sema

<div align="center">

<img src="https://sema-lang.com/logo.svg" alt="Sema" height="64">

# Sema

**A Lisp where LLM agents are language primitives, not an SDK** — compiled to a fast bytecode VM, shipped as a single binary.

[![Playground](https://img.shields.io/badge/try_it-sema.run-c8a855?style=flat)](https://sema.run)
[![Docs](https://img.shields.io/badge/docs-sema--lang.com-c8a855?style=flat)](https://sema-lang.com/docs/)
[![Version](https://img.shields.io/github/v/tag/sema-lisp/sema?label=version&color=c8a855&style=flat)](https://github.com/sema-lisp/sema/releases/latest)
[![Coverage](https://codecov.io/gh/sema-lisp/sema/graph/badge.svg)](https://codecov.io/gh/sema-lisp/sema)
[![License](https://img.shields.io/badge/license-MIT-c8a855?style=flat)](LICENSE)

[**Docs**](https://sema-lang.com/docs/) ·
[**Playground**](https://sema.run) ·
[**For Agents**](https://sema-lang.com/docs/for-agents) ·
[**Examples**](https://github.com/sema-lisp/sema/tree/main/examples) ·
[**Issues**](https://github.com/sema-lisp/sema/issues)

</div>

**Stop rewriting the agent loop.** Every LLM script grows the same scaffolding — retries, caching, cost caps, rate limits, tool dispatch, conversation state. Sema makes that scaffolding the runtime: your script stays the size of its idea, ships as a single binary, and your coding agent already speaks the language.

Sema is a Scheme-like Lisp where **prompts are s-expressions**, **conversations are persistent data structures**, and **LLM calls are just another form of evaluation** — with Clojure-style keywords (`:foo`), map literals (`{:key val}`), and vector literals (`[1 2 3]`).

## What It Looks Like

A coding agent with file tools, safety checks, and budget tracking — in ~40 lines:

```scheme
;; Define tools the LLM can call
(deftool read-file
  "Read a file's contents"
  {:path {:type :string :description "File path"}}
  (lambda (path)
    (if (file/exists? path) (file/read path) "File not found")))

(deftool edit-file
  "Replace text in a file"
  {:path {:type :string} :old {:type :string} :new {:type :string}}
  (lambda (path old new)
    (file/write path (string/replace (file/read path) old new))
    "Done"))

(deftool run-command
  "Run a shell command"
  {:command {:type :string :description "Shell command to run"}}
  (lambda (command) (:stdout (shell "sh" "-c" command))))

;; Create an agent with tools, system prompt, and spending limit
(defagent coder
  {:system (format "You are a coding assistant. Working directory: ~a" (sys/cwd))
   :tools [read-file edit-file run-command]
   :max-turns 20})   ; no :model → uses the configured default provider

;; Run it — budget is scoped, automatically restored after the block
(llm/with-budget {:max-cost-usd 0.50} (lambda ()
  (define result (agent/run coder "Add error handling to src/main.rs"))
  (println (:response result))
  (println (format "Cost: $~a" (:spent (llm/budget-remaining))))))
```

## Key Features

```scheme
;; Simple completion
(llm/complete "Explain monads in one sentence")

;; Structured data extraction — returns a map, not a string
(llm/extract
  {:vendor {:type :string} :amount {:type :number} :date {:type :string}}
  "Bought coffee for $4.50 at Blue Bottle on Jan 15")
;; => {:amount 4.5 :date "2025-01-15" :vendor "Blue Bottle"}

;; Classification
(llm/classify (list :positive :negative :neutral) "This product is amazing!")
;; => :positive

;; Multi-turn conversations as immutable data
(define conv (conversation/new {:model "claude-haiku-4-5-20251001"}))
(define conv (conversation/say conv "The secret number is 7"))
(define conv (conversation/say conv "What's the secret number?"))
(conversation/last-reply conv) ;; => "The secret number is 7."

;; Streaming
(llm/stream "Tell me a story" {:max-tokens 500})

;; Batch — all prompts sent concurrently
(llm/batch (list "Translate 'hello' to French"
                 "Translate 'hello' to Spanish"
                 "Translate 'hello' to German"))

;; Vision — extract structured data from images
(llm/extract-from-image
  {:text :string :background_color :string}
  "assets/logo.png")
;; => {:background_color "white" :text "Sema"}

;; Multi-modal chat — send images in messages
(define img (file/read-bytes "photo.jpg"))
(llm/chat (list (message/with-image :user "Describe this image." img)))

;; Cost tracking
(llm/set-budget 1.00)
(llm/budget-remaining) ;; => {:limit 1.0 :spent 0.05 :remaining 0.95}

;; Response caching — avoid duplicate API calls during development
(llm/with-cache (lambda ()
  (llm/complete "Explain monads")))

;; Cassettes — record real responses once, replay them in CI (no keys, no network)
(llm/with-cassette "fixtures/run.jsonl" {:mode :auto} (lambda ()
  (llm/complete "Explain monads")))

;; Fallback chains — automatic provider failover
(llm/with-fallback [:anthropic :openai :groq]
  (lambda () (llm/complete "Hello")))

;; In-memory vector store for semantic search (RAG)
(vector-store/create "docs")
(vector-store/add "docs" "id" (llm/embed "text") {:source "file.txt"})
(vector-store/search "docs" (llm/embed "query") 5)

;; Cross-encoder reranking — the retrieve-many → rerank-to-a-few RAG move
(llm/rerank "how do I read a file?"
            ["file/read returns a string" "http/get fetches a URL"]
            {:top-k 3})
;; => ({:index 0 :score 0.98 :document "file/read returns a string"} ...)

;; Text chunking for LLM pipelines
(text/chunk long-document {:size 500 :overlap 100})

;; Prompt templates
(prompt/render "Hello {{name}}" {:name "Alice"})
; => "Hello Alice"

;; Persistent key-value store
(kv/open "cache" "cache.json")
(kv/set "cache" "key" {:data "value"})
(kv/get "cache" "key")
```

## Supported Providers

All providers are auto-configured from environment variables — just set the API key and go.

| Provider              | Chat | Stream | Tools | Embeddings | Vision |
| --------------------- | ---- | ------ | ----- | ---------- | ------ |
| **Anthropic**         | ✅   | ✅     | ✅    | —          | ✅     |
| **OpenAI**            | ✅   | ✅     | ✅    | ✅         | ✅     |
| **Google Gemini**     | ✅   | ✅     | ✅    | —          | ✅     |
| **Ollama**            | ✅   | ✅     | ✅    | —          | ✅     |
| **Groq**              | ✅   | ✅     | ✅    | —          | —      |
| **xAI**               | ✅   | ✅     | ✅    | —          | —      |
| **Mistral**           | ✅   | ✅     | ✅    | —          | —      |
| **Moonshot**          | ✅   | ✅     | ✅    | —          | —      |
| **Jina**              | —    | —      | —     | ✅         | —      |
| **Voyage**            | —    | —      | —     | ✅         | —      |
| **Cohere**            | —    | —      | —     | ✅         | —      |
| **Any OpenAI-compat** | ✅   | ✅     | ✅    | —          | ✅     |
| **Custom (Lisp)**     | ✅   | —      | ✅    | —          | —      |

## It's Also a Real Lisp

Hundreds of built-in functions, tail-call optimization, macros, modules, error handling — not a toy.

```scheme
;; Closures, higher-order functions, TCO
(define (fibonacci n)
  (let loop ((i 0) (a 0) (b 1))
    (if (= i n) a (loop (+ i 1) b (+ a b)))))
(fibonacci 50) ;; => 12586269025

;; Full R7RS numeric tower — bignums, exact rationals, complex numbers
(expt 2 100)   ;; => 1267650600228229401496703205376
(+ 1/2 1/3)    ;; => 5/6
(sqrt -1)      ;; => 0+1i

;; Maps, keywords-as-functions, f-strings
(define person {:name "Ada" :age 36 :langs ["Lisp" "Rust"]})
(:name person) ;; => "Ada"
(println f"${(:name person)} knows ${(length (:langs person))} languages")

;; Destructuring
(let (({:keys [name age]} person))
  (println f"${name} is ${age}"))

;; Pattern matching with guards
(define (classify n)
  (match n
    (x when (> x 100) "big")
    (x when (> x 0)   "small")
    (_                 "non-positive")))

;; Functional pipelines
(->> (range 1 100)
     (filter even?)
     (map #(* % %))
     (take 5))
;; => (4 16 36 64 100)

;; Nested data access
(define config {:db {:host "localhost" :port 5432}})
(get-in config [:db :host])  ;; => "localhost"

;; Macros
(defmacro unless (test . body)
  `(if ,test nil (begin ,@body)))

;; Modules
(module utils (export square)
  (define (square x) (* x x)))

;; HTTP, JSON, regex, file I/O, crypto, CSV, datetime...
(define data (json/decode (http/get "https://api.example.com/data")))
```

> 📖 Full language reference, stdlib docs, and more examples at **[sema-lang.com/docs](https://sema-lang.com/docs/)**

## Try It Now

> **[sema.run](https://sema.run)** — Browser-based playground with 20+ example programs.
> No install required. Runs entirely in WebAssembly.

## Teach Your Coding Agent Sema in One Line

Sema is new, so your agent hasn't seen it. Fix that in one command — append the
agent crib sheet to your repo's `AGENTS.md` (and point `CLAUDE.md` at it):

```bash
curl -fsSL https://sema-lang.com/docs/for-agents.md >> AGENTS.md
ln -s AGENTS.md CLAUDE.md     # Claude Code, Cursor, etc. read this
```

[`for-agents.md`](https://sema-lang.com/docs/for-agents) is a compact working guide for
an LLM that already knows a Lisp. It covers the rules most likely to cause incorrect
generated code and links to [`/llms.txt`](https://sema-lang.com/llms.txt), a machine index
of every doc page. The agent can fetch only the page it needs (for example,
`/docs/llm/tools-agents.md`) instead of loading the whole manual. Every doc URL also
serves raw Markdown: append `.md` to a `sema-lang.com/docs/...` link to get the source.

## Installation

Install pre-built binaries (no Rust required):

```bash
# macOS / Linux
curl -fsSL https://sema-lang.com/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://github.com/sema-lisp/sema/releases/latest/download/sema-lang-installer.ps1 | iex"

# Homebrew (macOS / Linux)
brew install helgesverre/tap/sema-lang
```

Or install from [crates.io](https://crates.io/crates/sema-lang):

```bash
cargo install sema-lang
```

Or build from source:

```bash
git clone https://github.com/sema-lisp/sema
cd sema && cargo build --release
# Bin