AUTO-GIT Publisher – Autonomous Research-to-Code Pipeline

A multi-model autonomous agentic AI pipeline powered by LangGraph that turns simple ideas into working prototypes. This 19-node pipeline uses multiple AI models (Groq, OpenRouter, OpenAI, Anthropic, Ollama local fallback) with a hybrid router for 100% uptime. Personas are dynamically deployed according to the use case — only the main architecture flow is hard-coded, the execution path is entirely dynamic. Features a semantic cache using ChromaDB to prevent redundant LLM calls, a mutation testing engine that injects bugs via regex transformations to verify test robustness, and a strategy reasoner that classifies failures and forces new code paths when previous strategies have failed. Deployed live.

1. LangGraph State Machine: Directed acyclic graph (DAG) implementing cyclical processes for debate and self-healing. State defined as Python TypedDict with append-only event sourcing. 2. Hybrid Router (generate_with_fallback): Sequential circuit breaker pattern — Groq → OpenRouter → Local Ollama. Handles rate limits, timeouts, and internet drops seamlessly. 3. Consensus Selector: Three evaluation algorithms (Quality Scoring, Majority Vote, Ensemble Median) for selecting best output from parallel model generations. 4. Mutation Testing Engine: Regex-based bug injection (arithmetic swaps, logical flips, boolean inversions) to verify AI-generated test suites catch real bugs. 5. Strategy Reasoner: Classifies failures by type (architecture_flaw, wrong_api, syntax_error). Prevents stagnation by hashing strategies and forcing new paths on repeats. 6. Semantic Cache: ChromaDB vector storage with cosine similarity search. Bypasses LLM calls when similarity > 0.95, reducing cost and latency. 7. Self-Healing Loop: Automatic error detection, sandbox execution, rectification, and re-evaluation until quality threshold met.