🧠 STARKLANG — LLM Integration Interface Specification

(Jarvis Protocol – Conversational AI & Code-Augmenting Model Runtime)

This document defines the native integration spec for Large Language Models (LLMs) in STARKLANG — enabling programmable, embedded AI orchestration, semantic command interfaces, and self-augmenting code intelligence via the Jarvis Runtime Layer.

📌 Vision Statement

STARKLANG must treat AI models like functions, not black boxes.

• LLMs are callable compute units
• Prompting is just another function call
• AI inference is streamable, composable, and observable
• The language becomes self-aware, self-reasoning, and self-scaling

📐 Design Philosophy

Principle	Strategy
Prompt-as-code	@llm block or llm.call()
Modular model interface	LLMClient abstraction
Multi-model compatible	OpenAI, Claude, local, custom
AI orchestration DSL	Streaming prompt pipelines
Code intelligence loop	Self-reflective Jarvis runtime layer

🧠 Core Constructs

✅ 1️⃣ LLM Client Interface

let model = LLMClient(provider="openai", model="gpt-4")
let response = model.prompt("Summarize this text", context=article)

Method	Description
.prompt(query, context?)	One-shot prompt
.stream(query)	Token-wise streaming
.chain(prompts)	Multi-stage prompt chaining
.call_prompt(promptBlock)	Executes inline @llm block

✅ 2️⃣ @llm Block Syntax

@llm as summarize:
    system: "You are a summary assistant"
    user: "Summarize the following: {{text}}"

Usage:

let summary = summarize.call({ text: document })

✅ 3️⃣ Prompt Pipeline DSL

pipeline summarize_and_tag:
    @llm as summarize:
        user: "Summarize this: {{input}}"
    @llm as tagger:
        user: "Generate keywords for: {{input}}"

summarize_and_tag.run(input=document)

✅ 4️⃣ Semantic Prompt Macros

@llm as refactor:
    user: "Refactor this code: {{code}}"

let result = refactor.call({ code: my_code_block })

✅ 5️⃣ Tool-augmented Prompt Agents

@llm as code_critic:
    tools = [linter.suggest, profiler.summary]
    user: "Analyze this code and apply tools: {{code}}"

🔌 Runtime LLM Router

Feature	Description
Multi-provider abstraction	OpenAI, Anthropic, Local LLMs
Model fallback logic	Retry chains, routing logic
Token budget aware	Auto-truncate large prompts
Streaming hooks	Token-by-token event handlers

📦 Example: Embedded LLM Copilot

@llm as assistant:
    system: "You are a developer assistant"
    user: "{{query}}"

fn handle_user_input(text: String):
    let reply = assistant.call({ query: text })
    print(reply)

📊 Observability Hooks

• trace_prompt()
• emit_llm_metric(tokens, duration)
• log_llm_call(name, model, tokens)

🔐 Security Considerations

Concern	Mitigation
Prompt injection	Linter & sandbox prompt templates
Token cost audit	Model budget reporter
Data leakage	Local model fallback, inline redaction filters

🔮 Jarvis Runtime Protocol (Advanced)

Layer	Description
Prompt Resolver	Unifies block + dynamic prompting
AI Planner	Chains & orchestrates LLM tools
Semantic AST Interpreter	Understands code intent, passes to LLM
Reflection Engine	Lets code analyze & mutate itself

🔥 Jarvis AI Orchestration Primitives (Future-Ready Concepts)

Primitive	Description
@self_reflect	Code that analyzes itself and re-invokes
@intent_map	Auto-link prompts to code behavior
@plan_execution	AI agent decides optimal execution path
@jarvis_callstack	AI agent traces semantic call chain

✅ Summary

Feature	Status
LLMClient Interface	✅ Designed
@llm Block Syntax	✅ Specified
Prompt Pipeline DSL	✅ Drafted
Runtime Router	✅ Designed
Tool-Augmented Agents	✅ Designed
Jarvis Reflection Engine	⏳ Future
Patent-Worthy Concepts	✅ Ready

STARKLANG's Jarvis Protocol enables self-augmenting codebases and programmable AI-nativeness, making it the world's first truly intelligent programming language.