metauto-ai / gptswarm Goto Github PK

https://coveralls.io/github/metauto-ai/GPTSwarm

GPTSWARM_ROOT = find_project_root()

@mczhuge FYI this does not work with packages.

2024-05-01 17:00:25.721 | ERROR | swarm.graph.node:execute:141 - Node DirectAnswer failed to execute due to: RetryError: RetryError[<Future at 0x314e4fcd0 state=finished raised OpenAIError>]
2024-05-01 17:03:04.397 | ERROR | swarm.graph.node:execute:141 - Node DirectAnswer failed to execute due to: RetryError: RetryError[<Future at 0x314e4ff40 state=finished raised OpenAIError>]
2024-05-01 17:07:03.721 | ERROR | swarm.graph.node:execute:141 - Node DirectAnswer failed to execute due to: RetryError: RetryError[<Future at 0x314e84100 state=finished raised OpenAIError>]

Showing the above error when running the examples, the local openai connection is okay.

@Obs01ete @Wenyi-AI-Wang Hi, Dmitrii and Wenyi. Thanks for your effort!

These are the remain work for us.

• Please help with the dataset upload (git-lfs)
• Check the GitHub setting of project (automated test)
• Upload the ArXiv link and right format of paper citation

Hi,

Thanks for this useful framework!

There is an issue while running multi-agent commands that require asynchronous use of LMStudio API. For instance, running PYTHONPATH=. python experiments/run_mmlu.py --num-truthful-agents=3 --mode=OptimizedSwarm, the outputs of LMStudio seem to be not assigned to the relevant question but to random input questions.

I have not tested this with Open AI API to see if this is specifically an incompatibility issue of LMStudio.

My current temporary solution is not to use the asynchronous implementation but this is annoyingly very slow. Have you encountered this? Is there a way to still use your asynchronous implementation and not experience this issue?

Thanks so much!

This is one of the most approachable agent frameworks I have used. Thank you.

Would you mind giving me some advice on state management. I just need direction as there seem to be several ways of doing the same thing, and I don't want to start down the wrong (/unintended) path.

Question
I need agents to manage a hierarchy of objects. Which implies a user-navigable state machine.
[edit] Simplified the question

• For N states, would you use 1 uber-agent, or 1 agent per state
• Typically, no external tools needed, so I guess a DirectAnswer-type agent may suffice? Furthermore, I imagine a swarm would suffice (versus a DAG like CoT or ToT)
• Context/navigation state is truly global, ie not peculiar to each agent.id. Would you keep said state (somehow) in globalMemory, or as an inputs[x]/outputs[x] state that is flowed around, as extra properties in executions {} or in an env (per your crossword example)?

Thanks