# osama-fawad/Pekingman

Repository: https://github.com/osama-fawad/Pekingman
Canonical: https://ross.abutalabs.com/products/pekingman
Homepage: https://osama-fawad.github.io/Pekingman/
Language: HTML
License Family: other
Last push: 2026-07-07T21:03:27+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 35, longevity 4
- inputs: {"age_days": 57, "days_push": 57, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1175, forks 79 (observed 2026-08-28T04:03:51.939933+00:00)

## What it is
Pekingman is a multimodal AGI-oriented agent system that connects environmental perception, long-term memory, human-like reasoning, emotional consistency, and real-time behavioral response in one continuous agent loop. It targets lifelike virtual characters (NPCs, digital humans, metaverse avatars) with Unity-facing execution and a web monitoring panel.

## Use cases
- build lifelike NPCs with persistent long-term memory for simulation games
- create digital humans with consistent emotional expression across long interactions
- run embodied agents that sense and react to changing virtual environments
- prototype AGI-oriented agent loops with perception, memory, planning, and action modules
- connect agent decisions to Unity-based interactive simulations
- monitor agent module activity and shared state via a web panel

## When to choose
- you need NPCs or virtual characters with memory continuity and believable behavior over long-running sessions
- you want a modular research stack for embodied, multimodal agent experimentation
- your project uses Unity or simulation platforms and needs an agent decision layer

## When to avoid
- you need a production-ready, licensed, well-supported framework (no license is provided)
- you want a lightweight game AI solution for low-complexity worlds where scripted behavior suffices
- you need a mature, battle-tested agent framework with community support

## Facets
- artifact type: framework
- maturity: experimental
- function: agent-framework, machine-learning, nlp, simulation, monitoring
- domain: artificial-intelligence, simulation
- platform: cross-platform, game-engine
- tags: embodied-ai, npc-behavior, long-term-memory, multimodal-perception, emotional-consistency, unity-integration, digital-humans, agi-research, ai-agents, game-development, natural-language-processing, web-server

## Member repositories
- osama-fawad/Pekingman (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:51.939933+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 2026-08-30T06:28:14.009365+00:00, confidence not recorded.
  - readme: https://github.com/osama-fawad/Pekingman (fetched 2026-08-28T04:03:51.939933+00:00, sha 957d7158906c)
  - homepage: https://osama-fawad.github.io/Pekingman/ (fetched 2026-08-29T12:33:10.596945+00:00, sha 472efea41586)
- Data as of 2026-08-30T08:39:29.467469+00:00.