synvo-ai

synvo-ai / HippoCamp

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A benchmark for evaluating contextual agents on realistic multimodal personal-computer environments with profiling and factual-retention tasks.

19
0
100% credibility
Found Apr 04, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

HippoCamp is a benchmark with realistic personal computer file environments and evaluation tools for testing AI agents on multimodal search, retrieval, and reasoning tasks.

How It Works

1
🔍 Discover HippoCamp

You find this benchmark on GitHub or its project page and learn it's for testing AI helpers on everyday computer files like documents, photos, emails, and videos.

2
📥 Download sample computers

Grab ready-made collections of personal files from the dataset links, including small subsets to start quickly.

3
🚀 Set up your playground

Install a simple Python environment and prepare the file collections so everything is ready to test.

4
🐳 Launch a simulated computer

Start one of the personal computer setups with a single command, opening a safe Docker world full of realistic files.

5
🤖 Pick your AI helper

Choose from ready agents like ChatGPT, Claude, or Gemini to explore and answer questions about the files.

6
💬 Ask questions

Pose natural questions like 'What was my travel plan last month?' and watch the AI search files, read contents, and reason step-by-step.

📊 See scores and insights

Get automatic evaluations, compare agents on leaderboards, and understand strengths in search, perception, and reasoning.

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Star Growth

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AI-Generated Review

What is HippoCamp?

HippoCamp is a Python benchmark for testing contextual AI agents in realistic personal-computer setups packed with 42.4 GB of multimodal files—docs, images, audio, video, emails, calendars—like a real user's desktop. It delivers three user profiles with 2K+ files, 581 QA pairs split into factual retention (retrieve/reason over info) and profiling (infer user models from scattered evidence), plus 46K trajectory annotations to diagnose search, perception, and reasoning fails. Download datasets from Hugging Face, spin up Docker environments, and run evaluations via RAG pipelines or terminal agents.

Why is it gaining traction?

Unlike toy benchmarks, HippoCamp mirrors everyday PC chaos with time-stamped files across modalities, exposing agent weaknesses in multi-step retrieval and evidence aggregation—key for hippocampus-like memory tasks in LLMs. Developers dig the ready-to-run Docker images, batch eval scripts for ChatGPT/Claude/Gemini, and LLM-as-judge scoring, plus analysis plots for difficulty vs. performance. It's a github benchmark goldmine for evaluating large language models on chinese asr error correction or pedestrian action prediction analogs in desktop contexts.

Who should use this?

AI researchers benchmarking multimodal agents for desktop assistants, like those building RAG for personal data search. Teams evaluating benchmark underperformance in factual retention or profiling, especially for earthsea-style scientific exploration or 3d benchmark tasks. LLM devs probing github copilot rivals on gpu-heavy multimodal QA.

Verdict

Grab it if you're evaluating contextual agents—strong paper, HF dataset, and repro docs make setup straightforward despite 19 stars and 1.0% credibility signaling early maturity. Skip for production; it's a solid starting point for custom benchmarks.

(198 words)

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