responseiq 2.25.0

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

ResponseIQ

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

ResponseIQ is a self-healing infrastructure copilot. It reads your crash logs, loads the actual source code from your repositories using Tree-sitter AST parsing, and generates surgical fixes — complete with a rollback script, a Trust Gate audit trail, and a GitHub PR — all without requiring human intervention at 3am.

Zero config. Try it now:

pip install responseiq && responseiq demo

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How it works

# Pipe Kubernetes logs straight in — no plugin, no config
kubectl logs payment-svc --since=1h | responseiq --mode fix --target - --explain

# Or Docker
docker logs --tail 200 my-api | responseiq --mode fix --target - --explain

ResponseIQ will:

1. Filter out noise lines automatically
2. Resolve container paths like /app/services/auth.py to the real source files in your repos
3. Load the actual crashing functions via Tree-sitter AST into the AI context
4. Generate a fix with a full explanation of why it chose that approach
5. Run it through 7 safety guardrails before doing anything
6. Open a GitHub PR (or print the patch in dry-run mode if no token is set)
7. Write a REASONING.md audit log you can paste straight into your post-mortem

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What makes it different

Most log analysis tools pattern-match on error strings. ResponseIQ does something different: it reads your code.

When your auth service crashes at /app/services/auth_service.py:120, ResponseIQ resolves that container path to the actual file, loads the function with its surrounding context via Tree-sitter, and gives the AI exactly what it needs to understand the bug — not just the error message. That is why it generates a fix, not just a description.

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Benchmark

Evaluated against SWE-bench Verified — the same dataset used to rank SWE-agent, Devin, and OpenHands.

Model	Samples	Pass@1	Latency p50	API key needed
llama3.2 (local Ollama)	20	20%	29s	No

1-in-5 incidents get a correct, Trust-Gate-approved patch in ~30 seconds using a free local model. See reports/swe_bench_eval.md for the per-repo breakdown.

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Architecture

flowchart TD
    A([Log Input]) --> B[Noise Filter]
    B --> C[Concurrent Scan — asyncio.gather]
    C --> D{AI Classifier}
    D -->|HIGH / CRITICAL| E[Tree-sitter AST Source Loader]
    D -->|LOW| Z([Skip])
    E --> F[LLM Reasoning — Ollama / OpenAI]
    F --> G{Trust Gate — 7 guardrails}
    G -->|Approved| H[ProofBundle — SHA-256 sealed]
    G -->|Blocked| I([Human Review])
    H --> J[GitHub PR — githubkit]
    J --> K[PR Bot — /responseiq approve]

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Features

Core pipeline

What	How
Reads your actual source code	Tree-sitter AST loads the exact crashing functions, not just file paths
Multi-repo path resolution	Maps container paths like /app/services/auth.py to your real source — local or remote
Trust Gate — 7 guardrails	No bare except, no secrets, syntax valid, blast radius assessed, and more — every patch must pass all 7
SHA-256 proof chain	Every decision is sealed into a ProofBundle — SOC2-ready audit trail
GitHub PR bot	Opens a draft PR and responds to /responseiq approve, /responseiq reject, /responseiq explain
Rollback script	Generates an executable rollback_<id>.py alongside every patch
Works without any API key	Full rule-engine fallback is always available

New in v2.24 — Modern AI stack

Feature	What it does
--mode watch daemon	Continuous async tail of any log file; bursts are debounced and sent for AI analysis automatically
SSE streaming endpoint	POST /api/v1/incidents/analyze/stream emits live events (started → scrubbing → analyzing → critic → trust_gate → complete) so UIs update in real time
Critic / reviewer agent	A second fast-model pass reviews every proposed fix for logic errors and hidden regressions before the Trust Gate runs
LiteLLM multi-provider	Set RESPONSEIQ_USE_LITELLM=true to route calls through LiteLLM — supports 100+ providers with a single env-var switch
OTel GenAI conventions	All LLM calls emit gen_ai.* OpenTelemetry spans — plug any OTLP-compatible backend for cost and latency tracking
DSPy prompt optimiser	Scaffold for automatic prompt optimisation via DSPy; activate with responseiq[dspy] + RESPONSEIQ_DSPY_ENABLED=true
MCP server	responseiq-mcp exposes four tools (analyze_incident, get_remediation, run_trust_gate, open_pr) via the Model Context Protocol for agent integrations

Language-specific parsers

Dedicated parsers extract rich structured context — goroutine IDs, stack frames, exception chains, and framework signals — before the AI even sees the log.

Language / Framework	What it extracts
Go	panic message, goroutine states, stack frames with file + line, signal crashes (SIGSEGV, SIGABRT)
Node.js	TypeError/ReferenceError, V8 stack frames with column numbers, unhandled promise rejections
Spring Boot	Exception chain, root cause, Log4j/Logback lines with thread and logger
Django	Exception type, traceback frames, request path
FastAPI	Uvicorn/Starlette exception context
Python (built-in)	All Python tracebacks via KeywordParser

Infrastructure features

Feature	What it does
Post-apply watchdog	After applying a patch, monitors your error-rate metric (Datadog, Prometheus, or /health probe). Automatically executes the rollback script if the rate spikes above the configured threshold.
K8s YAML patcher	Edits Kubernetes Deployment manifests using ruamel.yaml — comments, quotes, and indentation survive the diff.
Stateful conversations	Each incident gets a persistent multi-turn AI session. Redis-backed in production; transparent in-memory fallback in dev/test.
PII scrubbing	Regex email redaction is always on. Set RESPONSEIQ_NER_SCRUB=true and install spaCy to add NER-level PERSON/ORG/location scrubbing.

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Quick start

CLI — local dev or CI

# 1. Install
pip install responseiq

# 2. Configure — 3 questions, writes .env, done
responseiq init

# 3. Scan
responseiq --mode scan --target ./logs/error.log

# 4. Fix with full explanation
responseiq --mode fix --target ./logs/error.log --explain

# 5. Shadow mode — read-only triage, nothing is changed
responseiq --mode shadow --target ./logs/ --shadow-report

# 6. Watch mode — continuous tail daemon (new in v2.24)
responseiq --mode watch --target ./logs/app.log

No LLM key? The rule-engine fallback activates automatically. responseiq init is optional.

LLM options:

# Local Ollama (free, no data leaves your machine — recommended)
LLM_BASE_URL=http://localhost:11434/v1 LLM_ANALYSIS_MODEL=llama3.2 responseiq --mode scan --target ./logs/

# OpenAI
OPENAI_API_KEY=sk-... responseiq --mode scan --target ./logs/

Pipe from anywhere:

kubectl logs -l app=api --since=1h        | responseiq --mode fix  --target - --explain
docker logs --tail 200 my-container       | responseiq --mode scan --target -
cat ./logs/app.log                        | responseiq --mode scan --target -

# Structured JSON / NDJSON
echo '{"level":"ERROR","message":"KeyError: email","service":"api"}' \
  | responseiq --mode scan --target -

Self-hosted API server

# Start server + Postgres
docker-compose up -d
curl http://localhost:8000/health

# Local dev with hot-reload
uv sync
uv run uvicorn responseiq.app:app --reload

Point your existing alert tools at POST /api/v1/incidents/ingest:

Platform	Integration
Datadog	Webhook → POST /api/v1/incidents/ingest
PagerDuty	Event Orchestration webhook
Sentry	Internal Integrations → Webhook URL
Alertmanager	Webhook receiver in alertmanager.yml
GitHub Actions	curl step in your workflow

SSE streaming — get live progress events while an incident is being analysed:

curl -N -X POST http://localhost:8000/api/v1/incidents/analyze/stream \
  -H 'Content-Type: application/json' \
  -d '{"log_text": "ERROR: NullPointerException at UserService.java:42"}'
# event: started
# event: scrubbing
# event: analyzing
# event: critic
# event: trust_gate
# event: complete

MCP server — expose ResponseIQ as an AI agent tool:

pip install 'responseiq[mcp]'
responseiq-mcp          # starts stdio MCP server
# Tools: analyze_incident · get_remediation · run_trust_gate · open_pr

Try the built-in demo

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

responseiq --mode scan   --target ./samples/crash.log
responseiq --mode fix    --target ./samples/crash.log --explain
responseiq --mode shadow --target ./samples/ --shadow-report

samples/ contains three real injected bugs with a pre-recorded crash log. No API key, no database, nothing to set up.

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.
------------------------------------------------------------

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SWE-bench

# Smoke run — 5 samples, no LLM key
uv run python scripts/swe_bench_eval.py --samples 5 --dry-run

# Full run (500 samples)
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 and reports/predictions.jsonl (compatible with the official swebench Docker harness).

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Development

uv sync
make lint    # ruff format --check + ruff check + mypy
make test    # pytest -n auto --dist=loadscope
make all     # lint + type-check + test + build + security audit

Project layout

src/responseiq/
  cli.py                        # CLI entry point (--mode scan|fix|shadow|watch)
  app.py                        # FastAPI server (webhooks + SSE streaming)
  mcp_server.py                 # MCP server — 4 agent tools (responseiq-mcp)
  ai/
    llm_service.py              # LLM calls with OTel GenAI spans + LiteLLM support
    dspy_optimizer.py           # DSPy prompt optimisation scaffold (opt-in)
  services/
    remediation_service.py      # Core LLM reasoning brain
    critic_service.py           # Second-pass critic/reviewer agent
    github_pr_service.py        # GitHub PR bot (githubkit)
    watchdog_service.py         # Post-apply error-rate monitor + auto-rollback
    conversation_service.py     # Stateful AI conversations (Redis + in-memory fallback)
  routers/
    streaming.py                # SSE streaming endpoint
  plugins/
    scan.py / fix.py / shadow.py / watch.py
    go_parser.py / nodejs_parser.py / spring_parser.py / django_parser.py / fastapi_parser.py
  utils/
    context_extractor.py        # Tree-sitter AST source loader
    multi_repo_resolver.py      # Maps container paths to local source files
    k8s_patcher.py              # Kubernetes YAML patcher (comment-preserving)
    log_scrubber.py             # PII redaction (email regex + optional spaCy NER)

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Disclaimer

ResponseIQ uses generative AI to suggest infrastructure and code fixes. AI can hallucinate — syntactically correct but functionally wrong patches are possible. Review every PR or patch before merging. The Trust Gate reduces risk but is not a substitute for human review. See the MIT License — no warranty implied.

For security issues see docs/SECURITY.md.