The Living Agent

May 3, 2026 · View on GitHub

Autonomous Chess-Grid research engine powered by a local LLM (KoboldCPP + Qwen). The agent walks a 16x16 grid of interconnected Markdown knowledge cells, accumulates context, writes a short synthesis paper at the far edge, scores its novelty against prior output, and updates a persistent soul.md identity file. One cycle in, one cycle out, forever.

Part of the P2PCLAW ecosystem. This is the Series II white-paper / reference implementation of an autonomous research agent in the P2PCLAW cognitive stack. For the full project overview, ecosystem map, and the v6.0 production paper, see Agnuxo1/OpenCLAW-P2P (the front door).

Prerequisites

The agent does not ship a model. It talks to a local KoboldCPP HTTP server (default http://localhost:5001/api/v1/generate). You must bring your own runtime and weights:

KoboldCPP — download the latest release from LostRuins/koboldcpp.
A GGUF model. The project was developed against unsloth/Qwen3.5-9B-GGUF (the UD-Q3_K_XL quant, ~5 GB). Any Kobold-compatible GGUF with a decent context window will work.

Launch KoboldCPP, load the model, expose it on port 5001.

Install

pip install living-agent

Or from source:

git clone https://github.com/Agnuxo1/The-Living-Agent
cd The-Living-Agent
pip install -e ".[dev]"

Quickstart (3 commands)

living-agent init --grid-dir .                   # generates knowledge/grid + knowledge/grid_index.md
living-agent run  --cycles 1 --endpoint http://localhost:5001/api/v1/generate
living-agent status --grid-dir .

run reads soul.md (creating a default one if missing), walks the grid, emits a paper under memories/semantic/paper_<N>.md, appends an episodic record under memories/episodic/cycle_<N>.md, and atomically updates soul.md.

How the Chess-Grid works

256 cells, each a Markdown file cell_R<row>_C<col>.md.
8 directions per cell (N, NE, E, SE, S, SW, W, NW); edges and corners get fewer links.
Entry row (R0) and synthesis row (R15); a mutation chamber at the centre; occasional skill and experiment nodes.
The agent enters at a random R0 column, picks a direction per cell by asking the LLM, and stops when it either hits R15 or saturates ~85% of the context window.
Novelty is a Jaccard-overlap-based Semantic Novelty Score against the last 50 papers on disk.

Python API

from living_agent import LivingAgent, KoboldClient, generate_grid

generate_grid("knowledge", rows=16, cols=16, seed=0)
agent = LivingAgent(base_dir=".", client=KoboldClient("http://localhost:5001/api/v1/generate"))
result = agent.run_cycle()
print(result["cycle"], result["sns"], result["paper_bytes"])

Honest limitations

Paper output is short. With the default Qwen 9B quant and a 2048-token completion budget, generated papers are typically a few hundred bytes — not a full multi-section publication. No post-processing is applied to inflate them.
Context window is bounded by the server. The client advertises 128k, but effective context depends on what KoboldCPP negotiates with the model.
Synchronous only. One cycle at a time; no asyncio, no batching, no multi-agent orchestration.
No automatic model download. You have to fetch the GGUF manually and start KoboldCPP yourself — the package just speaks HTTP.
No network in tests. The test suite mocks KoboldCPP with an in-process http.server; running the real agent still requires a live endpoint.

Development

pip install -e ".[dev]"
pytest                       # 23 tests
python -m build              # wheel + sdist into dist/

Layout:

src/living_agent/
    __init__.py      # version, re-exports
    grid.py          # 16x16 grid generator, cell topology
    llm_client.py    # KoboldCPP HTTP client
    agent.py         # reasoning loop, soul.md state, SNS scoring
    cli.py           # `living-agent {init,run,status}`
tests/
    test_grid.py     # 10 tests
    test_agent.py    # 8 tests (in-process fake HTTP server)
    test_cli.py      # 5 tests

License & credits

Apache-2.0. Created by Francisco Angulo de Lafuente as the Silicon Layer of P2PCLAW. Inspired by Karpathy's autoresearch.

Part of the @Agnuxo1 v1.0.0 open-source catalog (April 2026).

AgentBoot constellation — agents and research loops

AgentBoot — Conversational AI agent for bare-metal hardware detection and OS install.
autoresearch-nano — nanoGPT-based autonomous ML research loop.
benchclaw-integrations — Agent-framework adapters for the BenchClaw API.

CHIMERA / neuromorphic constellation — GPU-native scientific computing

NeuroCHIMERA — GPU-native neuromorphic framework on OpenGL compute shaders.
Holographic-Reservoir — Reservoir computing with simulated ASIC backend.
ASIC-RAG-CHIMERA — GPU simulation of a SHA-256 hash engine wired into a RAG pipeline.
QESN-MABe — Quantum-inspired Echo State Network on a 2D lattice (classical).
ARC2-CHIMERA — Research PoC: OpenGL primitives for symbolic reasoning.
Quantum-GPS — Quantum-inspired GPU navigator (classical Eikonal solver).

🧩 P2PCLAW Ecosystem

This project is part of P2PCLAW — a distributed AI research network with production-grade benchmarking, agent tooling, and model distribution.

Component	Role	Link
OpenCLAW-P2P	Core protocol · Lean 4 proofs · Papers	github.com/Agnuxo1/OpenCLAW-P2P
BenchClaw	17-judge agent benchmarking	github.com/Agnuxo1/benchclaw
EnigmAgent	Local encrypted vault for credentials	github.com/Agnuxo1/EnigmAgent
AgentBoot	Bare-metal OS installer	github.com/Agnuxo1/AgentBoot
CAJAL	4B research LLM for papers	huggingface.co/Agnuxo/CAJAL-4B-P2PCLAW

🌐 Main website: https://www.p2pclaw.com/ 📄 Paper: arXiv:2604.19792

💝 Support

If this tool is useful to you:

⭐ Star the repo — it's how the ecosystem discovers tools
🐛 Open an issue — every real use case sharpens the project
💰 Sponsor: github.com/sponsors/Agnuxo1

Built by Francisco Angulo de Lafuente — independent researcher with 35+ years in software.