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[FEATURE] Agent Booster: Ultra-Fast Code Application Engine (200x faster than Morph LLM)

📋 Summary

Build Agent Booster - a Rust-based vector semantic code merging engine that replaces expensive LLM-based code application APIs (like Morph LLM) with deterministic, vector-based AST merging.

Key Performance Targets:

  • ⚡ 200x faster than Morph LLM (30ms vs 6000ms)
  • 💰 100% cost savings ($0 vs $0.01+ per edit)
  • 📊 97-99% accuracy (comparable to Morph's 98%)
  • 🔒 100% local (privacy-first, offline-capable)
  • 🌍 Universal (Native Node.js, WASM, MCP server)

🎯 Motivation

Current Problem

Agentic-flow (and similar AI code assistants) currently rely on:

  1. LLM-based file rewrites - Slow (10-60 seconds), expensive ($0.01-0.10 per edit), non-deterministic
  2. Morph LLM API - Fast-ish (6 seconds), expensive ($0.01 per edit), requires API calls

Real-World Impact

For a typical development session with 100 code edits:

  • Current cost: $1-10 in API fees
  • Current time: 10-100 minutes of waiting
  • Privacy: Code sent to external APIs
  • Reliability: Depends on API availability

Proposed Solution

Agent Booster uses:

  • Tree-sitter AST parsing - Understand code structure
  • Vector embeddings - Semantic code understanding (pre-trained models)
  • HNSW similarity search - Find matching code locations
  • Smart merge strategies - Apply edits deterministically
  • Rust performance - Native speed, WASM portability

Expected Results (100 edits):

  • Cost: $0 (100% savings)
  • Time: 3-5 seconds (95% faster)
  • Privacy: 100% local
  • Reliability: No external dependencies

🏆 Success Criteria

Must Have (MVP)

  • 100x+ speedup vs Morph LLM
  • 95%+ accuracy on simple/medium edits
  • JavaScript & TypeScript support
  • Native Node.js addon (napi-rs)
  • NPM package published
  • Agentic-flow integration via .env
  • Comprehensive benchmarks vs Morph + Claude Sonnet 4

Should Have (v1.0)

  • WASM support (browser + edge)
  • MCP server for Claude Desktop/Cursor/VS Code
  • Standalone CLI (npx agent-booster)
  • 5+ language support (Python, Rust, Go, Java, C++)
  • Fallback to Morph LLM for low-confidence cases
  • Documentation site

Nice to Have (Future)

  • Fine-tuned custom models
  • Multi-file refactoring
  • VS Code extension
  • Real-time collaboration
  • Browser extension

🏗️ Technical Approach

Architecture

User (Agentic-flow / CLI / MCP Client)
           ↓
    JavaScript/TypeScript Layer
    (Auto-detects: Native > WASM)
           ↓
      Rust Core Library
           ↓
┌──────────┬──────────┬──────────┬─────────┐
│  Parser  │ Embedder │  Vector  │  Merger │
│ (Tree-   │  (ONNX   │  (HNSW)  │ (Smart  │
│ sitter)  │ Runtime) │          │Strategy)│
└──────────┴──────────┴──────────┴─────────┘

Technology Stack

Core:

  • Rust - Performance + memory safety
  • Tree-sitter - AST parsing (40+ languages)
  • ONNX Runtime - Local embedding inference
  • HNSW - Vector similarity search

Bindings:

  • napi-rs - Native Node.js addon (fastest)
  • wasm-bindgen - WebAssembly (portable)
  • TypeScript - Type-safe API

Models:

  • jina-embeddings-v2-base-code - 768-dim, best accuracy
  • all-MiniLM-L6-v2 - 384-dim, faster alternative

Data Flow

1. Parse original code → AST chunks (Tree-sitter)
2. Generate embeddings → 768-dim vectors (ONNX)
3. Build index → HNSW graph (in-memory)
4. Embed edit snippet → 768-dim vector
5. Vector search → Top-5 similar locations (cosine)
6. Select strategy → Based on similarity score
7. Apply merge → String manipulation + validation
8. Validate syntax → Tree-sitter re-parse

📊 Detailed Benchmark Plan

Baseline: Morph LLM

Models to Test:

  1. Claude Sonnet 4 (production default)
  2. Claude Opus 4 (max accuracy)
  3. Claude Haiku 4 (max speed)

Dataset:

  • 40 simple edits (function additions, renames, etc.)
  • 40 medium edits (async conversion, type additions)
  • 20 complex edits (refactoring, pattern changes)

Metrics:

  • Latency (p50, p95, p99, max)
  • Accuracy (exact match, semantic match, syntax valid)
  • Cost (per edit, per 100 edits)
  • Throughput (tokens/sec)

Agent Booster Benchmarks

Variants:

  1. Native addon (napi-rs) - Fastest
  2. WASM - Portable
  3. TypeScript fallback - Baseline

Metrics:

  • Same as baseline for fair comparison
  • Additional: Confidence scores, strategy distribution

Expected Results

Metric Morph + Sonnet 4 Agent Booster Improvement
Latency (p50) 5,800ms 35ms 166x
Accuracy 98.0% 96.8% -1.2pp
Cost/edit $0.01 $0.00 100%
Throughput 10,500 tok/s 1M+ tok/s 95x

🔌 Integration Plan

1. Agentic-Flow Integration

Environment Variables:

AGENT_BOOSTER_ENABLED=true
AGENT_BOOSTER_MODEL=jina-code-v2
AGENT_BOOSTER_CONFIDENCE_THRESHOLD=0.65
AGENT_BOOSTER_FALLBACK_TO_MORPH=true
MORPH_API_KEY=sk-morph-xxx  # Optional fallback

Tool Enhancement:

// Enhance edit_file tool
export const editFileTool = {
  async execute(params) {
    // Try Agent Booster first if enabled
    if (process.env.AGENT_BOOSTER_ENABLED === 'true') {
      const result = await booster.applyEdit(params);

      if (result.confidence >= threshold) {
        return { method: 'agent-booster', ...result };
      }

      // Fallback to Morph/LLM if low confidence
    }

    // Original behavior
    return fallbackToLLM(params);
  }
};

2. MCP Server

Start Server:

npx agent-booster mcp

Client Config (Claude Desktop):

{
  "mcpServers": {
    "agent-booster": {
      "command": "npx",
      "args": ["agent-booster", "mcp"],
      "env": {
        "AGENT_BOOSTER_MODEL": "jina-code-v2"
      }
    }
  }
}

Tools Exposed:

  • agent_booster_apply - Single edit
  • agent_booster_batch - Parallel batch edits
  • agent_booster_analyze - Workspace analysis
  • agent_booster_status - Server status

3. Standalone CLI

# Apply single edit
npx agent-booster apply src/main.ts "add error handling"

# Batch processing
npx agent-booster batch edits.json

# Watch mode
npx agent-booster watch src/

# MCP server
npx agent-booster mcp --port 3000

📁 Project Structure

agent-booster/
├── Cargo.toml                    # Rust workspace
├── README.md                     # Main docs
├── LICENSE                       # MIT/Apache-2.0
├── crates/
│   ├── agent-booster/           # Core Rust library
│   ├── agent-booster-native/    # napi-rs bindings
│   └── agent-booster-wasm/      # WASM bindings
├── npm/
│   ├── agent-booster/           # NPM package (auto-detection)
│   └── agent-booster-cli/       # Standalone CLI
├── benchmarks/
│   ├── datasets/                # Test code samples
│   ├── baselines/               # Morph LLM baselines
│   └── results/                 # Benchmark outputs
├── docs/
│   ├── architecture.md
│   ├── api.md
│   ├── benchmarks.md
│   └── integration.md
└── examples/
    ├── basic-usage.js
    ├── agentic-flow.js
    └── cli-usage.sh

🗓️ Implementation Roadmap

Phase 1: Foundation (Week 1-2) - @assignee

  • Setup Rust workspace (cargo init)
  • Implement tree-sitter parsing for JS/TS
  • Implement basic AST chunking
  • Setup benchmark framework
  • Run Morph LLM baseline benchmarks
  • Document baseline results

Phase 2: Core Engine (Week 3-4) - @assignee

  • Implement ONNX Runtime embedding generation
  • Implement HNSW vector similarity search
  • Implement merge strategies (replace, insert, append)
  • Implement confidence scoring
  • Add syntax validation
  • Run accuracy tests vs Morph LLM
  • Document accuracy comparison

Phase 3: Native Integration (Week 5) - @assignee

  • Build napi-rs native addon
  • Create NPM package with auto-detection
  • Write TypeScript definitions
  • Add comprehensive tests
  • Benchmark native performance
  • Document speedup results

Phase 4: WASM Support (Week 6) - @assignee

  • Build WASM bindings (wasm-bindgen)
  • Optimize WASM bundle size
  • Add browser compatibility tests
  • Benchmark WASM performance
  • Create browser examples

Phase 5: Agentic-flow Integration (Week 7) - @assignee

  • Design .env configuration
  • Create agent-booster tool in agentic-flow
  • Add fallback to Morph LLM
  • Write integration tests
  • Update agentic-flow documentation
  • Test with real workflows

Phase 6: MCP Server (Week 8) - @assignee

  • Implement MCP protocol server
  • Add workspace detection
  • Expose tools (apply, batch, analyze, status)
  • Add metrics resource
  • Test with Claude Desktop/Cursor/VS Code
  • Document MCP setup

Phase 7: CLI & SDK (Week 9) - @assignee

  • Build standalone CLI (npx agent-booster)
  • Add commands (apply, batch, watch, mcp, dashboard)
  • Add watch mode
  • Add batch processing
  • Create usage examples
  • Write CLI documentation

Phase 8: Documentation & Release (Week 10) - @assignee

  • Complete API documentation
  • Write architecture deep dive
  • Create comparison benchmarks
  • Record demo videos
  • Publish to crates.io
  • Publish to npm
  • Announce release (GitHub, Twitter, Reddit)

📚 Documentation Plan

README.md

  • Badges (crates.io, npm, CI, docs)
  • Quick start (3 examples: API, CLI, MCP)
  • Performance comparison table
  • Feature comparison vs Morph LLM
  • When to use Agent Booster vs Morph
  • Installation instructions
  • Use cases

docs/architecture.md

  • System architecture diagram
  • Module breakdown (parser, embedder, vector, merger)
  • Data flow diagram
  • Performance optimizations
  • Error handling strategy
  • Memory management

docs/benchmarks.md

  • Benchmark methodology
  • Test dataset description
  • Morph LLM baseline results
  • Agent Booster results
  • Comparison analysis
  • Visualizations (charts, graphs)

docs/integration.md

  • Agentic-flow setup
  • MCP server setup
  • CLI usage
  • Environment variables
  • Configuration options
  • Metrics & monitoring

docs/api.md

  • TypeScript API reference
  • Rust API reference
  • Configuration options
  • Error types
  • Usage examples

🧪 Testing Strategy

Unit Tests

  • Parser module (AST extraction, chunking)
  • Embeddings module (tokenization, inference)
  • Vector search module (HNSW indexing, similarity)
  • Merge module (strategy selection, application)

Integration Tests

  • End-to-end edit application
  • Multi-language support
  • Fallback to Morph LLM
  • MCP protocol compliance
  • CLI commands

Benchmark Tests

  • Morph LLM baseline (Claude Sonnet/Opus/Haiku)
  • Agent Booster variants (native/WASM/TypeScript)
  • Accuracy validation
  • Performance profiling
  • Memory usage

🚀 Release Checklist

v0.1.0 (MVP)

  • Core Rust library functional
  • Native Node.js addon working
  • NPM package published
  • Basic documentation
  • Benchmarks vs Morph LLM
  • Agentic-flow integration tested

v0.2.0 (Production Ready)

  • WASM support
  • MCP server
  • Standalone CLI
  • Comprehensive docs
  • 5+ language support
  • CI/CD setup

v1.0.0 (Stable)

  • API stability guarantee
  • Full test coverage (>80%)
  • Production deployments
  • Community feedback incorporated
  • Performance tuning complete
  • Security audit

📊 Metrics for Success

Performance KPIs

  • Latency (p50) < 50ms
  • Latency (p95) < 100ms
  • Throughput > 100 edits/sec
  • Memory usage < 500MB

Quality KPIs

  • Accuracy (simple) > 98%
  • Accuracy (medium) > 95%
  • Accuracy (complex) > 90%
  • Syntax errors < 1%

Adoption KPIs

  • 100+ GitHub stars
  • 1,000+ npm downloads
  • 10+ production users
  • 5+ community contributions

🤔 Open Questions

  1. Model Selection

    • Ship with one model or support multiple?
    • Should we fine-tune models for specific use cases?
    • Can we quantize models for smaller downloads?

  2. Fallback Strategy

    • Default to fallback enabled or disabled?
    • What confidence threshold is optimal?
    • How to learn from fallback cases?

  3. Language Support

    • Which languages to prioritize after JS/TS?
    • Should we support LSP for better parsing?
    • How to handle non-tree-sitter languages?

  4. Deployment

    • Offer hosted version for convenience?
    • Enterprise on-premise deployment guide?
    • Edge/serverless support?

  5. Business Model

    • Fully open source (MIT/Apache)?
    • Dual license (open + commercial)?
    • SaaS offering for enterprises?

📝 Related Documentation

🙋 Questions?

Please comment on this issue or join the discussion in:

📄 License

Agent Booster will be dual-licensed under MIT OR Apache-2.0

Let's build the future of AI code editing! 🚀