Autobolt — Autonomous Coding Agent

Autobolt is an autonomous, self-improving SWE (Software Engineering) agent that writes code, runs tests, reads errors, fixes itself, and iterates until tests pass. It runs entirely locally via Ollama — no API costs, no data leaves your machine.

Quick Start

# Install
pip install -e .

# CLI — chat with the agent
autobolt

# Server — web UI with WebSocket streaming
autobolt-server
# → http://localhost:8000

# Benchmark — measure solve rate on 10 built-in tasks
python -m distributed_agents.benchmark --model llama3.2:3b

# Real-world benchmark — run against real GitHub issues
python -m distributed_agents.real_benchmark --max-per-repo 3

What It Does

Architecture

┌─────────────────────────────────────────────────────┐
│  CLI (Rich)           │  Web UI + WebSocket         │
├─────────────────────────────────────────────────────┤
│  Iterative Developer     │  LLM (Ollama)  │  Tools  │
│  write → test → fix →    │  qwen3 /       │  12 fn  │
│  retest (max 5 iters)    │  llama3        │  calls  │
├─────────────────────────────────────────────────────┤
│  Memory Store  │  Project Scanner  │  Change Planner │
│  (SQLite)      │  deps/scripts    │  dependency-    │
│                │  /conventions     │  ordered files  │
├─────────────────────────────────────────────────────┤
│  Auto Quality Gate  │  Benchmark Harness           │
│  lint/typecheck     │  built-in + real GitHub      │
│  after every edit   │  issue pipeline              │
└─────────────────────────────────────────────────────┘

System Components

Benchmark Results (Calibration Suite)

Model: llama3.2:3b via Ollama (19 tok/s on M2 Pro)

Note: These are calibration tasks, not real-world GitHub issues. The same agent achieved 0% on real issues with a small model (qwen3:8b). The bottleneck is model capability, not the agent platform. Swap in a stronger model (DeepSeek V3, GPT-4o) and the infrastructure scales.

What It CAN'T Do (Yet)

Real-World Benchmark

12 configured repos with PR-first issue discovery. To run:

# Scan and run against all repos (takes hours on small models)
python -m distributed_agents.real_benchmark --max-per-repo 3 --max-retries 3

# Quick test on one issue
python -m distributed_agents.real_benchmark --issues "pytest-dev/pytest#14683"

# Export results as CSV
python -m distributed_agents.real_benchmark --export-csv results.csv

Requirements

• Python 3.10+
• Ollama with a model pulled (llama3.2:3b, qwen3:8b, etc.)
• Optional: GitHub CLI (gh) for real-issue benchmark

License

MIT