524 lines
14 KiB
Markdown
524 lines
14 KiB
Markdown
# [FEATURE] Agent Booster: Ultra-Fast Code Application Engine (200x faster than Morph LLM)
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## 📋 Summary
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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.
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**Key Performance Targets:**
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- ⚡ **200x faster** than Morph LLM (30ms vs 6000ms)
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- 💰 **100% cost savings** ($0 vs $0.01+ per edit)
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- 📊 **97-99% accuracy** (comparable to Morph's 98%)
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- 🔒 **100% local** (privacy-first, offline-capable)
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- 🌍 **Universal** (Native Node.js, WASM, MCP server)
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---
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## 🎯 Motivation
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### Current Problem
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Agentic-flow (and similar AI code assistants) currently rely on:
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1. **LLM-based file rewrites** - Slow (10-60 seconds), expensive ($0.01-0.10 per edit), non-deterministic
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2. **Morph LLM API** - Fast-ish (6 seconds), expensive ($0.01 per edit), requires API calls
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### Real-World Impact
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For a typical development session with 100 code edits:
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- **Current cost**: $1-10 in API fees
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- **Current time**: 10-100 minutes of waiting
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- **Privacy**: Code sent to external APIs
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- **Reliability**: Depends on API availability
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### Proposed Solution
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Agent Booster uses:
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- **Tree-sitter AST parsing** - Understand code structure
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- **Vector embeddings** - Semantic code understanding (pre-trained models)
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- **HNSW similarity search** - Find matching code locations
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- **Smart merge strategies** - Apply edits deterministically
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- **Rust performance** - Native speed, WASM portability
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**Expected Results** (100 edits):
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- **Cost**: $0 (100% savings)
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- **Time**: 3-5 seconds (95% faster)
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- **Privacy**: 100% local
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- **Reliability**: No external dependencies
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---
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## 🏆 Success Criteria
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### Must Have (MVP)
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- [ ] 100x+ speedup vs Morph LLM
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- [ ] 95%+ accuracy on simple/medium edits
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- [ ] JavaScript & TypeScript support
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- [ ] Native Node.js addon (napi-rs)
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- [ ] NPM package published
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- [ ] Agentic-flow integration via `.env`
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- [ ] Comprehensive benchmarks vs Morph + Claude Sonnet 4
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### Should Have (v1.0)
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- [ ] WASM support (browser + edge)
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- [ ] MCP server for Claude Desktop/Cursor/VS Code
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- [ ] Standalone CLI (`npx agent-booster`)
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- [ ] 5+ language support (Python, Rust, Go, Java, C++)
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- [ ] Fallback to Morph LLM for low-confidence cases
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- [ ] Documentation site
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### Nice to Have (Future)
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- [ ] Fine-tuned custom models
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- [ ] Multi-file refactoring
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- [ ] VS Code extension
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- [ ] Real-time collaboration
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- [ ] Browser extension
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---
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## 🏗️ Technical Approach
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### Architecture
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```
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User (Agentic-flow / CLI / MCP Client)
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↓
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JavaScript/TypeScript Layer
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(Auto-detects: Native > WASM)
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↓
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Rust Core Library
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↓
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┌──────────┬──────────┬──────────┬─────────┐
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│ Parser │ Embedder │ Vector │ Merger │
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│ (Tree- │ (ONNX │ (HNSW) │ (Smart │
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│ sitter) │ Runtime) │ │Strategy)│
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└──────────┴──────────┴──────────┴─────────┘
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```
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### Technology Stack
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**Core:**
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- **Rust** - Performance + memory safety
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- **Tree-sitter** - AST parsing (40+ languages)
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- **ONNX Runtime** - Local embedding inference
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- **HNSW** - Vector similarity search
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**Bindings:**
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- **napi-rs** - Native Node.js addon (fastest)
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- **wasm-bindgen** - WebAssembly (portable)
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- **TypeScript** - Type-safe API
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**Models:**
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- **jina-embeddings-v2-base-code** - 768-dim, best accuracy
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- **all-MiniLM-L6-v2** - 384-dim, faster alternative
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### Data Flow
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```
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1. Parse original code → AST chunks (Tree-sitter)
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2. Generate embeddings → 768-dim vectors (ONNX)
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3. Build index → HNSW graph (in-memory)
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4. Embed edit snippet → 768-dim vector
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5. Vector search → Top-5 similar locations (cosine)
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6. Select strategy → Based on similarity score
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7. Apply merge → String manipulation + validation
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8. Validate syntax → Tree-sitter re-parse
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```
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---
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## 📊 Detailed Benchmark Plan
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### Baseline: Morph LLM
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**Models to Test:**
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1. Claude Sonnet 4 (production default)
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2. Claude Opus 4 (max accuracy)
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3. Claude Haiku 4 (max speed)
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**Dataset:**
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- 40 simple edits (function additions, renames, etc.)
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- 40 medium edits (async conversion, type additions)
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- 20 complex edits (refactoring, pattern changes)
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**Metrics:**
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- Latency (p50, p95, p99, max)
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- Accuracy (exact match, semantic match, syntax valid)
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- Cost (per edit, per 100 edits)
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- Throughput (tokens/sec)
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### Agent Booster Benchmarks
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**Variants:**
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1. Native addon (napi-rs) - Fastest
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2. WASM - Portable
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3. TypeScript fallback - Baseline
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**Metrics:**
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- Same as baseline for fair comparison
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- Additional: Confidence scores, strategy distribution
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### Expected Results
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| Metric | Morph + Sonnet 4 | Agent Booster | Improvement |
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|--------|------------------|---------------|-------------|
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| Latency (p50) | 5,800ms | 35ms | **166x** |
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| Accuracy | 98.0% | 96.8% | -1.2pp |
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| Cost/edit | $0.01 | $0.00 | **100%** |
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| Throughput | 10,500 tok/s | 1M+ tok/s | **95x** |
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---
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## 🔌 Integration Plan
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### 1. Agentic-Flow Integration
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**Environment Variables:**
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```bash
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AGENT_BOOSTER_ENABLED=true
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AGENT_BOOSTER_MODEL=jina-code-v2
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AGENT_BOOSTER_CONFIDENCE_THRESHOLD=0.65
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AGENT_BOOSTER_FALLBACK_TO_MORPH=true
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MORPH_API_KEY=sk-morph-xxx # Optional fallback
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```
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**Tool Enhancement:**
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```typescript
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// Enhance edit_file tool
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export const editFileTool = {
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async execute(params) {
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// Try Agent Booster first if enabled
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if (process.env.AGENT_BOOSTER_ENABLED === 'true') {
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const result = await booster.applyEdit(params);
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if (result.confidence >= threshold) {
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return { method: 'agent-booster', ...result };
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}
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// Fallback to Morph/LLM if low confidence
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}
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// Original behavior
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return fallbackToLLM(params);
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}
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};
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```
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### 2. MCP Server
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**Start Server:**
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```bash
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npx agent-booster mcp
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```
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**Client Config (Claude Desktop):**
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```json
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{
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"mcpServers": {
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"agent-booster": {
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"command": "npx",
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"args": ["agent-booster", "mcp"],
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"env": {
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"AGENT_BOOSTER_MODEL": "jina-code-v2"
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}
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}
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}
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}
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```
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**Tools Exposed:**
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- `agent_booster_apply` - Single edit
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- `agent_booster_batch` - Parallel batch edits
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- `agent_booster_analyze` - Workspace analysis
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- `agent_booster_status` - Server status
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### 3. Standalone CLI
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```bash
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# Apply single edit
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npx agent-booster apply src/main.ts "add error handling"
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# Batch processing
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npx agent-booster batch edits.json
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# Watch mode
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npx agent-booster watch src/
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# MCP server
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npx agent-booster mcp --port 3000
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```
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---
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## 📁 Project Structure
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```
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agent-booster/
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├── Cargo.toml # Rust workspace
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├── README.md # Main docs
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├── LICENSE # MIT/Apache-2.0
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├── crates/
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│ ├── agent-booster/ # Core Rust library
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│ ├── agent-booster-native/ # napi-rs bindings
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│ └── agent-booster-wasm/ # WASM bindings
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├── npm/
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│ ├── agent-booster/ # NPM package (auto-detection)
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│ └── agent-booster-cli/ # Standalone CLI
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├── benchmarks/
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│ ├── datasets/ # Test code samples
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│ ├── baselines/ # Morph LLM baselines
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│ └── results/ # Benchmark outputs
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├── docs/
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│ ├── architecture.md
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│ ├── api.md
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│ ├── benchmarks.md
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│ └── integration.md
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└── examples/
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├── basic-usage.js
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├── agentic-flow.js
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└── cli-usage.sh
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```
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---
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## 🗓️ Implementation Roadmap
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### Phase 1: Foundation (Week 1-2) - @assignee
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- [ ] Setup Rust workspace (`cargo init`)
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- [ ] Implement tree-sitter parsing for JS/TS
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- [ ] Implement basic AST chunking
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- [ ] Setup benchmark framework
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- [ ] Run Morph LLM baseline benchmarks
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- [ ] Document baseline results
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### Phase 2: Core Engine (Week 3-4) - @assignee
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- [ ] Implement ONNX Runtime embedding generation
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- [ ] Implement HNSW vector similarity search
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- [ ] Implement merge strategies (replace, insert, append)
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- [ ] Implement confidence scoring
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- [ ] Add syntax validation
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- [ ] Run accuracy tests vs Morph LLM
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- [ ] Document accuracy comparison
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### Phase 3: Native Integration (Week 5) - @assignee
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- [ ] Build napi-rs native addon
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- [ ] Create NPM package with auto-detection
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- [ ] Write TypeScript definitions
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- [ ] Add comprehensive tests
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- [ ] Benchmark native performance
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- [ ] Document speedup results
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### Phase 4: WASM Support (Week 6) - @assignee
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- [ ] Build WASM bindings (wasm-bindgen)
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- [ ] Optimize WASM bundle size
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- [ ] Add browser compatibility tests
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- [ ] Benchmark WASM performance
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- [ ] Create browser examples
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### Phase 5: Agentic-flow Integration (Week 7) - @assignee
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- [ ] Design `.env` configuration
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- [ ] Create agent-booster tool in agentic-flow
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- [ ] Add fallback to Morph LLM
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- [ ] Write integration tests
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- [ ] Update agentic-flow documentation
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- [ ] Test with real workflows
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### Phase 6: MCP Server (Week 8) - @assignee
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- [ ] Implement MCP protocol server
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- [ ] Add workspace detection
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- [ ] Expose tools (apply, batch, analyze, status)
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- [ ] Add metrics resource
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- [ ] Test with Claude Desktop/Cursor/VS Code
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- [ ] Document MCP setup
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### Phase 7: CLI & SDK (Week 9) - @assignee
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- [ ] Build standalone CLI (`npx agent-booster`)
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- [ ] Add commands (apply, batch, watch, mcp, dashboard)
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- [ ] Add watch mode
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- [ ] Add batch processing
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- [ ] Create usage examples
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- [ ] Write CLI documentation
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### Phase 8: Documentation & Release (Week 10) - @assignee
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- [ ] Complete API documentation
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- [ ] Write architecture deep dive
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- [ ] Create comparison benchmarks
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- [ ] Record demo videos
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- [ ] Publish to crates.io
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- [ ] Publish to npm
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- [ ] Announce release (GitHub, Twitter, Reddit)
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---
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## 📚 Documentation Plan
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### README.md
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- [ ] Badges (crates.io, npm, CI, docs)
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- [ ] Quick start (3 examples: API, CLI, MCP)
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- [ ] Performance comparison table
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- [ ] Feature comparison vs Morph LLM
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- [ ] When to use Agent Booster vs Morph
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- [ ] Installation instructions
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- [ ] Use cases
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### docs/architecture.md
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- [ ] System architecture diagram
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- [ ] Module breakdown (parser, embedder, vector, merger)
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- [ ] Data flow diagram
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- [ ] Performance optimizations
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- [ ] Error handling strategy
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- [ ] Memory management
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### docs/benchmarks.md
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- [ ] Benchmark methodology
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- [ ] Test dataset description
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- [ ] Morph LLM baseline results
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- [ ] Agent Booster results
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- [ ] Comparison analysis
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- [ ] Visualizations (charts, graphs)
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### docs/integration.md
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- [ ] Agentic-flow setup
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- [ ] MCP server setup
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- [ ] CLI usage
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- [ ] Environment variables
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- [ ] Configuration options
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- [ ] Metrics & monitoring
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### docs/api.md
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- [ ] TypeScript API reference
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- [ ] Rust API reference
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- [ ] Configuration options
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- [ ] Error types
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- [ ] Usage examples
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---
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## 🧪 Testing Strategy
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### Unit Tests
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- [ ] Parser module (AST extraction, chunking)
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- [ ] Embeddings module (tokenization, inference)
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- [ ] Vector search module (HNSW indexing, similarity)
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- [ ] Merge module (strategy selection, application)
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### Integration Tests
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- [ ] End-to-end edit application
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- [ ] Multi-language support
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- [ ] Fallback to Morph LLM
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- [ ] MCP protocol compliance
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- [ ] CLI commands
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### Benchmark Tests
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- [ ] Morph LLM baseline (Claude Sonnet/Opus/Haiku)
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- [ ] Agent Booster variants (native/WASM/TypeScript)
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- [ ] Accuracy validation
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- [ ] Performance profiling
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- [ ] Memory usage
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---
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## 🚀 Release Checklist
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### v0.1.0 (MVP)
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- [ ] Core Rust library functional
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- [ ] Native Node.js addon working
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- [ ] NPM package published
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- [ ] Basic documentation
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- [ ] Benchmarks vs Morph LLM
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- [ ] Agentic-flow integration tested
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### v0.2.0 (Production Ready)
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- [ ] WASM support
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- [ ] MCP server
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- [ ] Standalone CLI
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- [ ] Comprehensive docs
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- [ ] 5+ language support
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- [ ] CI/CD setup
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### v1.0.0 (Stable)
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- [ ] API stability guarantee
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- [ ] Full test coverage (>80%)
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- [ ] Production deployments
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- [ ] Community feedback incorporated
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- [ ] Performance tuning complete
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- [ ] Security audit
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---
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## 📊 Metrics for Success
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### Performance KPIs
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- [ ] Latency (p50) < 50ms
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- [ ] Latency (p95) < 100ms
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- [ ] Throughput > 100 edits/sec
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- [ ] Memory usage < 500MB
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### Quality KPIs
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- [ ] Accuracy (simple) > 98%
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- [ ] Accuracy (medium) > 95%
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- [ ] Accuracy (complex) > 90%
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- [ ] Syntax errors < 1%
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### Adoption KPIs
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- [ ] 100+ GitHub stars
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- [ ] 1,000+ npm downloads
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- [ ] 10+ production users
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- [ ] 5+ community contributions
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---
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## 🤔 Open Questions
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1. **Model Selection**
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- Ship with one model or support multiple?
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- Should we fine-tune models for specific use cases?
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- Can we quantize models for smaller downloads?
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2. **Fallback Strategy**
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- Default to fallback enabled or disabled?
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- What confidence threshold is optimal?
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- How to learn from fallback cases?
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3. **Language Support**
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- Which languages to prioritize after JS/TS?
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- Should we support LSP for better parsing?
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- How to handle non-tree-sitter languages?
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4. **Deployment**
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- Offer hosted version for convenience?
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- Enterprise on-premise deployment guide?
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- Edge/serverless support?
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5. **Business Model**
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- Fully open source (MIT/Apache)?
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- Dual license (open + commercial)?
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- SaaS offering for enterprises?
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---
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## 📝 Related Documentation
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- **[Planning Overview](docs/plans/agent-booster/00-OVERVIEW.md)** - Full vision and objectives
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- **[Architecture Design](docs/plans/agent-booster/01-ARCHITECTURE.md)** - Technical deep dive
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- **[Integration Guide](docs/plans/agent-booster/02-INTEGRATION.md)** - Agentic-flow & MCP integration
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- **[Benchmark Plan](docs/plans/agent-booster/03-BENCHMARKS.md)** - Testing methodology
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- **[NPM SDK Design](docs/plans/agent-booster/04-NPM-SDK.md)** - Package structure
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---
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## 🙋 Questions?
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Please comment on this issue or join the discussion in:
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- [GitHub Discussions](https://github.com/your-org/agentic-flow/discussions)
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- [Discord](https://discord.gg/agentic-flow)
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---
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## 📄 License
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Agent Booster will be dual-licensed under MIT OR Apache-2.0
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---
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**Let's build the future of AI code editing! 🚀**
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