v5.24.0
11 次代码提交
| 作者 | SHA1 | 备注 | 提交日期 | |
|---|---|---|---|---|
 Asim Aslam
|
888dbbca4a |
Refactor AI tool handling and enhance CLI command documentation (#2920)
goreleaser / goreleaser (push) Has been cancelled
* refactor(ai): rename ToolSet to Tools, simplify wiring with WithTools
Move tool discovery/execution fully into the ai package as ai.Tools
(formerly ai.ToolSet), and simplify the usage model:
- NewTools(reg, ai.ToolClient(c)) takes the execution client as an
option instead of threading it through Handler(c) per call
- New ai.WithTools(tools) option wires the tool handler into a model
in one call, replacing ai.WithToolHandler(set.Handler(c))
- ai.DiscoverTools(reg) for one-shot discovery
Before:
set := ai.NewToolSet(reg)
list, _ := set.Discover()
m := ai.New(p, ai.WithToolHandler(set.Handler(client)))
After:
tools := ai.NewTools(reg, ai.ToolClient(client))
list, _ := tools.Discover()
m := ai.New(p, ai.WithTools(tools))
Update ai/flow, micro chat, README, ai integration doc, Atlas Cloud
guide, and blog posts 3/8/9/10.
* feat(cli): add per-interface commands (registry, broker, store, config)
Map go-micro's core interfaces onto the CLI so the framework's
building blocks are inspectable and manipulable from the terminal:
micro registry list/get/watch service discovery
micro broker publish/subscribe pub/sub messaging
micro store read/write/delete/list persistence
micro config get/dump dynamic config (from env)
Structured pluggably in cmd/micro/resource: each interface is one
file exposing a Command() func, all wired through a commandFuncs
slice in resource.go. Adding a new resource command is a single
file plus one slice entry. Shared printJSON/fail helpers keep
output and errors consistent across commands.
Each command's verbs mirror the interface methods. Output is JSON
for structured data, raw for single values. Update README and
getting-started with an "inspecting the framework" section.
* docs: update CLI README with all new commands
Add documentation for commands that were missing from the CLI README:
- micro new --template (crud, pubsub, api)
- micro api (standalone HTTP gateway)
- micro registry list/get/watch
- micro broker publish/subscribe
- micro store read/write/delete/list
- micro config get/dump
- micro chat (interactive LLM agent)
- micro flow run/exec (event-driven orchestration)
- micro mcp serve/list/test
Organized into sections: API Gateway, Inspecting the Framework
(registry, broker, store, config), and AI & Agents (chat, flow, mcp).
* refactor(ai): move History from caller to Request field
History is now pure state (no Generate method). Instead, pass it
via Request.History and call ai.Generate(ctx, model, req):
Before:
hist := ai.NewHistory("system prompt", 50)
resp, _ := hist.Generate(ctx, model, prompt, tools)
After:
hist := ai.NewHistory(50)
resp, _ := ai.Generate(ctx, model, &ai.Request{
Prompt: prompt,
SystemPrompt: "system prompt",
Tools: tools,
History: hist,
})
The model is always the thing you call. History is context you
pass in. ai.Generate() handles the bookkeeping: prepends
accumulated messages before the call, records the exchange after.
NewHistory no longer takes a system prompt (it belongs on the
Request, where it always did).
Update micro chat, ai/flow, and all blog posts/docs.
* refactor(ai): make History a plain message accumulator
History no longer has Generate or touches the model. It's just
Add/Messages/Reset/Len with truncation — a helper for building
Request.Messages across turns.
Before:
hist := ai.NewHistory(50)
resp, _ := ai.Generate(ctx, m, &ai.Request{History: hist, ...})
After:
hist := ai.NewHistory(50)
hist.Add("user", prompt)
resp, _ := m.Generate(ctx, &ai.Request{Messages: hist.Messages(), ...})
hist.Add("assistant", resp.Reply)
Remove History field from Request. Remove package-level
ai.Generate(ctx, model, req) wrapper — users call m.Generate()
directly, which is the interface method. History is a convenience
for accumulating messages, not a participant in generation.
Update micro chat, ai/flow, blog posts 9 and 10.
---------
Co-authored-by: Claude <noreply@anthropic.com>
|
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|
Asim Aslam
|
c4b4cbef25 |
refactor(ai): rename ToolSet to Tools, simplify wiring with WithTools (#2917)
Move tool discovery/execution fully into the ai package as ai.Tools (formerly ai.ToolSet), and simplify the usage model: - NewTools(reg, ai.ToolClient(c)) takes the execution client as an option instead of threading it through Handler(c) per call - New ai.WithTools(tools) option wires the tool handler into a model in one call, replacing ai.WithToolHandler(set.Handler(c)) - ai.DiscoverTools(reg) for one-shot discovery Before: set := ai.NewToolSet(reg) list, _ := set.Discover() m := ai.New(p, ai.WithToolHandler(set.Handler(client))) After: tools := ai.NewTools(reg, ai.ToolClient(client)) list, _ := tools.Discover() m := ai.New(p, ai.WithTools(tools)) Update ai/flow, micro chat, README, ai integration doc, Atlas Cloud guide, and blog posts 3/8/9/10. Co-authored-by: Claude <noreply@anthropic.com> |
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|
Asim Aslam
|
b97f45106d |
Update logo, add AI integration docs, and implement ai/flow package (#2913)
* feat: update Go Micro logo to interconnected nodes design
Replace the text-on-blue-square logo with a modern icon: three
teal nodes connected in a triangle, representing distributed
systems. Generated via Atlas Cloud. Clean at all sizes — works
as GitHub avatar, favicon, and nav bar icon.
* feat: new logo, AI integration architecture doc, and landing page CTA
Update logo to triangle-nodes icon + "Go Micro" text wordmark.
Save icon-only variant for favicon/avatar use.
Add docs/ai-integration.md — a single page that explains how the
AI stack fits together: services → registry → MCP gateway →
ai/tools → ai.Model → micro chat. Layer-by-layer with code
examples, provider table, and "what you don't need" section.
Add AI Integration to docs sidebar navigation (after Getting
Started). Update the landing page AI section with a direct CTA
button linking to the new doc.
* fix: restore original logo and add border-radius to all renders
Revert logo to original. Add border-radius: 8px to the logo img
in the landing page nav, docs layout nav, and blog layout nav
so the square logo renders with rounded corners everywhere.
Remove unused icon.png.
* feat(ai): add ai/flow package and micro flow CLI
Add ai/flow — event-driven LLM orchestration for go-micro. A Flow
subscribes to a broker topic, discovers services as tools, and
feeds each event into an LLM that decides which RPCs to call.
Key types:
- flow.New(name, opts...) creates a flow with trigger topic,
prompt template, provider config
- flow.Register(registry, broker, client) wires it into a service
- flow.Execute(ctx, data) runs the flow once (for testing/CLI)
- flow.Results() returns execution history
Add micro flow CLI with two subcommands:
- micro flow run: subscribe to a topic and react to events
- micro flow exec: one-shot execution with inline data
Both output JSON results with flow name, prompt, tool calls,
reply, answer, duration, and errors.
Example:
micro flow run --trigger events.user.created \
--prompt "New user: {{.Data}}. Send welcome email." \
--provider anthropic
micro flow exec --prompt "List all users" --provider anthropic
* docs: update flows blog post with ai/flow package and CLI examples
Add "Update: We Built It" section to blog/9 showing the ai/flow
package API, CLI usage for both event-driven and one-shot modes,
and what it does/doesn't do. Links the conceptual discussion to
the shipped implementation.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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Asim Aslam
|
c26d74a8a1 |
Add CRUD, pub/sub, and API gateway templates; update AI features (#2909)
* feat(website): redesign docs and blog layouts, add blog header images Redesign both layouts to match the new landing page: - Consistent nav bar with logo, Docs, Blog, GitHub, Reference, Home - Consistent footer with copyright and links - CSS custom properties for theming - Updated typography, spacing, and code block styling - Active sidebar link highlighting in docs - Dark mode support preserved Generate 4 blog header images via Atlas Cloud: - blog-deploy.png for post 1 (micro deploy) - blog-mcp.png for posts 2, 3, 7 (MCP-related) - blog-agents-demo.png for post 4 (agents demo) - blog-dx.png for post 5 (DX cleanup) - Reuse data-model.png for post 6 (model package) All 7 existing blog posts now have header images. * fix(website): prevent horizontal scroll on mobile landing page Add overflow-x: hidden on html and body. Set max-width: 100% and height: auto on all section and two-col images. Add overflow: hidden to .two-col grid. Constrain hero pre with max-width and overflow-x. Reduce font sizes and padding at mobile breakpoint. * feat(website): add images to remaining core doc pages Generate 5 more images via Atlas Cloud for docs: - registry.png: service discovery diagram - broker.png: pub/sub message broker pattern - transport.png: multi-transport layers (HTTP, gRPC, NATS) - config.png: dynamic configuration from multiple sources - observability.png: monitoring dashboard with metrics/traces Add images to registry.md, broker.md, transport.md, config.md, observability.md, and architecture.md. All 11 main doc pages now have header images. * feat: add sponsor logos to landing page, README images, and flows blog post Add Anthropic and Atlas Cloud sponsor logos to the landing page with links to their respective blog posts. Logos display at 0.7 opacity with hover effect. Add architecture and MCP agent images to the GitHub README for the Overview and MCP sections. Write blog post 9: "From Chat to Flows" — explores the concept of LLM-powered service orchestration. Compares micro chat's interactive model with persistent event-driven flows, shows how the existing building blocks (ai/tools, History, broker) could compose into a flow engine, discusses tradeoffs vs traditional orchestration (Step Functions, Temporal), and includes a working 15-line code example. Explicitly positions it as a concept for community feedback, not an announcement. * feat(website): add animated hero video to landing page Generate a 6-second hero video via Atlas Cloud's image-to-video API (gemini-omni-flash). Shows the microservices network diagram animating with data flowing between nodes. Replace the static hero image with an autoplay muted looping video element. Falls back to the static image via poster attribute and img fallback for browsers without video support. * feat(ai): add VideoModel interface with Atlas Cloud provider Add ai.VideoModel interface for video generation alongside Model and ImageModel. Supports text-to-video and image-to-video via VideoRequest with prompt, reference images, duration, aspect ratio, and resolution fields. Implement GenerateVideo for Atlas Cloud using their async API: POST /api/v1/model/generateVideo → poll /api/v1/model/prediction. Default model is gemini-omni-flash image-to-video. Polls every 5 seconds until completion or context cancellation. Register Atlas Cloud as a video provider via ai.RegisterVideo. Add 3 tests: registration, no-key error, compile-time interface check. Update ai/README.md with VideoModel docs. The ai package now covers all three modalities: - Model (text) — 7 providers - ImageModel (image) — 2 providers (Atlas Cloud, OpenAI) - VideoModel (video) — 1 provider (Atlas Cloud) --------- Co-authored-by: Claude <noreply@anthropic.com> |
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|
Asim Aslam
|
669481224c |
Enhance AI features with ImageModel, History, and website updates (#2907)
* feat(cli): add CRUD, pub/sub, and API gateway templates for micro new
Add --template flag to 'micro new' with three preset templates:
- crud: CRUD service with Create/Read/Update/Delete/List, in-memory
store with sync.RWMutex, UUID generation, pagination, and doc
comments with @example tags for MCP tool discovery.
- pubsub: Event-driven service with Publish/Stats RPCs and a
Subscribe method that hooks into the broker. Includes event
types with ID, type, source, data, and timestamp.
- api: API gateway service with Health and Endpoint RPCs, an
internal HTTP route table, and a response recorder for
proxying requests through RPC.
All templates include MCP-ready doc comments and work with
--no-mcp. The default template (no flag) is unchanged.
Usage:
micro new myservice --template crud
micro new myservice --template pubsub
micro new myservice --template api
* fix(ai): update Atlas Cloud provider to use actual API formats
Fix the Atlas Cloud image generation to use their real async API:
POST /api/v1/model/generateImage → poll /api/v1/model/prediction/{id}
instead of the OpenAI-compatible endpoint which doesn't exist.
Add Quality and OutputFormat fields to ai.ImageRequest for
provider-specific image parameters.
Update default text model from llama-3.3-70b (doesn't exist) to
deepseek-ai/DeepSeek-V3-0324 (their flagship model). Update
default image model to openai/gpt-image-2/text-to-image.
* feat(website): add AI-generated images to landing page, docs, and blog
Generate 5 images via Atlas Cloud's image API (gpt-image-2) to
elevate the website experience:
- hero.png: microservices network graph for landing page
- architecture.png: registry + broker architecture diagram
- mcp-agent.png: AI agent calling services via MCP
- developer-experience.png: terminal showing micro run/chat
- blog-atlas.png: Atlas Cloud unified API illustration
Add visual sections to the landing page with architecture,
MCP integration, and developer experience showcases. Add
images to docs index, MCP docs, and Atlas Cloud blog post.
All images resized to 1200px wide and optimized for web.
Generated using Atlas Cloud sponsor credits.
* feat(website): redesign landing page and add images to docs
Redesign the landing page from a centered card layout to a
full-width modern site with:
- Top navigation bar
- Hero section with gradient background and CTA buttons
- Full-width image showcase sections
- Two-column layout for architecture, MCP, and DX sections
- Feature grid with 6 capabilities
- Footer with links
- Responsive breakpoints for mobile
Generate 3 more images via Atlas Cloud for docs:
- getting-started.png for the getting started guide
- deployment.png for the deployment guide
- data-model.png for the data model docs
Add images to getting-started.md, model.md, and deployment.md.
---------
Co-authored-by: Claude <noreply@anthropic.com>
|
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|
Asim Aslam
|
86782e6d77 |
Add ImageModel interface and multi-turn conversation support (#2906)
* feat(ai): add ImageModel interface with Atlas Cloud and OpenAI support Add ai.ImageModel interface for text-to-image generation alongside the existing ai.Model for text. Uses the same options pattern (WithAPIKey, WithBaseURL) and the same provider registration system (RegisterImage/NewImage). Implement GenerateImage for Atlas Cloud and OpenAI providers via the OpenAI-compatible /v1/images/generations endpoint. Default image model is gpt-image-1. Responses return images as URL, base64, or both depending on the provider. Update Atlas Cloud blog post and integration guide with image generation examples. Update ai/README.md with ImageModel docs. * fix(website): widen docs content by reducing layout max-width to 1100px Remove the 800px max-width on .content (which left empty space on the right) and reduce the overall .layout and footer from 1400px to 1100px. With the 230px sidebar this gives ~830px of content width — readable and fills the page properly on desktop. * feat(ai): add History for multi-turn conversation state Add ai.History — a lightweight message accumulator that tracks user prompts, assistant replies, and tool call/result pairs across turns. FIFO truncation when message count exceeds the configured limit. System prompt is passed through on every Generate call. Wire History into micro chat so conversations are multi-turn by default (limit 50 messages). Add 'reset' command to clear history mid-session. 5 unit tests covering accumulation, truncation, reset, snapshot isolation, and tool call recording. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
1e2901ad0e |
feat(ai): add ImageModel interface with Atlas Cloud and OpenAI support (#2905)
goreleaser / goreleaser (push) Has been cancelled
Add ai.ImageModel interface for text-to-image generation alongside the existing ai.Model for text. Uses the same options pattern (WithAPIKey, WithBaseURL) and the same provider registration system (RegisterImage/NewImage). Implement GenerateImage for Atlas Cloud and OpenAI providers via the OpenAI-compatible /v1/images/generations endpoint. Default image model is gpt-image-1. Responses return images as URL, base64, or both depending on the provider. Update Atlas Cloud blog post and integration guide with image generation examples. Update ai/README.md with ImageModel docs. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
081e375f29 |
Add AI provider integration guide and new providers support (#2900)
* docs: add AI provider integration guide and Supported AI Providers section Add a step-by-step guide for AI infrastructure companies to implement ai.Model and contribute a provider to go-micro. Covers the full lifecycle: skeleton, tool call handling, tests, registration, and PR checklist. Add a "Supported AI Providers" section to the project README that lists current providers (Anthropic, OpenAI) in a table and links to the integration guide with a call-to-action for new providers and sponsors. Streamline the "Adding a New Provider" section in ai/README.md to point to the new guide instead of duplicating a full code listing. * fix: remove nonexistent Discord link from README * fix(website): set content container width to 800px on desktop Move the 800px max-width from .markdown-body up to .content so the entire content pane (not just the inner body) is sized correctly. The container now fills up to 800px beside the sidebar. * feat(ai): wire Atlas Cloud into server and auto-detection Import atlascloud provider in the micro server so it is available when running micro run / micro server. Add atlascloud to AutoDetectProvider so --ai_base_url with an atlascloud domain selects the right provider automatically. * feat(ai): add Google Gemini provider Add ai/gemini implementing ai.Model for Google's Gemini API. Uses the native generateContent endpoint with system_instruction, contents/parts, and functionDeclarations — not an OpenAI shim. Default model gemini-2.5-flash, auth via x-goog-api-key header. Wire into micro server imports and AutoDetectProvider (matches googleapis.com and google in base URL). Update README.md and ai/README.md with provider listing. * feat(ai): add Groq, Mistral, and Together AI providers Add three new OpenAI-compatible providers: - ai/groq: ultra-fast inference, default model llama-3.3-70b-versatile - ai/mistral: Mistral AI, default model mistral-large-latest - ai/together: Together AI, default model Llama-3.3-70B-Instruct-Turbo All three are wired into the micro server imports and AutoDetectProvider. README and ai/README updated with the full provider table. * feat(ai): add ai/tools helper and 'micro chat' interactive agent Extract the registry-discovery + RPC-execution loop from the web agent playground into a reusable ai/tools package: - tools.New(reg) creates a Set bound to a registry - Set.Discover() walks the registry and returns []ai.Tool with LLM-safe (underscored) names, remembering the mapping back to the original dotted form - Set.Handler(client) returns an ai.ToolHandler that resolves the safe name and issues the RPC Add cmd/micro/chat — an interactive 'micro chat' REPL that uses ai/tools to let users talk to their services through any registered AI provider. Supports --prompt for single-shot use, auto-detects the provider from --base_url, and falls back to the provider's conventional env var (ANTHROPIC_API_KEY, etc). Update README with the new command and the programmatic example. * feat(examples): add gRPC interop example Add examples/grpc-interop showing that any standard gRPC client can call a go-micro service — no go-micro SDK required on the client side. Includes: - proto/greeter.proto with generated Go, gRPC, and micro stubs - server/ using go-micro gRPC transport - client/ using stock google.golang.org/grpc (no go-micro imports) - README with Python example and explanation of how routing works Addresses the confusion from issue #2818 where users didn't know that go-micro gRPC services are callable by any gRPC client. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
60527e1fe8 |
Add AI provider integration guide and Atlas Cloud support (#2898)
* docs: add AI provider integration guide and Supported AI Providers section Add a step-by-step guide for AI infrastructure companies to implement ai.Model and contribute a provider to go-micro. Covers the full lifecycle: skeleton, tool call handling, tests, registration, and PR checklist. Add a "Supported AI Providers" section to the project README that lists current providers (Anthropic, OpenAI) in a table and links to the integration guide with a call-to-action for new providers and sponsors. Streamline the "Adding a New Provider" section in ai/README.md to point to the new guide instead of duplicating a full code listing. * feat(ai): add Atlas Cloud provider Add ai/atlascloud implementing ai.Model for Atlas Cloud's OpenAI-compatible chat completions API. Registers as "atlascloud" with default model llama-3.3-70b and base URL https://api.atlascloud.ai. Supports tool calling via ToolHandler. Includes 7 unit tests covering registration, defaults, init, generate-without-key, and stream-not-implemented. Update the Supported AI Providers table in README.md and the Supported Providers section in ai/README.md. * fix: remove nonexistent Discord link from README --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
defb2786ef |
update AI provider documentation (#2897)
goreleaser / goreleaser (push) Has been cancelled
* feat: add prometheus monitoring wrapper Reintroduces the Prometheus metrics wrapper previously available in the plugins repository, updated for go-micro v5. Exposes request count and latency histograms for handlers, subscribers, and outgoing client calls via NewHandlerWrapper, NewSubscriberWrapper, NewCallWrapper and NewClientWrapper, labelled with service/endpoint/status. Options cover namespace, subsystem, const labels, histogram buckets and a custom registerer; duplicate collectors (e.g. from multiple wrappers sharing the same config) are reused transparently via a cached metrics bundle. Fixes #2893 * fix(registry/etcd): clear lease/register caches on KeepAlive channel closure When the etcd client's long-lived KeepAlive channel closes (e.g. because the lease expired on the server side during a network partition), the previous cleanup only removed the channel bookkeeping. The stale entries in `leases` and `register` caused the next registerNode() heartbeat to hit the "unchanged hash" short-circuit and skip re-registration entirely, so the service permanently disappeared from etcd. Extract the cleanup into handleKeepAliveClosed and also drop the cached lease id and hash so the next heartbeat performs a full Grant+Put and the service recovers within one RegisterInterval. Regression introduced by #2822; fix is symmetric with the existing synchronous KeepAliveOnce recovery path that propagates rpctypes.ErrLeaseNotFound. * docs: add AI provider integration guide and Supported AI Providers section Add a step-by-step guide for AI infrastructure companies to implement ai.Model and contribute a provider to go-micro. Covers the full lifecycle: skeleton, tool call handling, tests, registration, and PR checklist. Add a "Supported AI Providers" section to the project README that lists current providers (Anthropic, OpenAI) in a table and links to the integration guide with a call-to-action for new providers and sponsors. Streamline the "Adding a New Provider" section in ai/README.md to point to the new guide instead of duplicating a full code listing. * Update contribution guidelines in README.md Removed Discord contact information for platform contributions. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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76bfeae456 |
Claude/update docs roadmap f zd2 j (#2880)
* docs: update all four documentation guides and mark Q2 complete - ai-native-services: add WithMCP one-liner, standalone gateway, WebSocket client example, and OpenTelemetry observability section - mcp-security: add OTel distributed tracing, WebSocket authentication (connection-level and per-message), DeniedReason audit field - tool-descriptions: add manual overrides with WithEndpointDocs and export formats section - agent-patterns: add LangChain/LlamaIndex SDK pattern and standalone gateway production pattern with Docker example - Update roadmap: mark Q2 documentation as complete, Q2 at 100% - Update status: reflect all recent completions, shift priorities https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add agent demo example and blog post Add examples/agent-demo with a multi-service project management app (projects, tasks, team) that demonstrates AI agents interacting with Go Micro services through MCP. Includes seed data and example prompts. Add blog post 4 "Agents Meet Microservices: A Hands-On Demo" walking through the example code and showing cross-service agent workflows. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: enable multiple services in a single binary Remove global state mutations from service and cmd option functions so that configuring one service no longer overwrites another's settings. Key changes: - service/options.go: remove all DefaultXxx global writes from option functions; newOptions() now creates fresh Server, Client, Store, and Cache per service while sharing Registry, Broker, and Transport - cmd/cmd.go: newCmd() uses local copies instead of pointers to package globals; Before() no longer mutates DefaultXxx vars - cmd/options.go: remove global mutations from all option functions - service/service.go: export ServiceImpl type for cross-package use - service/group.go: new Group type for multi-service lifecycle - micro.go: add Start/Stop to Service interface, expose Group and NewGroup convenience function - examples/multi-service: working example with two services https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * docs: highlight multi-service binary support Add multi-service section to README with code example, update features list, add to examples index, and note in status summary. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: unify service API and clean up developer experience - Unified service creation: micro.New("name", opts...) as canonical API - Clean handler registration: service.Handle(handler, opts...) accepts server.HandlerOption args directly, no need to reach through Server() - Unexported serviceImpl: users interact through Service interface only - Service groups use Service interface (not concrete type) - Fixed Stop() to properly propagate BeforeStop/AfterStop errors - Fixed store init: error-level log instead of fatal on init failure - Updated all examples to use consistent patterns - Updated README, getting-started, MCP docs, and guides - Added blog post about the DX cleanup https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * fix: add blog post 5 to blog index Blog post 5 (Developer Experience Cleanup) existed as a file but was missing from the blog index page. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: make micro new generate MCP-enabled services by default - main.go template includes mcp.WithMCP(":3001") by default - Handler template has agent-friendly doc comments with @example tags - Proto template has descriptive field comments - README includes MCP usage, Claude Code config, and tool description tips - Makefile adds mcp-tools, mcp-test, mcp-serve targets - go.mod updated to Go 1.22 - Added --no-mcp flag to opt out of MCP integration - Post-create output shows MCP endpoint URLs https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * docs: add MCP migration guide and troubleshooting guide - Migration guide: 3 approaches to add MCP to existing services (WithMCP one-liner, standalone gateway, CLI) - Troubleshooting guide: common issues with agents, WebSocket, Claude Code, auth, rate limiting, and performance https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * refactor: rename model/ package to ai/ for AI model providers The model/ package name conflicted with the conventional use of "model" for data models. Renamed to ai/ which better describes the package's purpose (AI provider abstraction for Anthropic, OpenAI, etc.) and frees up model/ for future data model layer use. - Rename model/ → ai/ with package name change - Update all Go imports from go-micro.dev/v5/model to go-micro.dev/v5/ai - Update cmd/micro/server/server.go references (model.X → ai.X) - Update all documentation and roadmap references - All tests pass, CLI builds successfully https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add model package for typed data access with CRUD and queries New model/ package provides a typed data model layer using Go generics. Supports structured CRUD operations, WHERE filters, ordering, pagination, and automatic schema creation from struct tags. Three backends: - memory: in-memory for development and testing - sqlite: embedded SQL for dev and single-node production - postgres: full PostgreSQL for production deployments Key features: - Generic Model[T] with Create/Read/Update/Delete/List/Count - Query builder: Where(), WhereOp(), OrderAsc/Desc(), Limit(), Offset() - Struct tags: model:"key" for primary key, model:"index" for indexes - Auto table creation from struct schema - 19 tests passing across memory and sqlite backends https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add model code generation to protoc-gen-micro Extend the micro plugin to generate model structs from proto messages annotated with // @model. Generated alongside client/server code in the same .pb.micro.go file. For a proto message like: // @model message User { string id = 1; string name = 2; } Generates: - UserModel struct with model:"key" and json tags - NewUserModel(db) factory returning *model.Model[UserModel] - UserModelFromProto(*User) *UserModel converter - (*UserModel).ToProto() *User converter Supports @model(table=custom_table, key=custom_field) options. Adds GetComments() to generator for plugin comment inspection. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add Model() to Service interface for Client/Server/Model trifecta Every service now exposes Client(), Server(), and Model() — call services, handle requests, and save/query data from the same interface. Includes README docs, blog post, and a full model guide on the docs site. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc --------- Co-authored-by: Claude <noreply@anthropic.com> |