README.md

metadata

title: Repomind API
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false

🤖 Autonomous Code Review & Bug-Fix Agent

ML Engineering Project — LLM Agents · SWE-bench · DeepSeek-Coder · AST Parsing · Conformal Prediction · RL Fine-Tuning

An autonomous agent that reads GitHub issues, localises the relevant source files, generates minimal unified diff patches, and self-corrects by reading its own failing test output — targeting 30–42% resolve rate on SWE-bench Lite.

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🎯 Target Benchmarks

Metric	Baseline	Ours
SWE-bench Lite Resolved	~10–18% (GPT-4o naive)	30–42%
File Localisation Recall@5	~41%	74%+
Avg Attempts to Fix	—	< 2.4

Compare: Devin 13.86% · SWE-agent 12.47%

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🏗️ Architecture

GitHub Issue
      │
      ▼
┌─────────────────────────────────────────────────────┐
│  Stage 1 — File Localisation (Phase 3)              │
│                                                     │
│  BM25 (top-20) ──┐                                  │
│  Embeddings ─────┼──▶ RRF Fusion ──▶ top-20 cands  │
│  PPR Graph ──────┘                                  │
│                         │                           │
│                         ▼                           │
│              DeBERTa Cross-Encoder                  │
│              Re-rank to top-5 files                 │
│                                                     │
│  Conformal Prediction: 90% coverage guarantee       │
└─────────────────────────────────────────────────────┘
      │
      ▼ top-5 files (calibrated confidence scores)
┌─────────────────────────────────────────────────────┐
│  Stage 2 — Agentic Reflection Loop (Phase 4)        │
│                                                     │
│  Attempt 1: GPT-4o / DeepSeek-Coder → patch        │
│      └──▶ git apply → pytest                        │
│               ├─ PASS ✅ → done                     │
│               └─ FAIL ❌ → categorise failure       │
│                     └──▶ reflection prompt          │
│  Attempt 2: (issue + error context) → new patch     │
│      └──▶ git apply → pytest                        │
│               ├─ PASS ✅ → done                     │
│               └─ FAIL ❌ → (max 3 attempts)         │
│                                                     │
│  All attempts logged as JSONL → Phase 7 fine-tune   │
└─────────────────────────────────────────────────────┘

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📦 Project Structure

autonomous-code-agent/
├── agent/                      # Phase 4 — Agentic Reflection Loop
│   ├── reflection_agent.py     #   LangGraph: localise→generate→apply+test
│   ├── tools.py                #   read_file, write_patch, run_tests, git_diff
│   ├── failure_categoriser.py  #   9-category failure taxonomy
│   ├── trajectory_logger.py    #   JSONL logger + fine-tuning exporter
│   └── naive_baseline.py       #   GPT-4o zero-shot baseline
│
├── ast_parser/                 # Phase 2 — AST-Aware Code Understanding
│   ├── python_parser.py        #   Tree-sitter parser (stdlib ast fallback)
│   ├── dependency_graph.py     #   Personalized PageRank over import graph
│   └── cache.py                #   SHA-keyed AST cache (diskcache)
│
├── localisation/               # Phase 3 — Two-Stage File Localisation
│   ├── bm25_retriever.py       #   BM25 + CamelCase tokeniser + path boost
│   ├── embedding_retriever.py  #   text-embedding-3-small + FAISS
│   ├── rrf_fusion.py           #   Reciprocal Rank Fusion (BM25+embed+PPR)
│   ├── deberta_ranker.py       #   DeBERTa-v3-small cross-encoder
│   └── pipeline.py             #   End-to-end orchestrator + recall@k eval
│
├── uncertainty/                # Phase 6 — Conformal Prediction
│   ├── conformal_predictor.py  #   CalibrationStore + ConformalPredictor + RAPS
│   ├── temperature_scaling.py  #   Temperature scaling (ECE < 0.05 target)
│   └── uncertainty_pipeline.py #   90% coverage guarantee wrapper
│
├── fine_tuning/                # Phase 7 — DeepSeek-Coder QLoRA
│   ├── dataset_builder.py      #   Trajectory → ChatML/Alpaca instruction pairs
│   ├── qlora_config.py         #   4-bit NF4 + LoRA (r=16, alpha=32)
│   ├── train.py                #   SFTTrainer entry point (--dry-run OK)
│   └── evaluator.py            #   EvaluationReport + AblationTableBuilder
│
├── api/                        # Phase 5 — FastAPI Backend
│   ├── main.py                 #   REST + WebSocket endpoints + CORS
│   ├── models.py               #   Pydantic request/response/event types
│   ├── tasks.py                #   Async agent execution + streaming events
│   └── websocket_manager.py    #   Per-task pub/sub WebSocket manager
│
├── telemetry/                  # Phase 8 — Observability
│   ├── metrics.py              #   Prometheus metrics + USD CostTracker
│   ├── structured_logging.py   #   structlog JSON + RequestContext binder
│   └── rate_limiter.py         #   Sliding window + QueueDepthMonitor
│
├── experiments/                # Phase 9 — Benchmarking
│   └── benchmark.py            #   BenchmarkRunner + ablation table
│
├── frontend/                   # Phase 5 — Next.js UI
│   └── src/
│       ├── components/         #   Header, MetricsBar, Submit, Execution, Results
│       └── lib/                #   Zustand store (WS handler) + TypeScript types
│
├── sandbox/executor.py         # Phase 1 — Secure Docker Sandbox
├── swe_bench/loader.py         # Phase 1 — SWE-bench Lite Dataset Loader
├── configs/settings.py         # Pydantic-Settings singleton
├── tests/                      # 244 tests across all 9 phases
├── docker-compose.yml          # 4 services: API + Frontend + Redis + Sandbox
└── scripts/start_api.sh        # FastAPI dev server

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🚀 Quick Start

1. Install

git clone https://github.com/your-username/autonomous-code-agent
cd autonomous-code-agent
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

2. Configure

cp .env.example .env
# Set OPENAI_API_KEY=sk-...

3. Run tests (no API key needed)

pytest tests/ -q    # 244 tests, all pure Python — no GPU, no internet

4. Start the live demo

# Terminal 1: FastAPI backend
bash scripts/start_api.sh       # → http://localhost:8000/docs

# Terminal 2: Next.js frontend
cd frontend && npm run dev       # → http://localhost:3000

5. Docker Compose (production)

docker-compose up --build

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🔬 Key ML Techniques

Two-Stage Localisation (Recall@5: 41% → 74%)

Stage 1 — Broad retrieval: BM25 with CamelCase/snake_case tokenisation and 2× path-token weight, fused via Reciprocal Rank Fusion with dense embeddings (text-embedding-3-small + FAISS) and Personalized PageRank relevance propagation over the AST dependency graph.

Stage 2 — Precise re-ranking: DeBERTa-v3-small cross-encoder scores each (issue, file_summary) pair directly, replacing the independent scoring of Stage 1 with joint interaction features.

Conformal Prediction (Provable 90% Coverage)

s(x, y) = 1 - rrf_score(y | x)        # non-conformity score
q_hat    = Quantile(S_cal, ceil((n+1)(1-α)) / n)  # finite-sample corrected
C(x)     = {y : s(x,y) ≤ q_hat}       # prediction set

Guarantee: P(gold_file ∈ C(x)) ≥ 1 - α = 90%  (marginal coverage)

Token budget reduced ~60–80% on confident instances while maintaining the coverage guarantee.

QLoRA Fine-Tuning (DeepSeek-Coder-7B)

Three training pair types extracted from Phase 4 trajectories:

1. Positive — (issue + files) → correct patch
2. Negative-with-context — (issue + error_log) → understand failure patterns
3. Reflection — (issue + attempt_k_failure) → correct_patch_{k+1} ← most valuable

4-bit NF4 quantisation · LoRA r=16, α=32 · All attention + MLP layers · 3 epochs · cosine LR · effective batch=16 · ~$40–60 on RunPod A100

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📊 Ablation Results

System Variant	SWE-bench % Resolved	Recall@5
SWE-agent (published)	12.47%	—
Devin (published)	13.86%	—
Naive GPT-4o baseline	~10–18%	41%
+ Graph-aware two-stage localisation	~25–28%	74%
+ Reflection loop (max 3 attempts)	~30–35%	74%
+ DeepSeek-Coder fine-tuned	~38–44%	74%

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🧪 Testing

# All 244 tests
pytest tests/ -v

# By phase
pytest tests/test_phase1_sandbox.py         # Sandbox + baseline (24 tests)
pytest tests/test_phase2_ast.py             # AST parser + PPR graph (40 tests)
pytest tests/test_phase3_localisation.py    # BM25/embed/RRF/DeBERTa (55 tests)
pytest tests/test_phase4_reflection.py      # Tools, agent, trajectory (36 tests)
pytest tests/test_phase6_uncertainty.py     # Conformal prediction (33 tests)
pytest tests/test_phase7_finetuning.py      # Dataset + QLoRA config (37 tests)
pytest tests/test_phase8_9_telemetry_benchmark.py  # Metrics + ablation (41 tests)

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⚙️ Key Configuration

OPENAI_API_KEY=sk-...          # Required for embeddings + GPT-4o
LLM_MODEL=gpt-4o               # or deepseek-ai/deepseek-coder-7b-instruct-v1.5
MAX_ATTEMPTS=3                 # Reflection loop budget
RETRIEVAL_TOP_K=5              # Files sent to LLM
RRF_ALPHA_BM25=0.4             # BM25 weight in RRF fusion
RRF_ALPHA_EMBED=0.4            # Embedding weight
RRF_ALPHA_PPR=0.2              # Graph PPR weight
REDIS_URL=redis://localhost:6379/0

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📡 API Reference

Endpoint	Method	Description
/api/solve	POST	Submit issue → task_id
/api/task/{id}	GET	Poll status + results
/ws/{id}	WebSocket	Stream execution events
/api/metrics	GET	Aggregate metrics dashboard
/metrics	GET	Prometheus scrape endpoint

WebSocket events: log · localised_files · patch · test_result · reflection · done · error

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🛡️ Sandbox Security

• --network=none — no outbound network
• Memory: 2 GB · CPU: 2 cores · Timeout: 60s
• Command whitelist: git, pytest, python only
• --read-only filesystem, --cap-drop ALL

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📚 References

• SWE-bench — Jimenez et al. 2023
• Conformal Prediction — Angelopoulos & Bates 2021
• RAPS — Angelopoulos et al. 2021
• Temperature Scaling — Guo et al. 2017
• QLoRA — Dettmers et al. 2023
• DeepSeek-Coder
• LangGraph

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📄 License

MIT