AgentMesh: Let Idle Computers Run Your AI Models While Everyone Takes It Easy.

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

Most computers spend their day the way a sloth does: switched on, barely moving, conserving energy. Meanwhile, developers who want to run an AI model have to buy a GPU, rent a cloud instance, or babysit a server.

AgentMesh connects those two groups so that both can do less.

It is an open-source platform that runs open-weight AI models on computers contributed by independent providers and exposes each deployment through an OpenAI-compatible API.

Calling a deployed model takes only your existing OpenAI client:

import os from openai import OpenAI client = OpenAI ( base_url = " https://agentmesh-riuz.onrender.com/api/v1/deployments/<id>/v1 ", api_key = os. environ [ " AGENTMESH_API_KEY " ], ) reply = client. chat. completions. create ( model = " qwen2.5:0.5b ", messages = [{ " role ": " user ", " content ": " Summarise this ticket " }], stream = True, ) Enter fullscreen mode Exit fullscreen mode Code meshach5667 / AgentMeshh AgentMesh AgentMesh lets developers deploy open-weight AI models on computers contributed by independent providers and call them through an OpenAI-compatible API. Workloads run inside resource limits on the provider's machine; the provider can see the data a workload handles Billing is prepaid, with Paystack top-ups and provider payouts. These run in Paystack test mode and have not yet been exercised against Paystack's live API.

The full documentation is in the dashboard at agent-mesh-ten.vercel.app/docs:

The Markdown files in docs/ hold the same material in more depth.