langswarm-core 0.0.16

A core framework for multi-agent LLM ecosystems

LangSwarm-Core

LangSwarm-Core is a framework designed to support multi-agent systems using Large Language Models (LLMs). It provides utilities, memory management, logging integration, and agent orchestration tools to build robust AI ecosystems with modularity and flexibility.

Features

• Agent Wrappers: Easily integrate with LangChain, OpenAI, Hugging Face, and LlamaIndex agents.
• Memory Management: Support for in-memory or external memory, with customizable options.
• Logging: Seamless integration with LangSmith for advanced logging and tracing.
• Factory Design: Create and manage multiple agents with configurable setups.
• Utilities: Helper functions for token management, text cleaning, and cost estimation.
• Registry: Centralized agent registry to manage and access agents dynamically.

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Installation

Prerequisites

• Python 3.8 or higher
• Install dependencies:

  pip install -r requirements.txt
  

From PyPI

pip install langswarm-core

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Usage

Quick Start

Here's an example of how to use LangSwarm-Core to create an agent and interact with it:

from core.factory.agents import AgentFactory

# Create a LangChain agent
agent = AgentFactory.create(
    name="example_agent",
    agent_type="langchain-openai",
    memory=[],
    langsmith_api_key="your-langsmith-api-key",
    model="gpt-4"
)

# Use the agent to respond to queries
response = agent.chat("What is LangSwarm?")
print(response)

Memory Integration

LangSwarm-Core supports memory out of the box. Here's how to initialize an agent with memory:

from core.factory.agents import AgentFactory

memory = []  # Initialize in-memory storage

agent = AgentFactory.create(
    name="memory_agent",
    agent_type="langchain-openai",
    memory=memory,
    model="gpt-4"
)

response = agent.chat("Remember this: LangSwarm is awesome.")
print(response)

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Components

Wrappers

Wrappers add modular capabilities such as:

• Memory management (MemoryMixin)
• Logging integration (LoggingMixin)

Utilities

Helper functions for:

• Token and cost estimation
• Text processing
• JSON and YAML validation

Factory

Use the AgentFactory to easily create and configure agents:

agent = AgentFactory.create(
    name="example",
    agent_type="llamaindex",
    documents=["Sample text for indexing"]
)

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Development

Setting Up the Environment

1. Clone the repository:

   git clone https://github.com/aekdahl/langswarm-core.git
   cd langswarm-core
   

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

Running Tests

Tests are located in the tests/ directory. Run them using pytest:

pytest

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Contributing

We welcome contributions! To get started:

1. Fork the repository.
2. Create a feature branch.
3. Make your changes and write tests.
4. Submit a pull request.

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Roadmap

• Add support for additional LLM providers.
• Expand orchestration capabilities with reinforcement learning agents.
• Develop CLI tools for managing agents.

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License

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

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Acknowledgments

LangSwarm-Core relies on several amazing libraries, including:

• LangChain
• Hugging Face Transformers
• LlamaIndex

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