Local Multi-LLM Chat Agent
Inspired by Clawdbot, an open-source local AI agent, this challenge focuses on building a client-side, web-based personal AI assistant using Vercel's AI SDK. The agent should demonstrate real-time streaming capabilities, the ability to integrate and switch between multiple LLM providers (e.g., OpenAI, Claude), and execute local 'tools' like file system access. A key aspect is prioritizing user privacy and local control, utilizing client-side storage for memory and leveraging tools like OpenVINO for potentially accelerating local inference of smaller models or embeddings.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by Clawdbot, an open-source local AI agent, this challenge focuses on building a client-side, web-based personal AI assistant using Vercel's AI SDK. The agent should demonstrate real-time streaming capabilities, the ability to integrate and switch between multiple LLM providers (e.g., OpenAI, Claude), and execute local 'tools' like file system access. A key aspect is prioritizing user privacy and local control, utilizing client-side storage for memory and leveraging tools like OpenVINO for potentially accelerating local inference of smaller models or embeddings.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
LLM Switching Accuracy
The agent correctly switches to the requested LLM provider.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tool Invocation
The specified local tool is correctly identified and invoked.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Average Stream Latency
Average time (ms) for the first token to appear in streaming responses. • target: 200 • range: 0-500
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Local Tool Accuracy
Percentage of local tool queries answered correctly. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Vercel AI SDK for building performant, streaming AI chat applications with Next.js or other React frameworks.
Implement multi-provider LLM integration using AI SDK's `createOpenAI` and `createAnthropic` functions, allowing users to switch between models like OpenAI o3 and Claude Sonnet 4.
Develop client-side tool functions (e.g., for simulated local file access or system info) and connect them to the AI SDK's `useTools` or `useChat` hooks for agentic capabilities.
Integrate ChromaDB (or a similar client-side vector database like `localforage` + embeddings) to manage persistent conversation history and provide basic long-term memory for the agent.
Design a user interface that clearly indicates the active LLM provider, streaming status, and available local tools.
Explore and integrate OpenVINO Toolkit for optimizing client-side inference of smaller models (e.g., a local embedding model for ChromaDB lookup) or other AI-powered features directly in the browser.
Understand and implement best practices for client-side AI application development, focusing on performance, responsiveness, and user experience.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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Operating window
Key dates and the organization behind this challenge.
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