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128 lines
5.3 KiB
Markdown
128 lines
5.3 KiB
Markdown
---
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layout: blog
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title: "Building the AI-Native Future of Go Micro with Claude"
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permalink: /blog/3
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description: "How Anthropic's Claude Max sponsorship accelerated Go Micro's MCP integration — from WebSocket transport to a full AI-native framework."
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---
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# Building the AI-Native Future of Go Micro with Claude
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<img src="/images/generated/blog-claude.jpg" alt="Claude AI powering Go Micro" style="width: 100%; border-radius: 8px; margin: 1rem 0 1.5rem;" />
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*March 4, 2026 • By the Go Micro Team*
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Go Micro was given access to **Claude Max** through Anthropic's open source sponsorship program. This post covers what we built with it, how the development process worked, and the vision that came out of it.
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## The Sponsorship
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Anthropic offers Claude Max to open source projects building on the Model Context Protocol. Go Micro's pitch was simple: every microservice should be an AI-callable tool with zero extra code. They agreed.
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What happened next was the most productive sprint in Go Micro's history. Claude didn't just assist — it became a collaborator. Features that would have taken weeks shipped in days.
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## What We Shipped
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### WebSocket Transport
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The MCP gateway needed persistent, bidirectional connections for real-time agents. We added a full WebSocket transport implementing JSON-RPC 2.0:
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```javascript
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const ws = new WebSocket("ws://localhost:3000/mcp/ws", {
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headers: { "Authorization": "Bearer my-token" }
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});
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// Discover and call tools over a single connection
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ws.send(JSON.stringify({
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jsonrpc: "2.0", id: 1,
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method: "tools/call",
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params: { name: "users.Users.Get", arguments: { id: "user-123" } }
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}));
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```
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Persistent connections, connection-level auth, concurrent requests. The agent playground in `micro run` uses this for interactive conversations with your services.
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### OpenTelemetry Tracing
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Every MCP tool call now creates an OpenTelemetry span:
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```
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Span: mcp.tool.call
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mcp.tool.name: users.Users.Get
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mcp.transport: websocket
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mcp.auth.status: allowed
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```
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Drop in your trace provider and agent activity flows into Jaeger, Grafana, or Datadog alongside your existing service traces. No trace provider configured? Zero overhead.
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### LlamaIndex SDK
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Following the LangChain integration, we built a LlamaIndex SDK for RAG workflows:
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```python
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from go_micro_llamaindex import GoMicroToolkit
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from llama_index.core.agent import ReActAgent
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toolkit = GoMicroToolkit.from_gateway("http://localhost:3000")
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agent = ReActAgent.from_tools(toolkit.get_tools(), llm=llm)
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# Agent can search docs AND call services
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response = agent.chat("Get the profile for user-123")
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```
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An agent that searches your documentation and calls your services in the same conversation.
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## What Came After
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The Claude sponsorship set a direction that kept going. Since then:
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**7 AI model providers** — Anthropic, OpenAI, Google Gemini, Atlas Cloud, Groq, Mistral, and Together AI. All implementing the same `ai.Model` interface, all swappable with one import.
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**Image and video generation** — `ai.ImageModel` and `ai.VideoModel` interfaces with Atlas Cloud as the first multi-modal provider. The images on this website were generated through the framework's own `ai` package.
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**`micro chat`** — an interactive CLI that discovers your services, exposes them as tools, and lets you orchestrate them through natural language. Multi-turn conversation with history.
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**`ai.Tools`** — a reusable package that turns registry discovery + client RPC into an `ai.ToolHandler`. Any service can reason about and call other services through an LLM.
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**Service templates** — `micro new --template crud` scaffolds a full CRUD service with typed proto, in-memory store, pagination, and MCP-ready doc comments.
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None of this was planned when the sponsorship started. It emerged from the velocity that Claude enabled.
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## The Development Process
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A note on what it's actually like to build a framework with Claude Code:
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The WebSocket transport went from zero to 14 passing tests in a single session. The OpenTelemetry integration was designed, implemented, and tested in another. The Gemini provider — which has a completely different API format from OpenAI — was researched, implemented, and passing tests in under an hour.
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This isn't about replacing engineering judgment. Every design decision, every interface, every architectural tradeoff was a conversation. Claude writes the code. The human decides what to build and why.
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The irony isn't lost on us: Go Micro is a framework for building services that AI agents can call, and it was itself built by an AI agent calling tools in the codebase. MCP works because we used MCP.
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## Try It
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```bash
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go install go-micro.dev/v5/cmd/micro@latest
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# Create a service
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micro new myservice
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cd myservice
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# Run with the agent playground
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micro run
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# Chat with your services
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ANTHROPIC_API_KEY=sk-ant-... micro chat --provider anthropic
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```
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See the [MCP documentation](/docs/mcp) for the full guide.
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---
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*Go Micro is an open source framework for distributed systems development. [Star us on GitHub](https://github.com/micro/go-micro) — 23K+ stars and growing.*
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*Thanks to Anthropic for the Claude Max sponsorship through their open source program.*
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<div class="post-nav">
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<div><a href="/blog/2">← Making Microservices AI-Native with MCP</a></div>
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<div><a href="/blog/">All Posts</a></div>
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<div><a href="/blog/4">Agents Meet Microservices →</a></div>
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</div>
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