Getting Started with Agent

To install the agent component, you'll need an existing Convex project. New to Convex? Go through the tutorial.

Run npm create convex or follow any of the quickstarts to set one up.

Installation

Install the component package:

npm install @convex-dev/agent

Create a convex.config.ts file in your app's convex/ folder and install the component by calling use:

// convex/convex.config.ts
import { defineApp } from "convex/server";
import agent from "@convex-dev/agent/convex.config";

const app = defineApp();
app.use(agent);

export default app;

Then run npx convex dev to generate code for the component. This needs to successfully run once before you start defining Agents.

Defining your first Agent

import { components } from "./_generated/api";
import { Agent } from "@convex-dev/agent";
import { openai } from "@ai-sdk/openai";

const agent = new Agent(components.agent, {
  name: "My Agent",
  chat: openai.chat("gpt-4o-mini"),
});

Using it:

import { action } from "./_generated/server";
import { v } from "convex/values";

export const helloWorld = action({
  args: { prompt: v.string() },
  handler: async (ctx, { prompt }) => {
    // const userId = await getAuthUserId(ctx);
    const { thread } = await agent.createThread(ctx, { userId });
    const result = await thread.generateText({ prompt });
    return result.text;
  },
});

If you get type errors about components.agent, ensure you've run npx convex dev to generate code for the component.

That's it! Next check out creating Threads and Messages.

Customizing the agent

The agent by default only needs a chat model to be configured. However, for vector search, you'll need a textEmbedding model. A name is helpful to attribute each message to a specific agent. Other options are defaults that can be over-ridden at each LLM call-site.

import { tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
import { Agent, createTool } from "@convex-dev/agent";
import { components } from "./_generated/api";

// Define an agent similarly to the AI SDK
const supportAgent = new Agent(components.agent, {
  // The chat completions model to use for the agent.
  chat: openai.chat("gpt-4o-mini"),
  // The default system prompt if not over-ridden.
  instructions: "You are a helpful assistant.",
  tools: {
    // Convex tool
    myConvexTool: createTool({
      description: "My Convex tool",
      args: z.object({...}),
      // Note: annotate the return type of the handler to avoid type cycles.
      handler: async (ctx, args): Promise<string> => {
        return "Hello, world!";
      },
    }),
    // Standard AI SDK tool
    myTool: tool({ description, parameters, execute: () => {}}),
  },
  // Embedding model to power vector search of message history (RAG).
  textEmbedding: openai.embedding("text-embedding-3-small"),
  // Used for fetching context messages. See https://docs.convex.dev/agents/context
  contextOptions,
  // Used for storing messages. See https://docs.convex.dev/agents/messages
  storageOptions,
  // Used for limiting the number of steps when tool calls are involved.
  // NOTE: if you want tool calls to happen automatically with a single call,
  // you need to set this to something greater than 1 (the default).
  maxSteps: 1,
  // Used for limiting the number of retries when a tool call fails. Default: 3.
  maxRetries: 3,
  // Used for tracking token usage. See https://docs.convex.dev/agents/usage-tracking
  usageHandler: async (ctx, { model, usage }) => {
    // ... log, save usage to your database, etc.
  },
});