Add LoopLimit: refuse a tool call repeated with identical arguments in
one Ask, with a self-heal message so the model changes approach. Catches
the no-progress loop that MaxSteps (count) and the gateway circuit
breaker (failures) miss. Enforced at the same tool-handler choke point as
MaxSteps/ApproveTool; on by default (lenient 3); AgentLoopLimit(0) to
disable. Tests cover repeats, distinct calls, disabled, and default-on.
Docs: new Agent Guardrails guide (MaxSteps/LoopLimit/ApproveTool, the
ApproveTool integration seam for external policy engines, and the
gateway's RateLimit/CircuitBreaker), nav + README + AGENT_DESIGN updates,
and blog/23 'Agent Guardrails'.
Co-authored-by: Claude <noreply@anthropic.com>
* feat(mcp): advertise x402 payment requirements in the tool catalog
/mcp/tools now includes each priced tool's payment requirements (amount,
network, asset, payTo) when payments are enabled, so an agent can see the
cost before calling and choose by price — a shoppable catalog, the
foundation for a tool marketplace. Free tools carry no payment block; the
shared Tool struct is copied when pricing so it isn't mutated. Tests cover
priced/free tools and payments-disabled. Documented in the payments guide.
* feat(x402): consumer client with a spend budget (pay-and-retry)
Add x402.Client, the consumer counterpart to Middleware: it settles 402
challenges automatically via a pluggable Payer, up to a spend Budget. A
call that would exceed the budget is refused before any payment is made,
and spend accumulates across calls — the spend cap that keeps an
autonomous, paying caller in bounds. Tests cover pay-within-budget,
refuse-over-budget, budget accumulation, and free endpoints, end to end
against the server Middleware with a mock facilitator and payer. Guide
documents the consumer side; agent-level AgentMaxSpend is the next step.
* chore: gofmt gateway/mcp/benchmark_test.go (trailing newline)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Follow-up to the merged x402 integration (#2964). Drop the commerce-y
'price' vocabulary for the protocol's own 'amount', and add per-tool
pricing as an operator concern (the way scopes/rate-limits are set at the
gateway).
- x402.Config: Price -> Amount (default), plus Amounts map for per-tool
overrides; AmountFor(tool) resolves per-tool -> default. Add a Require
primitive (per-request enforcement) and LoadConfig for an operator
config file.
- MCP gateway: enforce payment per-tool inside /mcp/call (where scopes
are enforced) using AmountFor, instead of a flat path-based middleware.
- CLI: --x402-price -> --x402-amount; add --x402-config (per-tool file)
to micro mcp serve and micro-mcp-gateway.
- docs: new Payments (x402) guide + nav + README section; blog/22
updated to Amount/Amounts and the config-file model.
Co-authored-by: Claude <noreply@anthropic.com>
A go-micro service can keep running while it has silently lost its
connection to the registry (etcd, Consul, …) — the process looks healthy
but other services can no longer discover it, and Kubernetes sees the
pod as fine. health.RegistryCheck(reg) probes connectivity via
ListServices and, registered as a critical check, makes /health/ready
report not-ready so a readiness probe can pull the pod from rotation.
- Works with any registry implementation (no interface change).
- Honors the check timeout: an unreachable/hung registry is reported
down rather than blocking the probe.
- Tests cover healthy, down, timeout, nil, and the not-ready integration.
- Documented in the health guide with the Kubernetes readiness example.
Co-authored-by: Claude <noreply@anthropic.com>
* docs: group README features by section (AI / Framework / DX)
The features table repeated 'AI' down the Category column. Split into
three grouped tables — AI, Framework, Developer experience & deployment —
dropping the repetitive column. Adds a Guardrails row (MaxSteps,
ApproveTool).
* test: flow-to-agent end-to-end in the harness
Proves 'Flow triggers, Agent reasons': a workflow with FlowAgent hands an
event to the registered conductor agent over RPC, which plans, creates
tasks, and delegates to comms — the whole chain over real RPC with only
the LLM mocked. Deterministic (shared in-memory registry, no sleeps),
passes under -race.
* blog: 'The Evolution of Microservices' (#19)
A technical history of distributed-systems eras — the monolith's
coordination cost, the distributed-systems tax, containers and
declarative orchestration, the service mesh, and the modular-monolith
correction — establishing the durable unit (named, typed, discoverable,
independently deployable) that every runtime wave required. Then the
technical argument for agents: an LLM tool call needs exactly a service
interface, so the caller shifts from deterministic code to a reasoner
that composes typed capabilities from intent, with the honest caveats
(non-determinism, cost, guardrails). Not a product pitch.
* docs: bump install version to v5.27.0
---------
Co-authored-by: Claude <noreply@anthropic.com>
* docs: group README features by section (AI / Framework / DX)
The features table repeated 'AI' down the Category column. Split into
three grouped tables — AI, Framework, Developer experience & deployment —
dropping the repetitive column. Adds a Guardrails row (MaxSteps,
ApproveTool).
* test: flow-to-agent end-to-end in the harness
Proves 'Flow triggers, Agent reasons': a workflow with FlowAgent hands an
event to the registered conductor agent over RPC, which plans, creates
tasks, and delegates to comms — the whole chain over real RPC with only
the LLM mocked. Deterministic (shared in-memory registry, no sleeps),
passes under -race.
* blog: 'The Evolution of Microservices' (#19)
A technical history of distributed-systems eras — the monolith's
coordination cost, the distributed-systems tax, containers and
declarative orchestration, the service mesh, and the modular-monolith
correction — establishing the durable unit (named, typed, discoverable,
independently deployable) that every runtime wave required. Then the
technical argument for agents: an LLM tool call needs exactly a service
interface, so the caller shifts from deterministic code to a reasoner
that composes typed capabilities from intent, with the honest caveats
(non-determinism, cost, guardrails). Not a product pitch.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* blog: 'Not Everything Should Be an Agent' (#18) on workflows
The workflow counterpart to the plan/delegate post: when the path is
known, use a deterministic Flow, not an autonomous agent. Frames flow vs
agent as two modes of the same building blocks, covers flow-triggers-
agent dispatch and the agent guardrails, and gives the simplest-first
guidance (single call -> workflow -> agent). Continues the arc from
blog 14/16/17; references Building Effective Agents in passing.
* docs: fix new-user onboarding friction
- README: lead Quick Start with a no-key 30-second path (micro new ->
micro run -> curl), then the AI --prompt path with an explicit
'export ANTHROPIC_API_KEY' so the headline command no longer fails
silently for users without a key.
- Unify all install versions to v5.26.0 (README + docs were split across
v5.16.0 / v5.25.0).
- Refresh the docs landing overview from the old 'microservices
framework' framing to 'services and agents', matching the README.
- getting-started: add Prerequisites (Go 1.21+, and that a provider key
is only needed for AI features).
- README features: Flows -> Workflows wording.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* docs: map go-micro onto Anthropic's workflows-vs-agents taxonomy
- new guide 'Agents and Workflows': adopts Anthropic's Building Effective
Agents vocabulary — workflow (predefined path) = flow, agent (dynamic
self-direction) = agent — maps the augmented-LLM building block and the
five workflow patterns onto go-micro, and shows routing (chat router)
and orchestrator-workers (conductor + plan/delegate) are already native.
- flow package doc reframed as a workflow (predefined path) per the same
taxonomy, with guidance on flow vs agent.
- nav + README link the new guide.
* feat: agent guardrails — step limit and tool approval hook
Anthropic's Building Effective Agents stresses stopping conditions and
human-in-the-loop checkpoints for autonomous agents. Add both as plain
options enforced at the tool-handler choke point — no provider changes,
no new abstraction:
- MaxSteps(n): bound tool executions per Ask; beyond the limit, actions
are refused and the model is told to stop and summarize.
- ApproveTool(fn): gate each action before it runs; returning false
blocks it and surfaces the reason to the model. The internal plan tool
is never gated.
Exposed at the micro package (AgentMaxSteps, AgentApproveTool, ApproveFunc).
Tests cover the limit, blocking, and that plan is not gated. Guardrails
section of the agents-and-workflows guide updated from 'active work' to
documented options.
* feat: flow can dispatch to an agent (flow triggers, agent reasons)
Unify the engine without collapsing the workflow/agent distinction. A
Flow with Agent set hands each event's rendered prompt to a named
registered agent over RPC (Agent.Chat) instead of running its own LLM
step — so the workflow stays the deterministic trigger and the agent is
the reasoning engine, with its plan, delegate, memory, and guardrails.
A plain flow is unchanged (single augmented-LLM step).
- flow.Agent(name) / micro.FlowAgent(name); flow stores the client and
skips model setup when dispatching.
- test: dispatch routes to comms.Agent.Chat with the rendered prompt and
records the reply.
- guide: 'Flow triggers, Agent reasons' section.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* feat: add plan and delegate as built-in agent tools
Give agents two self-capabilities, expressed as plain tools wired into
the existing tool handler — no harness or graph, consistent with
"services are the only abstraction":
- plan: record/update an ordered plan, persisted to store-backed memory
and surfaced in the system prompt on later turns (externalized
planning).
- delegate: hand a self-contained subtask to another agent.
Delegate-first — if the target names a registered agent it is called
via RPC; otherwise a focused ephemeral sub-agent is created with
agent.New + Ask in a fresh, isolated context (loads/persists no
history, no built-in tools, so it cannot re-delegate).
Both are added automatically to any non-ephemeral agent, so existing
micro.NewAgent services and micro chat routing get them for free.
Tests are hermetic (memory store + memory registry).
* feat: add agent-plan-delegate example and document plan/delegate
- examples/agent-plan-delegate: coordinator that plans multi-step work,
creates tasks with its own tools, and delegates notification to a
separate registered comms agent over RPC.
- integration tests driving the full Ask loop through a fake provider:
plan tool exposure + persistence, ephemeral delegation with isolated
context, delegate-first RPC routing to a registered agent.
- docs: README (Building Agents + features + examples), AGENT_DESIGN
(Built-in Capabilities), agent-patterns guide (Pattern 9), CLAUDE.md.
* docs: blog post and guide for plan & delegate
- blog/17: "Plan & Delegate: Deep Agents in Go" — what the feature is,
how plan and delegate work, and a runnable getting-started path.
- guides/plan-delegate: reference guide with the smallest-agent snippet,
plan/delegate semantics, and the multi-agent example; linked in nav.
- example: auto-detect provider/key from common env vars (ANTHROPIC_API_KEY,
OPENAI_API_KEY, ...) so 'export KEY && go run main.go' just works.
- onboarding: getting-started paths now include go mod init / go get and a
clone-and-run path, so a reader can actually run it from a cold start.
* refactor: reframe plan/delegate blog and clean up sub-agent construction
- blog/17 retitled "Agents That Plan and Delegate" and reframed around
intent (plan = state intent, delegate = direct it), positioned as the
next beat after blog 15/16 and tied to the existing store + agent RPC
rather than re-announcing them. "Deep agents" now a single in-passing
nod, matching how blog 14 references LangChain.
- agent: add unexported newEphemeral constructor for sub-agents instead
of type-asserting the public Agent interface to set an internal field;
matches the options-only construction idiom used elsewhere.
* feat: expose plan & delegate in the micro chat fallback
Add agent.Builtins(opts...) — returns the built-in tools plus a handler,
so the plan/delegate capabilities can be wired into a tool loop that
isn't a running Agent. micro chat's direct-service fallback now reuses
it (single source of truth, no duplicated handler logic), so planning
and delegation are available there too, not just for registered agents.
Adds a test for the accessor; notes CLI availability in the guide.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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>
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>
Add blog/8 announcing Atlas Cloud as an official Go Micro sponsor.
Covers the sponsorship, Atlas Cloud's platform (300+ models, OpenAI
compatibility, enterprise compliance), and how the integration
works with the ai package, ai/tools, micro chat, and micro run.
Add guides/atlascloud-integration.md with full setup instructions:
quick start, configuration options, environment variables, model
selection, tool calling with services, and provider swapping.
Add Atlas Cloud and AI Provider guides to the docs sidebar
navigation. Add sponsorship link to README header.
Co-authored-by: Claude <noreply@anthropic.com>
* 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.
* fix: strip /api prefix from MCP routes
Change /api/mcp/tools and /api/mcp/call to /mcp/tools and
/mcp/call. MCP is a first-class feature, not a sub-path of the
API proxy. Update server routes, playground template, scopes
template, run.go output, README, CLI README, and all docs.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* 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>
* 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>
* 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
---------
Co-authored-by: Claude <noreply@anthropic.com>
* 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
---------
Co-authored-by: Claude <noreply@anthropic.com>
* Update docs and roadmap to March 2026 with focus priorities
- ROADMAP.md: Updated from Nov 2025 to reflect Q1 completions and current state
- ROADMAP_2026.md: Updated status to March 2026, added model package as delivered
- CURRENT_STATUS_SUMMARY.md: Rewrote with March 2026 status and clear next priorities
- PROJECT_STATUS_2026.md: Added model package section, updated recommendations
- Website roadmap: Updated Q3 security status and timestamps
Key focus areas identified: documentation guides, multi-protocol MCP,
LlamaIndex SDK, and OpenTelemetry integration.
https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc
* Add CLAUDE.md and four documentation guides to fill doc gaps
- CLAUDE.md: Project guide with structure, build commands, and priorities
- ai-native-services.md: End-to-end tutorial building an MCP-enabled task service
- mcp-security.md: Production security guide (auth, scopes, rate limiting, audit)
- tool-descriptions.md: Best practices for writing Go comments that help agents
- agent-patterns.md: Six integration patterns from single-agent to event-driven
- Updated docs index with new "AI & Agents" section linking all four guides
These were the highest priority gaps identified in the roadmap analysis:
the framework has solid features that were under-documented.
https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc
---------
Co-authored-by: Claude <noreply@anthropic.com>
Major Features:
- Unified gateway architecture (micro run + micro server use same code)
- MCP (Model Context Protocol) integration as library package
- AI-accessible microservices with 3 lines of code
Gateway Unification:
- Created reusable gateway module (cmd/micro/server/gateway.go)
- Updated micro run to use unified gateway (removed duplicate code)
- Conditional authentication (disabled in dev, required in prod)
- Reduced code duplication, simplified maintenance
MCP Integration:
- New library package: gateway/mcp
- Automatic service discovery → MCP tools
- HTTP/SSE transport support (stdio coming soon)
- Works for both library users and CLI users
- CLI flags: --mcp-address for micro run and micro server
Documentation:
- ADR-010: Unified Gateway Architecture
- CLI & Gateway Guide for users
- MCP Gateway README and examples
- Blog post: Making Your Microservices AI-Native with MCP
Breaking Changes: None (fully backward compatible)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
micro build:
- Default: builds Go binaries to ./bin/
- Cross-compile with --os and --arch
- Docker is optional via --docker flag
micro deploy:
- Requires --ssh user@host
- Copies pre-built binaries (if ./bin/ exists)
- Or syncs source and builds on remote
- No Docker dependency
Go binaries are self-contained. No runtime needed.
Co-authored-by: Shelley <shelley@exe.dev>
Provides a test harness for running micro services in isolation:
h := testing.NewHarness(t)
defer h.Stop()
h.Name("users").Register(new(UsersHandler))
h.Start()
var rsp Response
h.Call("UsersHandler.Create", &req, &rsp)
Features:
- Isolated registry, transport, and broker per harness
- Simple API: Name(), Register(), Start(), Stop()
- Call helpers: Call(), CallContext()
- Assertions: AssertServiceRunning(), AssertCallSucceeds(), AssertCallFails()
- Access to underlying Client(), Server(), Registry()
Note: Due to go-micro's global defaults, each harness tests one service.
For multi-service testing, use integration tests or mocks.
Also fixes README.md example showing conflicting port 8080.
Co-authored-by: Shelley <shelley@exe.dev>
micro run now starts an HTTP gateway alongside your services:
- Web dashboard at http://localhost:8080
- API proxy at /api/{service}/{method}
- Health checks at /health
- Service listing at /services
The experience is now:
$ micro new helloworld
$ cd helloworld
$ micro run
Open http://localhost:8080 to see and call your services.
New flags:
--address :3000 # Custom gateway port
--no-gateway # Disable gateway (services only)
Updated documentation to make this the central experience.
Co-authored-by: Shelley <shelley@exe.dev>
Adds a new health package providing:
- /health endpoint for overall health status
- /health/live for Kubernetes liveness probes
- /health/ready for Kubernetes readiness probes
- Built-in checks: PingCheck, TCPCheck, HTTPCheck, DNSCheck
- Critical vs non-critical checks
- Concurrent check execution
- Configurable timeouts
- Service info metadata
Integrates with micro run's health check waiting when services
specify a port in their micro.mu configuration.
Example usage:
health.Register("database", health.PingCheck(db.Ping))
health.Register("redis", health.TCPCheck("localhost:6379", time.Second))
health.RegisterHandlers(mux)
Co-authored-by: Shelley <shelley@exe.dev>
* Initial plan
* docs: clarify gRPC server option ordering and service name usage
- Fix all examples to show Server option before Name option
- Add note explaining why option ordering matters
- Add new section on "Option Ordering Issue" in Common Errors
- Add new section on "Service Name vs Package Name" in Common Errors
- Update transport.md to show proper ordering with comment
Co-authored-by: asim <17530+asim@users.noreply.github.com>
* docs: refine comments based on code review feedback
- Clarify that Server ordering before Name is mandatory
- Remove confusing comment about Client ordering being for consistency
Co-authored-by: asim <17530+asim@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: asim <17530+asim@users.noreply.github.com>
Asim Aslam