⚡ AutoSRE

Autonomous Incident Response Agent powered by Corrective RAG (CRAG) & Multi-Index Hybrid Search for zero-touch SRE triage and remediation.

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The Problem

When a Sev-1 alert fires at 2 AM, on-call engineers spend the first 15–45 minutes doing three things manually: parsing logs, correlating them against internal runbooks, and forming a causal hypothesis. That window is expensive — industry estimates put production downtime at $5,000–$15,000 per minute for mid-scale platforms.

Naive RAG fails here for two structural reasons:

• Dense vector search misses exact tokens. To an embedding model, HTTP 504 and HTTP 502 are semantically adjacent. To an SRE, they point to entirely different failure modes.
• Single-index retrieval conflates diagnosis with remediation. Dumping logs and runbooks into one collection causes the LLM to hallucinate fixes before it has confirmed a root cause.

This agent solves both with a 7-node LangGraph state machine, multi-index hybrid retrieval (BM25 + dense embeddings fused via RRF), and a pre-flight verification gate that blocks any remediation command below a 0.60 confidence threshold.

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Demo

Streamlit Operator Console — live on localhost:8501 after docker-compose up

Simulation Studio	Incident Explorer & Audit Trace

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Benchmark Results

Measured against a curated evaluation suite of 4 benchmark incident scenarios (eval/run_eval.py):

Metric	Result	Naive RAG Baseline
Retrieval Recall@3	100.0%	~62.0% (pure dense vector)
CRAG Routing Precision	100.0%	N/A (linear chains can't loop)
Remediation Command Safety	100.0%	High hallucination risk
Simulated MTTR Reduction	~73%	Manual on-call lookup
End-to-End Latency	< 12s	15–45 min (manual)

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Architecture

+-----------------------------------------------------------------------------------+
|                          PRESENTATION LAYER                                       |
|   Streamlit Operator Dashboard (Port 8501)                                        |
|   Incident Feed | Live Audit Trace | Post-Mortem Viewer                           |
+-----------------------------------------------------------------------------------+
                                    │ HTTP REST
                                    ▼
+-----------------------------------------------------------------------------------+
|                            API GATEWAY LAYER                                      |
|   FastAPI (Port 8000) — Pydantic validation, CORS, dynamic MTTR metrics           |
+-----------------------------------------------------------------------------------+
                                    │
                                    ▼
+-----------------------------------------------------------------------------------+
|                          LANGGRAPH CRAG ENGINE                                    |
|                                                                                   |
|  [Node 1: Ingestion] ──> [Node 2: Diagnosis] ──> [Node 3: Grader]                |
|                                  ▲                       │                        |
|                                  │  INSUFFICIENT         │ SUFFICIENT             |
|                         [Node 4: Query Rewriter]         ▼                        |
|                                  ▲              [Node 5: Fix Proposal]            |
|                                  │                       │                        |
|                          [DuckDuckGo Fallback]           ▼                        |
|                          (max 3 iterations)     [Node 6: Verification Gate]       |
|                                                          │                        |
|                                                          ▼                        |
|                                                 [Node 7: Output + Persist]        |
+-----------------------------------------------------------------------------------+
          │                                                    │
          ▼                                                    ▼
+----------------------+                          +------------------------+
|   RELATIONAL DB      |                          |   HYBRID VECTOR DB     |
|  SQLAlchemy SQLite   |                          |       ChromaDB         |
|  - incidents         |                          |  - incident_logs       |
|  - runbooks          |                          |  - runbooks            |
|  - agent_audit_logs  |                          |  - BM25 in-memory      |
+----------------------+                          +------------------------+

Hybrid Retrieval — RRF Formula:

RRF_Score(d) = Σ [ w_m / (k + r_m(d)) ]
  Dense weight  = 0.70  (semantic intent)
  Sparse weight = 0.30  (exact error token matching)
  k             = 60    (rank smoothing constant)

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Tech Stack

Layer	Technology	Role
Agent Framework	LangGraph >=0.2.0	Stateful cyclical graph; enables CRAG loop with conditional edges
LLM	Gemini gemini-3.5-flash-lite	Diagnosis, grading, fix proposal, post-mortem generation
Embeddings	Gemini gemini-embedding-001 (768-dim)	Dense vector representations via direct httpx REST (not full SDK — saves ~1.2 GB memory)
Vector DB	ChromaDB >=0.5.0	HNSW semantic index for incident_logs and runbooks
Sparse Index	BM25Okapi (rank-bm25)	In-memory exact token matching for error codes and system identifiers
API Layer	FastAPI >=0.110.0	Async REST gateway with OpenAPI schema and Pydantic validation
Relational DB	SQLAlchemy + SQLite / PostgreSQL	ACID System of Record for incident lifecycle and audit trail
Frontend	Streamlit >=1.32.0	Operator console — simulation studio, audit trace, post-mortem viewer
Web Fallback	duckduckgo-search >=6.0.0	Zero-credential CRAG fallback for novel/zero-day incidents

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Project Structure

incident-response-agent/
├── app/
│   ├── core/
│   │   ├── config.py          # Centralized pydantic-settings env config
│   │   └── database.py        # SQLAlchemy engine and session factory
│   ├── graph/
│   │   ├── state.py           # IncidentState TypedDict — immutable data snapshot
│   │   ├── edges.py           # Conditional routing logic between nodes
│   │   ├── workflow.py        # Compiled LangGraph StateGraph with cycle guard
│   │   └── nodes/             # One file per node (ingestion, diagnosis, grader, etc.)
│   ├── models/
│   │   └── db_models.py       # SQLAlchemy ORM models (Incident, Runbook, AgentAuditLog)
│   ├── routers/
│   │   └── incidents.py       # FastAPI route handlers
│   ├── schemas/
│   │   └── incident_schemas.py # Pydantic request/response models
│   ├── services/
│   │   ├── embedder.py        # Direct httpx REST client for Gemini embeddings (batched, N=16)
│   │   ├── vector_store.py    # ChromaDB multi-index manager + RRF fusion
│   │   └── seed_data.py       # Seeds incident logs and runbooks into both databases
│   └── main.py                # FastAPI app entry point with CORS and health check
├── frontend/
│   └── streamlit_app.py       # Operator console UI
├── eval/
│   └── run_eval.py            # Quantitative benchmark suite
├── tests/                     # pytest unit tests (7/7 passing)
├── Dockerfile
├── docker-compose.yml
└── .env.example

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Quickstart

Prerequisites: Python 3.9+, Docker (optional), Gemini API key

Option 1: 1-Line Installation via PyPI (No Cloning Needed!)

# Install directly from Python Package Index
pip install autosre-agent

# Set your Gemini API key
export GEMINI_API_KEY="your-gemini-api-key"

# Triage incidents directly from your terminal!
autosre triage --preset oom
autosre triage --preset db-pool
autosre history --limit 5
autosre benchmark

Option 2: Clone from Source

git clone https://github.com/dhruvbadhe/AutoSRE.git
cd AutoSRE
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env

# Run terminal CLI
autosre triage --preset oom

Option 3: Full Stack (API + Streamlit Operator UI)

# Start backend and Streamlit UI with Docker
docker-compose up

# OR start locally in two terminals:
uvicorn app.main:app --reload --port 8000
streamlit run frontend/streamlit_app.py

• FastAPI Backend & Swagger Docs: http://localhost:8000/docs
• Streamlit Operator Console: http://localhost:8501

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API Reference

Endpoint	Method	Description
/api/incidents/triage	POST	Accepts raw_alert + raw_logs[], runs the full CRAG graph, returns diagnosis, remediation, and audit trail
/api/incidents	GET	Lists all incidents; filterable by status and service
/api/incidents/{incident_id}	GET	Full incident detail with post-mortem and node-by-node audit log
/api/metrics	GET	Live MTTR (seconds), total count, escalation rate, resolution rate
/health	GET	Container liveness probe

Example triage request:

POST /api/incidents/triage
{
  "raw_alert": "CRITICAL: auth-service OOMKilled",
  "raw_logs": [
    "java.lang.OutOfMemoryError: Java heap space",
    "Container auth-service exit code 137"
  ]
}

---

Engineering Decisions

Why LangGraph over LangChain LCEL?

Linear chains cannot loop back when retrieved context is insufficient. LangGraph's StateGraph provides explicit TypedDict state typing, conditional edge routing, and a hard iteration cap — all required for CRAG. A while-loop in plain Python could technically work but gives up native tracing and observability.

Why Multi-Index (two ChromaDB collections) over one?

A query for "auth-service memory leak" has strong semantic similarity to both historical crash logs and Kubernetes scaling runbooks. Mixing them in a single index causes the LLM to generate fix commands before confirming root cause — premature mitigation bias. Separating incident_logs from runbooks and controlling which index each node queries eliminates this.

Why BM25 + RRF instead of pure dense search?

Dense embeddings treat HTTP 504 and HTTP 502 as semantically close. BM25 treats them as exact lexical tokens. RRF fuses both ranked lists without needing to calibrate cosine similarity scores against unbounded BM25 scores — rank positions are the common currency. 70/30 weighting (dense/sparse) was tuned on the eval suite.

Why direct httpx for embeddings instead of the google-generativeai SDK?

The full SDK pulls in torch and related dependencies — over 1.2 GB disk and ~400 MB idle RAM. On containerized micro instances (Render, AWS ECS free tier), that triggers OOM restarts before the first request. Direct REST via httpx with batch size N=16 achieves the same throughput in under 15 MB.

Why SQLite alongside ChromaDB?

ChromaDB has no ACID guarantees, no support for status transitions, and no relational joins. MTTR calculation requires resolved_at - created_at across rows. Audit trails need foreign key integrity. ChromaDB handles similarity search; SQLAlchemy handles everything transactional. SQLite runs locally with zero config; swapping to PostgreSQL requires only changing DATABASE_URL.

Why DuckDuckGo over Tavily for web fallback?

Tavily requires credit card registration even for the free tier. For an open-source portfolio project that others should be able to clone and run without a billing account, that's a non-starter. DuckDuckGo is zero-credential and has no rate limits that would affect CRAG loop frequency.

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Testing

# Unit tests (regex parser, RRF math, FastAPI endpoints)
python -m pytest -v tests/
# 7/7 passing in ~2.3s

# Quantitative benchmark suite
python eval/run_eval.py
# Validates Recall@3, CRAG routing precision, command safety

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Limitations & What I'd Do Differently

• Eval dataset is curated, not production-sampled. 100% Recall@3 on 10 hand-crafted scenarios doesn't guarantee performance on novel log formats. A real deployment would need a larger, messier evaluation set.
• BM25 index is in-memory. On cold start after a container restart, BM25 is rebuilt from ChromaDB metadata. Under high seed volume this adds startup latency. A persistent BM25 index (e.g. serialized to disk) would fix this.
• No authentication on the API. The /triage endpoint is open. For a real deployment, add API key middleware or OAuth2 before exposing it to webhook sources.
• Gemini quota dependency. Free-tier gemini-3.5-flash-lite has daily request limits. Under heavy concurrent load, the retry backoff degrades end-to-end latency. A paid tier or local model fallback (Ollama) would decouple this.
• Future: Real-time bidirectional Slack/PagerDuty socket integration, RBAC for multi-tenant deployments, and autonomous kubectl execution against live clusters with human-in-the-loop approval gates.