Observed Signal · Aug 13, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive

Multi-Agent AI Pipeline Ships: LangGraph + RAG Lessons

Executive Signal Summary

An engineer describes building and deploying Doc2Slides, a live tool that converts PDFs into audience-tailored PowerPoint decks using a state-based multi-agent pipeline implemented with LangGraph. The pipeline comprises five agents (parser, summarizer, planner, writer, builder) and uses RAG with ChromaDB, OpenAI's GPT-4o-mini, FastAPI, and PostgreSQL on Railway. The author shares evaluation results (parser evals 100%, summarizer avg 4.4/5, RAG top-1 precision 42%), engineering tradeoffs (avoiding word-count heuristics, SQLite→Postgres dev/prod flow), and deferred work (hierarchical retrieval, content-aware slide allocation, multi-language support). The project source code and live demo are published.

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High Confidence

A practical technical write-up demonstrating a state-based multi-agent LLM pipeline, RAG evaluation insights, and engineering tradeoffs—useful to engineers building production GenAI tools but not industry-shifting.

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Key Takeaways & Evidence Grounding

  • Doc2Slides is a deployed tool that converts PDFs to audience-tailored .pptx presentations and is live on Railway.
  • The system uses a state-based multi-agent pipeline implemented in LangGraph with five agents: parser, summarizer (RAG), planner, writer, and builder.
  • Evaluation results: parser evals scored 100% (34/34), summarizer evals averaged 4.4/5, and RAG top-1 precision measured 42% (top-3 precision 57%).
  • Technology stack includes Python 3.13, FastAPI, OpenAI GPT-4o-mini, ChromaDB (vector DB), SQLAlchemy + PostgreSQL, and deployed via Railway.
  • Source code is published at github.com/manasviboineypally/doc2slides and a live demo is available at the Railway deployment URL.

Connected Companies & Entities

3 Entities mapped

“For Railway, the environment variable is: DATABASE_URL=postgresql+psycopg2://postgres:xxx@host:5432/railway”

“LLM | OpenAI GPT-4o-mini | Cheap enough for iteration, smart enough for structured output”

Ontology Mapping & Concepts

Large Language Models (LLM) & AICloud Data Warehouse / Data Lake#LangGraph#RAG top-1 precision#state-based multi-agent pipeline
Primary Source Grounding & Direct Attribution
Direct Origin Attribution
Primary Reporting: DEV Community•Published: Aug 13, 2026
Original Coverage Title: “Building a Multi-Agent AI Pipeline That Ships: LangGraph, RAG, and Evals That Matter”

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