rein-harness 0.1.0

ReIN — harness your code. An open-source agentic coding runtime.

ReIN

Harness your code.

Quick Start · Architecture · Usage · 中文文档

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ReIN is an open-source agentic coding runtime that implements a complete harness architecture — the control plane that orchestrates LLM calls, tool execution, hook lifecycle, permission control, and plugin systems.

It supports both Anthropic Claude (cloud) and local LLMs (Ollama, LM Studio, llama.cpp, vLLM) for fully offline operation.

Why ReIN?

Rein (n.) — a strap fastened to a bit, used to guide a horse. In software, the harness that guides an AI agent: intercepting, evaluating, executing, and extending every action it takes.

Most agentic coding tools are closed-source black boxes. ReIN opens up the full runtime:

• See exactly how LLM tool calls are orchestrated
• Hook into every lifecycle event (PreToolUse, PostToolUse, Stop, etc.)
• Control permissions at 5 layers (admin → user → project → command → hook)
• Extend with plugins (commands, agents, skills, hooks, MCP)
• Run offline with any local model — no API key needed

Quick Start

Prerequisites

• Python 3.11+
• An LLM backend (choose one):
  • Anthropic API key, or
  • Ollama / LM Studio / any OpenAI-compatible local server

Install

git clone https://github.com/BDeMo/ReIN.git
cd ReIN
pip install -r requirements.txt

Run

# Cloud mode (Anthropic Claude)
export ANTHROPIC_API_KEY=sk-ant-xxx
python -m rein direct

# Fully offline (Ollama)
ollama pull qwen2.5-coder:7b
python -m rein direct --local

# Custom local server (LM Studio / llama.cpp / vLLM)
python -m rein direct --local --local-url http://localhost:1234/v1 --local-model my-model

Architecture

rein/
├── core/
│   ├── harness.py          Core orchestrator — the heart of ReIN
│   ├── config.py           Multi-layer settings hierarchy
│   └── conversation.py     Session and message management
├── llm/
│   ├── provider.py         Abstract LLM interface
│   ├── anthropic_llm.py    Anthropic Claude (streaming + tool use)
│   └── local_llm.py        Local LLM (Ollama / LM Studio / llama.cpp / vLLM)
├── tools/
│   ├── registry.py         Tool registry and base class
│   ├── file_tools.py       Read / Write / Edit
│   ├── bash_tool.py        Bash with command filtering and security
│   └── search_tools.py     Grep / Glob
├── hooks/
│   ├── engine.py           Hook execution engine (command + prompt based)
│   └── types.py            9 lifecycle event types
├── permissions/
│   └── manager.py          5-layer permission model (allow / deny / ask)
├── plugins/
│   └── loader.py           Plugin discovery and loading
├── server/
│   └── app.py              FastAPI server with WebSocket streaming
├── client/
│   └── cli.py              Terminal client (direct + server modes)
└── main.py                 CLI entry point

Harness Pipeline

Every tool call passes through the full harness pipeline:

User Input
  → [UserPromptSubmit Hook]      Validate / preprocess
  → LLM streaming response       Generate text + tool calls
  → Tool call detected
    → [PreToolUse Hook]          Validate / modify / block
    → [Permission Check]         5-layer allow / deny / ask
    → Tool Execution             Run the tool
    → [PostToolUse Hook]         React / log / feedback
  → LLM continues                Feed result back
  → [Stop Hook]                  Validate task completion

Hook Events

Event	When	Purpose
PreToolUse	Before tool execution	Validate, modify, or block
PostToolUse	After tool execution	React, log, feedback
Stop	Before agent stops	Verify task completion
UserPromptSubmit	User sends message	Input preprocessing
SessionStart / SessionEnd	Session lifecycle	Init / cleanup
PreCompact	Before context compaction	Preserve critical info
Notification	Any notification	Logging, monitoring
SubagentStop	Subagent completes	Validate subagent output

Permission Layers

Layer 1  managed-settings.json       Enterprise admin (MDM deployable)
Layer 2  ~/.claude/settings.json     User global preferences
Layer 3  .claude/settings.json       Project-level settings
Layer 4  YAML frontmatter            Command / Agent tool whitelist
Layer 5  PreToolUse Hook             Runtime dynamic decisions

Usage

Direct Mode (simplest)

# Anthropic Claude
python -m rein direct

# Local LLM (Ollama)
python -m rein direct --local --local-model qwen2.5-coder:7b

# Custom system prompt
python -m rein direct --system-prompt "You are a Python expert."

Server + Client

# Terminal 1: server
python -m rein server --port 8765

# Terminal 2: client
python -m rein client --url ws://localhost:8765/ws/chat

# Local LLM server
python -m rein server --local --local-model llama3.1:8b

API

Endpoint	Method	Description
/health	GET	Health check
/api/tools	GET	List tools with schemas
/api/settings	GET	Current settings
/api/chat	POST	Non-streaming chat
/ws/chat	WebSocket	Streaming chat (full harness)

WebSocket Protocol

// Client → Server
{"type": "message", "content": "Read main.py", "system_prompt": "..."}

// Server → Client (streamed)
{"type": "text_delta",    "data": {"text": "I'll read..."}}
{"type": "tool_use",      "data": {"id": "...", "name": "Read", "input": {...}}}
{"type": "tool_result",   "data": {"tool_use_id": "...", "result": "..."}}
{"type": "usage",         "data": {"input_tokens": 150, "output_tokens": 80}}
{"type": "turn_complete", "data": {"stop_reason": "end_turn"}}

Local LLM

ReIN supports two tool-use modes:

Mode	How	Models
Native	OpenAI tool_call format	qwen2.5, llama3.1, mistral, functionary
Prompt-based	Schemas in prompt, parses ```tool_call blocks	Any model

Auto-detected from model name. Force native with --native-tools.

Compatible Servers

Server	Default URL	Install
Ollama	http://localhost:11434/v1	ollama serve
LM Studio	http://localhost:1234/v1	GUI
llama.cpp	http://localhost:8080/v1	./llama-server -m model.gguf
vLLM	http://localhost:8000/v1	vllm serve model
LocalAI	http://localhost:8080/v1	Docker

Recommended Models

Model	Size	Tool Use	Notes
qwen2.5-coder:7b	4.7 GB	Native	Best coding model at this size
qwen2.5-coder:1.5b	1.0 GB	Native	Fast, lightweight
llama3.1:8b	4.7 GB	Native	Strong general purpose
deepseek-coder-v2:16b	9.0 GB	Prompt	Excellent at code
codellama:7b	3.8 GB	Prompt	Meta's code model

Environment Variables

Variable	Description	Default
ANTHROPIC_API_KEY	Anthropic API key	—
CLAUDE_MODEL	Override model name	claude-sonnet-4-20250514
ANTHROPIC_BASE_URL	Override API URL	—
LOCAL_LLM_URL	Local server URL	http://localhost:11434/v1
LOCAL_LLM_MODEL	Local model name	qwen2.5-coder:7b

Dependencies

Package	Purpose
anthropic	Anthropic Claude API
httpx	Async HTTP for local LLMs
fastapi	API server
uvicorn	ASGI server
websockets	WebSocket client
pyyaml	YAML parsing

Acknowledgements

ReIN is inspired by and built upon ideas from:

• Anthropic — the Claude Code open-source plugin ecosystem and harness architecture
• Ollama — making local LLMs accessible to everyone
• FastAPI — elegant async Python web framework
• llama.cpp — efficient local model inference
• OpenAI — the tool-calling API convention adopted by local LLM servers

License

MIT