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convex-agents

Develop AI agents with Convex Agent for threads, tools, streaming, and RAG.

Updated Feb 9, 2025
Search Tags:#ai#llm#rag#agents#tools#workflows#convex
One-click install
npx skills add https://github.com/obada-jaras/nextboost-starter --skill convex-agents-obada-jaras
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convex-agents
Source: https://github.com/obada-jaras/nextboost-starter/tree/main/.cursor/skills/convex-agents
Command: npx skills add https://github.com/obada-jaras/nextboost-starter --skill convex-agents-obada-jaras

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @convex-dev/agent, ai, openai, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the creation of sophisticated AI agents by providing robust tools for managing conversations, integrating external functionalities, and orchestrating complex workflows within the Convex ecosystem.

Core Features & Use Cases

  • Persistent AI Agents: Build stateful agents with conversation history that survives restarts.
  • Real-time Streaming: Stream AI responses directly to clients for an interactive user experience.
  • Tool Integration: Enable agents to execute Convex functions or external APIs as tools.
  • RAG & Workflows: Implement Retrieval Augmented Generation for knowledge retrieval and orchestrate multi-step agent tasks.
  • Use Case: Develop a customer support agent that can access a knowledge base, create support tickets, and provide real-time answers to user queries.

Quick Start

Install the necessary packages and configure the Convex Agent component in your Convex project.

Frequently Asked Questions about convex-agents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build stateful AI agents with persistent conversation history using Convex?▼

To build stateful AI agents with Convex, use the Convex Agent component to manage threads, ensuring conversation history survives restarts and providing durable, stateful interactions for reliable user experiences.

Can I implement RAG and tool integration in my LLM workflows with Convex?▼

Yes, you can implement RAG and tool integration in LLM workflows with Convex by executing external APIs or Convex functions as tools, embedding text, and orchestrating retrieval augmented generation for knowledge retrieval.

What packages do I need to set up AI agents in a Convex project?▼

To set up AI agents in a Convex project, you need the @convex-dev/agent, ai, and openai packages to enable agent functionality, chat interactions, text embedding, and tool execution.

Does the Convex Agent component support real-time streaming for AI responses?▼

Yes, the Convex Agent component supports real-time streaming for AI responses, allowing you to stream LLM outputs directly to clients and create an interactive, real-time user experience.

What is the best way to orchestrate multi-step agent tasks in a Convex application?▼

The best way to orchestrate multi-step agent tasks in a Convex application is using the Convex Agent component, which facilitates durable workflows and orchestrates complex, stateful agent tasks with real-time updates.

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