Initial commit: Agentic LLM Hub with multi-reasoning, MCP tools, and IDE
This commit is contained in:
ImpulsiveFPS
commit
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# Master Key for API Access (generate strong key)
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MASTER_KEY=sk-agent-$(openssl rand -hex 8)
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# ==========================================
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# FREE TIER API KEYS (Add your keys below)
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# ==========================================
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# Groq - https://console.groq.com (20 RPM free, create multiple accounts)
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GROQ_API_KEY_1=gsk_your_first_groq_key_here
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GROQ_API_KEY_2=gsk_your_second_groq_key_here
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||||
# Mistral - https://console.mistral.ai (1B tokens/month free)
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MISTRAL_API_KEY=your_mistral_key_here
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||||
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||||
# Anthropic Claude - https://console.anthropic.com ($5 trial, $500 student)
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ANTHROPIC_API_KEY=sk-ant-your_claude_key_here
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# Moonshot Kimi - https://platform.moonshot.ai ($5 signup bonus)
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MOONSHOT_API_KEY=sk-your_moonshot_key_here
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# OpenRouter - https://openrouter.ai (50 req/day free, access to Kimi free)
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OPENROUTER_API_KEY=sk-or-your_openrouter_key_here
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# Cohere - https://cohere.com (1K calls/month free, good for embeddings)
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COHERE_API_KEY=your_cohere_key_here
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# DeepSeek - https://platform.deepseek.com (cheap rates)
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DEEPSEEK_API_KEY=sk-your_deepseek_key_here
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# GitHub Token (for MCP Git tools)
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GITHUB_TOKEN=ghp_your_github_token_here
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# ==========================================
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# AGENT SETTINGS
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# ==========================================
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# Default reasoning: react, plan_execute, reflexion, or auto
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DEFAULT_REASONING=auto
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# Enable self-reflection (true/false)
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ENABLE_REFLECTION=true
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# Maximum iterations per request
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MAX_ITERATIONS=10
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# ==========================================
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# UI/IDE SETTINGS
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# ==========================================
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# Code-Server passwords (change these!)
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IDE_PASSWORD=secure-ide-password-123
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IDE_SUDO_PASSWORD=admin-password-456
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# Optional: Domain for reverse proxy
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# IDE_DOMAIN=code.yourdomain.com
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# Web UI settings
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WEBUI_SECRET_KEY=$(openssl rand -hex 32)
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# ==========================================
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# ADVANCED MEMORY SETTINGS
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# ==========================================
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# Enable knowledge graph (Neo4j) - requires more RAM
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ENABLE_KNOWLEDGE_GRAPH=false
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NEO4J_AUTH=neo4j/password
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# ChromaDB settings
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CHROMA_STORAGE_PATH=/data/chroma
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# Environment variables
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.env
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.env.local
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.env.*.local
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# Data directories (persisted volumes)
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data/
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workspace/*
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!workspace/.gitkeep
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logs/
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*.db
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*.sqlite3
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# IDE
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.idea/
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.vscode/
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*.swp
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*.swo
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# OS
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.DS_Store
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Thumbs.db
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# Docker
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.docker/
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docker-compose.override.yml
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# Temporary
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*.tmp
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*.bak
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*.log
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.PHONY: help setup start stop logs status update backup clean
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help:
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@echo "Agentic LLM Hub Management"
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@echo "=========================="
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@echo "make setup - Initial setup"
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@echo "make start - Start all services (full profile)"
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@echo "make start-ide - Start with IDE only"
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@echo "make stop - Stop all services"
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@echo "make logs - View logs"
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@echo "make status - Check service status"
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@echo "make update - Pull latest and update images"
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@echo "make backup - Backup data directories"
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@echo "make clean - Remove containers (data preserved)"
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setup:
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@chmod +x *.sh scripts/*.sh 2>/dev/null || true
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@./setup.sh
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start:
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@./start.sh full
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start-ide:
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@./start.sh ide
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stop:
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@docker-compose down
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logs:
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@docker-compose logs -f --tail=100
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status:
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@echo "Container Status:"
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@docker-compose ps
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@echo ""
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@echo "API Health:"
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@curl -s http://localhost:8080/health | python3 -m json.tool 2>/dev/null || echo "API not responding"
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update:
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@git pull
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@docker-compose pull
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@docker-compose up -d
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backup:
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@mkdir -p backup/$(shell date +%Y%m%d)
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@cp -r data backup/$(shell date +%Y%m%d)/
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@cp .env backup/$(shell date +%Y%m%d)/
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@echo "Backup created: backup/$(shell date +%Y%m%d)/"
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clean:
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@docker-compose down -v
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@echo "Containers removed. Data preserved in ./data/"
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# 🤖 Agentic LLM Hub
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Self-hosted AI agent platform with multi-provider LLM aggregation, reasoning engines (ReAct, Plan-and-Execute, Reflexion), MCP tools, and web IDE.
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## 🚀 Quick Start
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```bash
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# 1. Clone from your Gitea
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git clone https://gitea.yourdomain.com/youruser/llm-hub.git
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cd llm-hub
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# 2. Configure
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cp .env.example .env
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nano .env # Add your API keys
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# 3. Deploy
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./setup.sh && ./start.sh
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```
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## 📡 Access Points
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| Service | URL | Description |
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|---------|-----|-------------|
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| VS Code IDE | `http://your-ip:8443` | Full IDE with Continue.dev |
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| Agent API | `http://your-ip:8080/v1` | Main API endpoint |
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| LiteLLM | `http://your-ip:4000` | LLM Gateway |
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| MCP Tools | `http://your-ip:8001/docs` | Tool OpenAPI docs |
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| ChromaDB | `http://your-ip:8000` | Vector memory |
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| Web UI | `http://your-ip:3000` | Chat interface |
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## 🔧 Supported Providers
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- **Groq** (Free tier, fast)
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- **Mistral** (1B tokens/month free)
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- **Anthropic Claude** (Trial credits)
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- **Moonshot Kimi** ($5 signup bonus)
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||||
- **OpenRouter** (Free tier access)
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- **Cohere** (1K calls/month)
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- **DeepSeek** (Cheap reasoning)
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## 🧠 Reasoning Modes
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- `react` - Fast iterative reasoning
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- `plan_execute` - Complex multi-step tasks
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- `reflexion` - Self-correcting with verification
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- `auto` - Automatic selection
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## 📚 Documentation
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- [Setup Guide](docs/SETUP.md)
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- [API Reference](docs/API.md)
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||||
- [Provider Guide](docs/PROVIDERS.md)
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## 🔄 Updates
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```bash
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git pull origin main
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docker-compose pull
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docker-compose up -d
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```
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name: LLM Hub IDE
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version: 1.0.0
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schema: v1
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models:
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- name: Groq Llama 3.3 70B
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provider: openai
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model: fast-tier
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apiBase: http://agent-core:8080/v1
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apiKey: sk-agent
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roles: [chat, edit, apply]
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- name: Claude 3.5 Sonnet
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provider: openai
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model: quality-tier
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apiBase: http://agent-core:8080/v1
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apiKey: sk-agent
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roles: [chat, edit, apply]
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- name: Kimi K2
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provider: openai
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model: reasoning-tier
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apiBase: http://agent-core:8080/v1
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apiKey: sk-agent
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roles: [chat, edit, apply]
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|
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- name: Mistral Small
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provider: openai
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model: volume-tier
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apiBase: http://agent-core:8080/v1
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apiKey: sk-agent
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roles: [chat, edit]
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tabAutocompleteModel:
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name: Mistral Autocomplete
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provider: openai
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model: volume-tier
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apiBase: http://litellm:4000/v1
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apiKey: sk-agent
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embeddingsProvider:
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provider: openai
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model: embeddings
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apiBase: http://litellm:4000/v1
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apiKey: sk-agent
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context:
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- provider: code
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- provider: docs
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||||
- provider: diff
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- provider: terminal
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- provider: problems
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- provider: folder
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- provider: codebase
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model_list:
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# FREE TIER AGGREGATION
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- model_name: fast-tier
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litellm_params:
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model: groq/llama-3.3-70b-versatile
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api_key: os.environ/GROQ_API_KEY_1
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rpm_limit: 20
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||||
- model_name: fast-tier
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litellm_params:
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model: groq/llama-3.1-8b-instant
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api_key: os.environ/GROQ_API_KEY_2
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rpm_limit: 20
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|
||||
- model_name: volume-tier
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||||
litellm_params:
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||||
model: mistral/mistral-small-latest
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api_key: os.environ/MISTRAL_API_KEY
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tpm_limit: 500000
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# CLAUDE & KIMI - Quality/Reasoning
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- model_name: quality-tier
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litellm_params:
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model: anthropic/claude-3-5-sonnet-20240620
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api_key: os.environ/ANTHROPIC_API_KEY
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rpm_limit: 5
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||||
|
||||
- model_name: claude-haiku
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litellm_params:
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model: anthropic/claude-3-haiku-20240307
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api_key: os.environ/ANTHROPIC_API_KEY
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rpm_limit: 10
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|
||||
# Kimi via OpenRouter (Free tier)
|
||||
- model_name: reasoning-tier
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||||
litellm_params:
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||||
model: openrouter/moonshotai/kimi-k2:free
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||||
api_key: os.environ/OPENROUTER_API_KEY
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||||
|
||||
# Kimi Direct (Ultra-cheap)
|
||||
- model_name: reasoning-tier
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||||
litellm_params:
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model: moonshot/kimi-k2-0711-preview
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||||
api_key: os.environ/MOONSHOT_API_KEY
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||||
tpm_limit: 100000
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||||
|
||||
- model_name: deepseek
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||||
litellm_params:
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model: deepseek/deepseek-chat
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api_key: os.environ/DEEPSEEK_API_KEY
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# Embeddings & Tools
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||||
- model_name: embeddings
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litellm_params:
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model: cohere/embed-english-v3.0
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api_key: os.environ/COHERE_API_KEY
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|
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# Local Fallback
|
||||
- model_name: local-llama
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litellm_params:
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model: ollama/llama3.1:8b
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api_base: http://ollama:11434
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||||
|
||||
router_settings:
|
||||
routing_strategy: "usage-based-routing"
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||||
timeout: 30
|
||||
num_retries: 3
|
||||
allowed_fails: 2
|
||||
cooldown_time: 60
|
||||
fallbacks:
|
||||
- fast-tier: ["volume-tier", "reasoning-tier"]
|
||||
- volume-tier: ["reasoning-tier", "local-llama"]
|
||||
- quality-tier: ["claude-haiku", "reasoning-tier"]
|
||||
|
||||
general_settings:
|
||||
master_key: os.environ/LITELLM_MASTER_KEY
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cache: true
|
||||
cache_params:
|
||||
type: redis
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||||
host: redis
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||||
port: 6379
|
||||
ttl: 3600
|
||||
retry_policy:
|
||||
TimeoutError: 3
|
||||
RateLimitError: 5
|
||||
log_level: info
|
||||
log_file: /app/logs/litellm.log
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||||
|
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@ -0,0 +1,35 @@
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{
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||||
"mcpServers": {
|
||||
"filesystem": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
|
||||
},
|
||||
"git": {
|
||||
"command": "uvx",
|
||||
"args": ["mcp-server-git"]
|
||||
},
|
||||
"github": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {
|
||||
"GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}"
|
||||
}
|
||||
},
|
||||
"fetch": {
|
||||
"command": "uvx",
|
||||
"args": ["mcp-server-fetch"]
|
||||
},
|
||||
"sqlite": {
|
||||
"command": "uvx",
|
||||
"args": ["mcp-server-sqlite", "/workspace/data.db"]
|
||||
},
|
||||
"memory": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-memory"]
|
||||
},
|
||||
"sequential-thinking": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-sequential-thinking"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,151 @@
|
|||
version: '3.8'
|
||||
|
||||
services:
|
||||
# Core Infrastructure
|
||||
redis:
|
||||
image: redis:7-alpine
|
||||
container_name: agent-redis
|
||||
restart: unless-stopped
|
||||
volumes:
|
||||
- ./data/redis:/data
|
||||
command: redis-server --appendonly yes --maxmemory 512mb --maxmemory-policy allkeys-lru
|
||||
networks:
|
||||
- agent-network
|
||||
|
||||
chromadb:
|
||||
image: chromadb/chroma:latest
|
||||
container_name: agent-memory-vector
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "8000:8000"
|
||||
volumes:
|
||||
- ./data/chroma:/chroma/chroma
|
||||
environment:
|
||||
- IS_PERSISTENT=TRUE
|
||||
- PERSIST_DIRECTORY=/chroma/chroma
|
||||
- ANONYMIZED_TELEMETRY=FALSE
|
||||
networks:
|
||||
- agent-network
|
||||
|
||||
# LLM Gateway
|
||||
litellm:
|
||||
image: ghcr.io/berriai/litellm:main-latest
|
||||
container_name: agent-gateway
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "4000:4000"
|
||||
volumes:
|
||||
- ./config/litellm_config.yaml:/app/config.yaml
|
||||
- ./logs:/app/logs
|
||||
environment:
|
||||
- DATABASE_URL=sqlite:///app/db.sqlite3
|
||||
- LITELLM_MASTER_KEY=${MASTER_KEY:-sk-agent}
|
||||
- REDIS_HOST=redis
|
||||
- REDIS_PORT=6379
|
||||
command: --config /app/config.yaml --port 4000
|
||||
networks:
|
||||
- agent-network
|
||||
|
||||
# Agent Core with Reasoning Engines
|
||||
agent-core:
|
||||
build:
|
||||
context: ./services/agent-core
|
||||
dockerfile: Dockerfile
|
||||
container_name: agent-core
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "8080:8080"
|
||||
volumes:
|
||||
- ./workspace:/workspace
|
||||
- ./config/agent:/app/config
|
||||
- ./data/agent:/app/data
|
||||
environment:
|
||||
- LLM_API_BASE=http://litellm:4000/v1
|
||||
- LLM_API_KEY=${MASTER_KEY:-sk-agent}
|
||||
- REDIS_URL=redis://redis:6379/0
|
||||
- CHROMA_URL=http://chromadb:8000
|
||||
- DEFAULT_REASONING_MODE=${DEFAULT_REASONING:-auto}
|
||||
depends_on:
|
||||
- litellm
|
||||
- redis
|
||||
- chromadb
|
||||
networks:
|
||||
- agent-network
|
||||
|
||||
# MCP Tool Gateway
|
||||
mcpo:
|
||||
build:
|
||||
context: ./services/mcpo
|
||||
dockerfile: Dockerfile
|
||||
container_name: agent-mcp-gateway
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "8001:8000"
|
||||
volumes:
|
||||
- ./workspace:/workspace:ro
|
||||
- ./config/mcp:/app/config
|
||||
networks:
|
||||
- agent-network
|
||||
profiles:
|
||||
- mcp
|
||||
|
||||
# VS Code Server with AI Assistant
|
||||
code-server:
|
||||
image: lscr.io/linuxserver/code-server:latest
|
||||
container_name: agent-ide
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "8443:8443"
|
||||
environment:
|
||||
- PUID=1000
|
||||
- PGID=1000
|
||||
- TZ=Etc/UTC
|
||||
- PASSWORD=${IDE_PASSWORD:-code}
|
||||
- SUDO_PASSWORD=${IDE_SUDO_PASSWORD:-sudo}
|
||||
- DEFAULT_WORKSPACE=/workspace
|
||||
volumes:
|
||||
- ./workspace:/workspace
|
||||
- ./data/code-server:/config
|
||||
- ./config/continue:/config/.continue:ro
|
||||
networks:
|
||||
- agent-network
|
||||
profiles:
|
||||
- ide
|
||||
|
||||
# Web UI
|
||||
open-webui:
|
||||
image: ghcr.io/open-webui/open-webui:main
|
||||
container_name: agent-ui
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "3000:8080"
|
||||
volumes:
|
||||
- ./data/open-webui:/app/backend/data
|
||||
environment:
|
||||
- OPENAI_API_BASE_URL=http://agent-core:8080/v1
|
||||
- OPENAI_API_KEY=${MASTER_KEY:-sk-agent}
|
||||
- ENABLE_SIGNUP=false
|
||||
- DEFAULT_MODELS=agent/orchestrator
|
||||
depends_on:
|
||||
- agent-core
|
||||
networks:
|
||||
- agent-network
|
||||
profiles:
|
||||
- ui
|
||||
|
||||
# Auto-updater
|
||||
watchtower:
|
||||
image: containrrr/watchtower
|
||||
container_name: agent-watchtower
|
||||
restart: unless-stopped
|
||||
volumes:
|
||||
- /var/run/docker.sock:/var/run/docker.sock
|
||||
environment:
|
||||
- WATCHTOWER_POLL_INTERVAL=86400
|
||||
- WATCHTOWER_CLEANUP=true
|
||||
networks:
|
||||
- agent-network
|
||||
|
||||
networks:
|
||||
agent-network:
|
||||
driver: bridge
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
# API Reference
|
||||
|
||||
## Base URL
|
||||
```
|
||||
http://your-server-ip:8080/v1
|
||||
```
|
||||
|
||||
## Authentication
|
||||
All requests require Bearer token:
|
||||
```
|
||||
Authorization: Bearer sk-agent-your-key
|
||||
```
|
||||
|
||||
## Endpoints
|
||||
|
||||
### POST /chat/completions
|
||||
Main agent endpoint.
|
||||
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"message": "Create a Python script to fetch weather data",
|
||||
"reasoning_mode": "plan_execute",
|
||||
"session_id": "unique-session-id",
|
||||
"max_iterations": 10
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"response": "Here\'s the Python script...",
|
||||
"reasoning_mode": "plan_execute",
|
||||
"session_id": "unique-session-id",
|
||||
"steps": [
|
||||
{"step_number": 1, "type": "plan", "content": "..."},
|
||||
{"step_number": 2, "type": "action", "content": "..."}
|
||||
],
|
||||
"metadata": {
|
||||
"model_used": "volume-tier",
|
||||
"auto_selected": true,
|
||||
"timestamp": "2024-..."
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Reasoning Modes
|
||||
|
||||
| Mode | Use Case | Speed | Accuracy |
|
||||
|------|----------|-------|----------|
|
||||
| `react` | Simple Q&A, debugging | Fast | Medium |
|
||||
| `plan_execute` | Complex multi-step tasks | Medium | High |
|
||||
| `reflexion` | Code review, critical tasks | Slow | Very High |
|
||||
| `auto` | Let system decide | Variable | Adaptive |
|
||||
|
||||
### GET /models
|
||||
List available models.
|
||||
|
||||
### GET /health
|
||||
Check system status.
|
||||
|
||||
### GET /sessions/{id}/history
|
||||
Retrieve conversation history.
|
||||
|
||||
## Examples
|
||||
|
||||
### Python
|
||||
```python
|
||||
import requests
|
||||
|
||||
response = requests.post(
|
||||
"http://localhost:8080/v1/chat/completions",
|
||||
headers={"Authorization": "Bearer sk-agent-xxx"},
|
||||
json={
|
||||
"message": "Refactor this code",
|
||||
"reasoning_mode": "reflexion"
|
||||
}
|
||||
)
|
||||
print(response.json()["response"])
|
||||
```
|
||||
|
||||
### cURL
|
||||
```bash
|
||||
curl -X POST http://localhost:8080/v1/chat/completions \
|
||||
-H "Authorization: Bearer sk-agent-xxx" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"message":"Hello","reasoning_mode":"auto"}'
|
||||
```
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
# Provider Setup Guide
|
||||
|
||||
## Free Tier Providers
|
||||
|
||||
### Groq (Fastest)
|
||||
- **URL**: https://console.groq.com
|
||||
- **Free Tier**: 20 RPM, variable TPM
|
||||
- **Models**: Llama 3.3 70B, Llama 3.1 8B
|
||||
- **Best For**: Speed, quick coding tasks
|
||||
- **Tip**: Create multiple accounts with different phones for load balancing
|
||||
|
||||
### Mistral (High Volume)
|
||||
- **URL**: https://console.mistral.ai
|
||||
- **Free Tier**: 1 billion tokens/month
|
||||
- **Models**: Mistral Small, Medium
|
||||
- **Best For**: High-volume processing, chatbots
|
||||
|
||||
### OpenRouter (Universal Access)
|
||||
- **URL**: https://openrouter.ai
|
||||
- **Free Tier**: 50 requests/day
|
||||
- **Access**: Kimi K2:free, Gemini Flash:free
|
||||
- **Best For**: Testing, fallback access
|
||||
|
||||
### Cohere (Embeddings)
|
||||
- **URL**: https://cohere.com
|
||||
- **Free Tier**: 1,000 calls/month
|
||||
- **Best For**: Embeddings, RAG systems
|
||||
|
||||
## Trial/Cheap Providers
|
||||
|
||||
### Anthropic Claude (Highest Quality)
|
||||
- **URL**: https://console.anthropic.com
|
||||
- **Trial**: $5 free credits (new users)
|
||||
- **Student**: $500 credits (apply with .edu)
|
||||
- **Cost**: $3/M input (Sonnet), $0.25/M (Haiku)
|
||||
- **Best For**: Complex reasoning, analysis, code review
|
||||
|
||||
### Moonshot Kimi (Best Value)
|
||||
- **URL**: https://platform.moonshot.ai
|
||||
- **Bonus**: $5 signup credit
|
||||
- **Cost**: $0.60/M input, $2.50/M output
|
||||
- **Context**: 128K tokens
|
||||
- **Best For**: Coding, long documents, Chinese content
|
||||
|
||||
### DeepSeek (Cheapest Reasoning)
|
||||
- **URL**: https://platform.deepseek.com
|
||||
- **Cost**: $0.14/M input, $0.28/M output
|
||||
- **Best For**: Reasoning tasks, math, code
|
||||
|
||||
## Configuration Priority
|
||||
|
||||
The system routes requests in this priority:
|
||||
|
||||
1. **Fast tasks** → Groq (free, instant)
|
||||
2. **High volume** → Mistral (1B tokens)
|
||||
3. **Complex coding** → Kimi (cheap, 128K context)
|
||||
4. **Quality critical** → Claude (expensive but best)
|
||||
5. **Fallback** → OpenRouter free tier
|
||||
|
||||
## Rate Limit Management
|
||||
|
||||
The router automatically:
|
||||
- Tracks RPM/TPM across all providers
|
||||
- Distributes load (multiple Groq accounts)
|
||||
- Falls back when limits approached
|
||||
- Caches responses to reduce API calls
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
# Setup Guide
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- **OS**: Debian 12, Ubuntu 22.04+, or Proxmox LXC
|
||||
- **RAM**: 4GB minimum (8GB recommended for IDE)
|
||||
- **Storage**: 20GB free space
|
||||
- **Network**: Internet access for API calls
|
||||
|
||||
## Quick Install
|
||||
|
||||
```bash
|
||||
# 1. Clone from your Gitea
|
||||
git clone https://gitea.yourdomain.com/username/llm-hub.git
|
||||
cd llm-hub
|
||||
|
||||
# 2. Run setup
|
||||
chmod +x setup.sh && ./setup.sh
|
||||
|
||||
# 3. Configure API keys
|
||||
nano .env
|
||||
|
||||
# 4. Start
|
||||
./start.sh full
|
||||
```
|
||||
|
||||
## Proxmox LXC Setup
|
||||
|
||||
On Proxmox host, create optimized container:
|
||||
|
||||
```bash
|
||||
pct create 100 local:vztmpl/debian-12-standard_12.7-1_amd64.tar.zst \
|
||||
--hostname llm-hub \
|
||||
--memory 8192 \
|
||||
--swap 1024 \
|
||||
--cores 4 \
|
||||
--rootfs local-lvm:20 \
|
||||
--features nesting=1,keyctl=1 \
|
||||
--net0 name=eth0,bridge=vmbr0,ip=dhcp
|
||||
|
||||
# Add to /etc/pve/lxc/100.conf:
|
||||
cat >> /etc/pve/lxc/100.conf << EOF
|
||||
lxc.cgroup.relative = 0
|
||||
lxc.apparmor.profile = unconfined
|
||||
lxc.cgroup.devices.allow = a
|
||||
EOF
|
||||
|
||||
pct start 100
|
||||
pct exec 100 -- bash -c "apt update && apt install -y curl git && curl -fsSL setup.sh | bash"
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
Edit `.env` file:
|
||||
|
||||
```bash
|
||||
# Required: At least one LLM provider
|
||||
GROQ_API_KEY_1=gsk_xxx
|
||||
MISTRAL_API_KEY=your_key
|
||||
|
||||
# Recommended: Multiple providers for redundancy
|
||||
ANTHROPIC_API_KEY=sk-ant-xxx
|
||||
MOONSHOT_API_KEY=sk-xxx
|
||||
OPENROUTER_API_KEY=sk-or-xxx
|
||||
|
||||
# UI Security
|
||||
IDE_PASSWORD=strong-password-here
|
||||
```
|
||||
|
||||
## Verification
|
||||
|
||||
```bash
|
||||
# Check health
|
||||
curl http://localhost:8080/health
|
||||
|
||||
# Test agent
|
||||
curl -X POST http://localhost:8080/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer sk-agent-xxx" \
|
||||
-d '{"message":"Hello","reasoning_mode":"react"}'
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**Docker not starting in LXC:**
|
||||
```bash
|
||||
# On Proxmox host, check config
|
||||
pct config 100 | grep features
|
||||
# Should show: features: nesting=1,keyctl=1
|
||||
```
|
||||
|
||||
**Permission denied on workspace:**
|
||||
```bash
|
||||
chown -R 1000:1000 workspace/
|
||||
```
|
||||
|
||||
**Port conflicts:**
|
||||
Edit `docker-compose.yml` to change port mappings (e.g., `8081:8080`)
|
||||
|
|
@ -0,0 +1,57 @@
|
|||
#!/bin/bash
|
||||
|
||||
ENV_FILE=".env"
|
||||
CONFIG_FILE="config/litellm_config.yaml"
|
||||
|
||||
echo "🔌 Add Provider to LLM Hub"
|
||||
echo "=========================="
|
||||
echo ""
|
||||
echo "1. Groq (Fast)"
|
||||
echo "2. Mistral (Volume)"
|
||||
echo "3. Anthropic Claude (Quality)"
|
||||
echo "4. Moonshot Kimi (Cheap/128K)"
|
||||
echo "5. OpenRouter (Free tier access)"
|
||||
echo "6. Cohere (Embeddings)"
|
||||
echo "7. DeepSeek (Cheap reasoning)"
|
||||
echo "8. Exit"
|
||||
read -p "Select (1-8): " choice
|
||||
|
||||
read -p "Enter API Key: " api_key
|
||||
|
||||
case $choice in
|
||||
1)
|
||||
read -p "Instance number (1,2,3...): " num
|
||||
var="GROQ_API_KEY_$num"
|
||||
echo "$var=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added Groq key as $var"
|
||||
;;
|
||||
2)
|
||||
echo "MISTRAL_API_KEY=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added Mistral"
|
||||
;;
|
||||
3)
|
||||
echo "ANTHROPIC_API_KEY=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added Claude (remember: expensive, use sparingly)"
|
||||
;;
|
||||
4)
|
||||
echo "MOONSHOT_API_KEY=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added Kimi (great for coding!)"
|
||||
;;
|
||||
5)
|
||||
echo "OPENROUTER_API_KEY=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added OpenRouter (access free tier models)"
|
||||
;;
|
||||
6)
|
||||
echo "COHERE_API_KEY=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added Cohere (embeddings)"
|
||||
;;
|
||||
7)
|
||||
echo "DEEPSEEK_API_KEY=$api_key" >> "$ENV_FILE"
|
||||
echo "✅ Added DeepSeek (cheap reasoning)"
|
||||
;;
|
||||
8) exit 0 ;;
|
||||
*) echo "Invalid choice" ; exit 1 ;;
|
||||
esac
|
||||
|
||||
read -p "Restart services to apply? (y/N): " restart
|
||||
[[ $restart =~ ^[Yy]$ ]] && docker-compose restart
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
#!/bin/bash
|
||||
|
||||
echo "🧠 LLM Hub Status"
|
||||
echo "================="
|
||||
echo ""
|
||||
|
||||
# Container status
|
||||
echo "📦 Containers:"
|
||||
docker-compose ps --services --filter "status=running" 2>/dev/null | while read service; do
|
||||
status=$(docker-compose ps -q "$service" | xargs docker inspect -f '{{.State.Status}}' 2>/dev/null)
|
||||
echo " $service: $status"
|
||||
done
|
||||
|
||||
echo ""
|
||||
echo "🔍 Health Checks:"
|
||||
|
||||
# API health
|
||||
if curl -s http://localhost:8080/health | grep -q "healthy"; then
|
||||
echo " ✅ Agent Core: Healthy"
|
||||
else
|
||||
echo " ❌ Agent Core: Not responding"
|
||||
fi
|
||||
|
||||
# LiteLLM
|
||||
if curl -s http://localhost:4000/health/liveliness | grep -q "true"; then
|
||||
echo " ✅ LiteLLM: Running"
|
||||
else
|
||||
echo " ❌ LiteLLM: Not responding"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "📊 Router Stats:"
|
||||
curl -s http://localhost:8080/health 2>/dev/null | python3 -m json.tool 2>/dev/null || echo " Unable to fetch stats"
|
||||
|
||||
echo ""
|
||||
echo "💾 Memory Usage:"
|
||||
docker stats --no-stream --format "table {{.Name}}\t{{.MemUsage}}" | grep -E "(agent-|NAME)" || true
|
||||
|
|
@ -0,0 +1,20 @@
|
|||
FROM python:3.11-slim
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# Install system dependencies
|
||||
RUN apt-get update && apt-get install -y \
|
||||
build-essential \
|
||||
git \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install Python dependencies
|
||||
COPY requirements.txt .
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
|
||||
# Copy application
|
||||
COPY main.py .
|
||||
|
||||
EXPOSE 8080
|
||||
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
|
||||
|
|
@ -0,0 +1,357 @@
|
|||
"""
|
||||
Agentic AI Core - Multi-Reasoning Engine
|
||||
Supports: ReAct, Plan-and-Execute, Reflexion
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
from typing import List, Dict, Any, Literal, Optional
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
from datetime import datetime
|
||||
import httpx
|
||||
import redis
|
||||
|
||||
app = FastAPI(
|
||||
title="Agentic AI Core",
|
||||
version="2.0.0",
|
||||
description="Multi-reasoning agent platform with memory and MCP integration"
|
||||
)
|
||||
|
||||
# Configuration
|
||||
LLM_API_BASE = os.getenv("LLM_API_BASE", "http://litellm:4000/v1")
|
||||
LLM_API_KEY = os.getenv("LLM_API_KEY", "sk-agent")
|
||||
DEFAULT_REASONING = os.getenv("DEFAULT_REASONING_MODE", "auto")
|
||||
|
||||
# Redis for short-term memory
|
||||
try:
|
||||
redis_client = redis.from_url(
|
||||
os.getenv("REDIS_URL", "redis://redis:6379"),
|
||||
decode_responses=True
|
||||
)
|
||||
except:
|
||||
redis_client = None
|
||||
|
||||
# ==========================================
|
||||
# DATA MODELS
|
||||
# ==========================================
|
||||
|
||||
class AgentRequest(BaseModel):
|
||||
message: str
|
||||
session_id: str = Field(default="default", description="Conversation thread ID")
|
||||
reasoning_mode: Literal["react", "plan_execute", "reflexion", "auto"] = DEFAULT_REASONING
|
||||
context_files: Optional[List[str]] = Field(default_factory=list)
|
||||
enable_memory: bool = True
|
||||
max_iterations: int = 10
|
||||
|
||||
class AgentStep(BaseModel):
|
||||
step_number: int
|
||||
type: Literal["thought", "action", "observation", "reflection", "plan"]
|
||||
content: str
|
||||
timestamp: datetime = Field(default_factory=datetime.now)
|
||||
|
||||
class AgentResponse(BaseModel):
|
||||
response: str
|
||||
reasoning_mode: str
|
||||
session_id: str
|
||||
steps: List[AgentStep] = Field(default_factory=list)
|
||||
metadata: Dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
# ==========================================
|
||||
# UTILITY FUNCTIONS
|
||||
# ==========================================
|
||||
|
||||
async def call_llm(messages: List[Dict], model: str = "auto", tools: Optional[List] = None) -> Dict:
|
||||
"""Call LLM through LiteLLM gateway"""
|
||||
async with httpx.AsyncClient() as client:
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 4000
|
||||
}
|
||||
if tools:
|
||||
payload["tools"] = tools
|
||||
|
||||
response = await client.post(
|
||||
f"{LLM_API_BASE}/chat/completions",
|
||||
headers={"Authorization": f"Bearer {LLM_API_KEY}"},
|
||||
json=payload,
|
||||
timeout=60.0
|
||||
)
|
||||
return response.json()
|
||||
|
||||
def determine_reasoning_mode(message: str, requested: str) -> str:
|
||||
"""Auto-select reasoning mode based on task complexity"""
|
||||
if requested != "auto":
|
||||
return requested
|
||||
|
||||
# Complexity indicators
|
||||
complexity_markers = [
|
||||
"plan", "design", "architecture", "steps", "implement",
|
||||
"build", "create", "project", "complex", "multi-step"
|
||||
]
|
||||
|
||||
msg_lower = message.lower()
|
||||
score = sum(1 for marker in complexity_markers if marker in msg_lower)
|
||||
|
||||
if score >= 3 or len(message) > 500:
|
||||
return "plan_execute"
|
||||
elif "review" in msg_lower or "check" in msg_lower or "verify" in msg_lower:
|
||||
return "reflexion"
|
||||
else:
|
||||
return "react"
|
||||
|
||||
# ==========================================
|
||||
# REASONING ENGINES
|
||||
# ==========================================
|
||||
|
||||
class ReActEngine:
|
||||
"""ReAct: Reasoning + Acting in interleaved steps"""
|
||||
|
||||
async def run(self, message: str, session_id: str) -> Dict:
|
||||
steps = []
|
||||
|
||||
# Initial thought
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a ReAct agent. Think step by step and act."},
|
||||
{"role": "user", "content": message}
|
||||
]
|
||||
|
||||
response = await call_llm(messages, model="fast-tier")
|
||||
|
||||
steps.append(AgentStep(
|
||||
step_number=1,
|
||||
type="thought",
|
||||
content="Initial analysis and reasoning"
|
||||
))
|
||||
|
||||
content = response["choices"][0]["message"]["content"]
|
||||
|
||||
return {
|
||||
"response": content,
|
||||
"steps": steps,
|
||||
"model_used": "fast-tier"
|
||||
}
|
||||
|
||||
class PlanAndExecuteEngine:
|
||||
"""Plan first, then execute step by step"""
|
||||
|
||||
async def run(self, message: str, session_id: str) -> Dict:
|
||||
steps = []
|
||||
|
||||
# Planning phase
|
||||
plan_messages = [
|
||||
{"role": "system", "content": "Create a step-by-step plan to accomplish the task."},
|
||||
{"role": "user", "content": f"Create a detailed plan for: {message}"}
|
||||
]
|
||||
|
||||
plan_response = await call_llm(plan_messages, model="volume-tier")
|
||||
plan = plan_response["choices"][0]["message"]["content"]
|
||||
|
||||
steps.append(AgentStep(
|
||||
step_number=1,
|
||||
type="plan",
|
||||
content=plan
|
||||
))
|
||||
|
||||
# Execution phase
|
||||
exec_messages = [
|
||||
{"role": "system", "content": "Execute the task following the provided plan."},
|
||||
{"role": "user", "content": f"Task: {message}\n\nPlan: {plan}\n\nExecute this plan:"}
|
||||
]
|
||||
|
||||
exec_response = await call_llm(exec_messages, model="reasoning-tier")
|
||||
result = exec_response["choices"][0]["message"]["content"]
|
||||
|
||||
steps.append(AgentStep(
|
||||
step_number=2,
|
||||
type="action",
|
||||
content="Execution completed"
|
||||
))
|
||||
|
||||
return {
|
||||
"response": result,
|
||||
"steps": steps,
|
||||
"model_used": "reasoning-tier",
|
||||
"plan": plan
|
||||
}
|
||||
|
||||
class ReflexionEngine:
|
||||
"""Execute with self-reflection and correction"""
|
||||
|
||||
async def run(self, message: str, session_id: str, max_iterations: int = 2) -> Dict:
|
||||
steps = []
|
||||
|
||||
# Initial execution
|
||||
messages = [
|
||||
{"role": "system", "content": "Solve the problem carefully."},
|
||||
{"role": "user", "content": message}
|
||||
]
|
||||
|
||||
response = await call_llm(messages, model="quality-tier")
|
||||
answer = response["choices"][0]["message"]["content"]
|
||||
|
||||
steps.append(AgentStep(
|
||||
step_number=1,
|
||||
type="action",
|
||||
content="Initial solution generated"
|
||||
))
|
||||
|
||||
# Reflection phase
|
||||
for i in range(max_iterations):
|
||||
reflect_messages = [
|
||||
{"role": "system", "content": "Critically evaluate the solution for errors or improvements."},
|
||||
{"role": "user", "content": f"Problem: {message}\n\nProposed Solution: {answer}\n\nIdentify any issues or improvements:"}
|
||||
]
|
||||
|
||||
reflect_response = await call_llm(reflect_messages, model="claude-haiku")
|
||||
reflection = reflect_response["choices"][0]["message"]["content"]
|
||||
|
||||
if "correct" in reflection.lower() and "no issues" in reflection.lower():
|
||||
break
|
||||
|
||||
steps.append(AgentStep(
|
||||
step_number=2+i,
|
||||
type="reflection",
|
||||
content=reflection
|
||||
))
|
||||
|
||||
# Improve based on reflection
|
||||
improve_messages = [
|
||||
{"role": "system", "content": "Improve the solution based on the critique."},
|
||||
{"role": "user", "content": f"Original: {answer}\n\nIssues found: {reflection}\n\nProvide improved solution:"}
|
||||
]
|
||||
|
||||
improve_response = await call_llm(improve_messages, model="quality-tier")
|
||||
answer = improve_response["choices"][0]["message"]["content"]
|
||||
|
||||
return {
|
||||
"response": answer,
|
||||
"steps": steps,
|
||||
"model_used": "quality-tier",
|
||||
"iterations": len(steps)
|
||||
}
|
||||
|
||||
# ==========================================
|
||||
# API ENDPOINTS
|
||||
# ==========================================
|
||||
|
||||
@app.post("/v1/chat/completions", response_model=AgentResponse)
|
||||
async def agent_endpoint(request: AgentRequest):
|
||||
"""
|
||||
Main agent endpoint with multiple reasoning strategies:
|
||||
- react: Fast iterative reasoning (good for simple tasks)
|
||||
- plan_execute: Plan then execute (good for complex tasks)
|
||||
- reflexion: Self-correcting (good for accuracy-critical tasks)
|
||||
- auto: Automatically select based on task complexity
|
||||
"""
|
||||
|
||||
# Determine reasoning mode
|
||||
mode = determine_reasoning_mode(request.message, request.reasoning_mode)
|
||||
|
||||
# Store message in memory if enabled
|
||||
if request.enable_memory and redis_client:
|
||||
key = f"session:{request.session_id}:history"
|
||||
redis_client.lpush(key, request.message)
|
||||
redis_client.ltrim(key, 0, 99) # Keep last 100 messages
|
||||
|
||||
try:
|
||||
# Route to appropriate reasoning engine
|
||||
if mode == "react":
|
||||
result = await ReActEngine().run(request.message, request.session_id)
|
||||
elif mode == "plan_execute":
|
||||
result = await PlanAndExecuteEngine().run(request.message, request.session_id)
|
||||
elif mode == "reflexion":
|
||||
result = await ReflexionEngine().run(
|
||||
request.message,
|
||||
request.session_id,
|
||||
max_iterations=2
|
||||
)
|
||||
else:
|
||||
# Default fallback
|
||||
result = await ReActEngine().run(request.message, request.session_id)
|
||||
|
||||
return AgentResponse(
|
||||
response=result["response"],
|
||||
reasoning_mode=mode,
|
||||
session_id=request.session_id,
|
||||
steps=result.get("steps", []),
|
||||
metadata={
|
||||
"model_used": result.get("model_used", "unknown"),
|
||||
"auto_selected": request.reasoning_mode == "auto",
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/v1/models")
|
||||
async def list_models():
|
||||
"""List available agent models"""
|
||||
return {
|
||||
"object": "list",
|
||||
"data": [
|
||||
{
|
||||
"id": "agent/orchestrator",
|
||||
"object": "model",
|
||||
"created": 1700000000,
|
||||
"owned_by": "llm-hub",
|
||||
"description": "Auto-selecting orchestrator"
|
||||
},
|
||||
{
|
||||
"id": "agent/react",
|
||||
"object": "model",
|
||||
"created": 1700000000,
|
||||
"owned_by": "llm-hub",
|
||||
"description": "ReAct reasoning - fast iterative"
|
||||
},
|
||||
{
|
||||
"id": "agent/plan-execute",
|
||||
"object": "model",
|
||||
"created": 1700000000,
|
||||
"owned_by": "llm-hub",
|
||||
"description": "Plan-and-Execute - complex tasks"
|
||||
},
|
||||
{
|
||||
"id": "agent/reflexion",
|
||||
"object": "model",
|
||||
"created": 1700000000,
|
||||
"owned_by": "llm-hub",
|
||||
"description": "Reflexion - self-correcting with verification"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
"""Health check endpoint"""
|
||||
redis_status = "connected" if redis_client and redis_client.ping() else "disconnected"
|
||||
|
||||
return {
|
||||
"status": "healthy",
|
||||
"version": "2.0.0",
|
||||
"capabilities": ["react", "plan_execute", "reflexion", "mcp", "memory"],
|
||||
"default_mode": DEFAULT_REASONING,
|
||||
"redis": redis_status,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
@app.get("/sessions/{session_id}/history")
|
||||
async def get_session_history(session_id: str, limit: int = 10):
|
||||
"""Retrieve conversation history for a session"""
|
||||
if not redis_client:
|
||||
return {"error": "Redis not available"}
|
||||
|
||||
key = f"session:{session_id}:history"
|
||||
history = redis_client.lrange(key, 0, limit - 1)
|
||||
|
||||
return {
|
||||
"session_id": session_id,
|
||||
"history": history,
|
||||
"count": len(history)
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8080)
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
fastapi==0.104.1
|
||||
uvicorn[standard]==0.24.0
|
||||
langgraph==0.2.53
|
||||
langchain==0.3.0
|
||||
langchain-openai==0.2.0
|
||||
langchain-chroma==0.1.4
|
||||
chromadb==0.5.0
|
||||
redis==5.0.1
|
||||
httpx==0.25.2
|
||||
tiktoken==0.5.1
|
||||
pydantic==2.5.0
|
||||
python-multipart==0.0.6
|
||||
aiofiles==23.2.1
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
FROM python:3.11-slim
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
RUN pip install mcpo uv
|
||||
|
||||
COPY servers.json .
|
||||
|
||||
EXPOSE 8000
|
||||
|
||||
CMD ["mcpo", "--config", "servers.json", "--host", "0.0.0.0", "--port", "8000"]
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
{
|
||||
"mcpServers": {
|
||||
"filesystem": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
|
||||
},
|
||||
"git": {
|
||||
"command": "uvx",
|
||||
"args": ["mcp-server-git"]
|
||||
},
|
||||
"fetch": {
|
||||
"command": "uvx",
|
||||
"args": ["mcp-server-fetch"]
|
||||
},
|
||||
"memory": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-memory"]
|
||||
},
|
||||
"sequential-thinking": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-sequential-thinking"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
BLUE='\033[0;34m'
|
||||
NC='\033[0m'
|
||||
|
||||
INSTALL_DIR="$(pwd)"
|
||||
|
||||
echo -e "${GREEN}🧠 Agentic AI Hub Setup${NC}"
|
||||
echo "======================="
|
||||
|
||||
# Detect Proxmox LXC
|
||||
if [ -f /proc/1/environ ] && grep -q "container=lxc" /proc/1/environ 2>/dev/null; then
|
||||
echo -e "${YELLOW}✓ LXC container detected${NC}"
|
||||
if ! grep -q "lxc.cgroup.relative" /etc/pve/lxc/*.conf 2>/dev/null; then
|
||||
echo -e "${YELLOW}⚠️ Tip: For LXC with Docker, add to /etc/pve/lxc/XXX.conf:${NC}"
|
||||
echo " lxc.cgroup.relative = 0"
|
||||
echo " lxc.apparmor.profile = unconfined"
|
||||
echo " lxc.cgroup.devices.allow = a"
|
||||
fi
|
||||
fi
|
||||
|
||||
# Check/install Docker
|
||||
if ! command -v docker &> /dev/null; then
|
||||
echo -e "${YELLOW}Installing Docker...${NC}"
|
||||
curl -fsSL https://get.docker.com | sh
|
||||
usermod -aG docker $USER || true
|
||||
systemctl enable docker
|
||||
systemctl start docker
|
||||
fi
|
||||
|
||||
if ! command -v docker-compose &> /dev/null && ! docker compose version &> /dev/null; then
|
||||
echo -e "${YELLOW}Installing Docker Compose...${NC}"
|
||||
apt-get update && apt-get install -y docker-compose-plugin
|
||||
fi
|
||||
|
||||
# Install Node.js for MCP
|
||||
if ! command -v node &> /dev/null || [ "$(node -v | cut -d'v' -f2 | cut -d'.' -f1)" != "20" ]; then
|
||||
echo -e "${YELLOW}Installing Node.js 20...${NC}"
|
||||
curl -fsSL https://deb.nodesource.com/setup_20.x | bash -
|
||||
apt-get install -y nodejs
|
||||
fi
|
||||
|
||||
# Install uv for Python tools
|
||||
if ! command -v uv &> /dev/null; then
|
||||
pip install uv || pip3 install uv
|
||||
fi
|
||||
|
||||
# Create directories
|
||||
echo -e "${BLUE}Creating directories...${NC}"
|
||||
mkdir -p {data/{redis,chroma,agent/{sessions,code-server,open-webui},neo4j},workspace,logs}
|
||||
mkdir -p services/{agent-core,mcpo}
|
||||
|
||||
# Set permissions
|
||||
chown -R 1000:1000 workspace data || true
|
||||
chmod +x *.sh scripts/*.sh 2>/dev/null || true
|
||||
|
||||
# Create .env if not exists
|
||||
if [ ! -f .env ]; then
|
||||
echo -e "${YELLOW}Creating .env file...${NC}"
|
||||
cp .env.example .env
|
||||
# Generate random keys
|
||||
sed -i "s/MASTER_KEY=.*/MASTER_KEY=sk-agent-$(openssl rand -hex 8)/" .env
|
||||
sed -i "s/WEBUI_SECRET_KEY=.*/WEBUI_SECRET_KEY=$(openssl rand -hex 32)/" .env
|
||||
echo -e "${GREEN}✓ .env created. Edit it to add your API keys.${NC}"
|
||||
fi
|
||||
|
||||
# Create workspace gitkeep
|
||||
touch workspace/.gitkeep
|
||||
|
||||
echo ""
|
||||
echo -e "${GREEN}✅ Setup complete!${NC}"
|
||||
echo ""
|
||||
echo "Next steps:"
|
||||
echo "1. Edit .env file: nano .env"
|
||||
echo "2. Add your API keys (Groq, Mistral, etc.)"
|
||||
echo "3. Start services: ./start.sh"
|
||||
echo ""
|
||||
echo "Documentation:"
|
||||
echo " - Setup: docs/SETUP.md"
|
||||
echo " - API: docs/API.md"
|
||||
echo " - Providers: docs/PROVIDERS.md"
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
#!/bin/bash
|
||||
|
||||
cd "$(dirname "$0")"
|
||||
|
||||
GREEN='\033[0;32m'
|
||||
BLUE='\033[0;34m'
|
||||
NC='\033[0m'
|
||||
|
||||
if [ ! -f .env ]; then
|
||||
echo "Error: .env file not found. Run ./setup.sh first."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
source .env
|
||||
|
||||
echo -e "${GREEN}🚀 Starting Agentic LLM Hub...${NC}"
|
||||
echo ""
|
||||
|
||||
# Determine profile
|
||||
PROFILE=${1:-full}
|
||||
|
||||
if [ "$PROFILE" = "minimal" ]; then
|
||||
echo "Mode: Minimal (core services only)"
|
||||
docker-compose up -d redis chromadb litellm agent-core
|
||||
elif [ "$PROFILE" = "ide" ]; then
|
||||
echo "Mode: Standard + IDE"
|
||||
docker-compose --profile ide up -d
|
||||
elif [ "$PROFILE" = "full" ]; then
|
||||
echo "Mode: Full (all services including MCP tools)"
|
||||
docker-compose --profile ide --profile mcp --profile ui up -d
|
||||
else
|
||||
echo "Usage: ./start.sh [minimal|ide|full]"
|
||||
echo " minimal - Core services only (lowest resources)"
|
||||
echo " ide - Core + VS Code IDE"
|
||||
echo " full - Everything including MCP tools and Web UI"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "Waiting for services..."
|
||||
sleep 5
|
||||
|
||||
# Get IP
|
||||
IP=$(hostname -I | awk '{print $1}')
|
||||
|
||||
echo ""
|
||||
echo -e "${GREEN}✅ Services started!${NC}"
|
||||
echo ""
|
||||
echo "Access Points:"
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
printf "${BLUE}%-22s${NC} %s\n" "Agent API:" "http://$IP:8080/v1"
|
||||
printf "${BLUE}%-22s${NC} %s\n" "VS Code IDE:" "http://$IP:8443"
|
||||
printf "${BLUE}%-22s${NC} %s\n" "LiteLLM Gateway:" "http://$IP:4000"
|
||||
printf "${BLUE}%-22s${NC} %s\n" "MCP Tools:" "http://$IP:8001/docs"
|
||||
printf "${BLUE}%-22s${NC} %s\n" "Web UI:" "http://$IP:3000"
|
||||
printf "${BLUE}%-22s${NC} %s\n" "Vector DB:" "http://$IP:8000"
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
echo ""
|
||||
echo "Test command:"
|
||||
echo "curl -X POST http://$IP:8080/v1/chat/completions \"
|
||||
echo " -H 'Content-Type: application/json' \"
|
||||
echo " -H 'Authorization: Bearer ${MASTER_KEY:0:20}...' \"
|
||||
echo " -d '{"message":"Hello","reasoning_mode":"react"}'"
|
||||
echo ""
|
||||
echo "View logs: docker-compose logs -f"
|
||||
echo "Stop: docker-compose down"
|
||||
Loading…
Reference in New Issue