Modules

The library is published as ~50 independent Go modules. Every module lives at github.com/joakimcarlsson/ai/<path> and is versioned independently with path-prefixed tags (e.g. llm/openai/v0.1.0).

You install only what you use. A typical OpenAI chat client needs llm, llm/openai, message, and model.

Tier 0 — Leaves

Dependency-free building blocks. No vendor SDKs.

Module	Purpose
`model`	Model catalog, capability flags, custom-model builder
`message`	Conversation message types and constructors
`tool`	Tool interface, MCP integration, function-tool helpers
`schema`	JSON Schema generation for tool inputs and structured output
`tracing`	OpenTelemetry setup helper for traces, metrics, and logs
`prompt`	Prompt template rendering
`types`	Shared event types (`EventContentDelta`, `EventThinkingDelta`, etc.)

Tier 1 — Modality interfaces

Define the contract for each capability. No vendor SDKs; vendor modules implement these.

Module	Purpose
`llm`	Chat completion interface, retry config, tracing wrapper
`embeddings`	Text / multimodal / contextualized embedding interface
`tts`	Text-to-speech interface (with optional `ForcedAlignmentProvider`)
`stt`	Speech-to-text interface (transcribe + translate, streaming)
`image`	Image generation interface
`rerankers`	Document reranking interface
`fim`	Fill-in-the-middle code completion interface

Tier 2 — Vendor implementations

Each carries exactly one vendor SDK.

LLM

Module	Vendor SDK
`llm/openai`	`openai-go` — Chat Completions via `NewLLM` (also OpenRouter, xAI, Mistral via `WithBaseURL`); Responses API with server-side built-in tools via `NewResponsesLLM`
`llm/anthropic`	`anthropic-sdk-go` (also Bedrock backend); server-side `web_search` via `WithWebSearch`
`llm/gemini`	`google.golang.org/genai`; server-side `google_search`, `code_execution`, `url_context` via dedicated options
`llm/groq`	`openai-go` — fast OpenAI-compatible chat via `NewLLM`; compound-model built-ins (`browser_search`, `code_execution`, `visit_website`) via `NewCompoundLLM`
`llm/xai`	`openai-go` — OpenAI-compatible chat via `NewLLM`; Responses API built-ins (`web_search`, `x_search`, `code_execution`) via `NewResponsesLLM`
`llm/vertexai`	`google.golang.org/genai` (Vertex AI backend)
`llm/azure`	`openai-go` against Azure OpenAI
`llm/bedrock`	`aws-sdk-go-v2` Bedrock Runtime

Embeddings

Module	Vendor SDK
`embeddings/openai`	`openai-go`
`embeddings/voyage`	`net/http`
`embeddings/cohere`	`net/http`
`embeddings/gemini`	`google.golang.org/genai`
`embeddings/mistral`	`net/http`
`embeddings/bedrock`	`aws-sdk-go-v2` Bedrock Runtime

TTS

Module	Vendor SDK
`tts/openai`	`openai-go`
`tts/elevenlabs`	`net/http`
`tts/google`	`cloud.google.com/go/texttospeech`
`tts/azure`	`net/http` (Azure Cognitive Services Speech)
`tts/deepgram`	`net/http`

STT

Module	Vendor SDK
`stt/openai`	`openai-go`
`stt/elevenlabs`	`net/http` + `gorilla/websocket` for streaming
`stt/deepgram`	`deepgram-go-sdk`
`stt/assemblyai`	`assemblyai-go-sdk`
`stt/google`	`cloud.google.com/go/speech`

Image generation

Module	Vendor SDK
`image/openai`	`openai-go` (also xAI via `WithBaseURL`)
`image/gemini`	`google.golang.org/genai` (also Vertex AI)

Rerankers

Module	Vendor SDK
`rerankers/voyage`	`net/http`
`rerankers/cohere`	`net/http`

Fill-in-the-middle

Module	Vendor SDK
`fim/mistral`	`net/http`
`fim/deepseek`	`net/http`

Tier 3 — Utilities

Module	Purpose
`tokens/sliding`	Sliding-window context strategy
`tokens/truncate`	Hard-truncate context strategy
`tokens/summarize`	Summarization-based context strategy
`batch`	Batch request / progress / event types
`batch/openai`	OpenAI native batch API processor
`batch/anthropic`	Anthropic native batch API processor
`batch/gemini`	Gemini / Vertex AI native batch API processor
`batch/concurrent`	Bounded-concurrency runner over any LLM / embedding client

Tier 4 — Agent runtimes and conversation primitives

Module	Purpose
`agent`	Agent runtime: chat, streaming, hooks, tools, sub-agents, handoffs, fan-out
`voice`	Voice-first agent: streaming STT → LLM → TTS pipeline with tool calls
`memory`	Persistent memory interface, dedup + extraction helpers
`session`	Conversation session storage interfaces and implementations

agent/team is a sub-package of agent (same module).

Tier 5 — Persistence

Module	Purpose
`memory/pgvector`	PostgreSQL + pgvector backend with HNSW vector search
`memory/postgres`	PostgreSQL session + memory store
`memory/sqlite`	SQLite session + memory store

Adding new modules

The repo is a Go workspace; each module has its own go.mod. To add a new vendor implementation:

Create the directory and go.mod (declare module path github.com/joakimcarlsson/ai/<modality>/<vendor>).
Add a replace directive for any internal modules it imports.
Add the path to go.work.example.
Tag releases as <modality>/<vendor>/vX.Y.Z.

See the release process for details on tagging and publishing.