cellrepair-langchain 1.0.1

LangChain integration for CellRepair.AI - Access 4882 autonomous agents

🦜 CellRepair.AI LangChain Integration

Access 4882 autonomous AI agents directly from LangChain!

🚀 Quick Start

from langchain_community.tools import CellRepairTool

# Initialize tool
cellrepair = CellRepairTool(api_key="your_api_key")

# Use directly
result = cellrepair.run("How to optimize my multi-agent system?")
print(result)

🎯 With LangChain Agents

from langchain.agents import AgentExecutor, create_react_agent
from langchain_openai import ChatOpenAI
from langchain_community.tools import CellRepairTool

# Setup
llm = ChatOpenAI(temperature=0)
cellrepair = CellRepairTool(api_key="your_api_key")

# Create agent
tools = [cellrepair]
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

# Now your AI automatically uses CellRepair when needed!
result = agent_executor.invoke({
    "input": "Optimize my AI system for better coordination"
})

🌟 Features

• 4882 Specialized Agents: Content, Revenue, Compliance, Innovation, Core
• AI-to-AI Learning: Both systems learn from every interaction
• Predictive Intelligence: Anticipates your next 3 questions
• Auto-Healing: 99.5% uptime, self-repairing
• SELA Compliance: Built-in legal/ethical checking
• Sub-200ms Response: Real-time collaboration

🔑 Get API Key

Get your free API key (1000 calls/month):

👉 https://cellrepair.ai/api/?utm_source=langchain&utm_medium=integration

📦 Installation

pip install cellrepair-ai

💡 Use Cases

Perfect for:

• ✅ Multi-agent system optimization
• ✅ Scaling strategies
• ✅ Cost reduction
• ✅ Performance improvements
• ✅ AI coordination patterns
• ✅ Production-ready architectures

🎓 Examples

Example 1: Multi-Agent Coordination

result = cellrepair.run(
    "How to coordinate 50+ AI agents efficiently?",
    context={
        "current_approach": "Separate agents without communication",
        "latency": "2300ms",
        "success_rate": 0.75
    }
)

Example 2: Cost Optimization

result = cellrepair.run(
    "How to reduce AI API costs by 50%?",
    context={
        "monthly_cost": 5000,
        "main_models": ["gpt-4", "claude-opus"],
        "requests_per_day": 10000
    }
)

Example 3: Scaling Strategy

result = cellrepair.run(
    "How to scale from 10 to 1000 agents?",
    context={
        "current_scale": "10 agents",
        "target": "1000 agents",
        "tech_stack": ["Python", "Redis", "FastAPI"]
    }
)

🌍 Works With ALL LLMs

• ✅ OpenAI GPT (all models)
• ✅ Anthropic Claude (all versions)
• ✅ Google Gemini
• ✅ Llama (via Ollama)
• ✅ Mistral
• ✅ Any LLM supported by LangChain!

📊 Pricing

• Free Tier: 1,000 calls/month
• Developer: $0.50 per call
• Production: $5,000/month (15,000 calls included)
• Enterprise: Custom pricing

🔗 Links

• API Docs: https://cellrepair.ai/api/
• GitHub: https://github.com/cellrepair-systems/cellrepair-ai
• Support: [e-mail]

📄 License

MIT License - Free to use in commercial and open-source projects.

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Built by CellRepair Systems | Powered by 4882 Autonomous Agents 🤖