langswarm-core 0.0.18

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, agent orchestration tools, and a factory-based approach to building robust AI ecosystems with modularity and flexibility.

Features

• Agent Factory: Dynamically create and configure agents for LangChain, OpenAI, Hugging Face, and LlamaIndex.
• Wrappers:
  • Memory Mixin: Seamless in-memory or external memory integration.
  • Logging Mixin: Advanced logging support using LangSmith and fallback loggers.
• Registry: A centralized agent registry for managing and accessing agents dynamically.
• Modular Architecture: Easily extend the framework by implementing additional mixins, factories, or agent types.

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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 langswarm.core.factory.agents import AgentFactory

# Set environment variables
os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'

# Create a LangChain agent
agent = AgentFactory.create(
    name="example_agent",
    agent_type="langchain-openai",
    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 langswarm.core.factory.agents import AgentFactory
from langchain.memory import ConversationBufferMemory

# Set environment variables
os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'

# Define memory for the agent
memory = ConversationBufferMemory()

# Create a LangChain agent with memory using AgentFactory
agent = AgentFactory.create(
    name="memory_agent",
    agent_type="langchain-openai",
    memory=memory,
    model="gpt-4"
)

# Interact with the agent
response1 = agent.chat("What is LangSwarm-Core?")
print(f"Agent: {response1}")

response2 = agent.chat("Can you tell me more about its features?")
print(f"Agent: {response2}")

# Showcase memory retention
response3 = agent.chat("What have we discussed so far?")
print(f"Agent: {response3}")

Customizing Memory

To use a different LangChain memory type, you can replace ConversationBufferMemory with another memory class. For example:

from langchain.memory import ConversationSummaryMemory

# Create a separate LLM for memory summarization
memory_llm = OpenAI(model="gpt-3.5-turbo", openai_api_key="your-openai-api-key")

# Define the memory module using the summarization LLM
memory = ConversationSummaryMemory(llm=memory_llm)

LangSmith Integration

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

from langswarm.core.factory.agents import AgentFactory

# Set environment variables
os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'

# Create a LangChain agent
agent = AgentFactory.create(
    name="example_agent",
    agent_type="langchain-openai",
    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)

Bring your own agent

LangSwarm-Core supports other agents out of the box. Here's how to wrap an external agent with LangSwarm:

Hugging Face

from transformers import pipeline
from langswarm.core.wrappers.generic import AgentWrapper

# Step 1: Create a Hugging Face pipeline
huggingface_agent = pipeline("text-generation", model="gpt2")

# Step 2: Wrap the Hugging Face agent using LangSwarm's AgentWrapper
wrapped_agent = AgentWrapper(
    name="my_agent",
    agent=huggingface_agent,
    memory=None,  # Optional: Add memory support if needed
    is_conversational=True  # Enable conversational context if needed (only for Hugging Face)
)

# Step 3: Interact with the wrapped agent
query = "Explain the concept of modular AI frameworks."
response = wrapped_agent.chat(query)
print(f"User: {query}")
print(f"Agent: {response}")

# Step 4: Add more interactions to showcase memory retention (if enabled)
wrapped_agent.chat("Can you elaborate on the benefits of modularity?")
response = wrapped_agent.chat("What was the initial query?")
print(f"Agent: {response}")

# Step 5: Reset the memory and start a new conversation
wrapped_agent.chat("Let's reset and discuss LangSwarm-Core.", reset=True)
response = wrapped_agent.chat("What is LangSwarm-Core?")
print(f"Agent: {response}")

Hugging Face

from langchain.llms import OpenAI
from langchain.chains import SimpleSequentialChain, LLMChain
from langchain.prompts import PromptTemplate
from langswarm.core.wrappers.generic import AgentWrapper

# Step 1: Create a LangChain LLM instance
llm = OpenAI(model="gpt-3.5-turbo", openai_api_key="your-openai-api-key")

# Step 2: Build a LangChain pipeline (e.g., a simple sequential chain)
template = PromptTemplate(template="What is {topic}? Explain in detail.")
llm_chain = LLMChain(llm=llm, prompt=template)

# Step 3: Wrap the LangChain agent using LangSwarm's AgentWrapper
wrapped_langchain_agent = AgentWrapper(
    name="langchain_agent",
    agent=llm_chain,
    memory=None,  # Optionally add memory
)

# Step 4: Interact with the wrapped LangChain agent
query = {"topic": "LangSwarm-Core"}
response = wrapped_langchain_agent.chat(query)
print(f"User: What is LangSwarm-Core?")
print(f"Agent: {response}")

# Step 5: Showcase conversational context (if memory is enabled)
wrapped_langchain_agent.chat("Can you summarize your explanation?")
response = wrapped_langchain_agent.chat("What was the topic of discussion?")
print(f"Agent: {response}")

# Step 6: Reset the agent and start a new conversation
wrapped_langchain_agent.chat("Reset and start over.", reset=True)
response = wrapped_langchain_agent.chat({"topic": "modular AI frameworks"})
print(f"Agent: {response}")

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Components

Factory

The AgentFactory provides a simple interface for creating agents:

• Supports LangChain, Hugging Face, OpenAI, and LlamaIndex agents.
• Configurable with memory, logging, and other custom parameters.

Wrappers

Wrappers extend agent capabilities:

• MemoryMixin: Adds memory management functionality.
• LoggingMixin: Integrates LangSmith for advanced logging.

Utilities

Helper functions include:

• Token and cost estimation.
• Text processing and cleaning.
• JSON, YAML, and Python code validation.

Registry

The AgentRegistry provides a centralized way to manage and query all agents created in the system.

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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

Run tests located in the tests/ directory using pytest:

pytest

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File Structure

• core/wrappers/: Contains mixins for memory and logging.
• core/factory/: Defines the AgentFactory for creating agents.
• core/registry/: Manages a centralized agent registry.
• core/utils/: Provides utility functions for validation, token management, and text processing.

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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.
• Introduce support for dynamic task allocation and meta-agent coordination.

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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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