provides knowledge retrieval and enhancement features Actual Value: Enables AI applications to possess enterprise-specific expertise, improving answer accuracy and professionalism

3. Workflow Automation - Simplifying Complex AI Tasks#

  • Offers visual workflow designer
  • Supports multi-step task orchestration and conditional branching Actual Value: Automates complex AI tasks, reduces manual intervention, improves application efficiency and reliability

4. Enterprise-Grade Security - Protecting Enterprise Data#

  • Built-in identity authentication and permission management
  • Supports JWT tokens and CSRF protection Actual Value: Ensures enterprise data security, meets compliance requirements, suitable for enterprise production deployment

Tech Stack & Integration#

Development Languages: Python, Vue.js, Shell Main Dependencies: Flask, Vue3, LangChain, PostgreSQL, Redis, Docker Integration Method: Web UI + API

Maintenance Status#

  • Development Activity: Active development with continuous updates
  • Recent Updates: Recent version updates
  • Community Response: Comprehensive documentation and example code provided

Commercial & License#

License: MIT

  • ✅ Commercial Use: Allowed
  • ✅ Modification: Permitted
  • ⚠️ Restrictions: Must include copyright and license notices

Documentation & Learning Resources#

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