codex/increment-3123
95 次代码提交
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 Codex
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4675804cd3 |
Add provider conformance JSON summary
Harness (E2E) / Harnesses (mock LLM) (push) Has been cancelled
Harness (E2E) / Provider harnesses (live LLM conformance) (push) Has been cancelled
Lint / golangci-lint (push) Has been cancelled
Run Tests / Unit Tests (push) Has been cancelled
Run Tests / Etcd Integration Tests (push) Has been cancelled
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5d1137d8f9 |
docs: add provider conformance matrix (#3106)
Co-authored-by: Codex <codex@openai.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> |
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a14b3e79e2 |
docs: add provider conformance matrix (#3120)
Co-authored-by: Codex <codex@openai.com> |
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72f8067e57 |
docs: finish constructor example cleanup (#3112)
Co-authored-by: Codex <codex@openai.com> |
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8533306e64 |
docs: align service constructor examples (#3111)
Co-authored-by: Codex <codex@openai.com> |
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384987da9f |
docs: align service constructor examples (#3110)
Co-authored-by: Codex <codex@openai.com> |
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fb14ccbc17 |
Add deterministic AI capability rows (#3087)
Co-authored-by: Codex <codex@openai.com> |
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8fc4f58a17 |
Add AI provider capability matrix (#3076)
Co-authored-by: Codex <codex@openai.com> |
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09cf00b5ec |
docs: mark harness Resilience as shipped (#3023)
* loop: establish the continuous-improvement charter + scheduled backbone Define the autonomous improvement loop (internal/docs/CONTINUOUS_IMPROVEMENT.md): full autonomy with correctness (build/test/lint) as the only gate, work sourced from roadmap + issues + an improvement radar + dogfooding, Claude Code driving and Codex executing scoped tasks, with brand/positioning and breaking API kept with the human. Add a durable scheduled GitHub Action (.github/workflows/continuous-improvement.yml) as the session-independent backbone — a safe no-op until an ANTHROPIC_API_KEY secret is added. * docs: mark harness Resilience as shipped Resilience (per-call timeout + context propagation + opt-in retry/backoff) landed in #3017/#3021; update the agent-harness status table from 'In progress' to 'Shipped'. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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8a19dc2d7c |
docs/blog: anchor the agent-harness positioning (#3015)
- Add the canonical concept doc 'The Agent Harness' (docs/guides/agent-harness.md) + nav entry: what the harness is, each piece mapped to a feature, honest about shipped vs in-progress. - Add blog post /blog/30 'Go Micro is an Agent Harness' (the public articulation; precise about what ships today vs the Now/Next roadmap). - Align the ROADMAP.md opening line to the agent-harness framing. Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL Co-authored-by: Claude <noreply@anthropic.com> |
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844ab4a14b |
Add provider-conformance harness, contract test, and reframe docs to 'agent harness' (#3006)
* docs: reposition go micro as agent harness * ci: rename universe workflow to harness |
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156b051191 | docs: fix service quickstart snippets (#3005) | ||
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a503628ef1 |
flow: add Loop — run a step until done, with a guaranteed ceiling (#3001)
Adds the agentic 'loop' to flows: flow.Loop(body, opts...) is a StepFunc that runs a body step repeatedly, carrying State across passes, until a stop condition fires or a hard iteration cap is reached. - Stop modes: flow.Until (code-defined predicate) and flow.UntilLLM (the model judges the goal met after each pass — the supervised 'Ralph' loop). Either firing stops the loop. - flow.LoopMax is the guardrail: the body never runs more than n times, so the loop always terminates and can't run up an unbounded bill. Hitting the cap returns the latest state rather than erroring. - flow.OnIteration reports per-pass progress. - Composes as a normal flow step (checkpointed by the step engine). - Exposed at the top level as micro.FlowLoop / FlowUntil / FlowUntilLLM / FlowLoopMax / FlowOnIteration, symmetric with the other Flow* helpers. Includes tests, an offline runnable example (examples/flow-loop), an 'Agent Loops' guide, and a CHANGELOG entry. Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL Co-authored-by: Claude <noreply@anthropic.com> |
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c3542127dd |
Enhance comparison guide for Go Micro and Google ADK (#2993)
* 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. --------- Co-authored-by: Claude <noreply@anthropic.com> |
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06659032a9 |
docs: install the CLI with @v6 instead of @latest (#2991)
Plain 'go install go-micro.dev/v6/cmd/micro@latest' fails on the public module proxy with a version-constraints conflict: the proxy has cached the sub-paths go-micro.dev/v6/cmd and .../cmd/micro as standalone v0/v1 modules (from old github.com/micro/go-micro tags, surfaced during an earlier vanity meta bug), so @latest resolves to v1.18.0 with a mismatched module path. A version-prefix query (@v6) sidesteps it: those cached sub-path modules have no v6.x.x versions, so Go falls back to the go-micro.dev/v6 root module and builds correctly. Verified against proxy.golang.org. Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL Co-authored-by: Claude <noreply@anthropic.com> |
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2cbd6264d7 |
support: advertise commercial support, consulting, and sponsorship (#2989)
* support: advertise commercial support, consulting, and sponsorship Adds a clear path to fund the project and pay for help, surfaced where people look: - SUPPORT.md + website /docs/support.html with a tier ladder (community, sponsor, support retainer, consulting) - Commercial Support / Consulting issue template (the GitHub inbound funnel) and an issue-chooser config linking Sponsors and docs - FUNDING.yml custom link to the support page; README section + nav entry Community support stays free via issues; paid support and consulting are scoped per engagement. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * ci: migrate golangci-lint to v2 config and enforce in CI (#2988) - Rewrite .golangci.yaml for the v2 schema: start from the standard linter set (errcheck, govet, ineffassign, staticcheck, unused) plus bodyclose, misspell, unconvert, usestdlibvars. Sensible exclusions: generated code, built-in presets, looser tests, SA1019 deprecations (coordinated migration is separate), and the ported protoc-gen-micro generator for unused. - Add a Lint workflow running golangci/golangci-lint-action with only-new-issues, so linting is enforced on new/changed code without a flag-day cleanup of the existing backlog. The pre-existing backlog (errcheck/unused/naming and a few real bugs the linter surfaces) is left for a dedicated follow-up so it can be reviewed on its own rather than buried in this wiring change. 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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aa7cd6dc3b |
Enhance support agent example and fix protoless service scaffolding (#2986)
goreleaser / goreleaser (push) Has been cancelled
* examples: support desk agent + blog walkthrough A real-world, runnable example (examples/support): customers/tickets/notify services become the agent's tools, a flow turns a ticket.created event into the agent's work, and an approval gate guards the one action that touches a customer. Runs with no API key (mock model) or against a live provider. Adds blog/28 'Building a Support Agent in Go' and indexes both. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * fix(new): protoless services by default; fix @latest install (#2985) micro new now scaffolds a reflection-based service by default — plain Go types registered via service.Handle, no .proto, no Makefile proto target. The generated project builds and runs with 'go run .' and zero external tooling. Protocol Buffers move behind --proto (the crud/pubsub/api templates imply it). When the proto workflow is used and protoc / protoc-gen-go / protoc-gen-micro are missing, print exact install instructions instead of failing with a cryptic plugin error. Also fixes the 'go install go-micro.dev/v6/cmd/micro@latest' version constraint conflict: the vanity go-import meta still advertised /v5, so Go fell back to the bare module and resolved an ancient v1.x tag. Advertise /v6 (keeping /v5 for existing users) and add a version-pin fallback note to the install docs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * fix(new,install): pin generated go.mod to current go-micro; lead install with prebuilt binary (#2985) - micro new now requires the exact go-micro version the CLI was built from (via build info), falling back to 'latest' for dev builds. An explicit require is also more robust than a bare import: 'go mod tidy' reliably resolves it, where a requireless go.mod could fail vanity discovery. - Make the precompiled binary (curl install.sh) the recommended install in the docs; demote 'go install' to a from-source option with the version-pin fallback note. - Sync the stale internal/scripts/install.sh to the working website script (it expected an old micro-OS-ARCH asset name; releases ship micro_OS_ARCH.tar.gz). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * website: add corrected nginx vanity-import config (#2985) The live go-micro.dev handler echoed the full request path into the go-import prefix ($host$1), so go install go-micro.dev/v6/cmd/micro@latest got prefix go-micro.dev/v6/cmd/micro — a package, not the module root — and Go fell back to the ancient v1.x tags (version constraints conflict). Add a dedicated /vN location that emits the module root (go-micro.dev/vN) for any sub-path, and make the catch-all advertise the current module roots instead of echoing arbitrary paths. 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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e5bf72abd8 |
docs: consolidate to a single agentic/DX roadmap (#2982)s
Replace the drifted, contradictory roadmap set (two public roadmaps, the AI-native-era business-model doc, stale status snapshots) with one canonical roadmap focused on agentic development and developer experience. - internal/website/docs/roadmap.md — the single source of truth: where we are (v6), the principles (build into what people run; CLI-first; the 0->1 and 0->hero getting-started contract; interaction; battle-tested), and prioritized work (cross-provider conformance + resilience now; durable agent loop, streaming, observability next). - ROADMAP.md — concise, points to the canonical. - roadmap-2026 + the internal ROADMAP_2026/STATUS docs — collapsed to pointers (keeps blog/CLAUDE links alive, removes drift). - CLAUDE.md — references the single roadmap + CHANGELOG for status. 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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f42c9d1d69 |
feat(a2a): agents can serve A2A directly, no gateway required (#2975)
Refactor the A2A handler into a reusable dispatcher + Invoke seam and expose NewAgentHandler(card, invoke) + Card(). An agent now serves its own A2A endpoint with AgentA2A(addr) / WithA2A — handling tasks in-process (no RPC hop, no separate gateway). The gateway and embedded agent share the same handler; the only difference is RPC vs in-process invocation. Docs, README, and changelog cover both deployment modes. Co-authored-by: Claude <noreply@anthropic.com> |
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307b94aab7 |
Implement A2A protocol gateway and update documentation (#2974)
goreleaser / goreleaser (push) Has been cancelled
* feat(a2a): Agent2Agent protocol gateway Add gateway/a2a — exposes registered agents over the open A2A protocol so agents on other frameworks can discover and call them. Agent Cards are generated from registry metadata (the same way the MCP gateway derives tools from service endpoints); incoming A2A tasks translate to the agent's existing Agent.Chat RPC, so there's no per-agent code. v1 is the synchronous JSON-RPC binding: message/send returns a completed Task, tasks/get retrieves it, and Agent Cards are served for discovery; streaming and push notifications are advertised as unsupported. Run with 'micro a2a serve' (cmd/micro/a2a). Tests cover card generation, message/send, tasks/get, listing, and unknown-method errors. * docs: A2A guide, README contents + A2A section, universe A2A check - Add a Contents table of contents at the top of the README and an A2A subsection under Building Agents. - Add the Agent2Agent (A2A) guide and register it in the docs nav. - Exercise the A2A gateway in the universe harness: the concierge agent is reached over A2A (message/send -> Agent.Chat -> completed task). * feat(a2a): outbound client — call external A2A agents Add a2a.Client (Send/Card) so a Go Micro agent or flow can call an agent on any framework by URL — the outbound counterpart to the gateway. Wired in two places: flow.A2A(url) as a workflow step (the cross-framework Dispatch), and agent delegate to an http(s) URL routes over A2A. The universe harness now drives the gateway through the client, exercising both directions. Tests cover client send/card and the round trip. * docs: A2A both-directions — guide, README, changelog, blog #26 --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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9fdcc24cce |
Implement durable execution and scoped state management for flows (#2972)
* docs: design note for flow steps + Checkpoint durable execution
* docs: fold in durable-execution decisions (State struct, single Step, run retention, retry)
* docs: rename State.Payload to State.Data
* feat(flow): ordered steps + Checkpoint durable execution
A flow can now be an ordered list of steps (a task with stages) instead
of a single LLM turn. State carries typed Data plus a Stage marker; each
step is checkpointed before and after via a pluggable Checkpoint
(store-backed by default), so a run survives a crash and resumes where it
stopped without re-running completed steps. Flow-level Retry with a
per-step override; runs retained for audit unless DeleteOnSuccess.
Step actions: Call (RPC), LLM (augmented turn), Dispatch (to an agent),
or any StepFunc. Single-step and agent-dispatch flows are unchanged.
* feat(flow): top-level re-exports + durable flow example
Expose the step/checkpoint API from the micro package (FlowSteps,
FlowStep, FlowState, FlowRetry, FlowWithCheckpoint, FlowCall/LLM/Dispatch,
Checkpoint, StoreCheckpoint) and add a runnable, key-free example
demonstrating crash + resume.
* docs: document durable flow steps (guide, README, CLI help)
* docs: blog post + changelog for durable workflows
* fix(flow): scope checkpoint keys by flow name (flow/{name}/runs/{id})
Run keys were flow/runs/{id} — a single global keyspace shared by every
flow on the default store. Namespace them by flow name so each flow's
state is kept apart. StoreCheckpoint now takes a scope argument (the flow
passes its name by default).
* feat(store): Scope handle; scope agent and flow state by name
Add store.Scope(s, database, table) — a store handle that confines every
operation to a database/table without mutating the shared store, so
co-located components don't clobber each other's table (the failure mode
of the global Init(Table(...)) approach).
Use it to keep each agent's memory and plan in its own table
(agent/{name}) and each flow's runs in its own (flow/{name}), instead of
one global table partitioned only by key prefix. Services already scope
by service name.
* feat: consistent state model — service store scoping, flow registry, list/history CLI
- service: scope store via store.Scope (database service / table name),
retiring the Init(store.Table(name)) global-mutation hack; bridge the
default store so handlers using store.DefaultStore stay isolated.
- flow: register in the registry as type=flow while running (with trigger
and step count), deregister on Stop. Live discovery, like agents.
- cli: micro flow list (registry), micro flow runs <name> (durable store),
micro agent history <name> (durable store). list = running, runs/history
= durable, mirroring the service model.
* test: mini-universe end-to-end harness + scheduled GitHub Action
internal/harness/universe boots a small but real go-micro world — four
services, a durable checkout flow that crashes at payment and resumes,
and a guardrailed agent with a tool wrapper reached over RPC — drives the
scenario, asserts the end state (10 checks), and shuts down. Everything
is real except the LLM (mocked), so it's deterministic and needs no key;
-provider anthropic runs it live. Exits non-zero on failure, so it's an
end-to-end test, not just a demo.
Adds .github/workflows/universe.yml (push/PR/daily/dispatch) running the
universe + existing harnesses on the mock provider, plus an opt-in job
that runs live when ANTHROPIC_API_KEY is set. 'make harness' runs them
locally.
* ci: run the live universe job against AtlasCloud (ATLASCLOUD_API_KEY)
* ci: run the live universe job only on schedule or manual dispatch
The deterministic mock job still runs on push/PR/daily; the live
(AtlasCloud) job runs daily and on manual workflow_dispatch only, so
changes don't burn API credits on every PR but can still be checked
against a real model on demand.
---------
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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e079e083da |
feat(agent): loop detection guardrail + document/blog agent guardrails (#2968)
Add LoopLimit: refuse a tool call repeated with identical arguments in one Ask, with a self-heal message so the model changes approach. Catches the no-progress loop that MaxSteps (count) and the gateway circuit breaker (failures) miss. Enforced at the same tool-handler choke point as MaxSteps/ApproveTool; on by default (lenient 3); AgentLoopLimit(0) to disable. Tests cover repeats, distinct calls, disabled, and default-on. Docs: new Agent Guardrails guide (MaxSteps/LoopLimit/ApproveTool, the ApproveTool integration seam for external policy engines, and the gateway's RateLimit/CircuitBreaker), nav + README + AGENT_DESIGN updates, and blog/23 'Agent Guardrails'. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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a1a57799c3 |
Add payment requirements to tool catalog and implement x402 client (#2966)
* feat(mcp): advertise x402 payment requirements in the tool catalog /mcp/tools now includes each priced tool's payment requirements (amount, network, asset, payTo) when payments are enabled, so an agent can see the cost before calling and choose by price — a shoppable catalog, the foundation for a tool marketplace. Free tools carry no payment block; the shared Tool struct is copied when pricing so it isn't mutated. Tests cover priced/free tools and payments-disabled. Documented in the payments guide. * feat(x402): consumer client with a spend budget (pay-and-retry) Add x402.Client, the consumer counterpart to Middleware: it settles 402 challenges automatically via a pluggable Payer, up to a spend Budget. A call that would exceed the budget is refused before any payment is made, and spend accumulates across calls — the spend cap that keeps an autonomous, paying caller in bounds. Tests cover pay-within-budget, refuse-over-budget, budget accumulation, and free endpoints, end to end against the server Middleware with a mock facilitator and payer. Guide documents the consumer side; agent-level AgentMaxSpend is the next step. * chore: gofmt gateway/mcp/benchmark_test.go (trailing newline) --------- Co-authored-by: Claude <noreply@anthropic.com> |
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e4d2a41c32 |
refactor(x402): Amount/Amounts naming + per-tool amounts + docs (#2965)
goreleaser / goreleaser (push) Has been cancelled
Follow-up to the merged x402 integration (#2964). Drop the commerce-y 'price' vocabulary for the protocol's own 'amount', and add per-tool pricing as an operator concern (the way scopes/rate-limits are set at the gateway). - x402.Config: Price -> Amount (default), plus Amounts map for per-tool overrides; AmountFor(tool) resolves per-tool -> default. Add a Require primitive (per-request enforcement) and LoadConfig for an operator config file. - MCP gateway: enforce payment per-tool inside /mcp/call (where scopes are enforced) using AmountFor, instead of a flat path-based middleware. - CLI: --x402-price -> --x402-amount; add --x402-config (per-tool file) to micro mcp serve and micro-mcp-gateway. - docs: new Payments (x402) guide + nav + README section; blog/22 updated to Amount/Amounts and the config-file model. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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550033dcce |
Optimize image formats and fix lease re-registration issue (#2959)
* perf: convert generated PNGs to optimized JPEGs (12MB -> 1.5MB) The landing and docs loaded 18 AI-generated PNGs at 0.5-1MB each. They're 1200x800 RGB illustrations with no transparency, so they recompress ~8x as progressive JPEG (quality 82) with no visible loss. Convert all, update every reference (.png -> .jpg), and drop the originals (including the unused hero.png). Generated images: 12.3MB -> 1.5MB. * fix(registry/etcd): re-register when a lease silently expires (#2956) The keepalive rework (long-lived KeepAlive instead of KeepAliveOnce) moved lease renewal entirely onto the keepalive goroutine; the 30s periodic Register now skips on the 'unchanged' check. The goroutine only reacted to the keepalive channel closing, so a lease that expired server-side without a prompt channel close (e.g. a partition that outlasted the 90s TTL) left the node de-registered from etcd while the cache still believed it was registered — and nothing re-registered it. That is the hidden-failure mode reported in #2956. React to a non-positive TTL keepalive response the same as a channel close: drop the cached lease/hash so the next Register performs a full re-registration. Extract the loop into keepAliveLoop and unit-test the TTL-expired, channel-closed, and healthy paths (no etcd required). --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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584a9f2132 |
feat(health): add RegistryCheck for registry connectivity (#2956) (#2958)
A go-micro service can keep running while it has silently lost its connection to the registry (etcd, Consul, …) — the process looks healthy but other services can no longer discover it, and Kubernetes sees the pod as fine. health.RegistryCheck(reg) probes connectivity via ListServices and, registered as a critical check, makes /health/ready report not-ready so a readiness probe can pull the pod from rotation. - Works with any registry implementation (no interface change). - Honors the check timeout: an unreachable/hung registry is reported down rather than blocking the probe. - Tests cover healthy, down, timeout, nil, and the not-ready integration. - Documented in the health guide with the Kubernetes readiness example. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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6488d8402d |
Organize README features, enhance testing, and update docs (#2955)
* docs: group README features by section (AI / Framework / DX) The features table repeated 'AI' down the Category column. Split into three grouped tables — AI, Framework, Developer experience & deployment — dropping the repetitive column. Adds a Guardrails row (MaxSteps, ApproveTool). * test: flow-to-agent end-to-end in the harness Proves 'Flow triggers, Agent reasons': a workflow with FlowAgent hands an event to the registered conductor agent over RPC, which plans, creates tasks, and delegates to comms — the whole chain over real RPC with only the LLM mocked. Deterministic (shared in-memory registry, no sleeps), passes under -race. * blog: 'The Evolution of Microservices' (#19) A technical history of distributed-systems eras — the monolith's coordination cost, the distributed-systems tax, containers and declarative orchestration, the service mesh, and the modular-monolith correction — establishing the durable unit (named, typed, discoverable, independently deployable) that every runtime wave required. Then the technical argument for agents: an LLM tool call needs exactly a service interface, so the caller shifts from deterministic code to a reasoner that composes typed capabilities from intent, with the honest caveats (non-determinism, cost, guardrails). Not a product pitch. * docs: bump install version to v5.27.0 --------- Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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d3be610367 |
Refactor README features and add end-to-end flow testing (#2954)
goreleaser / goreleaser (push) Has been cancelled
* 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> |
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Asim Aslam
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35bc58e5d2 |
Enhance onboarding experience and clarify workflows vs agents (#2953)
* 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> |
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Asim Aslam
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e416ea4a75 |
Enhance agent workflows with guardrails and documentation updates (#2952)
* 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> |
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Asim Aslam
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6a73608e9c |
rename coordinator agent to conductor in example and docs (#2950)
Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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cb33decd97 |
Add built-in plan and delegate tools for agents with examples (#2949)
* 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> |
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Asim Aslam
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9bed04ced0 |
Enhance micro run with interactive console and image compression (#2948)
Run Tests / Unit Tests (push) Has been cancelled
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> |
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Asim Aslam
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6731ee2f0c |
Update documentation and blogs for RPC-based agent architecture (#2942)
* 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> |
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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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e13ef7eed5 |
chore: update Discord invite link everywhere (#2935)
Replace discord.gg/jwTYuUVAGh and discord.gg/go-micro with discord.gg/WeMU5AGxD across all docs, blog posts, issue templates, security policy, and contrib READMEs. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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96280678ee |
Expose framework primitives via API gateway and MCP with auth control (#2925)
* feat: expose framework primitives via API gateway and MCP
Add registry, store, and broker as both HTTP routes and MCP tools
so AI agents and HTTP clients can inspect and operate the framework.
API gateway (/micro/* namespace):
GET /micro/registry List registered services
GET /micro/registry/{name} Describe a service
GET /micro/store List store keys
GET /micro/store/{key} Read a record
POST /micro/store/{key} Write a record
POST /micro/broker/{topic} Publish a message
MCP gateway (micro_* tool prefix):
micro_registry_list List services
micro_registry_get Describe a service
micro_store_list List keys
micro_store_read Read a record
micro_store_write Write a record
micro_broker_publish Publish a message
Framework tools use a Handler field on the MCP Tool struct for
direct dispatch (no RPC). Service tools continue to use RPC.
Rate limiters and circuit breakers are applied to framework
tools the same as service tools.
* fix: make framework internals opt-in on API and MCP gateways
Framework primitives (registry, broker, store) are now only
exposed when explicitly enabled:
API gateway: micro api --internal
MCP gateway: Options{Internal: true}
Off by default — user services are always exposed, framework
internals require the flag. Banner output only shows framework
routes when enabled.
* fix: always expose framework internals, gate by auth in production
Revert the --internal flag approach. Framework primitives (registry,
broker, store) are now always exposed:
- micro api: /micro/* routes always available (dev tool)
- MCP gateway: micro_* tools always registered. When Auth is
configured (production), they require micro:admin scope.
Without Auth (dev), they're open — same as all other tools.
This follows the existing pattern: micro run/api = dev (open),
micro server = production (auth + scopes). Framework internals
follow the same security model as user services.
Remove the Internal option from MCP Options. Remove --internal
flag from micro api.
Note: scope persistence depends on the store backend. The default
in-memory store does not survive restarts. Use MICRO_STORE=file
for persistent scopes in production.
* fix: correct DefaultStore comment — it's file-backed, not memory
* fix(server): don't recreate deleted admin user on restart
When the default admin account is deleted via the dashboard, set
a marker key (auth/.admin-deleted) in the store. On startup, skip
admin creation if the marker exists. This prevents the default
admin/micro credentials from reappearing after restart when the
user has intentionally removed them.
* fix: improve agent playground first-run UX and fix doc 404s
Agent playground:
- Add setup hint in empty state explaining how to get started
(click Settings, enter API key, type a prompt)
- Hide hint automatically when API key is already configured
- Add all 7 providers to dropdown (was only OpenAI + Anthropic)
- Include CLI fallback suggestion (micro chat)
Docs:
- Fix .md links to .html across all doc pages — Jekyll serves
.html files, not .md. Fixes 404s including the micro run guide.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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Asim Aslam
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3acf74a29a |
Refactor tool management and add CLI commands for interfaces (#2918)
* 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> |
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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
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88aa0cc666 |
Update logo, add AI integration docs, and enhance CLI features (#2915)
* 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>
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Asim Aslam
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985621f4b4 |
AI integration updates (#2911)
* 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. --------- 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
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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
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1e2901ad0e |
feat(ai): add ImageModel interface with Atlas Cloud and OpenAI support (#2905)
goreleaser / goreleaser (push) Has been cancelled
Add ai.ImageModel interface for text-to-image generation alongside the existing ai.Model for text. Uses the same options pattern (WithAPIKey, WithBaseURL) and the same provider registration system (RegisterImage/NewImage). Implement GenerateImage for Atlas Cloud and OpenAI providers via the OpenAI-compatible /v1/images/generations endpoint. Default image model is gpt-image-1. Responses return images as URL, base64, or both depending on the provider. Update Atlas Cloud blog post and integration guide with image generation examples. Update ai/README.md with ImageModel docs. Co-authored-by: Claude <noreply@anthropic.com> |
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Asim Aslam
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5968fce2d6 |
docs: add Atlas Cloud sponsorship blog post and integration guide (#2902)
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> |
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Asim Aslam
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a0ad9ee566 |
Claude/fix issue 2893 x3rpd (#2901)
* 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> |
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Asim Aslam
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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> |