convex-agents

To build persistent AI agents that maintain context across sessions, use Convex to store conversation threads and metadata. This approach ensures stateful agents survive application restarts while managing long-running tasks and durable workflows.

The best way to integrate RAG patterns into a Convex application is by using the Convex Agent Component alongside vector-based retrieval. This combination enables durable workflows and provides agents with knowledge access for context-aware responses.

Yes, you can run Convex functions as tools within an AI agent workflow. The orchestration pattern allows you to integrate tooling directly with OpenAI streaming responses to execute backend logic during conversations.

Yes, the Convex Agent Component works with OpenAI streaming responses to deliver real-time AI outputs to clients. It supports tool integration and vector-based retrieval while maintaining persistent conversation state.

You manage conversation threads for stateful AI agents in Convex by utilizing the Agent Component's persistent state capabilities. This stores context and metadata, allowing agents to maintain long-running tasks and continuity across restarts.

You need persistent state for AI agent workflows when your application requires maintaining conversation context, surviving server restarts, or orchestrating long-running tasks. Convex provides this durability alongside integrated tooling and streaming.