Introduction

DebuggAI is an AI‑enhanced application‑monitoring and debugging platform that closes the gap between writing code and fixing it. By combining lightweight runtime instrumentation with purpose‑built AI agents, DebuggAI detects, diagnoses, and recommends (or automatically applies) fixes for errors before they ever reach production.

What DebuggAI Does

Problem	Traditional tools	DebuggAI advantage
Time‑consuming “bug‑hunt” loop between coding and test runs	Logging, APM, Sentry‑style dashboards surface symptoms but rarely root causes	1‑line SDK captures deep runtime context → LLM‑powered agents trace failures to the exact file, line, or API call and propose fixes
Fragmented context across logs, stack traces, and repo	Manual jumping between editor, CI, and monitoring tools	Unified event model pipes structured data into the IDE for instant, inline insights
AI code assistants lack runtime awareness	Copilot/Cursor help write code, but not debug it	DebuggAI agents ingest live telemetry, making code suggestions aware of the actual error state

High‑Level Architecture

┌────────────────┐      SDK (1‑line)       ┌────────────────────┐
│  User Codebase │──── emit JSON events ──►│ DebuggAI Ingest API│
└────────────────┘                         └─────────┬──────────┘
                                                     │
                              Async workers enrich   │
                              with traces & metadata │
                                                     ▼
                                           ┌─────────────────────┐
                                           │ Vector / SQL stores │
                                           └─────────┬───────────┘
                                                     │
                               Context + code maps   │
                                                     ▼
                                      ┌─────────────────────────┐
                                      │  AI Diagnosis Agents    │
                                      └─────────┬───────────────┘
                                                │
                                     Proposed fix / RCA comment
                                                ▼
                              Surfaced inline in IDE (VS Code / JetBrains)

Core Components

1. Runtime SDK – < 40 KB client that auto‑collects stack traces, env vars, HTTP payloads, and breadcrumb logs.

2. Ingest & Processing – Django REST + Celery pipeline normalizes events, generates embeddings, and stores data in Postgres + LanceDB.

3. Repo Mapping Service – Syncs user repositories via GitHub App & build source maps to align stack traces with exact commit hashes and source lines.

4. AI Agent Orchestrator –

   • Retrieve relevant code snippets (RAG)
   • Read vector‑embedded error fingerprints
   • Produce a Root‑Cause Analysis + patch diff or PR

5. IDE Extensions – VS Code & JetBrains plugins that surface agent output as inline code lenses, diagnostics, and quick‑fix actions.

Roadmap Goals

Milestone	Target Date	Notes
Auto‑fix PR generation	May 2025	End‑to‑end flow for Python & Node repos
Live error replay in browser	July 2025	Deterministic input capture + session re‑hydration
Language coverage ≥ 6	September 2025	Add Java, Go, Ruby, PHP
Org‑wide analytics dashboard	November 2025	Error MTTR trends, agent ROI metrics

When to Use DebuggAI

• You already log errors but want actionable fixes, not just alerts.
• CI passes locally but fails in staging—DebuggAI pinpoints the drift.
• You ship rapidly and need AI guardrails that understand your codebase, environment, and data.

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DebuggAI turns every error report into a pull request, shrinking the feedback loop from hours to minutes. Drop in the SDK, connect your repo, and let the agents handle the rest.