Introduces an opt-in level-based reasoning toggle (low/medium/high) that
each provider translates to its native parameter:
- Anthropic: thinking.budget_tokens (1024/8000/24000), with temperature
forced to default and MaxTokens auto-grown above the budget.
- OpenAI/xAI/Groq via openaicompat: reasoning_effort string, gated by a
new Rules.SupportsReasoning predicate so non-reasoning models don't
receive the parameter. xAI uses Rules.MapReasoningEffort to remap
"medium" to "high" since its API only accepts low|high.
- Google: thinking_config.thinking_budget + include_thoughts:true.
- DeepSeek: SupportsReasoning=false (reasoner is always-on; the
reasoning_content trace was already extracted via openaicompat).
Reasoning content is surfaced as Response.Thinking on Complete and as
StreamEventThinking deltas during streaming. Provider-side: extracted
from Anthropic thinking content blocks, Google's part.Thought=true
parts, and the non-standard reasoning_content field that DeepSeek and
Groq emit (parsed out of raw JSON since openai-go doesn't type it).
Public API:
- llm.ReasoningLevel + ReasoningLow/Medium/High constants
- llm.WithReasoning(level) request option
- Model.WithReasoning(level) for baked-in defaults
- provider.Request.Reasoning, provider.Response.Thinking
- provider.StreamEventThinking
Tests cover Rules-based gating, MapReasoningEffort, reasoning_content
extraction (Complete + Stream), Anthropic budget mapping, and
temperature suppression when thinking is enabled. Existing behavior is
unchanged when Reasoning is the empty string.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Five OpenAI-compatible providers join the library as first-class constructors
(llm.DeepSeek, llm.Moonshot, llm.XAI, llm.Groq, llm.Ollama). Their wire-level
implementation is shared via a new v2/openaicompat package which is the
extracted guts of the old v2/openai provider; each provider supplies its own
Rules value to declare per-model constraints (e.g., DeepSeek Reasoner rejects
tools and temperature, Moonshot/xAI accept images only on *-vision* models,
Groq rejects audio input). v2/openai itself becomes a thin wrapper that sets
RestrictTemperature for o-series and gpt-5 models.
A new provider registry (v2/registry.go) exposes llm.Providers() and drives
the TUI's provider picker so adding a provider in future is a single-file
change.
The TUI at cmd/llm was migrated from v1 to v2 and moved to v2/cmd/llm. With
nothing else depending on v1, the v1 code at the repo root (all .go files,
schema/, internal/, provider/, root go.mod/go.sum) is deleted.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add provider-specific usage details, fix streaming usage, and return
usage from all high-level APIs (Chat.Send, Generate[T], Agent.Run).
Breaking changes:
- Chat.Send/SendMessage/SendWithImages now return (string, *Usage, error)
- Generate[T]/GenerateWith[T] now return (T, *Usage, error)
- Agent.Run/RunMessages now return (string, *Usage, error)
New features:
- Usage.Details map for provider-specific token breakdowns
(reasoning, cached, audio, thoughts tokens)
- OpenAI streaming now captures usage via StreamOptions.IncludeUsage
- Google streaming now captures UsageMetadata from final chunk
- UsageTracker.Details() for accumulated detail totals
- ModelPricing and PricingRegistry for cost computation
Closes#2
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Audio struct alongside Image for sending audio attachments to
multimodal LLMs. OpenAI uses input_audio content parts (wav/mp3),
Google Gemini uses genai.NewPartFromBytes, and Anthropic skips
audio gracefully since it's not supported.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Migrate speech-to-text transcription types and OpenAI transcriber
implementation from v1. Types are defined in provider/ to avoid
import cycles and re-exported via type aliases from the root package.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
v2 is a new Go module (v2/) with a dramatically simpler API:
- Unified Message type (no more Input marker interface)
- Define[T] for ergonomic tool creation with standard context.Context
- Chat session with automatic tool-call loop (agent loop)
- Streaming via pull-based StreamReader
- MCP one-call connect (MCPStdioServer, MCPHTTPServer, MCPSSEServer)
- Middleware support (logging, retry, timeout, usage tracking)
- Decoupled JSON Schema (map[string]any, no provider coupling)
- Sample tools: WebSearch, Browser, Exec, ReadFile, WriteFile, HTTP
- Providers: OpenAI, Anthropic, Google (all with streaming)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
steve