LlamaAgent LlamaAgent Framework

LlamaAgent is a production-ready AI agent framework that combines the power of multiple LLM providers with advanced reasoning capabilities, comprehensive tool integration, and enterprise-level security features.

Intelligent Agents

ReAct agents with advanced reasoning, tool integration, and multimodal capabilities.

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Extensible Tools

Comprehensive tool system with built-in tools and easy custom tool creation.

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Enterprise Ready

Production deployment with security, monitoring, and scalability features.

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LAUNCH: Quick Start

Installation

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pip install llamaagent

Basic Usage

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from llamaagent import ReactAgent, AgentConfig
from llamaagent.tools import CalculatorTool

# Create an agent
config = AgentConfig(
    name="MathAgent",
    tools=["calculator"],
    temperature=0.7
)

agent = ReactAgent(config=config, tools=[CalculatorTool()])

# Execute a task
response = await agent.execute("What is 25 * 4 + 10?")
print(response.content)  # "The result is 110"

Featured Key Features

Agent Advanced AI Capabilities

• Multi-Provider Support: OpenAI, Anthropic, Cohere, Together AI, Ollama
• Intelligent Reasoning: ReAct agents with chain-of-thought processing
• SPRE Framework: Strategic Planning & Resourceful Execution
• Multimodal Support: Text, vision, and audio processing
• Memory Systems: Advanced short-term and long-term memory

BUILD: Production-Ready Features

• FastAPI Integration: Complete REST API with OpenAPI docs
• Enterprise Security: Authentication, authorization, rate limiting
• Monitoring: Prometheus metrics, distributed tracing, health checks
• Scalability: Horizontal scaling with load balancing
• Docker & Kubernetes: Production deployment ready

Tools Developer Experience

• Extensible Architecture: Plugin system for custom tools
• Comprehensive Testing: 95%+ test coverage
• Rich Documentation: Complete API reference and tutorials
• CLI & Web Interface: Interactive command-line and web UI
• Type Safety: Full type hints and mypy compatibility

Results Performance

Architecture

graph TB
    A[Client Applications] --> B[API Gateway]
    B --> C[Agent Orchestrator]
    C --> D[ReAct Agents]
    C --> E[Planning Agents]
    C --> F[Multimodal Agents]
    
    D --> G[Tool Registry]
    E --> G
    F --> G
    
    G --> H[Calculator]
    G --> I[Code Executor]
    G --> J[Web Search]
    G --> K[Custom Tools]
    
    D --> L[Memory Systems]
    E --> L
    F --> L
    
    L --> M[Vector Database]
    L --> N[Redis Cache]
    L --> O[SQLite Storage]
    
    D --> P[LLM Providers]
    E --> P
    F --> P
    
    P --> Q[OpenAI]
    P --> R[Anthropic]
    P --> S[Cohere]
    P --> T[Ollama]

Target Use Cases

Customer Support

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from llamaagent import ReactAgent
from llamaagent.tools import DatabaseTool, EmailTool

support_agent = ReactAgent(
    config=AgentConfig(name="SupportAgent"),
    tools=[DatabaseTool(), EmailTool()]
)

Research Assistant

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from llamaagent.tools import WebSearchTool, PaperReaderTool

research_agent = ReactAgent(
    config=AgentConfig(name="ResearchAgent"),
    tools=[WebSearchTool(), PaperReaderTool()]
)

Code Analysis

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from llamaagent.tools import PythonREPLTool, CodeAnalyzerTool

code_agent = ReactAgent(
    config=AgentConfig(name="CodeAgent"),
    tools=[PythonREPLTool(), CodeAnalyzerTool()]
)

Security

• Authentication: JWT tokens with refresh mechanism
• Authorization: Role-based access control (RBAC)
• Rate Limiting: Configurable per-user limits
• Input Validation: Comprehensive sanitization
• Audit Logging: Complete audit trail
• Encryption: End-to-end encryption for sensitive data

Deployment

Docker

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docker run -p 8000:8000 llamaagent:latest

Kubernetes

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kubectl apply -f k8s/

Environment Variables

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LLAMAAGENT_API_KEY=your-api-key
LLAMAAGENT_MODEL=gpt-4
DATABASE_URL=postgresql://user:pass@localhost/llamaagent

Documentation Documentation

• Getting Started - Installation and basic usage
• User Guide - Comprehensive documentation
• API Reference - Complete API documentation
• Examples - Real-world use cases
• Deployment - Production deployment guide

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

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# Clone and setup
git clone https://github.com/yourusername/llamaagent.git
cd llamaagent
pip install -e ".[dev,all]"

# Run tests
pytest

# Submit PR
git checkout -b feature/your-feature
git commit -m "Add your feature"
git push origin feature/your-feature

License

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

Acknowledgments

• OpenAI for foundational AI models
• Anthropic for Claude integration
• The open-source community for inspiration
• All contributors and maintainers

Made with LOVE: by Nik Jois and the LlamaAgent community

For questions, support, or contributions, please contact nikjois@llamasearch.ai

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