* 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>
Run Tests / Etcd Integration Tests (push) Has been cancelled
goreleaser / goreleaser (push) Has been cancelled
* perf: compress hero image — 1.4MB to 80KB
Resized from 1536px to 1200px, converted to JPEG at quality 80.
80KB loads instantly vs 1.4MB stalling on slower connections.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* docs: micro run drops into interactive console
One command does everything — generate, start, and chat. No
separate micro chat step. The landing page shows micro run
dropping straight into the > prompt.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: interactive console in micro run, -d for detached mode
micro run now drops into an interactive chat console after services
start. The console discovers services, exposes them as tools, and
lets you talk to them through an LLM — same as micro chat but
built into the run experience.
- Detects MICRO_AI_PROVIDER and MICRO_AI_API_KEY from environment
- Falls back to provider-specific env vars (ANTHROPIC_API_KEY, etc.)
- If no API key, prints hint and blocks on Ctrl-C (no console)
- -d / --detach flag skips the console (background mode)
- Ctrl-C always shuts everything down
Removed adopters section from README.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
---------
Co-authored-by: Claude <noreply@anthropic.com>
* docs: reposition hero — framework for services and agents
Landing page: "Build Services and Agents in Go" — positions as a
framework, not a code generator. Tagline: "A framework for
microservices that AI agents can discover, use, and manage."
Hero command reverts to go get (the framework) instead of
micro run --prompt (a feature).
README matches: "framework for building services and agents in Go."
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* perf: compress hero image — 1.4MB to 80KB
Resized from 1536px to 1200px, converted to JPEG at quality 80.
80KB loads instantly vs 1.4MB stalling on slower connections.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
---------
Co-authored-by: Claude <noreply@anthropic.com>
* docs: update blog posts 15 and 16 to reflect RPC-based agents
Blog 15: replaced broker-based agent communication with RPC —
agents are services, they communicate via standard RPC, no pub/sub
hacks. Updated the framework mapping section.
Blog 16: added proto definition, micro call example, and explanation
that agents are real services with proto-defined endpoints. Updated
Ask() method name.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* docs: update agent design doc and blog posts for RPC-based agents
Rewrote AGENT_DESIGN.md — agents are services with proto-defined
Agent.Chat endpoints, communicate via RPC, no broker dependency.
Includes proto definition, CLI examples, generation output.
Blog 15: replaced broker references with RPC.
Blog 16: added proto definition and micro call example.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* docs: purge all stale broker-based agent references
Updated across all surfaces:
- README: agents described as services with RPC, Ask() not Chat(),
micro call example instead of micro agent chat
- Blog 15: replaced broker communication with RPC description
- Blog 16: replaced "coordinate through the broker" with RPC
- Getting started: agent is a service with proto endpoint, Ask()
not Chat(), added micro flow CLI commands (run/exec), expanded
CLI workflow table
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
---------
Co-authored-by: Claude <noreply@anthropic.com>
* docs: Agent interface design sketch
Proposes Agent as a top-level abstraction alongside Service in the
micro package. Agent manages services — scoped tools, system prompt,
conversation memory, registry-discoverable.
Design only, no implementation.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: Agent as a first-class abstraction
Introduce micro.NewAgent() alongside micro.New() — Agent is to
intelligence what Service is to capability.
Agent interface:
- Chat(ctx, message) (*Response, error) — core interaction method
- Run() — registers in registry, subscribes to broker, blocks
- Stop() — graceful shutdown
- Scoped tools — only sees endpoints of its assigned services
- Persistent memory — conversation history stored in store
- Agent-to-agent — communication via broker topics
Top-level API:
agent := micro.NewAgent("task-mgr",
micro.AgentServices("task"),
micro.AgentPrompt("You manage tasks."),
micro.AgentProvider("anthropic"),
)
agent.Run()
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: wire agents into chat router, add micro agent CLI, expose Flow
Three top-level abstractions:
micro.New("task") — Service (capability)
micro.NewAgent("task-mgr") — Agent (intelligence)
micro.NewFlow("onboard-user") — Flow (event-driven orchestration)
micro chat as router:
- Discovers agents from registry on startup
- Single agent: routes directly
- Multiple agents: LLM classifies intent, dispatches to right agent
via route_to_agent tool
- No agents: falls back to current direct-service behaviour
- Banner shows discovered agents
micro agent CLI:
- micro agent list — shows registered agents and their services
- micro agent describe <name> — shows agent details from registry
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: move flow to top level, update docs for three abstractions
Package structure now consistent:
service/ — Service (capability)
agent/ — Agent (intelligence)
flow/ — Flow (event-driven orchestration)
ai/flow/ kept as backward-compatible re-export.
Updated across all surfaces:
- CLAUDE.md: added agent/ and flow/ to project structure
- README: added "Building Agents" section with NewAgent() examples,
updated features table (Agents, Flows, Chat router), CLI table
(agent list, agent describe), docs links
- Website: features grid shows Services, Agents, Flows as the three
pillars alongside generation, MCP, and pluggable architecture
- micro.go: Flow imported from top-level flow/ package
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* docs: rewrite getting-started, fix ai-integration import paths
Getting started now covers all three abstractions:
- Service (write handlers, micro run, templates)
- Agent (micro.NewAgent, scoped tools, memory, CLI)
- Flow (event-driven LLM orchestration)
Leads with prompt-based generation, then manual service creation.
ai-integration.md: fixed flow import path from go-micro.dev/v5/ai/flow
to go-micro.dev/v5/flow, updated stack diagram to show agent/flow/chat.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* blog: Introducing micro.NewAgent()
Post 16 — announces Agent as a first-class abstraction. Shows the
API (NewAgent, AgentServices, AgentPrompt, AgentProvider), scoped
tools, persistent memory, multi-service agents, multi-agent systems,
and the three-abstraction comparison table (Service/Agent/Flow).
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: fix agent registration, blog post 16
Agent registration:
- Add node with address so mDNS can discover agents
- Store type and services in node metadata (mDNS requirement)
- Connect broker before subscribing, non-fatal if broker unavailable
- Print registration confirmation on Run()
Agent/chat discovery:
- Check both service-level and node-level metadata for type=agent
(mDNS stores metadata on nodes, not services)
Blog post 16: "Introducing micro.NewAgent()" — announces the Agent
abstraction with code examples, comparison table, multi-agent patterns.
Tested end-to-end: micro run → micro agent list discovers the agent →
micro chat routes to it → agent calls service endpoints.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: agents are proper services with RPC Chat endpoint
Refactored agent to use server.Server instead of fake registry entries.
An agent now:
- Creates a real RPC server with server.Name(agentName)
- Registers an Agent.Chat handler callable via standard RPC
- Sets server metadata type=agent, services=x,y for discovery
- No more fake addresses or broker hacks
micro chat calls agents via RPC (client.Call) instead of creating
local agent instances. The registry stays clean — agents are real
services with real endpoints.
Removed broker dependency from agent options. Agent-to-agent
communication is just RPC like everything else.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: agent uses proto-defined RPC interface
Added agent/proto/agent.proto with Agent service definition:
rpc Chat(ChatRequest) returns (ChatResponse)
Agent now implements the generated AgentHandler interface and
registers via pb.RegisterAgentHandler. The Chat endpoint is a
standard proto-based RPC callable by any go-micro client.
Renamed the programmatic API from Chat() to Ask() to avoid
collision with the proto handler method name.
micro chat calls agents via standard RPC with JSON-encoded
request/response — no special types needed on the caller side.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: generate agent alongside services, update all docs
micro run --prompt now generates an agent binary that manages all
the generated services. The agent reads MICRO_AI_PROVIDER and
MICRO_AI_API_KEY from the environment. micro run propagates these
when started with --prompt.
Run banner shows services and agents separately.
Updated README, getting-started guide, and landing page to show
the complete flow: generate → services + agent start → micro chat
routes to agent → agent orchestrates services.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
---------
Co-authored-by: Claude <noreply@anthropic.com>
Quick Start now flows through the full AI experience: generate →
review → run → chat → grow (mid-conversation service generation).
The reader sees the complete prompt-to-production story without
interruption.
"Writing Services" is a separate section below for developers who
want to understand the framework underneath. Shows Go code, doc
comments, @example tags, micro run, and scaffolding templates.
Features table and CLI table reordered: AI first, then framework.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
Co-authored-by: Claude <noreply@anthropic.com>
* docs: add binary install option to README quick start
Show curl install.sh first (no Go required), go install second.
Uses the existing install script at go-micro.dev/install.sh which
downloads pre-built binaries from GitHub releases.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* docs: regenerate hero image for new AI-native positioning
New hero shows terminal running micro run with service generation,
an AI agent orchestrating, and task/shipping/category service nodes.
Matches the "Microservices That AI Agents Can Use" headline.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
---------
Co-authored-by: Claude <noreply@anthropic.com>
Both surfaces now lead with the same line: "Go Micro is a framework
for building microservices that AI agents can use."
README:
- Leads with prompt generation + chat (the differentiator)
- Features as a compact table instead of paragraph-per-feature
- CLI workflow table
- Removed redundant sections, tightened to ~150 lines
Website:
- Hero: "Microservices That AI Agents Can Use"
- Hero command: micro run --prompt instead of go get
- Features grid reordered: AI tools, orchestration, generation first
- First two-col section: describe/generate/run/chat story
- Architecture and DX sections follow
Both tell the same story in the same order:
1. What it is (microservices framework)
2. What makes it different (every service is an AI tool)
3. How you use it (prompt → run → chat)
4. What's underneath (registry, RPC, store — all pluggable)
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
Co-authored-by: Claude <noreply@anthropic.com>
* feat: add micro new --prompt and micro run --prompt
Add AI-powered service generation: describe a system in natural
language and get real go-micro services with proto definitions,
handlers, doc comments, and MCP support.
micro new --prompt "a contact book with notes and tags" \
--provider anthropic
Generates:
contacts/ — CRUD service with name, email, phone fields
notes/ — notes linked to contacts
tags/ — tagging system
Each service gets:
proto/{name}.proto — domain model + CRUD endpoints
handler/{name}.go — in-memory store, @example tags for MCP
main.go — MCP-enabled, proper imports
go.mod + Makefile — compiles with go mod tidy + make proto
micro run --prompt does the same then starts all services.
The LLM designs the architecture (service names, fields, endpoints,
descriptions) and returns structured JSON. Code generation uses
the existing template patterns — the output is standard go-micro
code that compiles, runs, and is immediately callable via MCP
and micro chat. No AI dependency at runtime.
* feat: LLM generates real business logic with compile-fix loop
Rebuild the generate package so the LLM writes actual handler
code with business logic, not just CRUD scaffolding.
The flow is now:
1. LLM designs architecture (service names, fields, endpoints)
→ returns structured JSON
2. Proto, main.go, go.mod, Makefile generated deterministically
from the design (guaranteed to be correct)
3. go mod tidy + make proto compiles the protos
4. LLM generates handler code with REAL business logic
→ given the proto, endpoint descriptions, and go-micro patterns
5. go build — does it compile?
6. If no: feed errors back to LLM, get fixed code (up to 3 attempts)
7. If yes: service is ready
The handler prompt instructs the LLM to:
- Use sync.RWMutex for thread-safe in-memory state
- Include validation, edge cases, meaningful errors
- Write doc comments with @example tags for MCP
- Implement actual domain logic, not just map operations
Proto generation still uses deterministic templates (CRUD +
custom endpoints from the design spec) to guarantee correctness.
The compile-fix loop catches LLM mistakes automatically.
Both micro new --prompt and micro run --prompt use this flow.
* fix: handle edge cases in prompt-based generation
- Fix PATH for protoc-gen-micro in child processes
- Handle existing directories: skip structural files (main.go,
go.mod, Makefile) if dir exists, always regenerate proto,
only write placeholder handler if none exists
- Allow re-running micro new --prompt on same directory to
iterate on business logic without clobbering user edits
Tested end-to-end: "a simple todo list with tasks and categories"
generates 2 services (task-service, category-service) with real
business logic (validation, toggle complete, etc.), compiles
after 1 fix iteration, and runs with 6 MCP tools discovered.
* feat: auto-detect modified handlers on regeneration
Instead of requiring a --keep-handlers flag, the generate package now
tracks a SHA-256 hash of each generated handler in a .micro metadata
file. On re-run, if the user has edited the handler since generation,
it's left untouched. Unmodified handlers are regenerated normally.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: add tests, fix go.mod, gitignore, proto tracking, spinner
- Add 12 tests covering helpers, proto generation, hash tracking
- Fix go.mod: write minimal module file, let go mod tidy resolve deps
- Add .gitignore to prompt-generated services
- Protect user-edited proto files (same hash tracking as handlers)
- Add spinner during LLM calls so it doesn't look hung
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: signal handling, existing service discovery, help text
- Ctrl+C during generation now cancels LLM calls immediately via
signal-aware context; re-run picks up where it left off
- Design() scans for existing services in the working directory and
includes their proto definitions in the prompt, so the LLM extends
the system rather than redesigning from scratch
- Updated --prompt help text with usage examples on both new and run
- Listed all supported providers in flag descriptions
- Added discoverExisting test
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: show endpoints in run --prompt output, add micro chat hint
Print endpoint names and descriptions when designing services so users
see what was built. Add a micro chat hint to the run banner so users
know how to interact with their services after startup.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* fix: generated go.mod uses go 1.24 with explicit go-micro require
go 1.22 with no explicit require caused Go to resolve sub-packages
(gateway/mcp, client, server) as separate modules, hitting stale v1.18
tags. Pin to go 1.24 + require go-micro.dev/v5 v5.24.0 so go mod tidy
resolves all sub-packages from the root module correctly.
Tested end-to-end: 4 services generated and compiled successfully.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* fix: skip handler regeneration when proto unchanged
Compare proto hash before and after structure generation. If the proto
didn't change and the handler wasn't edited by the user, skip go mod
tidy, make proto, LLM handler generation, and compile-fix entirely.
Prints "(unchanged)" instead.
Reduces re-run of 4-service project from ~2 minutes to ~10 seconds.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: confirm design before generating code
Show the service design (names, endpoints) and prompt "Generate? [Y/n]"
before spending LLM time on handler generation. Applies to both
micro new --prompt and micro run --prompt. Default is yes (enter).
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* fix: use port :0 for MCP in generated multi-service projects
Each generated service had mcp.WithMCP(":3001") hardcoded, causing
port conflicts when running multiple services. Use :0 to auto-assign
a free port. micro run's central gateway handles unified MCP access.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: truncation detection, tool result display in chat
- Detect truncated LLM responses (unbalanced braces, doesn't end
with '}') and retry with a conciseness hint before falling through
to compile-fix
- Show tool call results in micro chat output (← for success, ✗ for
errors) so users can see what the LLM did
- Add Result/Error fields to ToolCall, populated by Anthropic provider
after tool execution
- Add isTruncated tests
Tested end-to-end with Anthropic: services generate, compile, start,
register, respond to RPC calls, and micro chat discovers and calls
tools correctly.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* fix: Anthropic tool loop, service naming, chat tool results
Anthropic provider:
- Fix tool execution loop to properly iterate (was re-processing all
tool calls instead of only new ones each round)
- Clean assistant content blocks before sending back (strip 'id' from
text blocks that Anthropic rejects on input)
- Include tools in follow-up requests so model can make additional calls
- Loop up to 10 rounds until model responds with text only
Service naming:
- Strip '-service' suffix from micro.New() name so services register
as 'task', 'category' instead of 'taskservice', 'categoryservice'
Chat:
- Show tool results (← for success) and errors (✗) in chat output
Tested end-to-end: create task → list tasks works as multi-step
orchestration through micro chat with Anthropic Claude.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: blog post 13 — from prompt to production
Covers the full micro run --prompt flow: design, generate, compile-fix,
run, and chat orchestration. Positions agent-as-orchestrator as the
answer to service coordination.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* fix: timeouts, max_tokens, TTY detection, smaller services
- Add 60s timeout on design, 90s on handler generation, 60s on
compile-fix LLM calls so hung providers don't block forever
- Bump Anthropic max_tokens from 4096 to 8192 to reduce truncation
- Add TTY detection: spinner prints static message in non-TTY (CI/pipes)
instead of ANSI escape codes
- Tighten prompts: max 200 lines per handler, 2-4 services, 5-8 fields,
explicit "services don't call each other" rule
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: chat suggests creating services when capabilities are missing
Update system prompt with the list of available services. When the user
asks for something no existing service can handle, the agent explains
what's available and suggests the exact micro new --prompt command to
create the missing service.
This is the natural evolution path: start with a few services, talk to
them via chat, and when the domain grows, the agent tells you what to
add. Each service stays small and focused.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: chat generates and starts services inline, drop -service suffix
Chat agent now has a micro_generate_service tool. When the user asks
for a capability that doesn't exist, the agent generates the service,
compiles it, starts it as a background process, waits for registration,
re-discovers tools, and uses the new endpoints immediately — all within
the conversation.
Service naming: design prompt now instructs LLM to return names without
'-service' suffix (e.g. 'task' not 'task-service'). buildMain keeps
TrimSuffix as safety net for backward compatibility.
Spawned processes are cleaned up when chat exits.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* docs: rewrite blog post 13 with inline service generation
Updated to reflect the full UX: services generate and start within
the chat conversation. Added the shipping example showing the agent
creating a service mid-conversation. Removed -service suffix from
all examples. Tightened the narrative around agent-as-orchestrator.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
* feat: persistent storage, README quickstart, auto-detect new services
Storage: generated handlers now use go-micro's store package instead
of in-memory maps. Data persists across restarts. The handler prompt
includes store API examples so the LLM generates correct store usage.
README: added "Generate From a Prompt" section with micro run --prompt
and micro chat examples, linking to blog post 13.
Watcher: micro run now scans for new service directories every 5s. When
micro chat generates a service, micro run detects the new directory,
builds it, starts it, and adds it to the watcher — fully automatic.
Added AddDir/Dirs methods to the watcher.
Blog: updated post 13 with persistent storage example and watcher note.
https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd
---------
Co-authored-by: Claude <noreply@anthropic.com>
* 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.
---------
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>
* 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.
* feat(cli): add micro api gateway command, clarify run vs server
Add 'micro api' — a standalone lightweight HTTP-to-RPC gateway:
- POST /{service}/{endpoint} proxies to RPC calls
- GET / lists all services and endpoints
- GET /{service} describes a service
- GET /health returns ok
- Supports Micro-Endpoint header for endpoint routing
- No dashboard, no auth, no hot reload — just the proxy
Update help text to clarify the three gateway modes:
- micro api: bare HTTP-to-RPC proxy
- micro run: development mode (hot reload + gateway + agent playground)
- micro server: production mode (dashboard + auth + JWT)
* docs: update README, getting started, and AI integration for all new features
Update the development workflow table in both README and getting
started to include all CLI commands: micro new --template,
micro api, micro chat, micro flow, micro call.
Getting started:
- Add CRUD template example to quick start
- Update workflow table with 8 stages
- Add AI Integration, MCP, and gRPC Interop to Next Steps
README:
- Add template flag to quick start example
- Update workflow table
- Reorder User Guides with AI Integration prominent
AI Integration doc:
- Update stack diagram to include micro api and ai/flow
- Add micro flow section with Go API and CLI examples
- Add micro api section
- Renumber layers (now 8 instead of 7)
---------
Co-authored-by: Claude <noreply@anthropic.com>
* 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>
Use height="26" on both logos for consistent alignment. Switch
Anthropic to the Wikimedia wordmark SVG (no padding) instead of
the logo.wine version which had excessive whitespace.
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>
* 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>
* 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>
* 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>
* feat: add agent platform showcase and blog post
Add a complete platform example (Users, Posts, Comments, Mail) that
mirrors micro/blog, demonstrating how existing microservices become
AI-accessible through MCP with zero code changes.
Includes blog post "Your Microservices Are Already an AI Platform"
walking through real agent workflows: signup, content creation,
commenting, tagging, and cross-service messaging.
https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc
* refactor: rename handler types to drop redundant Service suffix
UserService → Users, PostService → Posts, CommentService → Comments,
MailService → Mail. Matches micro/blog naming convention.
https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc
* refactor: consolidate top-level directories, reduce framework bloat
Move internal/non-public packages behind internal/ or into their
parent packages where they belong:
- deploy/ → gateway/mcp/deploy/ (Helm charts belong with the gateway)
- profile/ → service/profile/ (preset plugin profiles are a service concern)
- scripts/ → internal/scripts/ (install script is not public API)
- test/ → internal/test/ (test harness is not public API)
- util/ → internal/util/ (internal helpers shouldn't be imported externally)
Also fixes CLAUDE.md merge conflict markers and updates project
structure documentation.
All import paths updated. Build and tests pass.
https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc
* refactor: redesign model package to match framework conventions
Rename model.Database interface to model.Model (consistent with
client.Client, server.Server, store.Store). Remove generics in
favor of interface{}-based API with reflection.
Key changes:
- model.Model interface: Register once, CRUD infers table from type
- DefaultModel + NewModel() + package-level convenience functions
- Schema registered via Register(&User{}), no per-call schema passing
- Memory implementation as default (in model package, like store)
- memory/sqlite/postgres backends updated for new interface
- protoc-gen-micro generates RegisterXModel() instead of generic factory
- All docs, blog, and README updated
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
* 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
* 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
---------
Co-authored-by: Claude <noreply@anthropic.com>
Asim Aslam