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Lean-Agentic Architecture

Overview

A hybrid programming language combining Lean4's formal verification with blazingly fast compilation, actor-based agent orchestration, AI-driven optimization, and vector-backed agent memory.

Architecture Layers

┌─────────────────────────────────────────────────────────┐
│                   APPLICATIONS                          │
│  Policy-Verified RAG │ Agent Ops │ Memory Copilot      │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│              AI OPTIMIZATION LAYER                      │
│  LLM Compiler │ Auto-Vectorization │ Cost Routing      │
│  JIT Tiers    │ AgentDB Memory     │ ReasoningBank     │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│              AGENT RUNTIME                              │
│  Work-Stealing │ Message Passing │ Orchestration       │
│  Scheduler     │ Ref Caps        │ Primitives          │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│              LEAN-RUST CORE                             │
│  Elaborator │ Type Checker │ Proof Kernel │ Evaluator  │
│  Syntax     │ Unification  │ Inductives   │ WASM       │
└─────────────────────────────────────────────────────────┘

Swarm Organization

Swarm 1: Core Infrastructure (3 Agents)

  • System Architect: Core data structures, memory model, proof kernel
  • Elaborator Coder: Syntax, elaboration, unification, inductives
  • WASM Specialist: Compilation, incremental builds, WASM target

Swarm 2: Runtime & Applications (3 Agents)

  • Runtime Developer: Scheduler, message passing, orchestration
  • AI Optimization: LLM compiler, JIT, AgentDB, cost routing
  • Testing Specialist: Examples, tests, benchmarks, production validation

Performance Targets

Component Metric Target
Compilation Incremental (1 fn) <100ms
Agent Spawn Local <1ms
Message Send P99 <10ms
Vector Search P99 <10ms
Verification Overhead <10%
Cost per Task Average $0.10-$1.00

Technology Stack

  • Language: Rust 2021 edition
  • WASM: wasm-bindgen, web-sys
  • Compilation: Cranelift (debug), LLVM (production)
  • Runtime: Tokio work-stealing scheduler
  • Memory: AgentDB with Qdrant HNSW
  • Learning: ReasoningBank with trajectory tracking
  • Verification: Lean4-style proof kernel

Development Process

All agents use AgentDB ReasoningBank to:

  1. Track implementation trajectories
  2. Judge successful patterns
  3. Distill reusable strategies
  4. Optimize decision-making

Coordination via hooks:

npx claude-flow@alpha hooks pre-task --description "[task]"
npx claude-flow@alpha hooks post-edit --memory-key "swarm/[agent]/[key]"
npx claude-flow@alpha hooks post-task --task-id "[id]"

Build Instructions

See individual crate READMEs for detailed instructions.

Quick start:

cargo build --workspace
cargo test --workspace
cargo build --target wasm32-unknown-unknown -p leanr-wasm