Event-Driven
TypeScript AI AgentFramework
Build agents with open-source TypeScript framework.
Debug with complete LLM observability, traces, and evals.
Automate agents with triggers and actions.
Ship enterprise-grade multi-agents end-to-end, full code control, no black boxes.
Build AI agents with a type-safe, modular TypeScript framework
Docs
import { openai } from "@ai-sdk/openai";
import { Agent, VoltAgent, createTriggers } from "@voltagent/core";
import { createPinoLogger } from "@voltagent/logger";
import { honoServer } from "@voltagent/server-hono";
import { weatherTool } from "./tools/weather";
const logger = createPinoLogger({ name: "with-slack", level: "info" });
const slackAgent = new Agent({
name: "slack-agent",
instructions: "You are a Slack assistant.",
tools: [weatherTool],
model: openai("gpt-4o-mini"),
});
new VoltAgent({
agents: { slackAgent },
server: honoServer(),
logger,
triggers: createTriggers((on) => {
on.slack.messagePosted(async ({ payload, agents }) => {
const event = (payload as SlackMessagePayload | undefined) ?? {};
const channelId = event.channel;
const threadTs = event.thread_ts ?? event.ts;
const text = event.text ?? "";
const userId = event.user ?? "unknown-user";
if (!channelId || !text) {
logger.warn("Missing channel or text in Slack payload");
return;
}
await agents.slackAgent.generateText(
`Slack channel: ${channelId}\n` +
`Thread: ${threadTs ?? "new thread"}\n` +
`User: <@${userId}>\n` +
`Message: ${text}\n` +
`If the user asks for weather, call getWeather.`
);
});
}),
});

Used and Tested by Developers at
Enterprise-level AI agents
Complete toolkit for enterprise level AI agents
Design production-ready agents with unified APIs, tools, and memory.
123456789101112131415161718import { Agent } from '@voltagent/core'
import { openai } from '@ai-sdk/openai'
import { anthropic } from '@ai-sdk/anthropic'
const agent = new Agent({
model: openai("gpt-4o-mini"),
});
const anthropicAgent = new Agent({
model: anthropic('claude-3-haiku-20240307'),
});
Enable agents to invoke functions, interact with systems, and perform actions.
Seamlessly switch between different AI providers with a simple code update.
Experiment, fine-tune, and iterate your AI prompts in an integrated environment.
Store and recall interactions to enhance your agents intelligence and context.
Fast Growing Community
What are they saying?
Intelligent Coordination
Supervisor agent orchestration
Build powerful multi-agent systems with a central Supervisor Agent that coordinates specialized agents.
12345678910111213141516171819202122232425262728293031323334import { Agent } from "@voltagent/core";
import { openai } from "@ai-sdk/openai";
// Define supervisor agent
const supervisorAgent = new Agent({
name: "Supervisor Agent",
description: "You manage a workflow between specialized agents.",
llm: new(),
model: openai("gpt-4o-mini"),
subAgents: [storyAgent, translatorAgent]
});
// Define story agent
const storyAgent = new Agent({
name: "Story Agent",
description: "You are a creative story writer.",
llm: new(),
model: openai("gpt-4o-mini"),
});
// Define translator agent
const translatorAgent = new Agent({
name: "Translator Agent",
description: "Translate English text to German",
llm: new(),
model: openai("gpt-4o-mini"),
});
// Stream response from supervisor agent
const result = await supervisorAgent.streamText(
"Write a 100 word story in English."
);
for await (const chunk of result.textStream) {
console.log(chunk);
}
Workflow Chain API
Orchestrate your agents
Build complex agent workflows with a simple, declarative API
12345678createWorkflowChain()
.andAll({
id: "fetch-user-data-steps",
steps: [
andAgent(contentAgent),
andAgent(analysisAgent),
],
})
RAG
Accurate and context-aware responses
For advanced querying and dynamic analysis, integrate data into a knowledge base by syncing from diverse sources
INTEGRATIONS
Easily connect with 40+ apps in no time
Integrate your AI agents with your preferred tools and services effortlessly.
Stay in control at every stage
From tracking deployments to debugging and live interaction, VoltAgent gives you full visibility into your AI agents.
Deploy your Agents in seconds with VoltAgent Deployment.
Debug and analyze your VoltAgent-powered AI agent's behavior with visual flows.
Connect your VoltAgent-powered AI agents to popular observability platforms.


123456789101112131415161718import { VoltAgent } from "@voltagent/core"
import { LangfuseExporter } from "langfuse-vercel"
export const volt = new VoltAgent({
telemetry: {
serviceName: "ai",
enabled: true,
export: {
type: "custom",
exporter: new LangfuseExporter({
publicKey: process.env.LANGFUSE_PUBLIC_KEY,
secretKey: process.env.LANGFUSE_SECRET_KEY,
baseUrl: process.env.LANGFUSE_BASEURL,
}),
},
},
});
Interact with your AI agent through natural language chat interface.
Agent, analyze last month's social media campaign performance and provide recommendations for improvement.
Analysis complete. Last month's campaign reached 45% more users but had 12% lower conversion rate compared to previous campaigns. Key findings: 1) Video content performed 3x better than static images. 2) Posts published between 6-8pm had highest engagement. 3) Product demonstration posts generated most conversions. Recommendation: Increase video content by 40%, focus on product demonstrations, and schedule more posts during evening hours.
Create a content calendar for next month based on these insights. Include optimal posting times and content types.
Content calendar created. I've scheduled 15 posts across platforms with 60% video content focused on product demos. Primary posting times are Tuesdays and Thursdays 6-8pm, with additional posts Monday and Friday mornings based on secondary engagement peaks. I've included hooks for upcoming product launch and integrated seasonal marketing themes. Calendar has been added to your Marketing Projects workspace and synced with team collaboration tools.
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