MCPHub LabRegistryvolcano-agent-sdk
Kong

volcano agent sdk

Built by Kong • 390 stars

What is volcano agent sdk?

šŸŒ‹ Build AI agents that seamlessly combine LLM reasoning with real-world actions via MCP tools — in just a few lines of TypeScript.

How to use volcano agent sdk?

1. Install a compatible MCP client (like Claude Desktop). 2. Open your configuration settings. 3. Add volcano agent sdk using the following command: npx @modelcontextprotocol/volcano-agent-sdk 4. Restart the client and verify the new tools are active.
šŸ›”ļø Scoped (Restricted)
npx @modelcontextprotocol/volcano-agent-sdk --scope restricted
šŸ”“ Unrestricted Access
npx @modelcontextprotocol/volcano-agent-sdk

Key Features

Native MCP Protocol Support
Real-time Tool Activation & Execution
Verified High-performance Implementation
Secure Resource & Context Handling

Optimized Use Cases

Extending AI models with custom local capabilities
Automating system workflows via natural language
Connecting external data sources to LLM context windows

volcano agent sdk FAQ

Q

Is volcano agent sdk safe?

Yes, volcano agent sdk follows the standardized Model Context Protocol security patterns and only executes tools with explicit user-granted permissions.

Q

Is volcano agent sdk up to date?

volcano agent sdk is currently active in the registry with 390 stars on GitHub, indicating its reliability and community support.

Q

Are there any limits for volcano agent sdk?

Usage limits depend on the specific implementation of the MCP server and your system resources. Refer to the official documentation below for technical details.

Official Documentation

View on GitHub

CI License npm

šŸŒ‹ Volcano Agent SDK

The TypeScript SDK for Multi-Provider AI Agents

Build agents that chain LLM reasoning with MCP tools. Mix OpenAI, Claude, Mistral in one workflow. Parallel execution, branching, loops. Native retries, streaming, and typed errors.

šŸ“š Read the full documentation at volcano.dev →

✨ Features

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šŸ¤– Automatic Tool Selection

LLM automatically picks which MCP tools to call based on your prompt. No manual routing needed.

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🧩 Multi-Agent Crews

Define specialized agents and let the coordinator autonomously delegate tasks. Like automatic tool selection, but for agents.

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šŸ’¬ Conversational Results

Ask questions about what your agent did. Use .summary() or .ask() instead of parsing JSON.

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šŸ”§ 100s of Models

OpenAI, Anthropic, Mistral, Bedrock, Vertex, Azure. Switch providers per-step or globally.

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šŸ”„ Advanced Patterns

Parallel execution, branching, loops, sub-agent composition. Enterprise-grade workflow control.

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šŸ“” Streaming

Stream tokens in real-time as LLMs generate them. Perfect for chat UIs and SSE endpoints.

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šŸ›”ļø TypeScript-First

Full type safety with IntelliSense. Catch errors before runtime.

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šŸ“Š Observability

OpenTelemetry traces and metrics. Export to Jaeger, Prometheus, DataDog, or any OTLP backend.

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⚔ Production-Ready

Built-in retries, timeouts, error handling, and connection pooling. Battle-tested at scale.

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Explore all features →

Quick Start

Installation

npm install @volcano.dev/agent

That's it! Includes MCP support and all common LLM providers (OpenAI, Anthropic, Mistral, Llama, Vertex).

View installation guide →

Hello World with Automatic Tool Selection

import { agent, llmOpenAI, mcp } from "@volcano.dev/agent";

const llm = llmOpenAI({ 
  apiKey: process.env.OPENAI_API_KEY!, 
  model: "gpt-4o-mini" 
});

const weather = mcp("http://localhost:8001/mcp");
const tasks = mcp("http://localhost:8002/mcp");

// Agent automatically picks the right tools
const results = await agent({ llm })
  .then({ 
    prompt: "What's the weather in Seattle? If it will rain, create a task to bring an umbrella",
    mcps: [weather, tasks]  // LLM chooses which tools to call
  })
  .run();

// Ask questions about what happened
const summary = await results.summary(llm);
console.log(summary);

Multi-Agent Coordinator

import { agent, llmOpenAI } from "@volcano.dev/agent";

const llm = llmOpenAI({ apiKey: process.env.OPENAI_API_KEY! });

// Define specialized agents
const researcher = agent({ llm, name: 'researcher', description: 'Finds facts and data' })
  .then({ prompt: "Research the topic." })
  .then({ prompt: "Summarize the research." });

const writer = agent({ llm, name: 'writer', description: 'Creates content' })
  .then({ prompt: "Write content." });

// Coordinator autonomously delegates to specialists
const results = await agent({ llm })
  .then({
    prompt: "Write a blog post about quantum computing",
    agents: [researcher, writer]  // Coordinator decides when done
  })
  .run();

// Ask what happened
const post = await results.ask(llm, "Show me the final blog post");
console.log(post);

View more examples →

Documentation

šŸ“– Comprehensive Guides

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Questions or Feature Requests?

License

Apache 2.0 - see LICENSE file for details.

Global Ranking

-
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "volcano-agent-sdk": { "command": "npx", "args": ["volcano-agent-sdk"] } } }