responseiq 2.2.0

AI-Powered Infrastructure Copilot: The Self-Healing SRE.

ResponseIQ

"Don't just debug. Fix."

ResponseIQ is an AI-Native Self-Healing Infrastructure Copilot. Unlike traditional parsers that match regex strings, ResponseIQ reads your application logs, loads your actual source code into an LLM context, and generates surgical, context-aware remediation patches for incidents.

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📸 See It In Action

Above: ResponseIQ scanning a crash log, reading the service.py file mentioned in the stack trace, and proposing a specific code patch.

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✨ Key Features

• 🧠 AI-Native Analysis: Uses Generic AI reasoning instead of fragile regex parsing rules.
• 👁️ Context-Aware: Reads the local source files referenced in logs to understand why the crash happened.
• ⚡ Self-Healing: Can generate Pull Requests or apply patches directly (CLI mode).
• 🛡️ Battle-Tested: Includes "Sandbox Mode" to safely test remediation logic.

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🚀 Quick Start (CLI Tool)

For developers who want to fix bugs in their local environment or CI pipeline.

1. Install

pip install responseiq

2. Configure Credentials

ResponseIQ requires access to an LLM provider to reason about your code.

Currently supported: OpenAI

export OPENAI_API_KEY="sk-..."

3. Usage Examples

Example A: Analyze Local Logs Scan a directory of log files and get a report of active incidents.

responseiq --mode scan --target ./var/log/app/

Example B: The "Magic Fix" Analyze logs AND the current source code to generate a patch.

# Finds errors in logs, locates the source file, and explains the fix
responseiq --mode fix --target ./logs/error.log

Example C: CI/CD Pipeline Integration Run ResponseIQ as a step in your GitHub Action to auto-triage build failures.

# In your workflow
responseiq --mode scan --target ./build_logs.txt >> summary.md

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🏢 Platform Server (Self-Hosted)

For Platform Engineers who want to host a centralized incident response API (Webhook receiver for Datadog, PagerDuty, etc.).

Prerequisites

• Docker & Docker Compose
• OpenAI API Key configured in .env

Running with Docker

# 1. Start the API and Database
docker-compose up -d

# 2. The API is now available at http://localhost:8000
curl http://localhost:8000/health

Development Setup (Local)

We use UV for lightning-fast dependency management.

# Install dependencies
uv sync

# Run the API server with hot-reload
uv run uvicorn src.app:app --reload

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🧪 Development & Contributing

Workflow

1. Linting: make lint
2. Testing: make test
3. Format: make format

Project Structure

• src/cli.py: Entry point for the CLI tool.
• src/app.py: Entry point for the API Server.
• src/services/remediation_service.py: The core "Brain" that interfaces with the LLM.

License

MIT

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⚠️ Disclaimer & Liability

This tool uses Generative AI to suggest infrastructure and code fixes. By using ResponseIQ, you acknowledge that:

1. AI Can Hallucinate: The suggestions provided may be syntactically correct but functionally wrong or insecure.
2. Human Review is Mandatory: You must strictly review all Pull Requests or patches generated by this tool before deploying them.
3. No Warranty: As per the MIT License, the authors assume no liability for system outages, data loss, or security vulnerabilities resulting from the use of this software.

For security reporting instructions, please see SECURITY.md.