Today, we're open-sourcing Volcano SDK, a TypeScript SDK for building AI agents that combines LLM reasoning with real-world actions through MCP tools.
Why Volcano SDK? One reason: because 9 lines of code are faster to write and easier to manage than 100+.
Without Volcano SDK? You'd need 100+ lines handling tool schemas, context management, provider switching, error handling, and HTTP clients.Â
With Volcano SDK: 9 lines.
Look how we compress 100+ lines with the following example:
import { agent, llmOpenAI, llmAnthropic, mcp } from "volcano-ai";
// Setup: two LLMs, two MCP servers
const planner = llmOpenAI({ model: "gpt-5-mini", apiKey: process.env.OPENAI_API_KEY! });
const executor = llmAnthropic({ model: "claude-4.5-sonnet", apiKey: process.env.ANTHROPIC_API_KEY! });
const database = mcp("https://api.company.com/database/mcp");
const slack = mcp("https://api.company.com/slack/mcp");
// One workflow
await agent({ llm: planner })
.then({
prompt: "Analyze last week's sales data",
mcps: [database] // Auto-discovers and calls the right tools
})
.then({
llm: executor, // Switch to Claude
prompt: "Write an executive summary"
})
.then({
prompt: "Post the summary to #executives",
mcps: [slack]
})
.run();
That's it. GPT-5 queries the database, Claude writes the summary, and it posts to Slack. Context flows automatically between steps. Tool schemas are discovered automatically. Errors are retried. All in 9 lines of actual workflow code.
✅ 4 lines of setup — Two LLMs, two MCP servers
✅ Multi-LLM workflow — GPT-5 analyzes, Claude writes
✅ Automatic tool selection — SDK picks the right tools
✅ Chainable steps — Read like a story
✅ Context flows automatically — Each step sees previous results
✅ Production-ready — Built-in retries, timeouts, telemetry
Start building AI agents at volcano.dev.
At Kong, we build a lot of AI agents. Each time, we ended up writing the same code: managing conversation history, handling tool schemas, switching providers, implementing retries, connecting to MCP servers.
We looked at existing options:
We just wanted to chain LLM calls with different providers and use MCP tools without writing infrastructure code every time. It should be simple:
So, we built Volcano SDK. And now you can start building with it.
There are a few core concepts that you will find across Volcano that separate it from the pack:
We wanted to write agents like we write promises: one elegant chain that does exactly what it says, with the right tool for each job:
wait agent({ llm: planner })
.then({ prompt: "Analyze data", mcps: [db] })
.then({ llm: executor, prompt: "Write summary" })
.then({ prompt: "Post to Slack", mcps: [slack] })
.run();
Volcano is focused on one thing: multi-step workflows that mix different LLMs and MCP tools. It's TypeScript-first, type-safe, and small enough to understand in an afternoon, and it comes packed with easy-to-use features and multi-LLM support.
It’s time to start delivering on the agentic promise. It’s time to start building powerful AI agents with the Volcano SDK at volcano.dev. We can’t wait to see what you build.
MCP ACLs, Claude Code Support, and New Guardrails New providers, smarter routing, stronger guardrails — because AI infrastructure should be as robust as APIs We know that successful AI connectivity programs often start with an intense focus on how
What does the solution space look like so far? The solution landscape is complicated by the fact that MCP is still finding its footing, and there are many various OSS projects and vendors that are rapidly shipping “MCP support” in an attempt to take
Companies are charging headfirst into AI, with research around agentic AI in the enterprise finding as many as 9 out of 10 organizations are actively working to adopt AI agents. LLMs are being deployed, agentic workflows are getting created left
Kong Gateway is an API gateway and a core component of the Kong Konnect platform . Built on a plugin-based extensibility model, it centralizes essential functions such as proxying, routing, load balancing, and health checking, efficiently manag
If you're a builder, you likely keep sending your LLM credentials on every request from your agents and applications. But if you operate in an enterprise environment, you'll want to store your credentials in a secure third-party like HashiCorp Vault
Today, we're excited to announce the general availability of AI Manager in Kong Konnect, the platform to manage all of your API, AI, and event connectivity across all modern digital applications and AI agents. Kong already provides the fastest and m
Today at API Summit , we're introducing one of the biggest new releases of our AI Gateway technology : a new class of intelligent semantic plugins, new advanced load balancing capabilities for LLMs, and the official support for AWS Bedrock and GCP