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convex-agents

Develop persistent AI agents with Convex for thread management and tool integration.

2|Updated Feb 9, 2026
Search Tags:#llm#rag#ai agents#tools#workflows#convex
One-click install
npx skills add https://github.com/metaloozee/geoveda --skill convex-agents-metaloozee
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convex-agents
Source: https://github.com/metaloozee/geoveda/tree/main/.cursor/skills/convex-agents
Command: npx skills add https://github.com/metaloozee/geoveda --skill convex-agents-metaloozee

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 provides a robust framework for building sophisticated AI agents, enabling developers to manage conversations, integrate tools, and orchestrate complex workflows within the Convex ecosystem.

Core Features & Use Cases

  • Thread Management: Persistent storage and retrieval of conversation histories.
  • Tool Integration: Seamlessly connect agents to external functions and APIs.
  • RAG Patterns: Implement Retrieval Augmented Generation for knowledge-aware agents.
  • Workflow Orchestration: Design and execute multi-step agentic processes.
  • Use Case: Develop a customer support agent that can access a knowledge base, create support tickets, and provide real-time assistance, all managed within Convex.

Quick Start

Use the convex-agents skill to create a new conversation thread for the current user.

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 threads in Convex?▼

To build stateful AI agents in Convex, use the Convex Agent component for thread management, enabling persistent storage and retrieval of conversation histories alongside real-time updates.

What's the best way to implement RAG patterns for AI agents using Convex and OpenAI?▼

Implementing RAG patterns with Convex and OpenAI involves using the Convex Agent component to integrate text embedding functionalities, enabling knowledge-aware agents that retrieve contextual data for responses.

Can I orchestrate multi-step agentic workflows and integrate external tools within Convex?▼

Yes, you can orchestrate multi-step agentic workflows within Convex by using the agent component to design durable processes and seamlessly connect external functions and APIs as tools.

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

Yes, the Convex Agent component supports streaming responses, allowing developers to build AI chat applications that provide real-time assistance and immediate feedback during conversations.

Do I need OpenAI API access to use Convex for AI agent development?▼

Yes, OpenAI integration is required because the Convex Agent component relies on OpenAI APIs to handle underlying chat completions and text embedding functionalities for the agents.

Why use Convex for AI agent workflow orchestration instead of other stateless LLM tools?▼

Convex provides durable workflow orchestration for AI agents, ensuring persistent state management and real-time updates that stateless LLM tools lack when executing complex multi-step processes.

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