lite squad

Tool for working with a team of LLMs. Because sometimes two, or five, heads are better than one.

Usage

Install with pip:

pip install litesquad

Use in browser:

litesquad --web 

Use at command line:

litesquad "plan such and such project"
litesquad "such and such" --quick # get a quick answer, bypassing the squad

litesquad takes a few minutes to run when running in default (deep) mode. There's a lot of LLM calls going on under the hood.

Query is distributed to worker LLMs (gemini and sonnet direct, plus deepseek, mistral, and llama via OpenRouter). Another (grok) acts as a critic that gives feedback to the workers. They revise their response. An intermediate representation of these responses is extracted to pull the content and clustered into categories of suggestions (gpt5). A judge (opus) converts these suggestions into a final coherent answer for the user.

What about Fusion?

After I pushed version 0.0.2 I found out about OpenRouter's Fusion.

It has the same goal as litesquad, and a similar architecture, but is built by a professional team that can actually maintain the product. When I did testing of litesquad vs Fusion, Fusion consistently beat litesquad. They both produce good results, but if you want to use something (versus play in a workbench) I would recommend Fusion.

Also, my guess is there will be a squadron of these applications within a couple of years. It's just too obvious a need.

So. Many. APIs.

Todo: replace with OpenRouter API key only.

To run properly, litesquad currently needs API keys for Gemini, OpenAI, Anthropic, and OpenRouter. One OpenRouter key reaches the whole openrouter.ai catalog (deepseek, mistral, llama, grok, qwen, ...), which is how the worker roster grows wide without a key per provider. You can store your API keys in .env.

Basic tests

To see if api keys are working (from activated env): python -m litesquad.check_keys

Check on specific models: litesquad --check

Offline test: litesquad --smoke --mock

Caveats

This provides an interface to a swarm of LLMs to try to generate a better answer than when using a single LLM. Unlike Fusion, there is no tool-usage such as web calls from the LLMs. Just reasoning.

With apologies to squad.