* ai/minimax: complete provider surface (matrix, conformance, changelog)
Follow-up after merging the MiniMax provider (#3769), mirroring the Ollama
completeness pass (#3637):
- Add the `minimax` row to the AI provider capability matrix and blank-import
ai/minimax in provider_capabilities_test.go so the matrix stays enforced
against the registry.
- Add minimax to the stream-conformance allowlist (+ import) so its streaming
is actually exercised against the OpenAI-compatible SSE contract, not just
registered. It passes via the shared ai/internal/openaiapi path.
- Record the provider in CHANGELOG [Unreleased].
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL
* ai: update capabilities_test provider assertions for minimax
Adding the minimax blank-import to the shared ai_test binary (for stream
conformance) also registers it for TestRegisteredProviders / TestCapabilityRows
/ TestCapabilityMatrix in capabilities_test.go, which pin the exact provider
set. Update those assertions to include minimax. (Fixes the Unit Tests failure
my scoped `-run TestStreamProviders` check missed — go compiles all _test.go in
a package into one binary.)
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>
Follow-up cleanup after merging the Ollama provider (#3636):
- Add the `ollama` row to the AI provider capability matrix in the provider
guide, and blank-import `ai/ollama` in provider_capabilities_test.go so the
matrix stays enforced against the registry (the provider registers a stream
but wasn't imported in that test, so its row went unchecked).
- README: bump "7 LLM providers" → 8 and list Ollama (local + cloud); add its
default model (`llama3.2`) to the model table.
- Fix a fictional model name shipped in the example and package doc:
`gemma4:31b-cloud` → `gpt-oss:120b`. gemma4 doesn't exist, and the `-cloud`
suffix is for cloud models proxied through a local Ollama, not the direct
ollama.com/v1 endpoint the example uses.
- Record the provider and the new agent.BaseURL/micro.AgentBaseURL option in
the CHANGELOG [Unreleased] section.
Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL
Co-authored-by: Claude <noreply@anthropic.com>
Add a dedicated Ollama AI provider (ai/ollama/) that auto-detects
local vs cloud mode based on the base URL:
- Local Ollama: native /api/chat endpoint with NDJSON streaming
- Ollama Cloud: OpenAI-compatible /v1/chat/completions with SSE streaming
Both modes support tool calls with a multi-round execution loop.
Add agent.BaseURL option so agents can point at non-default LLM
endpoints (e.g. local Ollama, proxies). Wire it through micro.AgentBaseURL
at the top level.
Include a complete example (examples/agent-ollama/) demonstrating a
knowledge-base service with auto-discovered tools, a custom time tool,
streaming, and env-var configuration for local vs cloud.
Closes#3632
* atlascloud: env-selectable chat model; run conformance on a stronger model
The daily provider-conformance harness fails 4/5 harnesses on Atlas Cloud —
its default chat model answers agent/tool-use conformance prompts
conversationally instead of performing the task. Atlas is currently the only
provider with a key configured, so the whole live run is red.
Make the Atlas Cloud provider honor an ATLASCLOUD_MODEL env override (falling
back to the existing default), and set it in the harness workflow to a
stronger tool-use model (Qwen3, overridable via an Actions variable). No
change to the default for normal use.
* atlascloud: use minimaxai/minimax-m3 for conformance model
---------
Co-authored-by: Claude <noreply@anthropic.com>
Streaming is implemented now (v6.3.3); the old test expected
ErrStreamingUnsupported and failed. Replace it with a real SSE streaming
test (httptest) that also asserts stream_options.include_usage and the
final usage chunk.
* ai/atlascloud: surface token usage on streams
Request stream_options.include_usage and return the final usage chunk
as a Response with Usage set, so streaming callers can record usage.
* ai/openai: surface token usage on streams
Request stream_options.include_usage and return the final usage chunk
as a Response with Usage set, so streaming callers can record usage.
* Initial plan
* fix: remove extra blank line in ai/anthropic/anthropic.go (gofmt)
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
* ai: add WithMaxTokens option
Let callers cap response length; providers send max_tokens when set
(anthropic keeps its 8192 default otherwise).
* ai: add WithMaxTokens option
Let callers cap response length; providers send max_tokens when set
(anthropic keeps its 8192 default otherwise).
* ai: add WithMaxTokens option
Let callers cap response length; providers send max_tokens when set
(anthropic keeps its 8192 default otherwise).
* ai: add WithMaxTokens option
Let callers cap response length; providers send max_tokens when set
(anthropic keeps its 8192 default otherwise).
* ai/atlascloud: thread Request.Messages into the request
Fold conversation history (req.Messages) between the system prompt and
the final user prompt so multi-turn context reaches the model.
Refs #3292
* ai/openai: thread Request.Messages into the request
Fold conversation history (req.Messages) between the system prompt and
the final user prompt so multi-turn context reaches the model.
Refs #3292
* ai/anthropic: thread Request.Messages into the request
Fold conversation history (req.Messages) between the system prompt and
the final user prompt so multi-turn context reaches the model.
Refs #3292
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>
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>
* 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>
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>
* 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>
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>
* 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