* docs: add 'become a sponsor' call-to-action linking to Discord
Now that there are a couple of sponsors, invite more: a short CTA under
the Sponsors section in the README and on the landing page, pointing to
the Discord to get in touch.
* fix(health): remove duplicate RegistryCheck declaration
Two PRs (#2957 and #2958) each added a RegistryCheck to the health
package, leaving the package uncompilable on master (RegistryCheck
redeclared: health/registry.go vs health/health.go). Keep the
health.go implementation — it honors the check's context timeout so a
hung registry (e.g. an unreachable etcd) reports down instead of
blocking the probe — and remove the duplicate registry.go and its test.
registry_check_test.go already covers healthy/down/nil/timeout/not-ready.
* feat(agent): pluggable memory and custom tools
Make agents compose the way services do — pluggable pieces with working
defaults — by adding the two abstractions an agent needs beyond the model:
- Memory: a pluggable interface for conversation memory. The default is
store-backed and durable across restarts (the previous hardcoded
behavior, now behind an interface); supply your own with WithMemory
(in-memory, database, semantic store). NewMemory / NewInMemory provided.
- Custom tools: WithTool registers any function as a tool the agent can
call, so agents are no longer limited to orchestrating RPC services.
Both exposed at the micro package (AgentMemory, AgentTool, NewMemory,
NewInMemory). Behavior-preserving refactor of the agent's history into
the default Memory; tests cover persistence, in-memory, clear, custom
tool dispatch and errors. README + AGENT_DESIGN document the pluggable
composition (model / memory / tools / guardrails).
* blog: 'Doubling Down on Agents' (#20)
The vision post for making agents a first-class framework the way
services were: opinionated, batteries-included, pluggable. Frames an
agent as a composition of model + memory + tools + guardrails with
working defaults; introduces the new pluggable memory and custom tools;
makes the microagents argument (an agent for everything, distributed
like microservices); and lays out the three primitives — services,
agents, workflows — as one substrate, with an honest list of the gaps
still to fill (knowledge/retrieval, streaming, explicit loop).
---------
Co-authored-by: Claude <noreply@anthropic.com>
* docs: map go-micro onto Anthropic's workflows-vs-agents taxonomy
- new guide 'Agents and Workflows': adopts Anthropic's Building Effective
Agents vocabulary — workflow (predefined path) = flow, agent (dynamic
self-direction) = agent — maps the augmented-LLM building block and the
five workflow patterns onto go-micro, and shows routing (chat router)
and orchestrator-workers (conductor + plan/delegate) are already native.
- flow package doc reframed as a workflow (predefined path) per the same
taxonomy, with guidance on flow vs agent.
- nav + README link the new guide.
* feat: agent guardrails — step limit and tool approval hook
Anthropic's Building Effective Agents stresses stopping conditions and
human-in-the-loop checkpoints for autonomous agents. Add both as plain
options enforced at the tool-handler choke point — no provider changes,
no new abstraction:
- MaxSteps(n): bound tool executions per Ask; beyond the limit, actions
are refused and the model is told to stop and summarize.
- ApproveTool(fn): gate each action before it runs; returning false
blocks it and surfaces the reason to the model. The internal plan tool
is never gated.
Exposed at the micro package (AgentMaxSteps, AgentApproveTool, ApproveFunc).
Tests cover the limit, blocking, and that plan is not gated. Guardrails
section of the agents-and-workflows guide updated from 'active work' to
documented options.
* feat: flow can dispatch to an agent (flow triggers, agent reasons)
Unify the engine without collapsing the workflow/agent distinction. A
Flow with Agent set hands each event's rendered prompt to a named
registered agent over RPC (Agent.Chat) instead of running its own LLM
step — so the workflow stays the deterministic trigger and the agent is
the reasoning engine, with its plan, delegate, memory, and guardrails.
A plain flow is unchanged (single augmented-LLM step).
- flow.Agent(name) / micro.FlowAgent(name); flow stores the client and
skips model setup when dispatching.
- test: dispatch routes to comms.Agent.Chat with the rendered prompt and
records the reply.
- guide: 'Flow triggers, Agent reasons' section.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* feat: add plan and delegate as built-in agent tools
Give agents two self-capabilities, expressed as plain tools wired into
the existing tool handler — no harness or graph, consistent with
"services are the only abstraction":
- plan: record/update an ordered plan, persisted to store-backed memory
and surfaced in the system prompt on later turns (externalized
planning).
- delegate: hand a self-contained subtask to another agent.
Delegate-first — if the target names a registered agent it is called
via RPC; otherwise a focused ephemeral sub-agent is created with
agent.New + Ask in a fresh, isolated context (loads/persists no
history, no built-in tools, so it cannot re-delegate).
Both are added automatically to any non-ephemeral agent, so existing
micro.NewAgent services and micro chat routing get them for free.
Tests are hermetic (memory store + memory registry).
* feat: add agent-plan-delegate example and document plan/delegate
- examples/agent-plan-delegate: coordinator that plans multi-step work,
creates tasks with its own tools, and delegates notification to a
separate registered comms agent over RPC.
- integration tests driving the full Ask loop through a fake provider:
plan tool exposure + persistence, ephemeral delegation with isolated
context, delegate-first RPC routing to a registered agent.
- docs: README (Building Agents + features + examples), AGENT_DESIGN
(Built-in Capabilities), agent-patterns guide (Pattern 9), CLAUDE.md.
* docs: blog post and guide for plan & delegate
- blog/17: "Plan & Delegate: Deep Agents in Go" — what the feature is,
how plan and delegate work, and a runnable getting-started path.
- guides/plan-delegate: reference guide with the smallest-agent snippet,
plan/delegate semantics, and the multi-agent example; linked in nav.
- example: auto-detect provider/key from common env vars (ANTHROPIC_API_KEY,
OPENAI_API_KEY, ...) so 'export KEY && go run main.go' just works.
- onboarding: getting-started paths now include go mod init / go get and a
clone-and-run path, so a reader can actually run it from a cold start.
* refactor: reframe plan/delegate blog and clean up sub-agent construction
- blog/17 retitled "Agents That Plan and Delegate" and reframed around
intent (plan = state intent, delegate = direct it), positioned as the
next beat after blog 15/16 and tied to the existing store + agent RPC
rather than re-announcing them. "Deep agents" now a single in-passing
nod, matching how blog 14 references LangChain.
- agent: add unexported newEphemeral constructor for sub-agents instead
of type-asserting the public Agent interface to set an internal field;
matches the options-only construction idiom used elsewhere.
* feat: expose plan & delegate in the micro chat fallback
Add agent.Builtins(opts...) — returns the built-in tools plus a handler,
so the plan/delegate capabilities can be wired into a tool loop that
isn't a running Agent. micro chat's direct-service fallback now reuses
it (single source of truth, no duplicated handler logic), so planning
and delegation are available there too, not just for registered agents.
Adds a test for the accessor; notes CLI availability in the guide.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* 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>
Asim Aslam