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

Mistake	Why it breaks	Do instead
Calling generateText in a mutation	Mutations cannot make network calls and must be deterministic	Save the prompt with saveMessage, schedule an internalAction
v.id("threads") for thread ids	The threads table lives in the component, so ids are strings outside it	Use v.string()
Skipping npx convex dev after app.use(agent)	components.agent is not generated, so types fail	Run dev once before writing agent code
Returning the reply text from the action to the client	Loses the reply if the client disconnects, no reactivity	Let clients read listUIMessages; the saved message shows up on its own
Tools without .describe() on args	The model guesses what each field means and calls tools badly	Describe every zod field
Tool handler with no return type annotation	TypeScript circularity errors from ctx.runQuery	Add : Promise<...> to the handler
Exposing the message query with no auth check	Any client can read any thread	Call an authorizeThreadAccess helper first
stopWhen left at the default with tools defined	The model calls a tool and stops without a text reply	Set stopWhen: stepCountIs(n) with n > 1

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