lagents 0.1.1
L'Agent - Minimal experimental framework for building agents with local LLM deployments. Zero bloat, maximum simplicity.
Requires Python: >=3.8
Keywords: llm agents ollama local minimal framework llama vllm lagent

# L'Agent

**⚠️ EXPERIMENTAL MINIMAL AGENT FRAMEWORK ⚠️**

A minimal, experimental framework for building agents with local LLM deployments. Designed for **minimal overhead** and **maximum simplicity**. Currently configured for **Ollama** with full implementation. Includes stubs for other local LLM providers.

> **L'Agent** - The minimal agent framework for local LLMs

## 🎯 Project Status

**Current State**: Experimental / Early Development

- ✅ **Ollama**: Fully implemented and tested
- 📝 **llama.cpp**: Stub implementation (contributions welcome)
- 📝 **vLLM**: Stub implementation (contributions welcome)
- 📝 **Text Generation WebUI**: Stub implementation (contributions welcome)
- 📝 **LocalAI**: Stub implementation (contributions welcome)
- 📝 **LM Studio**: Stub implementation (contributions welcome)

## 🚀 Quick Start (Ollama)

```python
from agents import LocalOllamaClient, ChatAgent

# Create client
client = LocalOllamaClient(
    model_name="llama3:latest",
    api_base="http://localhost:11434"
)

# Create agent
agent = ChatAgent(client)

# Get response
response = agent.get_full_response([
    {"role": "user", "content": "What is Python?"}
])
print(response)
```

## 📦 Installation

### From PyPI (Recommended)

```bash
pip install lagents

# With distributed support (SOLLOL)
pip install lagents[distributed]
```

### From Source

```bash
git clone https://github.com/BenevolentJoker-JohnL/agents.git
cd agents
pip install -e .
```

## ✨ Features

**Minimal by Design:**
- **Zero Bloat**: Direct API calls with clean abstractions
- **Minimal Dependencies**: Only aiohttp and numpy (core)
- **Simple Architecture**: Easy to understand and extend
- **No Magic**: Explicit, straightforward code

**Core Capabilities:**
- **Provider Abstraction**: Common interface for all local LLM deployments
- **Built-in Agents**: Pre-configured agents for common tasks (optional)
- **Streaming Support**: Native async streaming
- **Distributed Mode**: Optional SOLLOL integration for Ollama clusters
- **Type-Safe**: Full type hints throughout

## 🏗️ Architecture

### Base Client Interface

All providers implement `BaseOllamaClient`:

```python
class BaseOllamaClient(abc.ABC):
    @abc.abstractmethod
    async def generate_embedding(text: str) -> List[float]:
        ...

    @abc.abstractmethod
    async def chat(messages, **options) -> AsyncGenerator[ChatResponse]:
        ...
```

### Implemented Providers

#### Ollama (Fully Implemented)

```python
from agents import LocalOllamaClient, DistributedOllamaClient

# Single node
client = LocalOllamaClient(
    model_name="llama3:latest",
    api_base="http://localhost:11434"
)

# Distributed (with SOLLOL)
client = DistributedOllamaClient(
    model_name="llama3:latest",
    nodes=["http://node1:11434", "http://node2:11434"]
)
```

### Stub Providers (Ready for Implementation)

See `agents/providers.py` for stub implementations:

```python
from agents import (
    LlamaCppClient,      # llama.cpp server
    VLLMClient,          # vLLM deployment
    TextGenWebUIClient,  # Oobabooga
    LocalAIClient,       # LocalAI
    LMStudioClient,      # LM Studio
)
```

**Note**: These will raise `NotImplementedError` until implemented. Contributions welcome!

## 🤖 Built-in Agents

- **ChatAgent**: Basic conversational agent
- **CodingAgent**: Code generation specialist
- **ReasoningAgent**: Analytical tasks
- **ResearchAgent**: Information synthesis
- **SummarizationAgent**: Text summarization
- **EmbeddingAgent**: Generate embeddings

### Custom Agents

```python
from agents import BaseAgent

class SQLAgent(BaseAgent):
    system_prompt = "You are an expert SQL developer..."

agent = SQLAgent(client)
```

## 📖 Documentation

- **[Quick Start](QUICKSTART.md)** - 5-minute tutorial
- **[Integration Guide](INTEGRATION_GUIDE.md)** - Use in applications
- **[Architecture](ARCHITECTURE.md)** - Design details
- **[Examples](agents/examples/)** - Working code samples

## 🧪 Examples

```bash
# Test framework
python test_as_library.py

# Example application
python example_app.py

# Individual examples
python agents/examples/basic_chat.py
python agents/examples/coding_agent.py
python agents/examples/embeddings.py
python agents/examples/multi_agent_workflow.py
```

## 🎯 Use Cases

### Web Applications

```python
from flask import Flask, jsonify, request
from agents import LocalOllamaClient, ChatAgent

app = Flask(__name__)
client = LocalOllamaClient("llama3:latest", "http://localhost:11434")
agent = ChatAgent(client)

@app.route('/chat', methods=['POST'])
def chat():
    message = request.json['message']
    response = agent.get_full_response([
        {"role": "user", "content": message}
    ])
    return jsonify({"response": response})
```

### Command-Line Tools

```python
from agents import LocalOllamaClient, ChatAgent
import sys

client = LocalOllamaClient("llama3:latest", "http://localhost:11434")
agent = ChatAgent(client)

query = " ".join(sys.argv[1:])
print(agent.get_full_response([{"role": "user", "content": query}]))
```

### Background Workers

```python
from celery import Celery
from agents import LocalOllamaClient, ResearchAgent

app = Celery('tasks', broker='redis://localhost:6379')
client = LocalOllamaClient("llama3:latest", "http://localhost:11434")
agent = ResearchAgent(client)

@app.task
def research_task(topic):
    return agent.get_full_response([
        {"role": "user", "content": f"Research: {topic}"}
    ])
```

## 🔧 Contributing

We welcome contributions, especially for implementing new LLM providers!

### Implementing a New Provider

1. See `agents/providers.py` for stub implementations
2. Implement `BaseOllamaClient` interface:
   - `async def generate_embedding(text: str) -> List[float]`
   - `async def chat(messages, **options) -> AsyncGenerator[ChatResponse]`
3. Follow the pattern in `LocalOllamaClient` (see `agents/ollama_framework.py`)
4. Add tests and examples
5. Submit a pull request

### Priority Providers

- **llama.cpp** - Lightweight C++ implementation
- **vLLM** - High-throughput serving
- **LocalAI** - OpenAI drop-in replacement
- **Text Generation WebUI** - Popular gradio interface
- **LM Studio** - User-friendly desktop app

## ⚠️ Important Notes

### Experimental Status

This is an **experimental framework** in early development:

- APIs may change without notice
- Not recommended for production use yet
- Limited testing and documentation
- Breaking changes possible

### Current Limitations

- Only Ollama is fully implemented
- Other providers are stubs requiring implementation
- Limited error handling in some edge cases
- Documentation may be incomplete

### Roadmap

- [ ] Implement llama.cpp client
- [ ] Implement vLLM client
- [ ] Implement LocalAI client
- [ ] Add comprehensive testing suite
- [ ] Add more utility functions
- [ ] Improve error handling
- [ ] Add caching layer
- [ ] Support multi-modal (images, audio)
- [ ] Function calling support

## 📁 Project Structure

```
OllamaAgent/
├── agents/                      # Main package
│   ├── __init__.py              # Public API
│   ├── ollama_framework.py      # Ollama clients (implemented)
│   ├── providers.py             # Other provider stubs
│   ├── agents.py                # Agent classes
│   ├── utils.py                 # Utility functions
│   └── examples/                # Usage examples
├── setup.py                     # Package installation
├── requirements.txt             # Dependencies
├── example_app.py               # Full application example
├── test_as_library.py           # Library usage tests
├── README.md                    # This file
├── QUICKSTART.md                # Quick start guide
├── INTEGRATION_GUIDE.md         # Integration patterns
└── ARCHITECTURE.md              # Design documentation
```

## 🧩 Dependencies

**Core** (minimal):
- `aiohttp>=3.8.0` - Async HTTP client
- `numpy>=1.20.0` - Vector operations

**Optional**:
- `sollol>=0.1.0` - Distributed Ollama load balancing

## 📄 License

MIT License - See [LICENSE](LICENSE) file for details

## 🤝 Support & Community

- **Issues**: Report bugs or request features on GitHub
- **Examples**: Check `agents/examples/` for working code
- **Documentation**: Read guides in `QUICKSTART.md` and `INTEGRATION_GUIDE.md`
- **Contributing**: See contributing guidelines above

## 🎓 Related Projects

Built with patterns from:
- [Hydra](https://github.com/yourusername/hydra) - Advanced reasoning engine
- [SynapticLlamas](https://github.com/yourusername/SynapticLlamas) - Multi-agent orchestration
- [FlockParser](https://github.com/yourusername/FlockParser) - Document processing with RAG
- [SOLLOL](https://github.com/yourusername/sollol) - Intelligent Ollama load balancer

## ⚡ Performance

**Ollama Implementation**:
- Direct HTTP calls (~1-5ms overhead)
- Connection pooling via aiohttp
- Streaming support (no buffering)
- Optional distributed mode with SOLLOL

## 🔒 Security

**Local Deployment Focus**:
- No API keys required
- All models run locally
- Full control over data
- No external API calls (except optional SOLLOL for Ollama clusters)

---

**Status**: Experimental Minimal Framework | **Philosophy**: Zero bloat, maximum simplicity | **Primary Use**: Ollama deployments | **Looking for**: Contributors to implement other providers!

Made for the local LLM community ❤️ | Keep it minimal, keep it simple.