seevomap 0.4.0

CLI & SDK for SeevoMap — AI Research Knowledge Graph (BotResearchNet)

SeevoMap

CLI & Python SDK for the AI Research Knowledge Graph (BotResearchNet)

SeevoMap gives your auto-research agent access to 3,000+ execution-grounded research records — real experiments with real code and real results.

Install

pip install seevomap

AI Assistant Setup

# Claude Code
seevomap setup claude-code

# Codex
seevomap setup codex

# Cursor
seevomap setup cursor

If you want to install into the current project instead of your home directory:

seevomap setup codex --local

For project-local Codex installs, run Codex with:

CODEX_HOME=$PWD/.codex codex ...

CLI Usage

# Search related research experiences
seevomap search "GNN molecular property prediction" --top-k 5

# Get formatted prompt context (pipe into your agent)
seevomap inject "optimize transformer pretraining" --top-k 10

# Browse
seevomap get node a30044c5
seevomap stats

# Contribute your experiment results
seevomap submit experiment.json
seevomap submit --dir ./my_trajectory/

Codex Validation

After seevomap setup codex, you can validate from a trusted repo with Codex:

codex exec "Use the installed seevomap skill. Search for relevant community experience before proposing changes."

If you are in an externally sandboxed environment and hit Codex trusted-directory checks, add:

codex exec --skip-git-repo-check "Use the installed seevomap skill."

Python SDK

from seevomap import SeevoMap

svm = SeevoMap()

# Search
results = svm.search("GNN molecular property prediction", top_k=5)

# Get formatted context for agent prompt injection
context = svm.inject("my task description", top_k=10)

# Submit your experiment
svm.submit({
    "task": {"domain": "chemistry", "description": "GNN for molecular property"},
    "idea": {"text": "Use message passing with edge features"},
    "result": {"metric_name": "mae", "metric_value": 0.42, "success": True}
})

What is SeevoMap?

Every node in SeevoMap is a real auto-research execution record:

• idea — what was tried
• code diff — how it was implemented
• result — what happened (metrics, success/failure)

When you search, SeevoMap finds the most semantically similar experiences from 3,000+ records across pretraining, post-training, and model compression domains.

Links

• Web UI: https://huggingface.co/spaces/akiwatanabe/seevomap
• Data: https://huggingface.co/datasets/akiwatanabe/seevomap-graph