AI Agent

AI Agents, built on Convex. Check out the docs here.

The Agent component is a core building block for building AI agents. It manages threads and messages, around which you Agents can cooperate in static or dynamic workflows.

• Agents provide an abstraction for using LLMs to represent units of use-case-specific prompting with associated models, prompts, Tool Calls, and behavior in relation to other Agents, functions, APIs, and more.
• Threads persist messages and can be shared by multiple users and agents (including human agents).
• Streaming text and objects using deltas over websockets so all clients stay in sync efficiently, without http streaming. Enables streaming from async functions.
• Conversation context is automatically included in each LLM call, including built-in hybrid vector/text search for messages in the thread and opt-in search for messages from other threads (for the same specified user).
• RAG techniques are supported for prompt augmentation from other sources, either up front in the prompt or as tool calls. Integrates with the RAG Component, or DIY.
• Workflows allow building multi-step operations that can span agents, users, durably and reliably.
• Files are supported in thread history with automatic saving to file storage and ref-counting.
• Debugging is enabled by callbacks, the agent playground where you can inspect all metadata and iterate on prompts and context settings, and inspection in the dashboard.
• Usage tracking is easy to set up, enabling usage attribution per-provider, per-model, per-user, per-agent, for billing & more.
• Rate limiting, powered by the Rate Limiter Component, helps control the rate at which users can interact with agents and keep you from exceeding your LLM provider's limits.

Read the associated Stack post here.

Read the docs for more details.

Play with the example:

git clone https://github.com/get-convex/agent.git
cd agent
npm run setup
npm run dev

Found a bug? Feature request? File it here.