work
21 次代码提交
| 作者 | SHA1 | 备注 | 提交日期 | |
|---|---|---|---|---|
 Asim Aslam
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5e9accfd45 | Add agent OpenTelemetry run observability (#3027) | ||
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Asim Aslam
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a8ea60120e |
agent: make model retries opt-in and harden retry backoff (#3021)
Follow-up to #3017. A Generate runs the whole tool-execution turn, so auto-retrying it re-runs already-executed (possibly side-effecting) tool calls. Default ModelMaxAttempts 3 -> 1: retries are now opt-in via ModelRetry. The timeout stays as a safety net. Also harden ai.GenerateWithRetry: always back off between retries (exponential, capped at 30s, default 200ms if unset) so an opt-in retry can't busy-loop the provider even with Backoff=0. Verified: go build, go test -race ./agent/... ./ai/, golangci-lint. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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ab2c092693 | Add agent model retry and timeout resilience (#3017) | ||
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Asim Aslam
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3e885308a0 |
lint: clear the golangci-lint backlog and enforce a blocking lint in CI (#2995)
Fixes #2988. Brings 'golangci-lint run ./...' to zero issues (was ~373): - errcheck: explicitly ignore fire-and-forget calls with '_ =' (and a small errcheck.exclude-functions list for response writes — json Encoder.Encode, http ResponseWriter.Write, fmt.Fprint*); genuine cases handled. - unused: remove dead code (unexported decls and dead test helpers) and the imports they orphaned. - staticcheck: ST1005 error strings, ST1016 receiver names, S1000/S1017/S1019/ S1023 simplifications, SA4004/SA4006/SA4010 dead code, SA1021 net.IP.Equal, SA6002 (store *[]byte in sync.Pool). - govet: fix a context leak (lostcancel) in internal/util/mdns and move t.Fatal/Fatalf out of goroutines (testinggoroutine) in tests. - ineffassign, unconvert: mechanical fixes. CI: the Lint workflow now runs a blocking full-tree 'golangci-lint run' on pushes and PRs (dropped only-new-issues now that the tree is clean). Verified: go build, go vet, test compilation, and unit tests for the behaviourally-touched packages all pass. Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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4311b73361 |
Enhance ADK vs Go Micro comparison and apply lint fixes (#2994)
* docs: compare Go Micro with Google ADK in the comparison guide Adds a 'vs Agent Frameworks (Google ADK)' section: ADK builds an agent, Go Micro builds the distributed system the agent lives in (agents are services in the mesh). Covers the category difference, a feature table, when to choose each, and MCP/A2A interoperability. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * docs: replace ADK comparison slogan with concrete explanation State plainly what each tool provides (ADK builds an agent process; Go Micro builds the surrounding service mesh) instead of marketing phrasing. * lint: apply golangci-lint autofixes; exclude ST1003 and demo errcheck Mechanical, behaviour-preserving fixes applied by 'golangci-lint run --fix': gofmt, misspell (US spelling), usestdlibvars (http.Method*/Status*), unconvert, and the auto-fixable staticcheck simplifications (QF*, S1017/S1019/S1023/S1039). Config: exclude ST1003 (remaining offenders are exported API renames, e.g. web.Id, which would break compatibility) and skip errcheck for examples/ and internal/harness/ (demo code where fire-and-forget is intentional). Build and test compilation verified. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * lint: WIP cleanup checkpoint (errcheck config + partial fixes) Checkpoint of an in-progress golangci-lint cleanup (background pass). Builds cleanly; lint is not yet zero. Follow-up commit will complete the cleanup and switch CI to a blocking full-tree lint. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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ca87efef2f |
feat(agent): expose run metadata + structured guardrail reasons to tool wrappers (#2981)
goreleaser / goreleaser (push) Has been cancelled
Closes the remaining ask in #2980 without adding a parallel callback API. ToolResult.Refused tags a guardrail block with a reason (ai.RefusedLoop / RefusedMaxSteps / RefusedApproval) so a wrapper can switch on it instead of parsing the message. ai.RunInfo (RunID, ParentID, Agent) rides on the context passed to the tool handler, giving wrappers run correlation and delegation lineage. Before/after/retry/failure were already covered by AgentWrapTool; this adds the metadata. Docs + tests included. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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c7657f73f4 |
Refactor agent plan storage, update docs, and release v6 (#2977)
goreleaser / goreleaser (push) Has been cancelled
* test(harness): read agent plan from the scoped store
The store-scoping change moved an agent's plan from the default table
key agent/{name}/plan to its own table (database "agent", table {name},
key "plan"). The plan-delegate harness tests still read the old key and
failed with 'not found'; read through store.Scope(mem, "agent", name)
like the agent does.
* docs: orient agents-first across README, landing, and docs overview
Lead with agents (then services and flows), surface MCP + A2A as the
interop story, and frame agents as services. Landing hero and feature
grid reordered agents-first with an A2A gateway card.
* v6: module path go-micro.dev/v6, TLS secure by default, NewService
Cut v6. Three breaking changes, bundled so the major bump is paid once:
- Module path go-micro.dev/v5 -> go-micro.dev/v6 across all imports + go.mod.
- TLS verification on by default (was off). MICRO_TLS_SECURE removed;
MICRO_TLS_INSECURE=true opts out for self-signed/dev.
- micro.NewService(name, opts...) is the canonical service constructor,
symmetric with NewAgent/NewFlow; micro.New kept as a deprecated alias;
the old name-less NewService(opts...) removed. Generators emit NewService.
Also ports the JWT auth token provider in-module (go-micro.dev/v6/auth/jwt/token
on golang-jwt/jwt/v5), dropping the v5-pinned github.com/micro/plugins/v5/auth/jwt
and the deprecated dgrijalva/jwt-go.
Docs/README/landing updated to v6 and @latest; v5->v6 migration guide added;
CHANGELOG cut as [6.0.0]. Blog posts left at their historical versions.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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Asim Aslam
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5e5d253abd |
feat(agent): tool-execution wrappers via WrapTool (#2969)
Restructure ai.ToolHandler to the structured, ctx-carrying shape that
mirrors a go-micro RPC handler:
func(ctx context.Context, call ai.ToolCall) ai.ToolResult
This reuses the existing ToolCall (with its correlation ID) and
ToolResult types instead of the flat (name, input)->(any, string)
signature, and adds ToolCall.Scan for typed argument access.
Add ai.ToolWrapper and the agent option WrapTool / micro.AgentWrapTool —
the tool-side analogue of client.CallWrapper and server.HandlerWrapper.
Reframe the built-in guardrails (MaxSteps, LoopLimit, ApproveTool) as
composed wrappers around a base handler; developer wrappers compose
outermost, so they observe every call and result, including refusals.
Update all provider call sites, the MCP server and chat handlers, the
integration harnesses, and docs to the new signature.
Co-authored-by: Claude <noreply@anthropic.com>
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Asim Aslam
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1bc886fa82 |
Introduce Agent abstraction and integrate with chat router (#2939)
* 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> |
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Asim Aslam
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d3c4981326 |
Enhance AI service generation with prompt-based architecture and logic (#2926)
goreleaser / goreleaser (push) Has been cancelled
* 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>
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Asim Aslam
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888dbbca4a |
Refactor AI tool handling and enhance CLI command documentation (#2920)
goreleaser / goreleaser (push) Has been cancelled
* refactor(ai): rename ToolSet to Tools, simplify wiring with WithTools
Move tool discovery/execution fully into the ai package as ai.Tools
(formerly ai.ToolSet), and simplify the usage model:
- NewTools(reg, ai.ToolClient(c)) takes the execution client as an
option instead of threading it through Handler(c) per call
- New ai.WithTools(tools) option wires the tool handler into a model
in one call, replacing ai.WithToolHandler(set.Handler(c))
- ai.DiscoverTools(reg) for one-shot discovery
Before:
set := ai.NewToolSet(reg)
list, _ := set.Discover()
m := ai.New(p, ai.WithToolHandler(set.Handler(client)))
After:
tools := ai.NewTools(reg, ai.ToolClient(client))
list, _ := tools.Discover()
m := ai.New(p, ai.WithTools(tools))
Update ai/flow, micro chat, README, ai integration doc, Atlas Cloud
guide, and blog posts 3/8/9/10.
* feat(cli): add per-interface commands (registry, broker, store, config)
Map go-micro's core interfaces onto the CLI so the framework's
building blocks are inspectable and manipulable from the terminal:
micro registry list/get/watch service discovery
micro broker publish/subscribe pub/sub messaging
micro store read/write/delete/list persistence
micro config get/dump dynamic config (from env)
Structured pluggably in cmd/micro/resource: each interface is one
file exposing a Command() func, all wired through a commandFuncs
slice in resource.go. Adding a new resource command is a single
file plus one slice entry. Shared printJSON/fail helpers keep
output and errors consistent across commands.
Each command's verbs mirror the interface methods. Output is JSON
for structured data, raw for single values. Update README and
getting-started with an "inspecting the framework" section.
* docs: update CLI README with all new commands
Add documentation for commands that were missing from the CLI README:
- micro new --template (crud, pubsub, api)
- micro api (standalone HTTP gateway)
- micro registry list/get/watch
- micro broker publish/subscribe
- micro store read/write/delete/list
- micro config get/dump
- micro chat (interactive LLM agent)
- micro flow run/exec (event-driven orchestration)
- micro mcp serve/list/test
Organized into sections: API Gateway, Inspecting the Framework
(registry, broker, store, config), and AI & Agents (chat, flow, mcp).
* refactor(ai): move History from caller to Request field
History is now pure state (no Generate method). Instead, pass it
via Request.History and call ai.Generate(ctx, model, req):
Before:
hist := ai.NewHistory("system prompt", 50)
resp, _ := hist.Generate(ctx, model, prompt, tools)
After:
hist := ai.NewHistory(50)
resp, _ := ai.Generate(ctx, model, &ai.Request{
Prompt: prompt,
SystemPrompt: "system prompt",
Tools: tools,
History: hist,
})
The model is always the thing you call. History is context you
pass in. ai.Generate() handles the bookkeeping: prepends
accumulated messages before the call, records the exchange after.
NewHistory no longer takes a system prompt (it belongs on the
Request, where it always did).
Update micro chat, ai/flow, and all blog posts/docs.
* refactor(ai): make History a plain message accumulator
History no longer has Generate or touches the model. It's just
Add/Messages/Reset/Len with truncation — a helper for building
Request.Messages across turns.
Before:
hist := ai.NewHistory(50)
resp, _ := ai.Generate(ctx, m, &ai.Request{History: hist, ...})
After:
hist := ai.NewHistory(50)
hist.Add("user", prompt)
resp, _ := m.Generate(ctx, &ai.Request{Messages: hist.Messages(), ...})
hist.Add("assistant", resp.Reply)
Remove History field from Request. Remove package-level
ai.Generate(ctx, model, req) wrapper — users call m.Generate()
directly, which is the interface method. History is a convenience
for accumulating messages, not a participant in generation.
Update micro chat, ai/flow, blog posts 9 and 10.
---------
Co-authored-by: Claude <noreply@anthropic.com>
|
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Asim Aslam
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c4b4cbef25 |
refactor(ai): rename ToolSet to Tools, simplify wiring with WithTools (#2917)
Move tool discovery/execution fully into the ai package as ai.Tools (formerly ai.ToolSet), and simplify the usage model: - NewTools(reg, ai.ToolClient(c)) takes the execution client as an option instead of threading it through Handler(c) per call - New ai.WithTools(tools) option wires the tool handler into a model in one call, replacing ai.WithToolHandler(set.Handler(c)) - ai.DiscoverTools(reg) for one-shot discovery Before: set := ai.NewToolSet(reg) list, _ := set.Discover() m := ai.New(p, ai.WithToolHandler(set.Handler(client))) After: tools := ai.NewTools(reg, ai.ToolClient(client)) list, _ := tools.Discover() m := ai.New(p, ai.WithTools(tools)) Update ai/flow, micro chat, README, ai integration doc, Atlas Cloud guide, and blog posts 3/8/9/10. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
b97f45106d |
Update logo, add AI integration docs, and implement ai/flow package (#2913)
* feat: update Go Micro logo to interconnected nodes design
Replace the text-on-blue-square logo with a modern icon: three
teal nodes connected in a triangle, representing distributed
systems. Generated via Atlas Cloud. Clean at all sizes — works
as GitHub avatar, favicon, and nav bar icon.
* feat: new logo, AI integration architecture doc, and landing page CTA
Update logo to triangle-nodes icon + "Go Micro" text wordmark.
Save icon-only variant for favicon/avatar use.
Add docs/ai-integration.md — a single page that explains how the
AI stack fits together: services → registry → MCP gateway →
ai/tools → ai.Model → micro chat. Layer-by-layer with code
examples, provider table, and "what you don't need" section.
Add AI Integration to docs sidebar navigation (after Getting
Started). Update the landing page AI section with a direct CTA
button linking to the new doc.
* fix: restore original logo and add border-radius to all renders
Revert logo to original. Add border-radius: 8px to the logo img
in the landing page nav, docs layout nav, and blog layout nav
so the square logo renders with rounded corners everywhere.
Remove unused icon.png.
* feat(ai): add ai/flow package and micro flow CLI
Add ai/flow — event-driven LLM orchestration for go-micro. A Flow
subscribes to a broker topic, discovers services as tools, and
feeds each event into an LLM that decides which RPCs to call.
Key types:
- flow.New(name, opts...) creates a flow with trigger topic,
prompt template, provider config
- flow.Register(registry, broker, client) wires it into a service
- flow.Execute(ctx, data) runs the flow once (for testing/CLI)
- flow.Results() returns execution history
Add micro flow CLI with two subcommands:
- micro flow run: subscribe to a topic and react to events
- micro flow exec: one-shot execution with inline data
Both output JSON results with flow name, prompt, tool calls,
reply, answer, duration, and errors.
Example:
micro flow run --trigger events.user.created \
--prompt "New user: {{.Data}}. Send welcome email." \
--provider anthropic
micro flow exec --prompt "List all users" --provider anthropic
* docs: update flows blog post with ai/flow package and CLI examples
Add "Update: We Built It" section to blog/9 showing the ai/flow
package API, CLI usage for both event-driven and one-shot modes,
and what it does/doesn't do. Links the conceptual discussion to
the shipped implementation.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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Asim Aslam
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c26d74a8a1 |
Add CRUD, pub/sub, and API gateway templates; update AI features (#2909)
* feat(website): redesign docs and blog layouts, add blog header images Redesign both layouts to match the new landing page: - Consistent nav bar with logo, Docs, Blog, GitHub, Reference, Home - Consistent footer with copyright and links - CSS custom properties for theming - Updated typography, spacing, and code block styling - Active sidebar link highlighting in docs - Dark mode support preserved Generate 4 blog header images via Atlas Cloud: - blog-deploy.png for post 1 (micro deploy) - blog-mcp.png for posts 2, 3, 7 (MCP-related) - blog-agents-demo.png for post 4 (agents demo) - blog-dx.png for post 5 (DX cleanup) - Reuse data-model.png for post 6 (model package) All 7 existing blog posts now have header images. * fix(website): prevent horizontal scroll on mobile landing page Add overflow-x: hidden on html and body. Set max-width: 100% and height: auto on all section and two-col images. Add overflow: hidden to .two-col grid. Constrain hero pre with max-width and overflow-x. Reduce font sizes and padding at mobile breakpoint. * feat(website): add images to remaining core doc pages Generate 5 more images via Atlas Cloud for docs: - registry.png: service discovery diagram - broker.png: pub/sub message broker pattern - transport.png: multi-transport layers (HTTP, gRPC, NATS) - config.png: dynamic configuration from multiple sources - observability.png: monitoring dashboard with metrics/traces Add images to registry.md, broker.md, transport.md, config.md, observability.md, and architecture.md. All 11 main doc pages now have header images. * feat: add sponsor logos to landing page, README images, and flows blog post Add Anthropic and Atlas Cloud sponsor logos to the landing page with links to their respective blog posts. Logos display at 0.7 opacity with hover effect. Add architecture and MCP agent images to the GitHub README for the Overview and MCP sections. Write blog post 9: "From Chat to Flows" — explores the concept of LLM-powered service orchestration. Compares micro chat's interactive model with persistent event-driven flows, shows how the existing building blocks (ai/tools, History, broker) could compose into a flow engine, discusses tradeoffs vs traditional orchestration (Step Functions, Temporal), and includes a working 15-line code example. Explicitly positions it as a concept for community feedback, not an announcement. * feat(website): add animated hero video to landing page Generate a 6-second hero video via Atlas Cloud's image-to-video API (gemini-omni-flash). Shows the microservices network diagram animating with data flowing between nodes. Replace the static hero image with an autoplay muted looping video element. Falls back to the static image via poster attribute and img fallback for browsers without video support. * feat(ai): add VideoModel interface with Atlas Cloud provider Add ai.VideoModel interface for video generation alongside Model and ImageModel. Supports text-to-video and image-to-video via VideoRequest with prompt, reference images, duration, aspect ratio, and resolution fields. Implement GenerateVideo for Atlas Cloud using their async API: POST /api/v1/model/generateVideo → poll /api/v1/model/prediction. Default model is gemini-omni-flash image-to-video. Polls every 5 seconds until completion or context cancellation. Register Atlas Cloud as a video provider via ai.RegisterVideo. Add 3 tests: registration, no-key error, compile-time interface check. Update ai/README.md with VideoModel docs. The ai package now covers all three modalities: - Model (text) — 7 providers - ImageModel (image) — 2 providers (Atlas Cloud, OpenAI) - VideoModel (video) — 1 provider (Atlas Cloud) --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
669481224c |
Enhance AI features with ImageModel, History, and website updates (#2907)
* feat(cli): add CRUD, pub/sub, and API gateway templates for micro new
Add --template flag to 'micro new' with three preset templates:
- crud: CRUD service with Create/Read/Update/Delete/List, in-memory
store with sync.RWMutex, UUID generation, pagination, and doc
comments with @example tags for MCP tool discovery.
- pubsub: Event-driven service with Publish/Stats RPCs and a
Subscribe method that hooks into the broker. Includes event
types with ID, type, source, data, and timestamp.
- api: API gateway service with Health and Endpoint RPCs, an
internal HTTP route table, and a response recorder for
proxying requests through RPC.
All templates include MCP-ready doc comments and work with
--no-mcp. The default template (no flag) is unchanged.
Usage:
micro new myservice --template crud
micro new myservice --template pubsub
micro new myservice --template api
* fix(ai): update Atlas Cloud provider to use actual API formats
Fix the Atlas Cloud image generation to use their real async API:
POST /api/v1/model/generateImage → poll /api/v1/model/prediction/{id}
instead of the OpenAI-compatible endpoint which doesn't exist.
Add Quality and OutputFormat fields to ai.ImageRequest for
provider-specific image parameters.
Update default text model from llama-3.3-70b (doesn't exist) to
deepseek-ai/DeepSeek-V3-0324 (their flagship model). Update
default image model to openai/gpt-image-2/text-to-image.
* feat(website): add AI-generated images to landing page, docs, and blog
Generate 5 images via Atlas Cloud's image API (gpt-image-2) to
elevate the website experience:
- hero.png: microservices network graph for landing page
- architecture.png: registry + broker architecture diagram
- mcp-agent.png: AI agent calling services via MCP
- developer-experience.png: terminal showing micro run/chat
- blog-atlas.png: Atlas Cloud unified API illustration
Add visual sections to the landing page with architecture,
MCP integration, and developer experience showcases. Add
images to docs index, MCP docs, and Atlas Cloud blog post.
All images resized to 1200px wide and optimized for web.
Generated using Atlas Cloud sponsor credits.
* feat(website): redesign landing page and add images to docs
Redesign the landing page from a centered card layout to a
full-width modern site with:
- Top navigation bar
- Hero section with gradient background and CTA buttons
- Full-width image showcase sections
- Two-column layout for architecture, MCP, and DX sections
- Feature grid with 6 capabilities
- Footer with links
- Responsive breakpoints for mobile
Generate 3 more images via Atlas Cloud for docs:
- getting-started.png for the getting started guide
- deployment.png for the deployment guide
- data-model.png for the data model docs
Add images to getting-started.md, model.md, and deployment.md.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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||
|
Asim Aslam
|
86782e6d77 |
Add ImageModel interface and multi-turn conversation support (#2906)
* feat(ai): add ImageModel interface with Atlas Cloud and OpenAI support Add ai.ImageModel interface for text-to-image generation alongside the existing ai.Model for text. Uses the same options pattern (WithAPIKey, WithBaseURL) and the same provider registration system (RegisterImage/NewImage). Implement GenerateImage for Atlas Cloud and OpenAI providers via the OpenAI-compatible /v1/images/generations endpoint. Default image model is gpt-image-1. Responses return images as URL, base64, or both depending on the provider. Update Atlas Cloud blog post and integration guide with image generation examples. Update ai/README.md with ImageModel docs. * fix(website): widen docs content by reducing layout max-width to 1100px Remove the 800px max-width on .content (which left empty space on the right) and reduce the overall .layout and footer from 1400px to 1100px. With the 230px sidebar this gives ~830px of content width — readable and fills the page properly on desktop. * feat(ai): add History for multi-turn conversation state Add ai.History — a lightweight message accumulator that tracks user prompts, assistant replies, and tool call/result pairs across turns. FIFO truncation when message count exceeds the configured limit. System prompt is passed through on every Generate call. Wire History into micro chat so conversations are multi-turn by default (limit 50 messages). Add 'reset' command to clear history mid-session. 5 unit tests covering accumulation, truncation, reset, snapshot isolation, and tool call recording. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
1e2901ad0e |
feat(ai): add ImageModel interface with Atlas Cloud and OpenAI support (#2905)
goreleaser / goreleaser (push) Has been cancelled
Add ai.ImageModel interface for text-to-image generation alongside the existing ai.Model for text. Uses the same options pattern (WithAPIKey, WithBaseURL) and the same provider registration system (RegisterImage/NewImage). Implement GenerateImage for Atlas Cloud and OpenAI providers via the OpenAI-compatible /v1/images/generations endpoint. Default image model is gpt-image-1. Responses return images as URL, base64, or both depending on the provider. Update Atlas Cloud blog post and integration guide with image generation examples. Update ai/README.md with ImageModel docs. Co-authored-by: Claude <noreply@anthropic.com> |
||
|
Asim Aslam
|
081e375f29 |
Add AI provider integration guide and new providers support (#2900)
* docs: add AI provider integration guide and Supported AI Providers section Add a step-by-step guide for AI infrastructure companies to implement ai.Model and contribute a provider to go-micro. Covers the full lifecycle: skeleton, tool call handling, tests, registration, and PR checklist. Add a "Supported AI Providers" section to the project README that lists current providers (Anthropic, OpenAI) in a table and links to the integration guide with a call-to-action for new providers and sponsors. Streamline the "Adding a New Provider" section in ai/README.md to point to the new guide instead of duplicating a full code listing. * fix: remove nonexistent Discord link from README * fix(website): set content container width to 800px on desktop Move the 800px max-width from .markdown-body up to .content so the entire content pane (not just the inner body) is sized correctly. The container now fills up to 800px beside the sidebar. * feat(ai): wire Atlas Cloud into server and auto-detection Import atlascloud provider in the micro server so it is available when running micro run / micro server. Add atlascloud to AutoDetectProvider so --ai_base_url with an atlascloud domain selects the right provider automatically. * feat(ai): add Google Gemini provider Add ai/gemini implementing ai.Model for Google's Gemini API. Uses the native generateContent endpoint with system_instruction, contents/parts, and functionDeclarations — not an OpenAI shim. Default model gemini-2.5-flash, auth via x-goog-api-key header. Wire into micro server imports and AutoDetectProvider (matches googleapis.com and google in base URL). Update README.md and ai/README.md with provider listing. * feat(ai): add Groq, Mistral, and Together AI providers Add three new OpenAI-compatible providers: - ai/groq: ultra-fast inference, default model llama-3.3-70b-versatile - ai/mistral: Mistral AI, default model mistral-large-latest - ai/together: Together AI, default model Llama-3.3-70B-Instruct-Turbo All three are wired into the micro server imports and AutoDetectProvider. README and ai/README updated with the full provider table. * feat(ai): add ai/tools helper and 'micro chat' interactive agent Extract the registry-discovery + RPC-execution loop from the web agent playground into a reusable ai/tools package: - tools.New(reg) creates a Set bound to a registry - Set.Discover() walks the registry and returns []ai.Tool with LLM-safe (underscored) names, remembering the mapping back to the original dotted form - Set.Handler(client) returns an ai.ToolHandler that resolves the safe name and issues the RPC Add cmd/micro/chat — an interactive 'micro chat' REPL that uses ai/tools to let users talk to their services through any registered AI provider. Supports --prompt for single-shot use, auto-detects the provider from --base_url, and falls back to the provider's conventional env var (ANTHROPIC_API_KEY, etc). Update README with the new command and the programmatic example. * feat(examples): add gRPC interop example Add examples/grpc-interop showing that any standard gRPC client can call a go-micro service — no go-micro SDK required on the client side. Includes: - proto/greeter.proto with generated Go, gRPC, and micro stubs - server/ using go-micro gRPC transport - client/ using stock google.golang.org/grpc (no go-micro imports) - README with Python example and explanation of how routing works Addresses the confusion from issue #2818 where users didn't know that go-micro gRPC services are callable by any gRPC client. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
60527e1fe8 |
Add AI provider integration guide and Atlas Cloud support (#2898)
* docs: add AI provider integration guide and Supported AI Providers section Add a step-by-step guide for AI infrastructure companies to implement ai.Model and contribute a provider to go-micro. Covers the full lifecycle: skeleton, tool call handling, tests, registration, and PR checklist. Add a "Supported AI Providers" section to the project README that lists current providers (Anthropic, OpenAI) in a table and links to the integration guide with a call-to-action for new providers and sponsors. Streamline the "Adding a New Provider" section in ai/README.md to point to the new guide instead of duplicating a full code listing. * feat(ai): add Atlas Cloud provider Add ai/atlascloud implementing ai.Model for Atlas Cloud's OpenAI-compatible chat completions API. Registers as "atlascloud" with default model llama-3.3-70b and base URL https://api.atlascloud.ai. Supports tool calling via ToolHandler. Includes 7 unit tests covering registration, defaults, init, generate-without-key, and stream-not-implemented. Update the Supported AI Providers table in README.md and the Supported Providers section in ai/README.md. * fix: remove nonexistent Discord link from README --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
defb2786ef |
update AI provider documentation (#2897)
goreleaser / goreleaser (push) Has been cancelled
* feat: add prometheus monitoring wrapper Reintroduces the Prometheus metrics wrapper previously available in the plugins repository, updated for go-micro v5. Exposes request count and latency histograms for handlers, subscribers, and outgoing client calls via NewHandlerWrapper, NewSubscriberWrapper, NewCallWrapper and NewClientWrapper, labelled with service/endpoint/status. Options cover namespace, subsystem, const labels, histogram buckets and a custom registerer; duplicate collectors (e.g. from multiple wrappers sharing the same config) are reused transparently via a cached metrics bundle. Fixes #2893 * fix(registry/etcd): clear lease/register caches on KeepAlive channel closure When the etcd client's long-lived KeepAlive channel closes (e.g. because the lease expired on the server side during a network partition), the previous cleanup only removed the channel bookkeeping. The stale entries in `leases` and `register` caused the next registerNode() heartbeat to hit the "unchanged hash" short-circuit and skip re-registration entirely, so the service permanently disappeared from etcd. Extract the cleanup into handleKeepAliveClosed and also drop the cached lease id and hash so the next heartbeat performs a full Grant+Put and the service recovers within one RegisterInterval. Regression introduced by #2822; fix is symmetric with the existing synchronous KeepAliveOnce recovery path that propagates rpctypes.ErrLeaseNotFound. * docs: add AI provider integration guide and Supported AI Providers section Add a step-by-step guide for AI infrastructure companies to implement ai.Model and contribute a provider to go-micro. Covers the full lifecycle: skeleton, tool call handling, tests, registration, and PR checklist. Add a "Supported AI Providers" section to the project README that lists current providers (Anthropic, OpenAI) in a table and links to the integration guide with a call-to-action for new providers and sponsors. Streamline the "Adding a New Provider" section in ai/README.md to point to the new guide instead of duplicating a full code listing. * Update contribution guidelines in README.md Removed Discord contact information for platform contributions. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
|
76bfeae456 |
Claude/update docs roadmap f zd2 j (#2880)
* docs: update all four documentation guides and mark Q2 complete - ai-native-services: add WithMCP one-liner, standalone gateway, WebSocket client example, and OpenTelemetry observability section - mcp-security: add OTel distributed tracing, WebSocket authentication (connection-level and per-message), DeniedReason audit field - tool-descriptions: add manual overrides with WithEndpointDocs and export formats section - agent-patterns: add LangChain/LlamaIndex SDK pattern and standalone gateway production pattern with Docker example - Update roadmap: mark Q2 documentation as complete, Q2 at 100% - Update status: reflect all recent completions, shift priorities https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add agent demo example and blog post Add examples/agent-demo with a multi-service project management app (projects, tasks, team) that demonstrates AI agents interacting with Go Micro services through MCP. Includes seed data and example prompts. Add blog post 4 "Agents Meet Microservices: A Hands-On Demo" walking through the example code and showing cross-service agent workflows. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: enable multiple services in a single binary Remove global state mutations from service and cmd option functions so that configuring one service no longer overwrites another's settings. Key changes: - service/options.go: remove all DefaultXxx global writes from option functions; newOptions() now creates fresh Server, Client, Store, and Cache per service while sharing Registry, Broker, and Transport - cmd/cmd.go: newCmd() uses local copies instead of pointers to package globals; Before() no longer mutates DefaultXxx vars - cmd/options.go: remove global mutations from all option functions - service/service.go: export ServiceImpl type for cross-package use - service/group.go: new Group type for multi-service lifecycle - micro.go: add Start/Stop to Service interface, expose Group and NewGroup convenience function - examples/multi-service: working example with two services https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * docs: highlight multi-service binary support Add multi-service section to README with code example, update features list, add to examples index, and note in status summary. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: unify service API and clean up developer experience - Unified service creation: micro.New("name", opts...) as canonical API - Clean handler registration: service.Handle(handler, opts...) accepts server.HandlerOption args directly, no need to reach through Server() - Unexported serviceImpl: users interact through Service interface only - Service groups use Service interface (not concrete type) - Fixed Stop() to properly propagate BeforeStop/AfterStop errors - Fixed store init: error-level log instead of fatal on init failure - Updated all examples to use consistent patterns - Updated README, getting-started, MCP docs, and guides - Added blog post about the DX cleanup https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * fix: add blog post 5 to blog index Blog post 5 (Developer Experience Cleanup) existed as a file but was missing from the blog index page. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: make micro new generate MCP-enabled services by default - main.go template includes mcp.WithMCP(":3001") by default - Handler template has agent-friendly doc comments with @example tags - Proto template has descriptive field comments - README includes MCP usage, Claude Code config, and tool description tips - Makefile adds mcp-tools, mcp-test, mcp-serve targets - go.mod updated to Go 1.22 - Added --no-mcp flag to opt out of MCP integration - Post-create output shows MCP endpoint URLs https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * docs: add MCP migration guide and troubleshooting guide - Migration guide: 3 approaches to add MCP to existing services (WithMCP one-liner, standalone gateway, CLI) - Troubleshooting guide: common issues with agents, WebSocket, Claude Code, auth, rate limiting, and performance https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * refactor: rename model/ package to ai/ for AI model providers The model/ package name conflicted with the conventional use of "model" for data models. Renamed to ai/ which better describes the package's purpose (AI provider abstraction for Anthropic, OpenAI, etc.) and frees up model/ for future data model layer use. - Rename model/ → ai/ with package name change - Update all Go imports from go-micro.dev/v5/model to go-micro.dev/v5/ai - Update cmd/micro/server/server.go references (model.X → ai.X) - Update all documentation and roadmap references - All tests pass, CLI builds successfully https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add model package for typed data access with CRUD and queries New model/ package provides a typed data model layer using Go generics. Supports structured CRUD operations, WHERE filters, ordering, pagination, and automatic schema creation from struct tags. Three backends: - memory: in-memory for development and testing - sqlite: embedded SQL for dev and single-node production - postgres: full PostgreSQL for production deployments Key features: - Generic Model[T] with Create/Read/Update/Delete/List/Count - Query builder: Where(), WhereOp(), OrderAsc/Desc(), Limit(), Offset() - Struct tags: model:"key" for primary key, model:"index" for indexes - Auto table creation from struct schema - 19 tests passing across memory and sqlite backends https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add model code generation to protoc-gen-micro Extend the micro plugin to generate model structs from proto messages annotated with // @model. Generated alongside client/server code in the same .pb.micro.go file. For a proto message like: // @model message User { string id = 1; string name = 2; } Generates: - UserModel struct with model:"key" and json tags - NewUserModel(db) factory returning *model.Model[UserModel] - UserModelFromProto(*User) *UserModel converter - (*UserModel).ToProto() *User converter Supports @model(table=custom_table, key=custom_field) options. Adds GetComments() to generator for plugin comment inspection. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc * feat: add Model() to Service interface for Client/Server/Model trifecta Every service now exposes Client(), Server(), and Model() — call services, handle requests, and save/query data from the same interface. Includes README docs, blog post, and a full model guide on the docs site. https://claude.ai/code/session_01GkduEhcrqcG45rdfYh8dAc --------- Co-authored-by: Claude <noreply@anthropic.com> |