AgentLang 🤖✨

A purpose-built language for LLM-powered agents.
Written by agents. Executed by runtimes. Trusted by design.

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🚀 Overview

AgentLang is a programming language specifically designed for the "Agentic Era." Unlike general-purpose languages like Python or TypeScript, which were optimized for human authorship, AgentLang is optimized for Large Language Model (LLM) generation, token efficiency, and autonomous execution.

It introduces goal-oriented control flow, first-class uncertainty/confidence types, privacy-preserving Zero Knowledge Proof (ZKP) integration, and a high-performance hybrid runtime.

⚠️ Disclaimer

AgentLang is currently a Research & Education project.

While the ultimate intent is to build a robust, production-ready language and ecosystem for the Agentic Era, the current implementation serves primarily as a proof-of-concept and learning vehicle for combining Rust, actor-model concurrency, zero-knowledge proofs, and LLM-centric parsing. APIs, syntax, and features are subject to rapid, breaking changes.

🌟 Key Features

• Goal-Oriented Execution: Replace fragile loops and try/catch blocks with native GOAL directives featuring built-in RETRY, ON_FAIL, and DEADLINE.
• First-Class Confidence: Use AS confidence types to let agents make decisions based on their own certainty (e.g., IF {city.confidence} > 0.9).
• Privacy by Default: Sensitive data is automatically encrypted and protected via zk-STARKs (Zero Knowledge Proofs), allowing agents to prove permissions without revealing raw secrets.
• Trust-Aware Communication: Every inter-agent message is cryptographically signed and verifiable via a federated Agent Registry.
• Elixir-Inspired Runtime: A pure Rust/Tokio architecture utilizing Ractor for massive concurrency and fault-tolerant OTP-style orchestration, natively powering cryptography and ZK-proof generation.
• MCP Compatible: Native support for the Model Context Protocol (MCP), making it compatible with the existing ecosystem of tools and data sources.

📝 Example Syntax

GOAL plan_trip
  SET origin = "London"
  SET destination = "New York"
  
  // Parallel execution with error handling
  PARALLEL
    GOAL search_flights
      FROM {origin} TO {destination}
      RESULT INTO {flights}
      RETRY 3
    END
    GOAL search_hotels
      IN {destination}
      RESULT INTO {hotels}
    END
  GATHER INTO {itinerary}
  ON_ALL_FAIL GOAL notify_human
  
  // Confidence-driven logic
  IF {itinerary.flights.confidence} > 0.85
    USE book_flight
      flight_id {itinerary.flights[0].id}
      CONFIRM_WITH human
      AUDIT_TRAIL true
    END
  END
END

🔍 How It Compares

AgentLang sits in a unique whitespace: A natively compiled, memory-safe language that treats AI uncertainty, agent-to-agent trust, and Zero Knowledge privacy as first-class syntactical primitives.

Ecosystem	Examples	AgentLang Difference
Agent Frameworks	LangGraph, AutoGen, CrewAI	These are libraries on top of Python/TS requiring heavy boilerplate (try/except, retry loops). AgentLang is a DSL with native semantics (GOAL, ON_FAIL, RETRY).
Prompting DSLs	LMQL, Guidance, DSPy	These focus on the micro-level (constraining a single LLM response). AgentLang focuses on macro-level orchestration, parallel execution, and state.
Crypto Agent Networks	Fetch.ai, Autonolas	These use Python SDKs to connect agents to ledgers. AgentLang bakes zk-STARKs and data sensitivity (AS sensitive) directly into the compiler types for guaranteed privacy.
Actor-Model Languages	Elixir/Erlang, Pony	These lack concepts for LLM "confidence thresholds" or "hallucination recovery". AgentLang fuses Actor Model supervision with AI-native primitives.

🏗️ Architecture

AgentLang uses a Pure Rust architecture designed for massive throughput and zero-cost safety:

1. Orchestration (Tokio + Ractor): Every GOAL is a supervised task. We use the Ractor framework to provide OTP-style supervision trees, ensuring that one failing agent cannot crash the system.
2. Safety Layer: Built-in support for WebAssembly (WASM) allows for sandboxed agent execution and hot-swappable logic without restarting the host.
3. High-Performance Primitives:
   • winterfell: Fast zk-STARK proof generation.
   • Tonic: gRPC-based inter-agent communication.
   • sqlx: Type-safe, asynchronous database access for memory scopes.
   • nom: High-speed, zero-copy parsing of .al source files.

🗺️ Roadmap

• Phase 1: TypeScript-based Interpreter (PoC)
• Phase 2: Full Grammar & Type Validation
• Phase 3: Native Rust Actor-Model Orchestrator (Ractor)
• Phase 4: Rust-based Crypto & ZK-Proof Engine
• Phase 5: Federated Agent Registry

📄 License

AgentLang is licensed under the Apache 2.0 License.

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Generated by AgentLang Spec v1.0 · April 2026