# neural-maze/philoagents-course

When Philosophy meets AI

Repository: https://github.com/neural-maze/philoagents-course
Canonical: https://ross.abutalabs.com/products/philoagents-course
Language: Python
License: MIT
License Family: permissive
Topics: agent, agent-based-simulation, agentic-workflow, groq, langgraph, mongodb, opik, rag
Last push: 2025-10-20T19:13:27+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 48, release rhythm 35, longevity 42
- inputs: {"age_days": 601, "days_push": 317, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1537, forks 329 (observed 2026-08-28T04:05:00.049075+00:00)

## What it is
An open-source course teaching how to build an AI-powered game simulation engine where agents impersonate historical philosophers like Plato and Turing. It covers agentic RAG systems, LLMOps, and production engineering using LangGraph, Groq, MongoDB, Opik, FastAPI, and Docker.

## Use cases
- learn to build AI agents for game NPCs
- build an agentic RAG system from scratch
- simulate philosopher characters in a game
- learn LLMOps best practices with LangGraph
- deploy an AI agent as a RESTful API
- create AI-powered character simulation engines

## When to choose
- you want a hands-on course on building agentic applications
- you want to learn production RAG and LLMOps patterns
- you want to add AI-driven NPCs to a game
- you prefer learning with real tools like LangGraph, MongoDB, and Groq

## When to avoid
- you need a production-ready game engine rather than a course
- you want a no-code or beginner AI tutorial
- you need non-Python or non-LLM agent frameworks
- you only want a library to import rather than educational material

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, rag, llm-inference, machine-learning
- domain: artificial-intelligence, large-language-models, education, tutorials
- platform: python, self-hosted
- tags: agentic-workflow, langgraph, llmops, npc-simulation, philosophy, course, fastapi, groq, mongodb, opik, ai-agents, retrieval-augmented-generation, game-development, docker

## Member repositories
- neural-maze/philoagents-course (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.049075+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:31:01.461761+00:00, confidence not recorded.
  - readme: https://github.com/neural-maze/philoagents-course (fetched 2026-08-28T04:05:00.049075+00:00, sha 75b62b977bd6)
- Data as of 2026-08-30T08:39:29.467469+00:00.