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7 次代码提交

作者 SHA1 备注 提交日期
Asim Aslam c7657f73f4 Refactor agent plan storage, update docs, and release v6 (#2977)
goreleaser / goreleaser (push) Has been cancelled
* test(harness): read agent plan from the scoped store

The store-scoping change moved an agent's plan from the default table
key agent/{name}/plan to its own table (database "agent", table {name},
key "plan"). The plan-delegate harness tests still read the old key and
failed with 'not found'; read through store.Scope(mem, "agent", name)
like the agent does.

* docs: orient agents-first across README, landing, and docs overview

Lead with agents (then services and flows), surface MCP + A2A as the
interop story, and frame agents as services. Landing hero and feature
grid reordered agents-first with an A2A gateway card.

* v6: module path go-micro.dev/v6, TLS secure by default, NewService

Cut v6. Three breaking changes, bundled so the major bump is paid once:

- Module path go-micro.dev/v5 -> go-micro.dev/v6 across all imports + go.mod.
- TLS verification on by default (was off). MICRO_TLS_SECURE removed;
  MICRO_TLS_INSECURE=true opts out for self-signed/dev.
- micro.NewService(name, opts...) is the canonical service constructor,
  symmetric with NewAgent/NewFlow; micro.New kept as a deprecated alias;
  the old name-less NewService(opts...) removed. Generators emit NewService.

Also ports the JWT auth token provider in-module (go-micro.dev/v6/auth/jwt/token
on golang-jwt/jwt/v5), dropping the v5-pinned github.com/micro/plugins/v5/auth/jwt
and the deprecated dgrijalva/jwt-go.

Docs/README/landing updated to v6 and @latest; v5->v6 migration guide added;
CHANGELOG cut as [6.0.0]. Blog posts left at their historical versions.

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-06-18 11:55:35 +01:00
Asim Aslam 550033dcce Optimize image formats and fix lease re-registration issue (#2959)
* perf: convert generated PNGs to optimized JPEGs (12MB -> 1.5MB)

The landing and docs loaded 18 AI-generated PNGs at 0.5-1MB each. They're
1200x800 RGB illustrations with no transparency, so they recompress ~8x
as progressive JPEG (quality 82) with no visible loss. Convert all,
update every reference (.png -> .jpg), and drop the originals (including
the unused hero.png). Generated images: 12.3MB -> 1.5MB.

* fix(registry/etcd): re-register when a lease silently expires (#2956)

The keepalive rework (long-lived KeepAlive instead of KeepAliveOnce)
moved lease renewal entirely onto the keepalive goroutine; the 30s
periodic Register now skips on the 'unchanged' check. The goroutine only
reacted to the keepalive channel closing, so a lease that expired
server-side without a prompt channel close (e.g. a partition that
outlasted the 90s TTL) left the node de-registered from etcd while the
cache still believed it was registered — and nothing re-registered it.
That is the hidden-failure mode reported in #2956.

React to a non-positive TTL keepalive response the same as a channel
close: drop the cached lease/hash so the next Register performs a full
re-registration. Extract the loop into keepAliveLoop and unit-test the
TTL-expired, channel-closed, and healthy paths (no etcd required).

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-06-10 08:24:49 +01:00
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>
2026-05-30 16:20:38 +01:00
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>
2026-05-30 14:21:35 +01:00
Asim Aslam a9421e5b7e Update logo design, add AI integration documentation and blog post (#2916)
* 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.

* docs: add micro chat blog post

Write blog/10 — a dedicated post for micro chat covering:
- What it does (interactive LLM agent for services)
- How it works (ai/tools → ai.History → ai.Model stack)
- Multi-turn conversation examples
- All provider options and env vars
- Single prompt mode for scripting
- Why it works (registry metadata + doc comments = tool descriptions)
- Programmatic usage with the same building blocks
- Link to micro flow as the event-driven counterpart

Add to blog index. Update blog 9 nav to link forward.

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-05-30 13:46:05 +01:00
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>
2026-05-29 17:13:16 +01:00
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>
2026-05-29 15:52:35 +01:00