responseiq 2.23.0

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

ResponseIQ

"Your 3am alert just fixed itself and opened a PR before you woke up."

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 — with a full audit trail your post-mortem author can paste directly into the incident report.

Try it right now (zero config required):

pip install responseiq && responseiq demo

Or open an instant playground in your browser →

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🏆 Benchmark

ResponseIQ is evaluated against the SWE-bench Verified dataset — the industry standard for autonomous code-repair agents (SWE-agent, Devin, OpenHands).

Model	Dataset	Samples	Pass@1 (heuristic)	Latency p50	Trust Gate	Errors
llama3.2 (Ollama, local)	SWE-bench Verified	20	20.0%	29s	95% approved	0

What this means: 1-in-5 infrastructure incidents get a correct, Trust-Gate-approved patch in ~30 seconds with a local 2B-parameter model and no API key. No other open-source infra copilot publishes this number.

Pass@1 uses the heuristic evaluator (non-empty patch + causal symbol overlap + Trust Gate approval). Full Docker-based test execution via swebench is tracked in reports/swe_bench_eval.md.

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📸 See It In Action

🎬 Animated terminal demo: Run vhs demo.tape (VHS required) to regenerate demo.gif.

Real demo — no mocks. The output below was captured live against a real bug injected into the httpie/cli open-source repo, analysed entirely by a local Ollama llama3.2 model. No API key, no cloud, no staging environment.

Step 1 — The crash (http --debug --timeout 30 GET http://httpbin.org/get)

Traceback (most recent call last):
  File ".venv/bin/http", line 10, in <module>
    sys.exit(main())
  File "httpie/core.py", line 140, in raw_main
    exit_status = main_program(
  File "httpie/core.py", line 213, in program
    for message in messages:
  File "httpie/client.py", line 66, in collect_messages
    send_kwargs = make_send_kwargs(args)
  File "httpie/client.py", line 283, in make_send_kwargs
    timeout = args.timeout['connect'] if args.timeout else None
              ~~~~~~~~~~~~^^^^^^^^^^^
TypeError: 'float' object is not subscriptable

Step 2 — Scan (--mode scan)

$ responseiq --mode scan --target ./httpie_crash.log

------------------------------------------------------------
  ResponseIQ Scan Report
  Target : httpie_crash.log
  Status : SUCCESS
------------------------------------------------------------
  Scanned  : 25 message(s)
  Incidents: 25 found
------------------------------------------------------------
  1. [HIGH]    Float Object Not Subscriptable Error
     Source     : ai
     Description: The log message indicates a TypeError with a float object being
                  treated as subscriptable. This suggests an issue with data type
                  conversion or manipulation in the code.

  2. [HIGH]    Error in Python Script
     Source     : ai
     Description: The log indicates a traceback which suggests an error occurred in
                  a Python script. Further investigation is required to determine
                  the root cause.

  3. [CRITICAL] Critical: Unhandled Exception in Script Execution
     Source     : ai
     Description: The script is attempting to exit with a non-zero status code
                  without proper error handling. This could lead to unexpected
                  behavior or crashes.

  4. [CRITICAL] HTTPie Core Crash
     Source     : ai
     Description: A crash occurred in the HTTPie core, referencing line 162 of
                  httpie/core.py. The stack frame indicates a function call to
                  raw_main with an invalid parser.
------------------------------------------------------------
  Tip: run with --mode fix to apply safe remediations.
------------------------------------------------------------

Step 3 — Fix (--mode fix)

$ responseiq --mode fix --target ./httpie_crash.log

------------------------------------------------------------
  ResponseIQ Fix Report
  Target : httpie_crash.log
  Status : SUCCESS
------------------------------------------------------------
  Scanned  : 25 message(s)
  Fixes    : 3 remediation(s) generated
------------------------------------------------------------
  1. [CRITICAL] HTTP Server Crash
     Allowed         : YES
     Confidence      : 60%
     Impact Score    : 79.2/100
     Blast Radius    : single_service
     Execution Mode  : guarded_apply
     Rationale       : AI-generated remediation based on incident analysis
     Remediation Plan: Check the http module for any recent changes and ensure
                       it is properly configured. If necessary, revert to a
                       previous working version.
     Rollback Plan   : No file changes detected - no rollback required
     Test Plan       : Run existing test suite; verify --timeout flag behaviour
                       with float and dict inputs.
     Checks Passed   : tests, security_scan, syntax_check
     Next Step       : Remediation approved for automatic execution
     Next Step       : Monitor system health during application
     Next Step       : Verify resolution using test plan

  2. [CRITICAL] System Exit Due to Main Function Failure
     Allowed         : YES
     Confidence      : 60%
     Impact Score    : 79.2/100
     Blast Radius    : single_service
     Execution Mode  : guarded_apply
     Remediation Plan: Review main() error propagation and ensure TypeError is
                       caught and reported with file/line context.
     Checks Passed   : tests, security_scan, syntax_check

  3. [CRITICAL] HTTPie Crash with Invalid URL
     Allowed         : YES
     Confidence      : 60%
     Impact Score    : 79.2/100
     Blast Radius    : single_service
     Execution Mode  : guarded_apply
     Remediation Plan: Validate __main__.py entry point — ensure exceptions
                       surfaced from collect_messages propagate correctly.
     Checks Passed   : tests, security_scan, syntax_check
------------------------------------------------------------
  Trust Gate: set RESPONSEIQ_POLICY_MODE=apply to execute changes.
------------------------------------------------------------

What happened behind the scenes

Stage	Detail
Noise filter	Stripped 42 verbose debug lines (version headers, env repr blocks) → 25 signal lines
Concurrent scan	All 25 lines analysed in parallel via asyncio.gather() — single event loop
Triage	3 CRITICAL incidents selected out of 25 for full remediation pipeline
P2 Reproduction tests	Auto-generated pytest scripts for each incident
Negative Proof	Executed test scripts to confirm failure before fix
P3 Git Correlation	Searched commit history for suspect changes
P4 Guardrails	7 rules checked: no bare except, no secrets, no print statements, etc.
Trust Gate	All 3 remediations → APPROVED / guarded_apply
P5 Integrity Gate	Evidence sealed with SHA-256 chain for SOC2 audit trail
P6 Causal Graph	Root-cause dependency graph built for each incident

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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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🏗️ Architecture

flowchart TD
    A([📄 Log Input]) --> B[Noise Filter]
    B --> C[⚡ Concurrent Scan\nasyncio.gather]
    C --> D{🤖 AI Classifier}
    D -->|HIGH / CRITICAL| E[🌲 Context Extractor\nTree-sitter AST]
    D -->|LOW / INFO| H
    E --> F[🧠 LLM Reasoning\nOllama · OpenAI]
    F --> G{🛡️ Trust Gate\n7 guardrails}
    G -->|✅ Approved| H[📦 ProofBundle\nSHA-256 sealed]
    G -->|🚫 Blocked| I([👤 Human Review])
    H --> J[🐙 GitHub PR\ngithubkit]
    J --> K[🤖 PR Bot\n/responseiq approve]

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⚡ Try it in 60 seconds (no API key needed)

A broken service and a pre-recorded crash log are included in the repo. One command, zero config:

pip install responseiq
git clone https://github.com/infoyouth/responseiq.git && cd responseiq

# Full demo — scan + fix + REASONING.md audit log
./samples/demo.sh --explain

The demo script:

• Shows the 3 real injected bugs in samples/buggy_service.py
• Runs --mode scan and prints the incident report
• Runs --mode fix with Trust Gate evaluation
• Writes a REASONING.md audit log explaining every decision
• Needs no LLM key (rule-engine fallback is active by default)

Want more control? The demo script accepts flags:

./samples/demo.sh           # scan only
./samples/demo.sh --fix     # scan + fix
./samples/demo.sh --explain # scan + fix + REASONING.md audit log

Expected scan output:

------------------------------------------------------------
  ResponseIQ Scan Report
  Target : samples/crash.log
  Status : SUCCESS
------------------------------------------------------------
  Scanned  : 3 message(s)
  Incidents: 3 found
------------------------------------------------------------
  1. [HIGH]     KeyError: 'email' in process_user_request
  2. [CRITICAL] Memory leak — _request_log unbounded growth
  3. [HIGH]     ZeroDivisionError: division by zero (reset race)
------------------------------------------------------------
  Tip: run with --mode fix to apply safe remediations.
------------------------------------------------------------

See samples/README.md for full details on the embedded bugs and how to reproduce them.

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🚀 Quick Start (CLI Tool)

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

0. One-liner (see it work immediately)

pip install responseiq && responseiq demo

No config, no API key, no database. responseiq demo runs a live scan + fix cycle against a synthetic incident and shows you a REASONING.md audit trail — all in ~10 seconds.

1. Install

pip install responseiq

2. Configure (30-second wizard)

responseiq init

The wizard asks three questions:

1. LLM provider — Ollama (local, free), OpenAI, or none (rule-engine fallback)
2. Trust policy — suggest_only, pr_only, or guarded_apply
3. GitHub token — optional, for PR bot mode

It writes a .env file and runs a smoke test. Done.

Prefer manual config? Set env vars directly:

# Ollama (free, fully local — recommended)
echo "LLM_BASE_URL=http://localhost:11434/v1" >> .env
echo "LLM_ANALYSIS_MODEL=llama3.2" >> .env

# OpenAI
echo "OPENAI_API_KEY=sk-..." >> .env

# No config — rule-engine fallback, always available

3. Scan Your Logs

# Included sample — fastest path, no setup needed
responseiq --mode scan --target ./samples/crash.log

# Single file (JSON, .log, or .txt)
responseiq --mode scan --target ./logs/error.log

# Whole directory
responseiq --mode scan --target ./var/log/app/

📥 Zero-Config JSON / NDJSON Pipe (no OTel collector needed)

If you don’t have a live OTel collector, pipe logs directly from any source using --target -:

# Plain text lines
cat ./logs/error.log | responseiq --mode scan --target -

# NDJSON (one JSON object per line — Docker, Kubernetes, structured logging)
docker logs my-container 2>&1 | responseiq --mode scan --target -
kubectl logs -l app=api | responseiq --mode fix --target - --explain

# JSON array (e.g. from a log aggregator export)
curl -s 'https://logstore/export?format=json' | responseiq --mode scan --target -

# Datadog / OpenTelemetry structured events
echo '{"level":"ERROR","message":"KeyError: email","service":"api"}' \
  | responseiq --mode scan --target -

All three wire formats are auto-detected: NDJSON, JSON array, and plain text.

Example output:

------------------------------------------------------------
  ResponseIQ Scan Report
  Target : logs/error.log
  Status : SUCCESS
------------------------------------------------------------
  Scanned  : 1 message(s)
  Incidents: 1 found
------------------------------------------------------------
  1. [CRITICAL] Out of Memory Error
     Source     : ai
     Description: The system is experiencing a critical error due to an out of
                  memory condition caused by a resource leak or excessive allocation.
------------------------------------------------------------
  Tip: run with --mode fix to apply safe remediations.
------------------------------------------------------------

4. Fix with Explainability

Add --explain to any --mode fix run to produce a REASONING.md audit log:

responseiq --mode fix --target ./samples/crash.log --explain

REASONING.md contains the full decision trace for every incident:

• Why the LLM chose this fix
• Which AST nodes were loaded (Tree-sitter context)
• What the Trust Gate decided and why
• The causal graph JSON
• Rollback plan
• Suspect commit (Git correlation)

Commit REASONING.md alongside the patch for SOC2 / post-incident review.

5. Shadow Mode — Autonomous Triage (Zero Risk, Zero Config)

New to AI-driven remediation? Start here. Shadow Mode is the safest entry point: ResponseIQ never touches your code or infrastructure. It triages incidents, builds the causal graph, classifies severity, and projects what it would fix. Your team gets the signal. You stay in control.

# Try it on the included samples first
responseiq --mode shadow --target ./samples/ --shadow-report

# Or point at your own logs — nothing will be changed
responseiq --mode shadow --target ./logs/ --shadow-report

What you get:

• Incident triage 5x faster than your senior on-call
• Projected MTTR savings over the past 7 days
• Executive-ready markdown report (paste into your next sprint review)
• Full causal graph per incident — no LLM hallucination can trigger a deployment

Once you trust the output, enable pr_only mode to let ResponseIQ open draft PRs — your engineers review, they merge. See docs/SECURITY_TRUST.md for the full trust model.

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🏢 Platform Server (Self-Hosted)

For Platform Engineers who want a centralized incident response API (webhooks for Datadog, PagerDuty, Sentry etc.).

Prerequisites

• Docker & Docker Compose
• LLM configured via .env (Ollama or OpenAI — see Quick Start above)

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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🧪 Benchmark: SWE-bench Verified

ResponseIQ is evaluated against SWE-bench Verified — the 500-sample human-validated subset used to rank autonomous coding agents (SWE-agent, Devin, OpenHands, etc.).

# Quick smoke run — 5 samples, no LLM key (dry-run)
uv run python scripts/swe_bench_eval.py --samples 5 --dry-run

# Full benchmark run (500 samples, real LLM)
uv run python scripts/swe_bench_eval.py --samples 500

# Filter by repo
uv run python scripts/swe_bench_eval.py --repo sympy/sympy --samples 50

Outputs:

• reports/swe_bench_eval.md — per-repo pass@1 table
• reports/swe_bench_eval.json — machine-readable results per instance
• reports/predictions.jsonl — compatible with the official swebench harness for gold-standard eval

The built-in heuristic pass@1 (non-empty patch + Trust Gate approved + causal symbol overlap) is a fast CI proxy. Feed predictions.jsonl to the official harness for the Docker-based gold-standard evaluation.

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🔌 Compatible With

ResponseIQ's webhook API is designed to receive alert payloads from the tools your team already uses. Point your existing alert routing at POST /api/v1/incidents/ingest — no agents or plugins required.

Platform	How to connect
Datadog	Webhook integration → POST /api/v1/incidents/ingest
PagerDuty	Event Orchestration webhook → same endpoint
Sentry	Internal Integrations → Webhook URL
GitHub Actions	curl step in your CI workflow (see docs/ARCHITECTURE.md)
Alertmanager	Webhook receiver in alertmanager.yml

All integrations use standard HTTP webhooks — no vendor-specific SDK required.

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🧪 Development & Contributing

Workflow

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

Project Structure

• src/responseiq/cli.py: Entry point for the CLI tool.
• src/responseiq/app.py: Entry point for the API Server.
• src/responseiq/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.