ColabGPU Agent Lab

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A GPU-accelerated, fully reproducible agent research lab that runs entirely inside a single Google Colab notebook. The goal is to make agent research measurable, deterministic, and GPU-native while staying lightweight enough to run on free or Pro Colab GPUs.

Why this exists

Most agent frameworks are CPU-bound, opaque, and hard to reproduce. ColabGPU Agent Lab is the opposite:

• Colab-first: one-click runnable, no local setup.
• GPU-accelerated where it matters: embeddings, memory search, planning rollouts, and simulation.
• Deterministic benchmarks: fixed seeds and metrics you can trust.
• Notebook-as-a-paper: structured like a research artifact you can export.

What you get (initial roadmap)

1) GPU-Accelerated Cognitive Stack

A clear, inspectable dataflow with explicit GPU offload targets.

[Perception] → [GPU Memory] → [Planner] → [Tool Exec] → [Reflection]

Planned GPU usage:

• FAISS-GPU for memory similarity search.
• GPU embeddings for rapid context retrieval.
• Vectorized rollouts for planning and simulation.

2) Agent Stress-Test Suite

A set of deterministic, GPU-batched cognitive benchmarks:

Test	What it Measures
Tool Maze	Tool selection reasoning
Memory Drift	Long-horizon recall
Deception Detection	Self-consistency
Recursive Planning	Depth vs compute
Energy Budget	Reasoning efficiency

Each test outputs:

• Seeded run artifacts
• Metrics (accuracy, cost proxy, step counts)
• Plots for quick comparison

3) Live GPU Telemetry Overlay

A lightweight, notebook-native telemetry panel to monitor:

• GPU memory
• Tokens/sec (or tokens/step)
• Planning depth
• Memory growth
• Cost proxy

4) Notebook-as-a-Paper

A single notebook structured as:

1. Abstract
2. Method
3. Experiments
4. Results
5. Reproducibility

Export to PDF to get a research-ready artifact.

Proposed repo structure

colabgpu-agent-lab/
├── notebooks/
│   └── colabgpu_agent_lab.ipynb
├── src/
│   ├── agents/
│   ├── benchmarks/
│   ├── memory/
│   ├── planner/
│   ├── telemetry/
│   └── utils/
├── assets/
│   └── figures/
├── data/
│   └── seeds/
└── README.md

Suggested stack (GPU-friendly)

• PyTorch + CUDA for compute
• FAISS-GPU for memory retrieval
• cuDF/cuML (optional) for fast metric aggregation
• Plotly or Altair for notebook-native plots
• NVML (via pynvml) for GPU telemetry

First benchmark suite (v0)

1. Tool Maze
   • Tiny deterministic environment with tools and rewards
   • Measures decision quality under tool constraints
2. Memory Drift
   • Sliding-window tasks with long-horizon recall
   • Measures retention vs. compute budget
3. Recursive Planning
   • Depth-limited tree search with known optimal solutions
   • Measures quality vs. planning depth

Quick start (planned)

1. Open the notebook in Colab.
2. Run the setup cell to install GPU dependencies.
3. Select a benchmark and an agent policy.
4. Run experiments and export results.

Status

This repository is a design and roadmap starter for the full Colab notebook and benchmark harness.

If you want me to proceed, I can:

• Generate the notebook skeleton
• Implement the first benchmark environments
• Add the GPU telemetry overlay
• Set up deterministic experiment exports