llm

llm

Small, native, per-provider model adapters for agentcore / jess agents, behind one domain-named port: llm.LLM.

Why

agentcore.ChatModel is the interface an agent talks to. llm.LLM is that same contract under a name that belongs to your domain, so the rest of your code never imports a vendor SDK. Each provider adapter is:

• Native — it speaks the provider's own API, not an OpenAI-compatibility shim layered over a different provider.
• An anti-corruption layer — the only package allowed to import that provider's SDK, translating it to and from agentcore message / tool / stream-event types.

Add a provider by adding a subpackage; the rest of your agent doesn't change.

What it's not

Not a gateway. If you want one process that fronts every provider behind an OpenAI-compatible endpoint, with routing, fallbacks, load balancing, budgets, and a proxy server, that's LiteLLM or OpenRouter. llm is the opposite shape:

• A library, not a server. No proxy, no gateway, no daemon. You import it; calls go straight from your process to the provider.
• Native, not OpenAI-flattened. Each adapter speaks its provider's real API and can surface provider-specific behavior (Anthropic's thinking/effort), instead of collapsing everything to a lowest-common-denominator shape.
• No orchestration baked in. Routing, fallback, retries, load balancing, response caching, cost and budget tracking are the agent's or caller's job, not this layer's.
• No giant model registry. You add the one or two adapters you actually use; nothing else ships.

One port (llm.LLM), native adapters behind it. That's the whole scope.

Install

go get github.com/guygrigsby/llm

Requires Go 1.26+.

The port

type LLM interface {
	Generate(ctx context.Context, messages []agentcore.Message, tools []agentcore.ToolSpec, opts ...agentcore.CallOption) (*agentcore.LLMResponse, error)
	GenerateStream(ctx context.Context, messages []agentcore.Message, tools []agentcore.ToolSpec, opts ...agentcore.CallOption) (<-chan agentcore.StreamEvent, error)
	SupportsTools() bool
}

Anything satisfying llm.LLM is an agentcore.ChatModel, so it drops straight into jess.WithModel. agentcore types cross the boundary freely — they are the ubiquitous language jess is built on, not an isolated vendor. The isolated vendor is each provider's SDK, which only that provider's adapter imports.

Adapters

anthropic

github.com/guygrigsby/llm/anthropic — native Anthropic adapter on the official anthropic-sdk-go. Adaptive thinking + effort; never sends temperature/top_p/top_k. Full streaming and tool support.

m, err := anthropic.New(anthropic.Config{APIKey: key, Model: "claude-sonnet-5"})
if err != nil {
	return err
}
agent := jess.New(jess.WithModel(m) /* , ... */)

deepseek

github.com/guygrigsby/llm/deepseek — native DeepSeek adapter over its OpenAI-format chat-completions API, using only net/http (no SDK dependency). Built for cheap one-shot work such as summaries and extraction; tool-calling and true token streaming are not wired yet (GenerateStream returns the whole result as one terminal event). Wire those before using it as a primary conversational model.

m, err := deepseek.New(deepseek.Config{APIKey: key, Model: "deepseek-chat"})

kimi

github.com/guygrigsby/llm/kimi — native Kimi (Moonshot) adapter over its OpenAI-format chat-completions API, using only net/http (no SDK dependency). Full scope: real SSE token streaming and tool calling, so kimi-k3 works as a primary conversational model. Kimi-specific extensions beyond standard OpenAI are wired too: reasoning_effort (mapped from the call's thinking level, with the model's reasoning_content surfaced as a thinking block), partial mode (prefix continuation, triggered by "partial": true metadata on a trailing assistant message), and an EstimateTokens helper over the /tokenizers/estimate-token-count endpoint.

m, err := kimi.New(kimi.Config{APIKey: key, Model: "kimi-k3"})
if err != nil {
	return err
}
agent := jess.New(jess.WithModel(m) /* , ... */)

License

MIT. See LICENSE.