Convex agents

Produces a chat or tool calling agent backed by @convex-dev/agent, with thread history stored in Convex and a reactive message list for the UI. The one rule: every LLM call runs inside an action. Mutations save the prompt and schedule the action; they never call a model.

When to reach for this

• Adding a chat assistant with persistent conversation history
• Letting an LLM call your queries and mutations as tools
• Streaming a model reply to one or more clients
• Answering questions over your own documents (RAG)
• Chaining several LLM steps into a durable job that survives restarts

Install and register

npm install @convex-dev/agent ai @ai-sdk/openai zod
npx convex env set OPENAI_API_KEY sk-...

// 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;

Run npx convex dev once so components.agent is generated before defining an agent.

Define an agent

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

export const supportAgent = new Agent(components.agent, {
  name: "Support Agent",
  languageModel: openai.chat("gpt-4o-mini"),
  instructions: "You are a support assistant. Answer briefly and cite docs when possible.",
  // Lets the model call tools and then respond, up to 5 steps
  stopWhen: stepCountIs(5),
});

name tags each saved message with the agent that wrote it. Everything except name can be overridden per call.

Create a thread and generate a reply

Save the user prompt in a mutation, then schedule an internal action that generates the reply. Clients subscribed to the thread see the new message without the action returning anything.

// convex/chat.ts
import { v } from "convex/values";
import { mutation, internalAction, QueryCtx, MutationCtx } from "./_generated/server";
import { components, internal } from "./_generated/api";
import { saveMessage } from "@convex-dev/agent";
import { supportAgent } from "./agent";

// Throws unless the signed in user owns the thread
async function authorizeThreadAccess(ctx: QueryCtx | MutationCtx, threadId: string) {
  const identity = await ctx.auth.getUserIdentity();
  if (!identity) throw new Error("Not authenticated");
  const thread = await ctx.runQuery(components.agent.threads.getThread, { threadId });
  if (!thread || thread.userId !== identity.subject) throw new Error("Unauthorized");
}

export const startThread = mutation({
  args: {},
  returns: v.string(),
  handler: async (ctx) => {
    const identity = await ctx.auth.getUserIdentity();
    if (!identity) throw new Error("Not authenticated");
    const { threadId } = await supportAgent.createThread(ctx, { userId: identity.subject });
    return threadId;
  },
});

export const sendMessage = mutation({
  args: { threadId: v.string(), prompt: v.string() },
  returns: v.null(),
  handler: async (ctx, args) => {
    await authorizeThreadAccess(ctx, args.threadId);
    const { messageId } = await saveMessage(ctx, components.agent, {
      threadId: args.threadId,
      prompt: args.prompt,
    });
    await ctx.scheduler.runAfter(0, internal.chat.generateReply, {
      threadId: args.threadId,
      promptMessageId: messageId,
    });
    return null;
  },
});

export const generateReply = internalAction({
  args: { threadId: v.string(), promptMessageId: v.string() },
  returns: v.null(),
  handler: async (ctx, args) => {
    // promptMessageId makes retries safe: the same prompt is reused, never duplicated
    await supportAgent.generateText(
      ctx,
      { threadId: args.threadId },
      { promptMessageId: args.promptMessageId },
    );
    return null;
  },
});

Thread ids are strings, not v.id(...), since the table lives inside the component.

List messages for the UI

// convex/chat.ts (continued)
import { paginationOptsValidator } from "convex/server";
import { listUIMessages } from "@convex-dev/agent";
import { query } from "./_generated/server";

export const listMessages = query({
  args: { threadId: v.string(), paginationOpts: paginationOptsValidator },
  handler: async (ctx, args) => {
    await authorizeThreadAccess(ctx, args.threadId);
    return await listUIMessages(ctx, components.agent, args);
  },
});

// src/Chat.tsx
import { useUIMessages } from "@convex-dev/agent/react";
import { api } from "../convex/_generated/api";

function Chat({ threadId }: { threadId: string }) {
  const { results, status, loadMore } = useUIMessages(
    api.chat.listMessages,
    { threadId },
    { initialNumItems: 20 },
  );
  return (
    <div>
      {results.map((m) => (
        <div key={m.key} data-role={m.role}>{m.text}</div>
      ))}
      {status === "CanLoadMore" && <button onClick={() => loadMore(20)}>Older</button>}
    </div>
  );
}

listUIMessages merges tool calls and the assistant text that follows them into one UIMessage, which keeps rendering simple.

One tool

Tools are defined with createTool and get a ctx that includes runQuery, runMutation, userId, and threadId. Annotate the handler return type to avoid circular type errors.

// convex/tools.ts
import { createTool } from "@convex-dev/agent";
import { z } from "zod";
import { api } from "./_generated/api";

export const searchOrders = createTool({
  description: "Find the current user's orders that match a search term",
  args: z.object({
    term: z.string().describe("Product name or order number to look for"),
  }),
  handler: async (ctx, args): Promise<Array<{ id: string; status: string }>> => {
    return await ctx.runQuery(api.orders.search, { term: args.term });
  },
});

Pass it to the agent with tools: { searchOrders } in the constructor or at the call site. For tool error handling, runtime tools with closures, and delta streaming to the client, open references/tools-and-streaming.md.

Retrieval and multi step jobs

For embedding documents, searching them with @convex-dev/rag or a hand rolled vector index, injecting results into the prompt, and running several LLM steps as a durable @convex-dev/workflow job, open references/rag-and-workflows.md.

Common mistakes

Checklist

• app.use(agent) in convex.config.ts and npx convex dev has run
• Provider key stored with npx convex env set, never in client code
• Agent has a name, languageModel, and instructions
• Prompts saved in a mutation, replies generated in an internalAction with promptMessageId
• Thread and message ids typed as v.string()
• Message list query checks thread ownership before calling listUIMessages
• Every tool arg has a zod .describe() and the handler has a return type
• stopWhen: stepCountIs(n) set when tools are in play
• Client uses useUIMessages rather than reading action return values

Docs

• https://docs.convex.dev/llms.txt
• https://docs.convex.dev/agents
• https://docs.convex.dev/agents/agent-usage
• https://docs.convex.dev/agents/tools
• https://www.convex.dev/components/agent