agents-on-langchain 0.1.0.dev202501300233

A lightweight Python package for creating agents on Langchain for Agentic platforms

A lightweight framework to create agents based on the LangChain BaseModel interface.

The idea is to make it easy to a multi-agent platform.

Usage

Basic Example

(TODO)

Complex Journal Multi-Agent Platform

(TODO)

Philosophy

I went with three guiding principles in writing this model.

1. Everything is an agent: tools, buses, orchestrators are all agents.
2. Final code should show the flow: You should be able scale while being able to see how agents connect to each other. This means that each agent relationship should be at most one line of code.
3. Agents are minimal building blocks: one prompt per agent, one vector store per agent, one model per agent.

Deployment

The mode of deployment is as follows: Make sure that there is a model and an API, based on the BaseModelin LangChain. Put all agents in the same piece of code. Run this code on a loop - the loop can also be an "orchestrator" agent.

Note that you can also host the model on the same pod / instance / computer as the agents. This is how I (the author) tested it.

Development

Requirements

Just Docker. If you want to develop, you can use the .devcontainer on VS Code, you don't need to install anything.

This works with VS Code, however, if you want to use another IDE, you can also use the Dockerfile.dev to create your development environment.

Testing

Run:

docker-compose run --rm --build test

Contributing

1. Branch out
2. Add new code.
3. Add tests.
4. Push.
5. Make a pull request.