Completion & Chat

Completion

llm/complete

Send a single prompt string and get a completion back.

;; Simple completion
(llm/complete "Say hello in 5 words" {:max-tokens 50})

With options:

(llm/complete "Explain monads"
  {:model "claude-haiku-4-5-20251001"
   :max-tokens 200
   :temperature 0.3
   :system "You are a Haskell expert."})

llm/stream

Stream a completion, printing chunks as they arrive.

(llm/stream "Tell me a story" {:max-tokens 200})

With a callback function:

(llm/stream "Tell me a story"
  (fn (chunk) (display chunk))
  {:max-tokens 200})

llm/stream returns the full accumulated response string once streaming finishes — so you can show the live stream and keep the final text:

(define story
  (llm/stream "Tell me a story" (fn (c) (display c)) {:max-tokens 200}))
;; `story` is the complete text after the stream ends.

Chat

llm/chat

Send a list of messages and get a response. Supports system, user, and assistant messages.

(llm/chat
  (list (message :system "You are a helpful assistant.")
        (message :user "What is Lisp? One sentence."))
  {:max-tokens 100})

When you pass :tools, llm/chat runs the tool-execution loop for you (see Tools & Agents). Two options bound it: :tool-mode :none lets the model see the tools but never auto-executes them, and :max-tool-rounds N caps the loop (default 10).

Multi-Modal Chat

Send messages that include images alongside text using message/with-image.

;; Load an image and ask the LLM about it
(define img (file/read-bytes "photo.jpg"))
(define msg (message/with-image :user "Describe this image." img))
(llm/chat (list msg))

Combine with regular messages:

(llm/chat
  (list (message :system "You are an image analyst.")
        (message/with-image :user "What text is in this image?" (file/read-bytes "doc.png"))))

The image must be a bytevector. Media type (PNG, JPEG, GIF, WebP, PDF) is detected automatically from magic bytes. See Vision Extraction for structured data extraction from images.

llm/send

Send a prompt value (composed from prompt expressions) to the LLM.

(define review-prompt
  (prompt
    (system "You are a code reviewer. Be concise.")
    (user "Review this function.")))

(llm/send review-prompt {:max-tokens 200})

Options

All completion and chat functions accept an options map with these keys:

Key	Description
:model	Model name (e.g. "claude-haiku-4-5-20251001")
:max-tokens	Maximum tokens in response
:temperature	Sampling temperature (0.0–1.0)
:system	System prompt (for llm/complete)
:reasoning-effort	Reasoning effort for thinking models — see below
:tools	List of tool values (see Tools & Agents)
:timeout	Per-call HTTP timeout in milliseconds (network providers; non-streaming)
:tags / :metadata	Observability tags/metadata — see Backend Compatibility

Reasoning effort

:reasoning-effort controls how much a reasoning/thinking model deliberates before answering. It takes a keyword or string: :minimal, :low, :medium, :high, :none, or :xhigh. It is a single portable option — Sema maps it to each provider's native control, so the same code works everywhere:

(llm/complete "Prove that sqrt(2) is irrational."
  {:model "gpt-5.4-mini" :reasoning-effort :high :max-tokens 4000})

Provider	Mapped to
OpenAI	native reasoning_effort (gpt-5 / o-series)
Anthropic	extended thinking — effort sets the thinking budget_tokens (and raises max_tokens above it; temperature is forced to default while thinking)
Gemini	thinkingConfig.thinkingBudget (:none/:minimal disable thinking)

Models and providers that don't support reasoning effort ignore the option (no-op). It is also accepted by llm/chat and per-run on agent/run ({:reasoning-effort :high}).