`ai/flow` was an alias-only package re-exporting go-micro.dev/v6/flow (the
canonical location). It had no callers anywhere in the repo. Remove it; users
should import `go-micro.dev/v6/flow` directly (identical types/functions).
Classified as a Removed (breaking) change in the CHANGELOG since it deletes a
public import path — see the PR for the versioning note.
Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL
Co-authored-by: Claude <noreply@anthropic.com>
* 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
Asim Aslam