# An Open-Source Framework for Emergent Multi-Agent Social Simulations > "This serves as a **complex** mirror to real-world systems of power, belief, and **emergent social behavior**.” The architecture leverages **LangGraph** for cycle management and **ChromaDB** for persistent storage. It runs locally on consumer-grade hardware by utilizing **Gemma** models for their high intelligence-to-parameter ratio. Interventions are managed asynchronously through a **Human-in-the-Loop (HITL)** interface, enabling real-time **state injections** that influence the simulation's trajectory via a Telegram-based command protocol. ### **Persistence and Long-Context Management** To achieve infinite uptime, the framework implements **hierarchical recursive summarization**. This ensures the "World Engine" maintains historical awareness without exceeding context limits, while **LangGraph checkpointers** provide durability across system reboots. * * * ### **Orchestration: LangGraph and the Router Pattern** The backbone of this infinite loop is **LangGraph**, which facilitates a stateful, cyclic graph architecture. It allows nodes to represent distinct agents—such as the Logic Engine and Narrative agents—while edges define the flow of information through a persistent **Observe-Think-Act** loop. To bypass the strict limits of consumer cards, the framework utilizes **llama.cpp’s router mode**. This implementation uses a **Least Recently Used (LRU) eviction policy**, dynamically swapping models between the GPU and the **system RAM**. ![](https://cdn.hashnode.com/uploads/covers/69d10b786792e486f66795a1/afb009a9-ff32-45b3-aa7e-65f502d62f34.png align="center") ### **The Quantization Standard** By reducing model weights from 16-bit to 4-bit, we significantly lower the VRAM footprint—allowing a 9B model that would normally require **18GB** to fit into just **5.8GB** of VRAM, thus maintaining high reasoning capabilities on a local workstation. This precision reduction preserves the vast majority of the model's emergent behavior while leaving critical headroom for the **KV Cache**, which stores the context of the current conversation. For extended simulations, we further utilize **4-bit KV cache quantization** to reduce context penalty by up to **50%**, enabling deeper historical awareness within the hardware's physical limits. ### **VRAM Reference by Model and Quantization** *These figures are planning ranges for local use. Real usage will be slightly higher once the runtime, context window, and KV cache are active.*

Model	Q2	Q4 (Default)	Q5	FP16	Practical Application
E2B	~0.8 GB	~1.5 GB	~1.8 GB	~4 GB	Minimal agents; CPU-heavy setups.
E4B	~2 GB	~3.5 GB	~4.2 GB	~8 GB	Standard starting point for local agents.
26B A4B	~9 GB	~14 GB	~17 GB	~52 GB	High-tier social complexity; requires 24GB GPU.
31B	~12 GB	~20 GB	~24 GB	~62 GB	Maximum local reasoning; 24GB VRAM required.

## Join the Experiment. This is an invitation to build your own scenarios—whether they be utopian social experiments or harrowing studies of civilizational collapse. %[https://soundcloud.com/mihai-vancea-909027674/building-persistent-ai]