OpenMonica

🤖 Open-Source AI Copilot & Personal Agent Platform

Monica的开源替代品 | 企业级私有化部署 | Multi-Agent协作系统

English | 简体中文 | Documentation | Demo | Discord

🚀 Your Personal AI Agent, Fully Under Your Control

🌟 What is OpenMonica?

OpenMonica is an open-source AI copilot and personal agent platform, built as a privacy-first, self-hosted alternative to Monica AI. Unlike cloud-based AI assistants that store your data on third-party servers, OpenMonica empowers organizations and teams to deploy their own AI agent infrastructure with complete data sovereignty.

Think of it as Monica + Manus AI, but:

✅ 100% Open Source - Full transparency, community-driven development
✅ Self-Hosted - Deploy on your own infrastructure (cloud or on-premise)
✅ Privacy-First - Your data never leaves your servers
✅ Enterprise-Ready - Multi-user support for teams and organizations
✅ Extensible - MCP-based tool ecosystem with 11+ built-in agents
✅ Cost-Effective - No per-user subscription fees, pay only for infrastructure

🎯 Core Capabilities

OpenMonica is not just a knowledge base or chatbot—it's a general-purpose AI agent that can autonomously execute complex tasks across multiple domains:

Capability	Description	Powered By
💬 Chat	Multi-model conversations (GPT-4o, Claude 3.7, DeepSeek, Qwen, etc.)	LLM Router + Load Balancer
🔍 Search	Real-time web search with cited sources	`websearch_mcp` (Tavily API)
📖 Read	Document understanding, OCR, PDF/image analysis	MinerU + Mistral Vision + RAG
✍️ Write	Content generation, articles, reports, creative writing	`artifact_mcp` + LLM
💻 Code	Execute Python/JavaScript/Bash, debug code, build apps	`sandbox_mcp` (E2B runtime)
🌍 Translate	Multi-language translation and localization	`translate_mcp`
🎨 Create	Generate images, videos, diagrams, charts	`text2image_mcp`, `text2video_mcp`
📧 Assist	Email management, calendar scheduling, task automation	`mail_mcp`
🧠 Remember	Learn user preferences, context-aware personalization	`memory_mcp` + Vector DB
🤔 Reason	Multi-step reasoning, complex problem-solving	`sequentialthinking_mcp`
📊 Analyze	Data analysis, visualization, knowledge graphs	RAG + Graph DB

🆚 OpenMonica vs Monica AI

Feature	Monica AI	OpenMonica
Deployment Model	☁️ Cloud SaaS (monica.im)	🏠 Self-Hosted / Private Cloud
Data Privacy	Stored on Monica's servers	🔒 100% on your infrastructure
Pricing	$9.9 - $28.99/month per user	✅ Open-source, pay only for compute
Customization	Limited to predefined bots	🔧 Fully customizable agents & tools
Knowledge Base	Personal notes & documents	🏢 Enterprise knowledge systems + RAG
Multi-User Support	Personal use only	👥 Team/Organization accounts
Tool Ecosystem	Closed, proprietary plugins	🔌 MCP-based, 100% open & extensible
LLM Support	Managed by Monica	🎛️ BYO API keys, self-hosted models (Ollama)
On-Premise Deployment	❌ Not available	✅ Fully supported (Docker/K8s)
Source Code Access	❌ Closed source	✅ MIT License, community-driven

🏗️ Architecture Overview

OpenMonica is built on a microservices architecture with a multi-agent collaboration system at its core. Each service is independently scalable and fault-tolerant.

graph TB
    subgraph "Client Layer"
        A[Web App<br/>React + TypeScript]
        B[Browser Extension<br/>Coming Soon]
        C[Desktop App<br/>Coming Soon]
        D[Mobile App<br/>Coming Soon]
    end

    subgraph "API Gateway"
        E[Nginx / Traefik<br/>Load Balancer]
    end

    subgraph "Core Services"
        F[User Management<br/>Auth + RBAC]
        G[Main Agent<br/>RAG + Orchestration]
        H[File Server<br/>OCR + Vectorization]
    end

    subgraph "MCP Tool Ecosystem"
        I1[knowledge_mcp<br/>KB Search]
        I2[websearch_mcp<br/>Web Search]
        I3[sandbox_mcp<br/>Code Exec]
        I4[artifact_mcp<br/>Content Gen]
        I5[translate_mcp<br/>Translation]
        I6[memory_mcp<br/>User Context]
        I7[mail_mcp<br/>Email]
        I8[... 11+ Tools]
    end

    subgraph "Data Layer"
        J[PostgreSQL 16<br/>+ pgvector]
        K[Redis 7<br/>Cache + Session]
        L[MinIO / S3<br/>Object Storage]
    end

    subgraph "External AI Services"
        M[OpenAI API<br/>GPT-4o, o1]
        N[Anthropic API<br/>Claude 3.7]
        O[DeepSeek API<br/>DeepSeek-V3]
        P[Local Models<br/>Ollama]
    end

    A --> E
    B --> E
    C --> E
    D --> E
    E --> F
    E --> G
    E --> H
    G --> I1
    G --> I2
    G --> I3
    G --> I4
    G --> I5
    G --> I6
    G --> I7
    G --> I8
    F --> J
    F --> K
    G --> J
    G --> K
    H --> J
    H --> L
    G --> M
    G --> N
    G --> O
    G --> P

    style A fill:#3b82f6
    style G fill:#10b981
    style I1 fill:#f59e0b
    style I2 fill:#f59e0b
    style I3 fill:#f59e0b
    style I4 fill:#f59e0b
    style I5 fill:#f59e0b
    style I6 fill:#f59e0b
    style I7 fill:#f59e0b
    style I8 fill:#f59e0b

🧩 Multi-Agent Collaboration

OpenMonica uses Model Context Protocol (MCP) to orchestrate multiple specialized agents that collaborate to solve complex tasks:

sequenceDiagram
    participant User
    participant MainAgent as Main Agent<br/>(Orchestrator)
    participant KnowledgeAgent as Knowledge Agent
    participant WebAgent as Web Search Agent
    participant CodeAgent as Code Execution Agent
    participant ArtifactAgent as Artifact Generator

    User->>MainAgent: "Analyze our Q4 sales data and create a dashboard"
    MainAgent->>KnowledgeAgent: Retrieve sales data from KB
    KnowledgeAgent-->>MainAgent: Sales data (JSON)
    MainAgent->>WebAgent: Get industry benchmarks
    WebAgent-->>MainAgent: Benchmark data
    MainAgent->>CodeAgent: Run data analysis (Python)
    CodeAgent-->>MainAgent: Analysis results
    MainAgent->>ArtifactAgent: Generate interactive dashboard (HTML)
    ArtifactAgent-->>MainAgent: Dashboard code
    MainAgent-->>User: Present dashboard + insights

✨ Key Features

🎯 For Individual Users

Personal AI Assistant: Chat with multiple LLMs in one interface (GPT-4, Claude, DeepSeek, etc.)
Knowledge Base: Upload documents (PDF, Word, images) and ask questions with RAG-powered answers
Web Browsing: Get real-time information from the internet with cited sources
Code Execution: Write and run Python/JavaScript code in a secure sandbox environment
Content Creation: Generate articles, images, diagrams, and more
Multi-Language Support: Translate content and chat in 100+ languages
Privacy: All your data stays on your own servers

🏢 For Organizations & Teams

Multi-User Management: Create accounts for team members with role-based access control (RBAC)
Shared Knowledge Bases: Build organization-wide knowledge repositories
Custom Agents: Configure specialized agents for different departments (HR, Sales, Engineering, etc.)
Admin Dashboard: Monitor usage, costs, and agent performance
SSO Integration: Connect with existing identity providers (LDAP, OAuth, SAML)
Audit Logging: Track all agent interactions for compliance and security
Resource Isolation: Per-user/per-team resource limits and quotas

🛠️ For Developers

MCP Tool Ecosystem: Extend OpenMonica with custom tools using the Model Context Protocol
API-First Design: RESTful APIs for all services with OpenAPI documentation
Webhook Support: Integrate with external systems (Slack, Discord, GitHub, etc.)
Plugin Development: Build custom agents and connectors
Local LLM Support: Run with self-hosted models (Ollama, vLLM, LM Studio)
Observability: Prometheus metrics, OpenTelemetry tracing, structured logging

🚀 Quick Start

Prerequisites

Docker 24+ and Docker Compose 2.20+
8GB+ RAM (16GB recommended for production)
PostgreSQL 16+ (or use the included Docker setup)
Redis 7+ (or use the included Docker setup)
API keys for LLMs (OpenAI, Anthropic, DeepSeek, etc.) OR Ollama for local models

1. Clone the Repository

git clone --recurse-submodules https://github.com/yourusername/OpenMonica.git
cd OpenMonica

2. Configure Environment Variables

# Copy example configs
cp .env.example .env
cp modules/OpenMonica_UserManagement/.env.example modules/OpenMonica_UserManagement/.env
cp modules/OpenMonica_main/.env.example modules/OpenMonica_main/.env
cp modules/OpenMonica_fileserver/.env.example modules/OpenMonica_fileserver/.env

# Edit .env files with your settings:
# - Database credentials
# - Redis connection
# - MinIO/S3 settings
# - LLM API keys (OpenAI, Anthropic, DeepSeek, etc.)
# - JWT secret for authentication

3. Launch with Docker Compose

# Start all services
docker-compose up -d

# Check service health
docker-compose ps

# View logs
docker-compose logs -f

4. Access OpenMonica

Web App: http://localhost:3000
API Documentation: http://localhost:8000/docs
Admin Panel: http://localhost:3000/admin

Default Credentials (change immediately!):

Username: admin
Password: admin123

5. Create Your First Agent

Navigate to Settings → Agents
Click Create New Agent
Configure:
- Name: "Sales Assistant"
- Description: "Helps with sales data analysis and reporting"
- Tools: Enable knowledge_mcp, websearch_mcp, sandbox_mcp
- Knowledge Bases: Link to "Sales Documents" KB
Start chatting with your agent!

📦 Installation Methods

Option 1: Docker Compose (Recommended)

See Quick Start above.

Option 2: Kubernetes Deployment

For production deployments with high availability and auto-scaling:

# Install with Helm
helm repo add openmonica https://charts.openmonica.io
helm install openmonica openmonica/openmonica \
  --namespace openmonica \
  --create-namespace \
  --values values-production.yaml

See Kubernetes Deployment Guide for details.

Option 3: Manual Installation

For development or custom deployments:

# 1. Install PostgreSQL 16+ with pgvector
sudo apt install postgresql-16 postgresql-16-pgvector

# 2. Install Redis 7+
sudo apt install redis-server

# 3. Install Python 3.11+ and dependencies
cd modules/OpenMonica_UserManagement
pip install -e .

cd ../OpenMonica_main
pip install -e .

cd ../OpenMonica_fileserver
pip install -e .

# 4. Install Node.js 20+ and build frontend
cd ../OpenMonica_frontend
npm install
npm run build

# 5. Configure and start services
# See detailed instructions in docs/deployment/manual.md

🔧 Configuration

LLM Providers

OpenMonica supports multiple LLM providers out of the box:

Provider	Models	API Key Required	Self-Hosted Option
OpenAI	GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo	✅ Yes	❌ No
Anthropic	Claude 3.7 Sonnet, Claude 3.5 Haiku	✅ Yes	❌ No
DeepSeek	DeepSeek-V3, DeepSeek-Coder	✅ Yes	❌ No
Google	Gemini 1.5 Pro, Gemini 2.0 Flash	✅ Yes	❌ No
Ollama	Llama 3, Mistral, Qwen, etc.	❌ No	✅ Yes
vLLM	Any HuggingFace model	❌ No	✅ Yes

Configure in modules/OpenMonica_main/.env:

# OpenAI
OPENAI_API_KEY=sk-...
OPENAI_BASE_URL=https://api.openai.com/v1

# Anthropic
ANTHROPIC_API_KEY=sk-ant-...

# DeepSeek
DEEPSEEK_API_KEY=sk-...
DEEPSEEK_BASE_URL=https://api.deepseek.com

# Ollama (local)
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3:70b

MCP Tools Configuration

Enable or disable specific tools in modules/OpenMonica_main/config/tools.yaml:

tools:
  knowledge_mcp:
    enabled: true
    priority: high
  websearch_mcp:
    enabled: true
    api_key: ${TAVILY_API_KEY}
  sandbox_mcp:
    enabled: true
    timeout: 300
  mail_mcp:
    enabled: false  # Disable if not needed

Knowledge Base Settings

Configure OCR and vectorization in modules/OpenMonica_fileserver/.env:

# OCR Engine
OCR_ENGINE=mineru  # Options: mineru, mistral_vision, tesseract
OCR_LANGUAGE=eng,chi_sim

# Vector Embedding Model
EMBEDDING_MODEL=bge-m3  # Options: bge-m3, voyage-ai, openai
EMBEDDING_DIMENSION=1024

# Chunk Settings
CHUNK_SIZE=512
CHUNK_OVERLAP=50

📚 Use Cases

1️⃣ Personal Productivity Assistant

Scenario: Freelance consultant managing multiple clients

Setup:

Create separate knowledge bases for each client
Enable mail_mcp for email management
Enable translate_mcp for international clients
Enable memory_mcp to remember client preferences

Result: AI assistant that:

Summarizes client emails and suggests responses
Answers questions about past projects using client documents
Translates communications with non-English clients
Remembers each client's communication style and preferences

2️⃣ Enterprise Knowledge Hub

Scenario: 500-person tech company with scattered documentation

Setup:

Deploy OpenMonica on company Kubernetes cluster
Create knowledge bases:
- engineering-docs (technical specs, architecture docs)
- hr-policies (employee handbook, benefits)
- sales-materials (pitch decks, case studies)
Configure RBAC: Engineers → engineering-docs, Everyone → hr-policies, Sales → sales-materials

Result:

Employees get instant answers from company knowledge base
Reduces support tickets to HR and IT by 40%
New hires onboard 2x faster with AI-guided documentation

3️⃣ Research & Data Analysis

Scenario: Academic research team analyzing papers and datasets

Setup:

Upload research papers to knowledge base (PDF OCR)
Enable websearch_mcp for latest papers from arXiv
Enable sandbox_mcp for running data analysis code
Enable artifact_mcp for generating visualizations

Result: AI agent that:

Summarizes research papers and extracts key findings
Finds related papers from the web
Runs statistical analysis on datasets
Generates charts and diagrams for presentations

4️⃣ Customer Support Automation

Scenario: SaaS company with knowledge base of support articles

Setup:

Import support docs and FAQs into knowledge base
Create custom agent with knowledge_mcp + mail_mcp
Integrate with customer support email (forwarding rules)

Result:

AI drafts responses to common support questions
Human agents review and send (HITL - human-in-the-loop)
Response time reduced from 4 hours to 15 minutes
Support team focuses on complex issues

🛣️ Roadmap

✅ Version 0.1.0 (Current - Q1 2025)

Core RAG engine with multi-model support
User management and authentication
File upload, OCR, and vectorization
11 MCP tool servers (knowledge, websearch, sandbox, etc.)
Web application (React + TypeScript)
Docker Compose deployment
Basic admin dashboard

🚧 Version 0.2.0 (Q2 2025)

Browser Extension (Chrome, Firefox, Edge)
- Chat anywhere on the web (like Monica)
- Summarize web pages
- Translate selected text
- Quick AI commands with keyboard shortcuts
Enhanced Knowledge Graph (Neo4j integration)
Voice Interaction (Speech-to-text, text-to-speech)
Advanced Analytics Dashboard (usage stats, cost tracking)
SSO Integration (OAuth 2.0, SAML 2.0, LDAP)

🔮 Version 0.3.0 (Q3 2025)

Desktop Application (Windows, macOS, Linux via Electron)
- System tray integration
- Hotkey activation (Cmd+Shift+Space)
- Offline mode with local LLMs
Mobile Apps (iOS, Android via React Native)
Team Collaboration Features
- Shared chat threads
- @mentions and notifications
- Team knowledge bases
Workflow Automation (Zapier-like automation builder)

🌟 Version 0.4.0 (Q4 2025)

Multi-Modal Enhancements
- Image understanding (vision models)
- Audio processing (transcription, analysis)
- Video summarization
Advanced Agent Capabilities
- Reinforcement learning (OpenManus-RL style)
- Long-term memory and context
- Goal-oriented task execution
Enterprise Features
- Usage quotas and billing
- Department-level isolation
- Compliance reports (GDPR, HIPAA)

🎯 Version 1.0.0 (Q1 2026)

Production-Ready Release
- 99.9% uptime SLA
- Multi-region deployment support
- Auto-scaling and load balancing
- Comprehensive security audit
Marketplace
- Community-built agents and tools
- One-click installation of plugins
- Revenue sharing for developers
AI Model Fine-Tuning
- Fine-tune models on organization data
- Custom embeddings for domain-specific KB

🏛️ Architecture Details

Technology Stack

Layer	Technology	Purpose
Frontend	React 18 + TypeScript 5.7	Modern, type-safe UI
Build Tool	Vite 6.0	Lightning-fast development
UI Library	Ant Design 5 + Tailwind CSS	Beautiful, responsive components
State Management	Zustand 4.5	Lightweight, performant state
Backend	Python 3.11+ (FastAPI, Starlette)	Async, high-performance APIs
Database	PostgreSQL 16 + pgvector 0.7	Relational data + vector search
Cache	Redis 7	Session management, rate limiting
Object Storage	MinIO / AWS S3	Document and media storage
Message Queue	Redis Streams / RabbitMQ	Async task processing
Orchestration	Docker Compose / Kubernetes	Container management
Observability	Prometheus + Grafana + Loki	Metrics, logs, tracing

Security Architecture

graph LR
    A[User Request] -->|HTTPS/TLS 1.3| B[Nginx/Traefik]
    B -->|JWT Token| C{Auth Service}
    C -->|Valid| D[API Gateway]
    C -->|Invalid| E[401 Unauthorized]
    D -->|RBAC Check| F{Permission Service}
    F -->|Authorized| G[Core Services]
    F -->|Denied| H[403 Forbidden]
    G -->|Encrypted| I[(PostgreSQL<br/>AES-256-GCM)]
    G -->|Encrypted| J[(MinIO<br/>SSE-S3)]

    style A fill:#3b82f6
    style C fill:#f59e0b
    style F fill:#f59e0b
    style I fill:#10b981
    style J fill:#10b981

Security Features:

🔐 Authentication: JWT tokens with refresh mechanism, bcrypt password hashing (10 rounds)
🛡️ Authorization: RBAC (Role-Based Access Control) + ABAC (Attribute-Based Access Control)
🔒 Encryption:
- At-rest: AES-256-GCM for database and object storage
- In-transit: TLS 1.3 for all HTTP communications
🚨 Security Headers: CSP, HSTS, X-Frame-Options, X-Content-Type-Options
📝 Audit Logging: All user actions and agent interactions logged
🕵️ Intrusion Detection: Rate limiting, IP whitelisting, anomaly detection

Observability Stack

# Prometheus metrics
- http_request_duration_seconds (API latency)
- rag_query_latency_seconds (RAG pipeline performance)
- llm_token_usage_total (LLM cost tracking)
- vector_search_results_count (search quality)

# Grafana dashboards
- System Overview (CPU, memory, disk)
- API Performance (requests/sec, error rates)
- LLM Usage & Costs (tokens, API calls, $$$)
- User Activity (active users, chat sessions)

# Loki log aggregation
- Structured JSON logs from all services
- Query by user_id, session_id, trace_id
- Alert on errors and anomalies

🧪 Performance Benchmarks

Vector Search Performance

Tested on PostgreSQL 16 with pgvector 0.7.4, 10M vectors (1024 dimensions):

Operation	Latency (p50)	Latency (p99)	Throughput
Vector Search (Top 10)	23ms	48ms	1,200 QPS
Hybrid Search (Vector + Full-text)	35ms	67ms	850 QPS
Document Retrieval	15ms	28ms	2,000 QPS

Configuration: 8-core CPU, 32GB RAM, NVMe SSD, ivfflat index with 100 lists.

LLM Inference Performance

Provider	Model	Latency (TTFT)*	Throughput (tokens/s)	Cost (per 1M tokens)
OpenAI	GPT-4o	450ms	120	$2.50 / $10.00
Anthropic	Claude 3.7 Sonnet	380ms	150	$3.00 / $15.00
DeepSeek	DeepSeek-V3	320ms	180	$0.27 / $1.10
Ollama (Local)	Llama 3 70B	800ms	35	$0 (self-hosted)

*TTFT = Time To First Token

Document Processing Performance

Tested on 8-core CPU, 16GB RAM:

Document Type	Pages/Files	Processing Time	Throughput
PDF (Text)	100 pages	45 seconds	133 pages/min
PDF (Scanned - OCR)	100 pages	8 minutes	12.5 pages/min
Word Documents	500 files	3 minutes	166 files/min
Images (OCR)	1000 images	15 minutes	66 images/min

🤝 Contributing

We welcome contributions from the community! OpenMonica is built by developers, for developers.

Ways to Contribute

🐛 Report Bugs: Open an issue on GitHub Issues
💡 Feature Requests: Share your ideas in Discussions
📝 Documentation: Improve guides, tutorials, and API docs
🔧 Code Contributions: Submit pull requests (see CONTRIBUTING.md)
🌐 Translations: Help translate OpenMonica into your language
🧩 Build Tools: Create MCP tools and share with the community

Development Setup

# 1. Fork and clone the repository
git clone --recurse-submodules https://github.com/yourusername/OpenMonica.git
cd OpenMonica

# 2. Create a feature branch
git checkout -b feature/my-awesome-feature

# 3. Set up development environment
docker-compose -f docker-compose.dev.yml up -d

# 4. Install dependencies
cd modules/OpenMonica_main
pip install -e ".[dev]"  # Includes pytest, black, mypy

cd ../OpenMonica_frontend
npm install

# 5. Run tests
pytest
npm run test

# 6. Make your changes and commit
git add .
git commit -m "feat: add awesome feature"

# 7. Push and create PR
git push origin feature/my-awesome-feature

Code Standards

Python: Follow PEP 8, use black for formatting, mypy for type checking
TypeScript: Follow Airbnb style guide, use Prettier for formatting
Commits: Use Conventional Commits format
Tests: Maintain >80% code coverage
Documentation: Update docs for any user-facing changes

📖 Documentation

Quick Start Guide - Get up and running in 5 minutes
Architecture Overview - System design and components
API Reference - RESTful API documentation
MCP Tool Development - Build custom agents and tools
Deployment Guide - Docker, Kubernetes, manual installation
Configuration - Environment variables and settings
Troubleshooting - Common issues and solutions
FAQ - Frequently asked questions

Module-Specific Documentation

User Management Service - Authentication, RBAC, user APIs
Main Agent Service - RAG engine, MCP orchestration, LLM integration
File Server Service - OCR, vectorization, storage management
Frontend Application - React app, UI components, state management

🌐 Community & Support

Community Channels

💬 Discord: Join our Discord server for real-time chat
💭 GitHub Discussions: Ask questions and share ideas
🐦 Twitter/X: Follow @OpenMonica for updates
📧 Mailing List: Subscribe to [email protected]

Getting Help

Check the Documentation: Most questions are answered in docs
Search Existing Issues: Someone may have already reported your issue
Ask in Discord: Get help from the community in real-time
Open a Discussion: For open-ended questions or feature ideas
File an Issue: For confirmed bugs or feature requests

Commercial Support

For organizations requiring:

Dedicated support (SLA-backed)
Custom feature development
Training and onboarding
Consulting and architecture review

Contact us at: [email protected]

📜 License

OpenMonica is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 OpenMonica Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software...

What this means:

✅ Free to use for commercial and non-commercial purposes
✅ Modify and distribute as you wish
✅ No attribution required (but appreciated!)
❌ No warranty or liability from the authors

🙏 Acknowledgments

OpenMonica stands on the shoulders of giants. We're grateful to:

Monica AI - Inspiration for the personal AI assistant concept
OpenManus - Pioneering open-source AI agents
MetaGPT - Multi-agent collaboration research
LangChain - RAG and LLM tooling
FastAPI - High-performance Python web framework
React - Powering the frontend
PostgreSQL - Reliable, powerful database
pgvector - Vector similarity search for Postgres

And to all our contributors who make this project possible! 🎉

📊 Star History

Built with ❤️ by the OpenMonica community

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OpenMonica

🤖 Open-Source AI Copilot & Personal Agent Platform

🌟 What is OpenMonica?

🎯 Core Capabilities

🆚 OpenMonica vs Monica AI

🏗️ Architecture Overview

🧩 Multi-Agent Collaboration

✨ Key Features

🎯 For Individual Users

🏢 For Organizations & Teams

🛠️ For Developers

🚀 Quick Start

Prerequisites

1. Clone the Repository

2. Configure Environment Variables

3. Launch with Docker Compose

4. Access OpenMonica

5. Create Your First Agent

📦 Installation Methods

Option 1: Docker Compose (Recommended)

Option 2: Kubernetes Deployment

Option 3: Manual Installation

🔧 Configuration

LLM Providers

MCP Tools Configuration

Knowledge Base Settings

📚 Use Cases

1️⃣ Personal Productivity Assistant

2️⃣ Enterprise Knowledge Hub

3️⃣ Research & Data Analysis

4️⃣ Customer Support Automation

🛣️ Roadmap

✅ Version 0.1.0 (Current - Q1 2025)

🚧 Version 0.2.0 (Q2 2025)

🔮 Version 0.3.0 (Q3 2025)

🌟 Version 0.4.0 (Q4 2025)

🎯 Version 1.0.0 (Q1 2026)

🏛️ Architecture Details

Technology Stack

Security Architecture

Observability Stack

🧪 Performance Benchmarks

Vector Search Performance

LLM Inference Performance

Document Processing Performance

🤝 Contributing

Ways to Contribute

Development Setup

Code Standards

📖 Documentation

Module-Specific Documentation

🌐 Community & Support

Community Channels

Getting Help

Commercial Support

📜 License

🙏 Acknowledgments

📊 Star History

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