Agent Skills for Production LangGraph Agents

Demo from the Medium article: Stop Stuffing Your System Prompt: Build Scalable Agent Skills in LangGraph.

Demonstrates progressive knowledge loading, skill-based domain modularization, and tool-driven skill activation.

Note: This code has evolved beyond the version published with the article. Notable upgrades:

• Introduced a reusable async BaseAgent (graphs/core/) with LLM retry classification (transient vs permanent), tool-call pairing safety, and Langfuse-managed prompts.
• Split skills_agent into typed state + slim nodes, replacing the original monolithic utils/nodes.py.
• Stopped vendoring the Aegra runtime — the FastAPI server, persistence, and migrations are now installed via aegra-cli, keeping only the graphs and tests in this repo.

The Agent Skills concepts in the article still apply; the surrounding implementation has been hardened and simplified.

How it runs

This repo is a thin overlay on top of Aegra — the FastAPI server, persistence, and migrations are installed via aegra-cli. The local code is just:

aegra.json          # graph registration
graphs/core/        # reusable BaseAgent
graphs/skills_agent/ # the demo agent + skills
tests/              # unit tests for graphs/

make install pulls the latest aegra-cli. The repo was last verified against 0.7.2 (see the comment in pyproject.toml). Don't vendor upstream source here — patch upstream and re-run make install instead.

Quick start

make install
cp .env.example .env  # set OPENAI_API_KEY
make dev              # runs on http://localhost:4242

Tests

make test

Credits

• LangGraph by LangChain
• Aegra by Muhammad Ibrahim
• n8n Skills Repository by haunchen