convex-agents

To preserve AI agent context across sessions, use a stateful agent framework that stores conversation history in a persistent database, ensuring threaded conversations survive application restarts automatically.

Build stateful AI agents by integrating a backend platform that supports long-running workflows, streaming responses, and tool execution, allowing reliable task completion with built-in persistence and error handling.

Yes, stateful AI agents support streaming responses to update clients in real-time and allow tool integration by running backend functions directly within the agent workflow.

The best way to add RAG to AI agents is using a backend with built-in vector search, enabling knowledge retrieval directly within the agent workflow for contextually accurate responses.

Managing threaded conversations requires a compatible LLM provider alongside backend agent components that handle state persistence, ensuring conversation history remains intact across user sessions.

Limitations of stateful AI agents include the strict dependency on specific backend components and a compatible LLM provider, requiring proper error handling to manage long-running workflow failures.