ProjectBuild an Agentic Content Studio: the explanation · Module 8: The agent pipeline with LangGraph · part 10 of 22
Part 10 · Module 8: The agent pipeline with LangGraph

Module 8: The agent pipeline with LangGraph

23 min read·
About 45 minutes · Needs: Module 2 (the solution: one prompt, three agents, one spec), Module 5 (the low-level design of the deck spec) · Code: backend/app/ai/pipeline.py, backend/app/ai/context.py

Where this fits

WHERE THIS FITS Slidesmith Studio this module BROWSER React FRONT DOOR AND API nginx, FastAPI BACKGROUND AGENTS LangGraph MODELS Ollama RENDERING Playwright DATA Postgres 17 OPERATIONS evals, Docker, Cloud Run Studio app React pages /render page same renderer nginx proxy, headers Auth sessions, CSRF Brand kit onboarding, uploads Projects deck spec, edits Job runner progress over SSE Research search, facts Strategist outline Designer slide JSON Check fit, else repair LangGraph pipeline state graph, JSON repair Ideas post ideas from pillars qwen3.8:27b Ollama, local GPU Web search Ollama API Chromium render, measure Exports PDF, PNG, ZIP, HTML PostgreSQL users, decks, jobs Uploads disk or GCS Evaluation suites, gate Insights usage, timings Containers Docker Compose Deploy Cloud Run, CI/CD
The pipeline (blue: this module) runs the three agents and the layout check in order, calls the model (amber) through each agent, and is started by the job runner.

The job runner starts a generation and hands it to the pipeline. The pipeline decides which agent runs when, what each one receives, and what happens when one of them fails.

The rest of this project is yours to keep

This project is bought on its own, once, and stays readable afterwards, including the parts added to it later.

Or open everything with a plan