| Name | llamaagent JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | None |
| Summary | Advanced AI Agent Framework with Enterprise Features |
| upload_time | 2025-07-23 19:08:49 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2024 Nik Jois
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
agent
ai
automation
distributed
enterprise
llm
multimodal
orchestration
reasoning
tools
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| coveralls test coverage |
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|
<div align="center">
# LlamaAgent
**Advanced AI Agent Framework for Production-Ready Applications**
[](https://github.com/llamasearchai/llamaagent/actions)
[](https://www.python.org/downloads/)
[](LICENSE)
[](https://github.com/llamasearchai/llamaagent)
*Empowering developers to build intelligent, scalable AI agents with enterprise-grade reliability*
</div>
---
## Overview
LlamaAgent is a comprehensive AI agent framework designed for production environments. It provides a robust foundation for building intelligent agents that can reason, use tools, maintain memory, and integrate seamlessly with modern AI providers.
### Key Features
- **Multi-Provider LLM Support**: OpenAI, Anthropic, Cohere, Together AI, and more
- **Advanced Reasoning**: ReAct pattern implementation with chain-of-thought capabilities
- **Tool Integration**: Extensible tool system with calculator, Python REPL, and custom tools
- **Memory Management**: Persistent memory with vector storage capabilities
- **Production Ready**: Comprehensive error handling, logging, and monitoring
- **FastAPI Integration**: RESTful API endpoints for web applications
- **Docker Support**: Containerized deployment with Kubernetes manifests
- **Comprehensive Testing**: 38+ tests with 100% pass rate
## Quick Start
### Installation
```bash
# Install from PyPI
pip install llamaagent
# Or install from source
git clone https://github.com/llamasearchai/llamaagent.git
cd llamaagent
pip install -e ".[dev]"
```
### Basic Usage
```python
from llamaagent.agents.react import ReactAgent
from llamaagent.agents.base import AgentConfig
from llamaagent.llm.providers.openai_provider import OpenAIProvider
from llamaagent.types import TaskInput
# Configure the agent
config = AgentConfig(
name="MyAgent",
description="A helpful AI assistant",
tools_enabled=True
)
# Initialize LLM provider
provider = OpenAIProvider(
model_name="gpt-4",
api_key="your-api-key"
)
# Create the agent
agent = ReactAgent(config=config, llm_provider=provider)
# Execute a task
task = TaskInput(
id="task-1",
task="Calculate the square root of 144 and explain the process"
)
result = await agent.arun(task)
print(result.content)
```
## Architecture
LlamaAgent follows a modular architecture designed for scalability and maintainability:
```
├── agents/ # Agent implementations (ReAct, reasoning chains)
├── llm/ # LLM provider integrations
├── tools/ # Tool system and implementations
├── memory/ # Memory management and storage
├── api/ # FastAPI web interfaces
├── monitoring/ # Observability and metrics
├── security/ # Authentication and validation
└── types/ # Core type definitions
```
## Advanced Features
### Tool System
```python
from llamaagent.tools.calculator import CalculatorTool
from llamaagent.tools.python_repl import PythonREPLTool
# Register custom tools
agent.register_tool(CalculatorTool())
agent.register_tool(PythonREPLTool())
```
### Memory Management
```python
from llamaagent.memory.vector_memory import VectorMemory
# Configure persistent memory
memory = VectorMemory(
embedding_model="text-embedding-3-large",
storage_path="./agent_memory"
)
agent.set_memory(memory)
```
### FastAPI Integration
```python
from llamaagent.api.main import create_app
# Create web API
app = create_app()
# Run with: uvicorn main:app --host 0.0.0.0 --port 8000
```
## Configuration
### Environment Variables
```bash
# LLM Provider Keys
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
COHERE_API_KEY=your_cohere_key
# Database Configuration
DATABASE_URL=postgresql://user:pass@localhost/db
REDIS_URL=redis://localhost:6379
# Monitoring
ENABLE_METRICS=true
LOG_LEVEL=INFO
```
### Configuration File
```yaml
# config/default.yaml
agent:
name: "ProductionAgent"
max_iterations: 10
timeout: 300
llm:
provider: "openai"
model: "gpt-4"
temperature: 0.7
max_tokens: 2000
tools:
enabled: true
timeout: 30
memory:
enabled: true
type: "vector"
max_entries: 10000
```
## Deployment
### Docker
```bash
# Build the image
docker build -t llamaagent:latest .
# Run the container
docker run -p 8000:8000 -e OPENAI_API_KEY=your_key llamaagent:latest
```
### Kubernetes
```bash
# Deploy to Kubernetes
kubectl apply -f k8s/
```
### Docker Compose
```bash
# Full stack deployment
docker-compose up -d
```
## API Reference
### Core Endpoints
- `POST /agents/execute` - Execute agent task
- `GET /agents/{agent_id}/status` - Get agent status
- `POST /tools/execute` - Execute tool directly
- `GET /health` - Health check endpoint
### OpenAI Compatible API
```bash
# Chat completions
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
## Testing
```bash
# Run all tests
pytest tests/ -v
# Run with coverage
pytest tests/ --cov=src --cov-report=html
# Run specific test categories
pytest tests/unit/ -v # Unit tests
pytest tests/integration/ -v # Integration tests
pytest tests/e2e/ -v # End-to-end tests
```
## Monitoring and Observability
### Metrics
LlamaAgent provides comprehensive metrics for production monitoring:
- Request/response times
- Success/failure rates
- Token usage and costs
- Agent performance metrics
- Tool execution statistics
### Logging
```python
import logging
from llamaagent.monitoring.logging import setup_logging
# Configure structured logging
setup_logging(level=logging.INFO, format="json")
```
### Health Checks
```bash
# Check system health
curl http://localhost:8000/health
# Detailed diagnostics
curl http://localhost:8000/diagnostics
```
## Security
### Authentication
```python
from llamaagent.security.authentication import APIKeyAuth
# Configure API key authentication
auth = APIKeyAuth(api_keys=["your-secret-key"])
app.add_middleware(auth)
```
### Input Validation
```python
from llamaagent.security.validator import InputValidator
# Validate and sanitize inputs
validator = InputValidator()
safe_input = validator.sanitize(user_input)
```
## Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
# Clone the repository
git clone https://github.com/llamasearchai/llamaagent.git
cd llamaagent
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install development dependencies
pip install -e ".[dev]"
# Run pre-commit hooks
pre-commit install
```
### Code Quality
```bash
# Format code
black src/ tests/
isort src/ tests/
# Lint code
ruff check src/ tests/
# Type checking
mypy src/
# Security scan
bandit -r src/
```
## Examples
### Basic Agent
```python
# examples/basic_agent.py
import asyncio
from llamaagent.agents.react import ReactAgent
from llamaagent.agents.base import AgentConfig
from llamaagent.llm.providers.mock_provider import MockProvider
from llamaagent.types import TaskInput
async def main():
config = AgentConfig(name="BasicAgent")
provider = MockProvider(model_name="test-model")
agent = ReactAgent(config=config, llm_provider=provider)
task = TaskInput(
id="example-1",
task="Explain quantum computing in simple terms"
)
result = await agent.arun(task)
print(f"Agent Response: {result.content}")
if __name__ == "__main__":
asyncio.run(main())
```
### Multi-Agent System
```python
# examples/multi_agent.py
import asyncio
from llamaagent.spawning.agent_spawner import AgentSpawner
from llamaagent.orchestration.adaptive_orchestra import AdaptiveOrchestra
async def main():
spawner = AgentSpawner()
orchestra = AdaptiveOrchestra()
# Spawn multiple specialized agents
research_agent = await spawner.spawn_agent("researcher")
analysis_agent = await spawner.spawn_agent("analyst")
writer_agent = await spawner.spawn_agent("writer")
# Orchestrate collaborative task
result = await orchestra.execute_collaborative_task(
task="Write a comprehensive report on AI safety",
agents=[research_agent, analysis_agent, writer_agent]
)
print(f"Collaborative Result: {result}")
if __name__ == "__main__":
asyncio.run(main())
```
## Benchmarks
LlamaAgent includes comprehensive benchmarking against industry standards:
- **GAIA Benchmark**: General AI Assistant evaluation
- **SPRE Evaluation**: Structured Problem Reasoning
- **Custom Benchmarks**: Domain-specific performance testing
```bash
# Run benchmarks
python -m llamaagent.benchmarks.run_all --provider openai --model gpt-4
```
## Roadmap
- [ ] Multi-modal agent support (vision, audio)
- [ ] Advanced reasoning patterns (Tree of Thoughts, Graph of Thoughts)
- [ ] Federated learning capabilities
- [ ] Enhanced security features
- [ ] Performance optimizations
- [ ] Extended tool ecosystem
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
- **Documentation**: [https://llamaagent.readthedocs.io](https://llamaagent.readthedocs.io)
- **Issues**: [GitHub Issues](https://github.com/llamasearchai/llamaagent/issues)
- **Discussions**: [GitHub Discussions](https://github.com/llamasearchai/llamaagent/discussions)
- **Email**: [nikjois@llamasearch.ai](mailto:nikjois@llamasearch.ai)
## Acknowledgments
Built with love by [Nik Jois](https://github.com/nikjois) and the LlamaSearch AI team.
Special thanks to the open-source community and all contributors who make this project possible.
---
<div align="center">
<strong>LlamaAgent - Empowering the Future of AI Agents</strong>
</div>
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"description": "<div align=\"center\">\n\n\n\n# LlamaAgent\n\n**Advanced AI Agent Framework for Production-Ready Applications**\n\n[](https://github.com/llamasearchai/llamaagent/actions)\n[](https://www.python.org/downloads/)\n[](LICENSE)\n[](https://github.com/llamasearchai/llamaagent)\n\n*Empowering developers to build intelligent, scalable AI agents with enterprise-grade reliability*\n\n</div>\n\n---\n\n## Overview\n\nLlamaAgent is a comprehensive AI agent framework designed for production environments. It provides a robust foundation for building intelligent agents that can reason, use tools, maintain memory, and integrate seamlessly with modern AI providers.\n\n### Key Features\n\n- **Multi-Provider LLM Support**: OpenAI, Anthropic, Cohere, Together AI, and more\n- **Advanced Reasoning**: ReAct pattern implementation with chain-of-thought capabilities\n- **Tool Integration**: Extensible tool system with calculator, Python REPL, and custom tools\n- **Memory Management**: Persistent memory with vector storage capabilities\n- **Production Ready**: Comprehensive error handling, logging, and monitoring\n- **FastAPI Integration**: RESTful API endpoints for web applications\n- **Docker Support**: Containerized deployment with Kubernetes manifests\n- **Comprehensive Testing**: 38+ tests with 100% pass rate\n\n## Quick Start\n\n### Installation\n\n```bash\n# Install from PyPI\npip install llamaagent\n\n# Or install from source\ngit clone https://github.com/llamasearchai/llamaagent.git\ncd llamaagent\npip install -e \".[dev]\"\n```\n\n### Basic Usage\n\n```python\nfrom llamaagent.agents.react import ReactAgent\nfrom llamaagent.agents.base import AgentConfig\nfrom llamaagent.llm.providers.openai_provider import OpenAIProvider\nfrom llamaagent.types import TaskInput\n\n# Configure the agent\nconfig = AgentConfig(\n name=\"MyAgent\",\n description=\"A helpful AI assistant\",\n tools_enabled=True\n)\n\n# Initialize LLM provider\nprovider = OpenAIProvider(\n model_name=\"gpt-4\",\n api_key=\"your-api-key\"\n)\n\n# Create the agent\nagent = ReactAgent(config=config, llm_provider=provider)\n\n# Execute a task\ntask = TaskInput(\n id=\"task-1\",\n task=\"Calculate the square root of 144 and explain the process\"\n)\n\nresult = await agent.arun(task)\nprint(result.content)\n```\n\n## Architecture\n\nLlamaAgent follows a modular architecture designed for scalability and maintainability:\n\n```\n\u251c\u2500\u2500 agents/ # Agent implementations (ReAct, reasoning chains)\n\u251c\u2500\u2500 llm/ # LLM provider integrations\n\u251c\u2500\u2500 tools/ # Tool system and implementations\n\u251c\u2500\u2500 memory/ # Memory management and storage\n\u251c\u2500\u2500 api/ # FastAPI web interfaces\n\u251c\u2500\u2500 monitoring/ # Observability and metrics\n\u251c\u2500\u2500 security/ # Authentication and validation\n\u2514\u2500\u2500 types/ # Core type definitions\n```\n\n## Advanced Features\n\n### Tool System\n\n```python\nfrom llamaagent.tools.calculator import CalculatorTool\nfrom llamaagent.tools.python_repl import PythonREPLTool\n\n# Register custom tools\nagent.register_tool(CalculatorTool())\nagent.register_tool(PythonREPLTool())\n```\n\n### Memory Management\n\n```python\nfrom llamaagent.memory.vector_memory import VectorMemory\n\n# Configure persistent memory\nmemory = VectorMemory(\n embedding_model=\"text-embedding-3-large\",\n storage_path=\"./agent_memory\"\n)\nagent.set_memory(memory)\n```\n\n### FastAPI Integration\n\n```python\nfrom llamaagent.api.main import create_app\n\n# Create web API\napp = create_app()\n\n# Run with: uvicorn main:app --host 0.0.0.0 --port 8000\n```\n\n## Configuration\n\n### Environment Variables\n\n```bash\n# LLM Provider Keys\nOPENAI_API_KEY=your_openai_key\nANTHROPIC_API_KEY=your_anthropic_key\nCOHERE_API_KEY=your_cohere_key\n\n# Database Configuration\nDATABASE_URL=postgresql://user:pass@localhost/db\nREDIS_URL=redis://localhost:6379\n\n# Monitoring\nENABLE_METRICS=true\nLOG_LEVEL=INFO\n```\n\n### Configuration File\n\n```yaml\n# config/default.yaml\nagent:\n name: \"ProductionAgent\"\n max_iterations: 10\n timeout: 300\n\nllm:\n provider: \"openai\"\n model: \"gpt-4\"\n temperature: 0.7\n max_tokens: 2000\n\ntools:\n enabled: true\n timeout: 30\n\nmemory:\n enabled: true\n type: \"vector\"\n max_entries: 10000\n```\n\n## Deployment\n\n### Docker\n\n```bash\n# Build the image\ndocker build -t llamaagent:latest .\n\n# Run the container\ndocker run -p 8000:8000 -e OPENAI_API_KEY=your_key llamaagent:latest\n```\n\n### Kubernetes\n\n```bash\n# Deploy to Kubernetes\nkubectl apply -f k8s/\n```\n\n### Docker Compose\n\n```bash\n# Full stack deployment\ndocker-compose up -d\n```\n\n## API Reference\n\n### Core Endpoints\n\n- `POST /agents/execute` - Execute agent task\n- `GET /agents/{agent_id}/status` - Get agent status\n- `POST /tools/execute` - Execute tool directly\n- `GET /health` - Health check endpoint\n\n### OpenAI Compatible API\n\n```bash\n# Chat completions\ncurl -X POST \"http://localhost:8000/v1/chat/completions\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"gpt-4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]\n }'\n```\n\n## Testing\n\n```bash\n# Run all tests\npytest tests/ -v\n\n# Run with coverage\npytest tests/ --cov=src --cov-report=html\n\n# Run specific test categories\npytest tests/unit/ -v # Unit tests\npytest tests/integration/ -v # Integration tests\npytest tests/e2e/ -v # End-to-end tests\n```\n\n## Monitoring and Observability\n\n### Metrics\n\nLlamaAgent provides comprehensive metrics for production monitoring:\n\n- Request/response times\n- Success/failure rates\n- Token usage and costs\n- Agent performance metrics\n- Tool execution statistics\n\n### Logging\n\n```python\nimport logging\nfrom llamaagent.monitoring.logging import setup_logging\n\n# Configure structured logging\nsetup_logging(level=logging.INFO, format=\"json\")\n```\n\n### Health Checks\n\n```bash\n# Check system health\ncurl http://localhost:8000/health\n\n# Detailed diagnostics\ncurl http://localhost:8000/diagnostics\n```\n\n## Security\n\n### Authentication\n\n```python\nfrom llamaagent.security.authentication import APIKeyAuth\n\n# Configure API key authentication\nauth = APIKeyAuth(api_keys=[\"your-secret-key\"])\napp.add_middleware(auth)\n```\n\n### Input Validation\n\n```python\nfrom llamaagent.security.validator import InputValidator\n\n# Validate and sanitize inputs\nvalidator = InputValidator()\nsafe_input = validator.sanitize(user_input)\n```\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\n# Clone the repository\ngit clone https://github.com/llamasearchai/llamaagent.git\ncd llamaagent\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run pre-commit hooks\npre-commit install\n```\n\n### Code Quality\n\n```bash\n# Format code\nblack src/ tests/\nisort src/ tests/\n\n# Lint code\nruff check src/ tests/\n\n# Type checking\nmypy src/\n\n# Security scan\nbandit -r src/\n```\n\n## Examples\n\n### Basic Agent\n\n```python\n# examples/basic_agent.py\nimport asyncio\nfrom llamaagent.agents.react import ReactAgent\nfrom llamaagent.agents.base import AgentConfig\nfrom llamaagent.llm.providers.mock_provider import MockProvider\nfrom llamaagent.types import TaskInput\n\nasync def main():\n config = AgentConfig(name=\"BasicAgent\")\n provider = MockProvider(model_name=\"test-model\")\n agent = ReactAgent(config=config, llm_provider=provider)\n\n task = TaskInput(\n id=\"example-1\",\n task=\"Explain quantum computing in simple terms\"\n )\n\n result = await agent.arun(task)\n print(f\"Agent Response: {result.content}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n### Multi-Agent System\n\n```python\n# examples/multi_agent.py\nimport asyncio\nfrom llamaagent.spawning.agent_spawner import AgentSpawner\nfrom llamaagent.orchestration.adaptive_orchestra import AdaptiveOrchestra\n\nasync def main():\n spawner = AgentSpawner()\n orchestra = AdaptiveOrchestra()\n\n # Spawn multiple specialized agents\n research_agent = await spawner.spawn_agent(\"researcher\")\n analysis_agent = await spawner.spawn_agent(\"analyst\")\n writer_agent = await spawner.spawn_agent(\"writer\")\n\n # Orchestrate collaborative task\n result = await orchestra.execute_collaborative_task(\n task=\"Write a comprehensive report on AI safety\",\n agents=[research_agent, analysis_agent, writer_agent]\n )\n\n print(f\"Collaborative Result: {result}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n## Benchmarks\n\nLlamaAgent includes comprehensive benchmarking against industry standards:\n\n- **GAIA Benchmark**: General AI Assistant evaluation\n- **SPRE Evaluation**: Structured Problem Reasoning\n- **Custom Benchmarks**: Domain-specific performance testing\n\n```bash\n# Run benchmarks\npython -m llamaagent.benchmarks.run_all --provider openai --model gpt-4\n```\n\n## Roadmap\n\n- [ ] Multi-modal agent support (vision, audio)\n- [ ] Advanced reasoning patterns (Tree of Thoughts, Graph of Thoughts)\n- [ ] Federated learning capabilities\n- [ ] Enhanced security features\n- [ ] Performance optimizations\n- [ ] Extended tool ecosystem\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Support\n\n- **Documentation**: [https://llamaagent.readthedocs.io](https://llamaagent.readthedocs.io)\n- **Issues**: [GitHub Issues](https://github.com/llamasearchai/llamaagent/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/llamasearchai/llamaagent/discussions)\n- **Email**: [nikjois@llamasearch.ai](mailto:nikjois@llamasearch.ai)\n\n## Acknowledgments\n\nBuilt with love by [Nik Jois](https://github.com/nikjois) and the LlamaSearch AI team.\n\nSpecial thanks to the open-source community and all contributors who make this project possible.\n\n---\n\n<div align=\"center\">\n <strong>LlamaAgent - Empowering the Future of AI Agents</strong>\n</div>\n",
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