tomevault-io Convex Agents
Build persistent, stateful AI agents with Convex including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration.
Documentation Sources
Before implementing, do not assume; fetch the latest documentation:
- Primary: https://docs.convex.dev/ai
- Convex Agent Component: https://www.npmjs.com/package/@convex-dev/agent
- For broader context: https://docs.convex.dev/llms.txt
Instructions
Why Convex for AI Agents
- Persistent State - Conversation history survives restarts
- Real-time Updates - Stream responses to clients automatically
- Tool Execution - Run Convex functions as agent tools
- Durable Workflows - Long-running agent tasks with reliability
- Built-in RAG - Vector search for knowledge retrieval
Setting Up Convex Agent
npm install @convex-dev/agent ai openai
// convex/agent.ts
import { Agent } from "@convex-dev/agent";
import { components } from "./_generated/api";
import { OpenAI } from "openai";
const openai = new OpenAI();
export const agent = new Agent(components.agent, {
chat: openai.chat,
textEmbedding: openai.embeddings,
});
Thread Management
// convex/threads.ts
import { mutation, query } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
// Create a new conversation thread
export const createThread = mutation({
args: {
userId: v.id("users"),
title: v.optional(v.string()),
},
returns: v.id("threads"),
handler: async (ctx, args) => {
const threadId = await agent.createThread(ctx, {
userId: args.userId,
metadata: {
title: args.title ?? "New Conversation",
createdAt: Date.now(),
},
});
return threadId;
},
});
// List user's threads
export const listThreads = query({
args: { userId: v.id("users") },
returns: v.array(v.object({
_id: v.id("threads"),
title: v.string(),
lastMessageAt: v.optional(v.number()),
})),
handler: async (ctx, args) => {
return await agent.listThreads(ctx, {
userId: args.userId,
});
},
});
// Get thread messages
export const getMessages = query({
args: { threadId: v.id("threads") },
returns: v.array(v.object({
role: v.string(),
content: v.string(),
createdAt: v.number(),
})),
handler: async (ctx, args) => {
return await agent.getMessages(ctx, {
threadId: args.threadId,
});
},
});
Sending Messages and Streaming Responses
// convex/chat.ts
import { action } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
import { internal } from "./_generated/api";
export const sendMessage = action({
args: {
threadId: v.id("threads"),
message: v.string(),
},
returns: v.null(),
handler: async (ctx, args) => {
// Add user message to thread
await ctx.runMutation(internal.chat.addUserMessage, {
threadId: args.threadId,
content: args.message,
});
// Generate AI response with streaming
const response = await agent.chat(ctx, {
threadId: args.threadId,
messages: [{ role: "user", content: args.message }],
stream: true,
onToken: async (token) => {
// Stream tokens to client via mutation
await ctx.runMutation(internal.chat.appendToken, {
threadId: args.threadId,
token,
});
},
});
// Save complete response
await ctx.runMutation(internal.chat.saveResponse, {
threadId: args.threadId,
content: response.content,
});
return null;
},
});
Tool Integration
Define tools that agents can use:
// convex/tools.ts
import { tool } from "@convex-dev/agent";
import { v } from "convex/values";
import { api } from "./_generated/api";
// Tool to search knowledge base
export const searchKnowledge = tool({
name: "search_knowledge",
description: "Search the knowledge base for relevant information",
parameters: v.object({
query: v.string(),
limit: v.optional(v.number()),
}),
handler: async (ctx, args) => {
const results = await ctx.runQuery(api.knowledge.search, {
query: args.query,
limit: args.limit ?? 5,
});
return results;
},
});
// Tool to create a task
export const createTask = tool({
name: "create_task",
description: "Create a new task for the user",
parameters: v.object({
title: v.string(),
description: v.optional(v.string()),
dueDate: v.optional(v.string()),
}),
handler: async (ctx, args) => {
const taskId = await ctx.runMutation(api.tasks.create, {
title: args.title,
description: args.description,
dueDate: args.dueDate ? new Date(args.dueDate).getTime() : undefined,
});
return { success: true, taskId };
},
});
// Tool to get weather
export const getWeather = tool({
name: "get_weather",
description: "Get current weather for a location",
parameters: v.object({
location: v.string(),
}),
handler: async (ctx, args) => {
const response = await fetch(
`https://api.weather.com/current?location=${encodeURIComponent(args.location)}`
);
return await response.json();
},
});
Agent with Tools
// convex/assistant.ts
import { action } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
import { searchKnowledge, createTask, getWeather } from "./tools";
export const chat = action({
args: {
threadId: v.id("threads"),
message: v.string(),
},
returns: v.string(),
handler: async (ctx, args) => {
const response = await agent.chat(ctx, {
threadId: args.threadId,
messages: [{ role: "user", content: args.message }],
tools: [searchKnowledge, createTask, getWeather],
systemPrompt: `You are a helpful assistant. You have access to tools to:
- Search the knowledge base for information
- Create tasks for the user
- Get weather information
Use these tools when appropriate to help the user.`,
});
return response.content;
},
});
RAG (Retrieval Augmented Generation)
// convex/knowledge.ts
import { mutation, query } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
// Add document to knowledge base
export const addDocument = mutation({
args: {
title: v.string(),
content: v.string(),
metadata: v.optional(v.object({
source: v.optional(v.string()),
category: v.optional(v.string()),
})),
},
returns: v.id("documents"),
handler: async (ctx, args) => {
// Generate embedding
const embedding = await agent.embed(ctx, args.content);
return await ctx.db.insert("documents", {
title: args.title,
content: args.content,
embedding,
metadata: args.metadata ?? {},
createdAt: Date.now(),
});
},
});
// Search knowledge base
export const search = query({
args: {
query: v.string(),
limit: v.optional(v.number()),
},
returns: v.array(v.object({
_id: v.id("documents"),
title: v.string(),
content: v.string(),
score: v.number(),
})),
handler: async (ctx, args) => {
const results = await agent.search(ctx, {
query: args.query,
table: "documents",
limit: args.limit ?? 5,
});
return results.map((r) => ({
_id: r._id,
title: r.title,
content: r.content,
score: r._score,
}));
},
});
Workflow Orchestration
// convex/workflows.ts
import { action, internalMutation } from "./_generated/server";
import { v } from "convex/values";
import { agent } from "./agent";
import { internal } from "./_generated/api";
// Multi-step research workflow
export const researchTopic = action({
args: {
topic: v.string(),
userId: v.id("users"),
},
returns: v.id("research"),
handler: async (ctx, args) => {
// Create research record
const researchId = await ctx.runMutation(internal.workflows.createResearch, {
topic: args.topic,
userId: args.userId,
status: "searching",
});
// Step 1: Search for relevant documents
const searchResults = await agent.search(ctx, {
query: args.topic,
table: "documents",
limit: 10,
});
await ctx.runMutation(internal.workflows.updateStatus, {
researchId,
status: "analyzing",
});
// Step 2: Analyze and synthesize
const analysis = await agent.chat(ctx, {
messages: [{
role: "user",
content: `Analyze these sources about "${args.topic}" and provide a comprehensive summary:\n\n${
searchResults.map((r) => r.content).join("\n\n---\n\n")
}`,
}],
systemPrompt: "You are a research assistant. Provide thorough, well-cited analysis.",
});
// Step 3: Generate key insights
await ctx.runMutation(internal.workflows.updateStatus, {
researchId,
status: "summarizing",
});
const insights = await agent.chat(ctx, {
messages: [{
role: "user",
content: `Based on this analysis, list 5 key insights:\n\n${analysis.content}`,
}],
});
// Save final results
await ctx.runMutation(internal.workflows.completeResearch, {
researchId,
analysis: analysis.content,
insights: insights.content,
sources: searchResults.map((r) => r._id),
});
return researchId;
},
});
Examples
Complete Chat Application Schema
// convex/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";
export default defineSchema({
threads: defineTable({
userId: v.id("users"),
title: v.string(),
lastMessageAt: v.optional(v.number()),
metadata: v.optional(v.any()),
}).index("by_user", ["userId"]),
messages: defineTable({
threadId: v.id("threads"),
role: v.union(v.literal("user"), v.literal("assistant"), v.literal("system")),
content: v.string(),
toolCalls: v.optional(v.array(v.object({
name: v.string(),
arguments: v.any(),
result: v.optional(v.any()),
}))),
createdAt: v.number(),
}).index("by_thread", ["threadId"]),
documents: defineTable({
title: v.string(),
content: v.string(),
embedding: v.array(v.float64()),
metadata: v.object({
source: v.optional(v.string()),
category: v.optional(v.string()),
}),
createdAt: v.number(),
}).vectorIndex("by_embedding", {
vectorField: "embedding",
dimensions: 1536,
}),
});
React Chat Component
import { useQuery, useMutation, useAction } from "convex/react";
import { api } from "../convex/_generated/api";
import { useState, useRef, useEffect } from "react";
function ChatInterface({ threadId }: { threadId: Id<"threads"> }) {
const messages = useQuery(api.threads.getMessages, { threadId });
const sendMessage = useAction(api.chat.sendMessage);
const [input, setInput] = useState("");
const [sending, setSending] = useState(false);
const messagesEndRef = useRef<HTMLDivElement>(null);
useEffect(() => {
messagesEndRef.current?.scrollIntoView({ behavior: "smooth" });
}, [messages]);
const handleSend = async (e: React.FormEvent) => {
e.preventDefault();
if (!input.trim() || sending) return;
const message = input.trim();
setInput("");
setSending(true);
try {
await sendMessage({ threadId, message });
} finally {
setSending(false);
}
};
return (
<div className="chat-container">
<div className="messages">
{messages?.map((msg, i) => (
<div key={i} className={`message ${msg.role}`}>
<strong>{msg.role === "user" ? "You" : "Assistant"}:</strong>
<p>{msg.content}</p>
</div>
))}
<div ref={messagesEndRef} />
</div>
<form className="input-form">
<input
value={input}
=> setInput(e.target.value)}
placeholder="Type your message..."
disabled={sending}
/>
<button type="submit" disabled={sending || !input.trim()}>
{sending ? "Sending..." : "Send"}
</button>
</form>
</div>
);
}
Best Practices
- Never run
npx convex deployunless explicitly instructed - Never run any git commands unless explicitly instructed
- Store conversation history in Convex for persistence
- Use streaming for better user experience with long responses
- Implement proper error handling for tool failures
- Use vector indexes for efficient RAG retrieval
- Rate limit agent interactions to control costs
- Log tool usage for debugging and analytics
Common Pitfalls
- Not persisting threads - Conversations lost on refresh
- Blocking on long responses - Use streaming instead
- Tool errors crashing agents - Add proper error handling
- Large context windows - Summarize old messages
- Missing embeddings for RAG - Generate embeddings on insert
References
- Convex Documentation: https://docs.convex.dev/
- Convex LLMs.txt: https://docs.convex.dev/llms.txt
- Convex AI: https://docs.convex.dev/ai
- Agent Component: https://www.npmjs.com/package/@convex-dev/agent
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