AI Agents Overview

AI Agents

Nulang’s AI capabilities live in the optional nulang-ai library crate. An agent is a named record of configuration — model, system prompt, tools, memory, pricing — that the runtime spawns like an actor through the generic PerformAsync effect mechanism. No special AI bytecodes or language extensions are needed. You interact with an agent through the ask operator, which is a synchronous request/reply call.

Declaring an Agent

agent Assistant = {
    model: "gpt-4o",
    system_prompt: "You are a helpful assistant.",
    memory: { max_turns: 10 }
}

The full set of agent configuration fields:

Field	Type	Description
model	String	LLM model identifier (e.g. "gpt-4o", "llama3.1")
system_prompt	String	System prompt prepended to every conversation
tools	[String]	List of function names exposed as tools (see Tools)
memory	{ max_turns: Int }	Episodic memory — conversation history window
semantic_memory	{ dimensions: Int }	Vector embeddings for fact recall
procedural_memory	{ namespace: String }	Learned patterns/skills
pricing	{ input: Float, output: Float }	Per-token pricing for cost tracking
fallback	[{ model: String, ... }]	Fallback models on failure
retry	{ max_attempts: Int, ... }	Retry configuration

All fields except model and system_prompt are optional.

Spawning and Asking

Spawn an agent like an actor, then call it with ask:

agent Assistant = {
    model: "gpt-4o",
    system_prompt: "You are helpful.",
    memory: { max_turns: 10 }
}

let a = spawn Assistant {} in
ask a ask("What is an actor model?")

spawn Assistant {} in ... creates a running agent instance and returns its reference. The ask a ask("prompt") form is a synchronous request/reply — it blocks the caller until the agent responds. Inside a scheduler-driven actor or workflow, LLM.ask suspends non-blockingly instead (see Signals, Timers & Queries).

Tools

Expose Nulang functions as agent tools with the @tool annotation:

@tool(description: "Adds two integers.")
fn add(x: Int, y: Int) -> Int { x + y }

agent Calculator = {
    model: "gpt-4o",
    system_prompt: "You are a calculator.",
    tools: [add]
}

let calc = spawn Calculator {} in
ask calc ask("What is 2 + 2?")

The @tool(description: "...") annotation attaches a human-readable description. The agent’s LLM can invoke the tool during its response; the runtime executes the Nulang function and feeds the result back.

Providers

Nulang’s LLM client is provider-agnostic. The model field selects the provider:

Provider	Example model	Configuration
OpenAI	gpt-4o	OPENAI_API_KEY env var
Ollama	llama3.1	Local Ollama server on localhost:11434

Pipeline orchestration is available via the Rust nulang-ai crate (Pipeline::new(), Pipeline::stage(), Pipeline::run()) and can be accessed through the runtime API. A language-level pipeline expression is pending.

Next

• Memory — episodic, semantic, and procedural memory subsystems
• Multi-Agent Patterns — pipelines, debates, and supervisor teams

Note: The agent keyword is currently Experimental and is proposed for deprecation in favor of plain actor declarations that import nlc.ai (RFC 0004). The keyword remains functional and will continue to work through at least two major language versions.