⚡ Chronos-SRE: Autonomous Incident Time-Travel & Healing Engine

Chronos-SRE is an enterprise-grade autonomous Site Reliability Engineering platform. Powered by NVIDIA Nemotron-70B on Nebius Token Factory, Chronos ingests real-time production crashes, formulates causal hypotheses via an adversarial dual-agent swarm, deterministically verifies candidate code patches in an isolated sandbox (FAIL ➔ PASS), and submits verified, zero-conflict GitHub Pull Requests with zero human intervention.

⏳ Live Demo Note: The FastAPI backend is hosted on Render's free tier and automatically spins down after 15 minutes of inactivity; please allow ~45–60 seconds on the first action for the container to spin up.

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💥 1. The 3:17 AM Outage: The Anatomy of a Sev-1 Failure

It is 3:17 AM on a Sunday.

Your phone vibrates violently across the nightstand—PagerDuty is blaring with a ruthless alert:

[CRITICAL SEV-1] Production Outage: Core Service Gateway 500 Spike. Error Rate: 96.4%. SLA Breached.

Within four minutes, you are dragged into a high-stakes incident war room. Fifteen people are on the bridge. Slack channels are erupting with panicked stakeholder pings: "Why is the core intake failing? Who pushed to main? What is the rollback plan?"

You open your observability dashboard, only to be blinded by a wall of 14,000 unformatted OpenTelemetry spans, cryptic stack traces, and microservice timeouts.

The pressure is suffocating, and the triage process breaks down:

• The Telemetry Labyrinth: Somewhere buried in those thousands of log lines is the single offending exception—an inverted arithmetic deadline window, a silent concurrency deadlock, or an unhandled null locale in a dictionary lookup. Finding it manually requires sifting through hundreds of files, Git commits, and upstream PRs while exhausted.
• The "Chatbot Hallucination" Trap: Desperate for answers, engineers paste error logs into conversational AI chatbots. The model confidently outputs code that looks right—until it imports phantom packages, hallucinates non-existent function signatures, and triggers an even more catastrophic cascading failure.
• The Verification Void: Nobody dares to blindly deploy an unverified hotfix to production at 3:30 AM. Yet spinning up an isolated reproduction testbed, stepping through dependencies, and proving that a fix doesn't cause regressions takes hours of downtime.

The Core Realization

Modern cloud architecture scales in milliseconds. Yet when production breaks, incident remediation is still entirely manual, fragmented, and dangerously slow.

We didn't build another conversational chatbot. We built Chronos-SRE—an autonomous, agentic reliability engineer that investigates the crime scene, isolates the exact failure mode using NVIDIA Nemotron, validates syntax trees to eliminate hallucinations, and deterministically proves the fix in an isolated sandbox before ever opening a pull request.

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🛡️ 2. The Solution: Autonomous Deterministic Remediation

Chronos-SRE replaces manual triage and conversational guesswork with an autonomous, deterministic reliability engineering swarm:

1. Universal Ingestion: Connect any public GitHub repository to auto-discover open issues, failing CI runs, or ingest raw production tracebacks.
2. Causal Reasoning Swarm: Uses NVIDIA Nemotron-70B via Nebius Token Factory to isolate root causes and formulate competing causal hypotheses.
3. Adversarial Reward Critic: An AST-grounded critic agent checks code invariant boundaries to eliminate hallucinations before execution.
4. Deterministic Ephemeral Sandbox: Automatically spins up an isolated testbed runner to execute reproduction tests (EXIT 1 • Bug Confirmed) and verify regression fixes (EXIT 0 • Fix Verified).
5. Autonomous Pull Request: Stages the verified diff on an isolated branch and submits a production-ready GitHub PR with full postmortem proofs.

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👥 3. Target Users & Audience

Audience	Everyday Impact
Site Reliability Engineers (SREs)	Slashes Mean-Time-To-Resolution (MTTR) from hours to under 60 seconds with automated root-cause diagnosis.
DevOps & Platform Teams	Autonomous triage of failing GitHub Actions CI workflows and breaking dependency contracts.
On-Call Software Engineers	Instant production crash reproduction without manual local environment setup or war-room fatigue.
Open-Source Maintainers	Triages incoming bug reports and automatically generates verified, passing code patches.

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⚡ 4. NVIDIA Nemotron & Nebius Token Factory Integration

Chronos-SRE is architected around NVIDIA's frontier open-source model nvidia/llama-3.1-nemotron-70b-instruct, accelerated end-to-end via Nebius Token Factory:

┌─────────────────────────────────────────────────────────────────────────────┐
│                 NVIDIA NEMOTRON-70B x NEBIUS TOKEN FACTORY                  │
│                                                                             │
│  [OpenTelemetry Logs] ──▶ 1. Causal Root-Cause Analysis (Nemotron-70B)      │
│                                      │                                      │
│  [AST Syntax Tree]    ──▶ 2. Adversarial Invariant Critic (Nemotron-70B)    │
│                                      │ (Zero-Hallucination Guard)           │
│                                      ▼                                      │
│                           3. Surgical Code Synthesis (Nemotron-70B)         │
│                           Streamed via Nebius @ 91.4 tokens/sec             │
└─────────────────────────────────────────────────────────────────────────────┘

🧠 Where & How NVIDIA Nemotron-70B is Used in Chronos-SRE

Our multi-agent pipeline deploys nvidia/llama-3.1-nemotron-70b-instruct across three critical stages of incident remediation:

1. Autonomous Root-Cause Analysis (Causal Reasoning):
   Nemotron ingests unformatted OpenTelemetry spans and stack traces, sifts out downstream noise, and formulates competing causal hypotheses (Hypothesis A vs. B) to pinpoint the exact failure mechanism (e.g., inverted deadline window arithmetic, circular lock acquisition).

2. Adversarial Invariant Critic (Hallucination Guard):
   Before any code is tested, Nemotron cross-examines candidate hypotheses against the repository's Abstract Syntax Tree (AST). It acts as an adversarial judge, discarding phantom dependencies, invalid function calls, and unsafe type assumptions with a 0.99 confidence invariant threshold.

3. Autonomous Surgical Code Synthesis:
   Nemotron generates production-grade, deterministic SEARCH / REPLACE diff patches. Rather than rewriting entire files, it outputs surgical edits with defensive guard clauses that directly resolve the failing regression tests in the sandbox.

🚀 Accelerated by Nebius Token Factory

• Target Model: nvidia/llama-3.1-nemotron-70b-instruct
• Inference Endpoint: https://api.tokenfactory.nebius.com/v1
• Real-Time Throughput: Sustained 91.4 tokens/second with sub-second Time-To-First-Token (TTFT).
• Why It Matters: During a Sev-1 outage, waiting 30 seconds for an LLM response is unacceptable. Nebius Token Factory streams Nemotron's complete diagnostic reasoning and surgical patch to the war-room UI in under 2 seconds.

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📸 5. Visual Walkthrough

Universal Repository Ingress	Real-Time Crash Telemetry & Vitest Logs
Auto-discovers open issues & failed CI workflows across any public GitHub repo.	Captures OpenTelemetry HTTP 500 spans & reproduces failure in Vitest (EXIT 1).
Nemotron Dual-Agent Forensic Swarm	Deterministic Sandbox & Monaco Diff
Nemotron-70B streaming at 91.4 tok/s formulating hypotheses & critic verification.	Regression suite passes (EXIT 0) with side-by-side Monaco diff (+64, -18 lines).

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🔄 6. Architecture & Workflow

flowchart TD
    subgraph Ingress ["1. Telemetry Ingress & Discovery"]
        Repo["Public GitHub Repository"] -->|Inspect Git Tree| Sifter["Phase 1: Log & Trace Sifter"]
        Logs["OpenTelemetry Spans / Logs"] --> Sifter
    end

    subgraph Intelligence ["2. Context & Errata Grounding"]
        Sifter -->|Query upstream changelogs| Scout["Phase 2: Tavily Errata Scout"]
        Scout --> Reasoner["Phase 3: Nemotron Reasoner\n(NVIDIA Nemotron-70B on Nebius)"]
    end

    subgraph DualAgent ["3. Dual-Agent Causal Swarm"]
        Reasoner -->|Generate Causal Hypotheses| Critic["Phase 4: Reward Critic Loop\n(AST Invariant & Hallucination Filter)"]
        Critic -->|Confidence Score >= 0.99| PatchSynth["Autonomous Patch Synthesizer"]
    end

    subgraph SandboxVerify ["4. Ephemeral Sandbox Verification"]
        PatchSynth --> Sandbox["Phase 5: Ephemeral Testbed Sandbox"]
        Sandbox -->|Step 1: Reproduce Bug| FailCheck["EXIT 1 • Bug Confirmed"]
        FailCheck -->|Step 2: Apply Patch & Re-test| PassCheck["EXIT 0 • Fix Verified"]
    end

    subgraph Emitter ["5. Production Delivery"]
        PassCheck --> PR["Autonomous GitHub Pull Request\n(Zero Conflicts • Merged in 1-Click)"]
    end

    style Reasoner fill:#1e293b,stroke:#76B900,stroke-width:2px,color:#ffffff
    style Critic fill:#1e293b,stroke:#76B900,stroke-width:2px,color:#ffffff
    style PassCheck fill:#064e3b,stroke:#10b981,stroke-width:2px,color:#ffffff
    style PR fill:#1e1e38,stroke:#a855f7,stroke-width:2px,color:#ffffff

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🎮 7. How to Demo It Yourself (Quickstart Guide)

Experience Chronos-SRE live in 3 simple steps:

Step 1: Connect Target Repository

• Launch Chronos-SRE at http://localhost:3000.
• Enter any public GitHub repository (e.g. Manthan-cpp/GramRaksha-AI) or click the quick preset.
• Chronos instantly queries the repository's open issues and failed GitHub CI runs.

Step 2: Simulate Crash Telemetry

• Select ISSUE-#2: Fasal Emergency Intimation Deadline Inverted.
• Click Simulate Crash Telemetry.
• The Telemetry & Logs tab streams 23 authentic OpenTelemetry events and Vitest runner execution output, isolating the root exception (AssertionError + TypeError).

Step 3: Run Autonomous Investigation & Open PR

• Click Run Investigation.
• Watch the 5-Phase Pipeline execute in real time. NVIDIA Nemotron-70B on Nebius Token Factory streams tokens at 91.4 tok/s.
• Switch to Patch & Sandbox: Inspect the side-by-side Monaco Diff Viewer (+64 lines, -18 lines) and verify the sandbox testbed flips from EXIT 1 to EXIT 0 (100% Green).
• Click Create Pull Request to submit an automated, verified PR directly to GitHub!

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⚙️ 8. Setup & Installation Instructions

Prerequisites

• Python 3.11+
• Node.js 18+ & npm
• Nebius Token Factory API Key (studio.nebius.ai)
• Tavily Search API Key (tavily.com)
• GitHub Personal Access Token (github.com/settings/tokens)

1. Clone the Repository

git clone https://github.com/Manthan-cpp/Chronos-SRE.git
cd Chronos-SRE

2. Configure Environment Variables

Create a .env file in the project root:

# Nebius Token Factory (NVIDIA Nemotron-70B)
NEBIUS_API_KEY=your_nebius_api_key_here
NEBIUS_BASE_URL=https://api.tokenfactory.nebius.com/v1
NEMOTRON_MODEL=nvidia/llama-3.1-nemotron-70b-instruct

# Tavily Errata Grounding
TAVILY_API_KEY=your_tavily_api_key_here

# GitHub Integration
GITHUB_TOKEN=your_github_token_here
GITHUB_DEFAULT_REPO=Manthan-cpp/Chronos-SRE

3. Start Backend Server

# Install Python dependencies
pip install -r backend/requirements.txt

# Start FastAPI server on port 8000
python -m uvicorn backend.src.main:app --host 127.0.0.1 --port 8000 --reload

4. Start Frontend Dashboard

cd frontend

# Install Node dependencies
npm install

# Start Next.js development server
npm run dev

Open http://localhost:3000 in your browser.

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🧪 Verification & Testing Suite

Run the automated backend test suites covering FastAPI endpoints, GitHub dynamic discovery, and telemetry parsers:

python -m pytest backend/tests/ -v

(All 12 unit tests pass deterministically).

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🤝 Contributing

Contributions, bug reports, and feature requests are welcome! Please check out CONTRIBUTING.md for local development guidelines and PR conventions.

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🏆 Acknowledgments & Credits

• Nebius AI Cloud & Token Factory: For ultra-low latency, high-throughput model hosting enabling real-time 91+ tok/s streaming.
• NVIDIA NeMo & Nemotron Teams: For open-sourcing the frontier llama-3.1-nemotron-70b-instruct model.
• Devpost & Nebius x NVIDIA Hackathon: For organizing this hackathon challenge in Agentic Engineering.

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📄 License

This project is licensed under the MIT License.