pip install cognis-geoaoi
geoaoi scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ geoaoi-emit --version
geoaoi 0.1.0

$ geoaoi-emit --help
usage: geoaoi [-h] [--version] [--format {table,json}]
              {bbox,geofence,diff} ...

Area-of-interest GEOINT helper: bbox, geofence, change diffs.

positional arguments:
  {bbox,geofence,diff}
    bbox                compute bounding box of a coordinate log
    geofence            check point membership in a geofence
    diff                diff two coordinate logs into change events

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}

Blocks above are real geoaoi output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"feed_name": "My GeoAOI Feed",
"actor": {
"name": "John Doe"
},
"objects": [
{
"id": "1234567890",
"type": "indicator",
"count": 1,
"data": {
"indicator_types": ["IP"],
"values": ["192.168.1.100"]
}
}
]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install geoaoi
   

2. Compute the bounding box of a coordinate log (CSV, or - for stdin):

   geoaoi bbox track.csv
   

3. Check point membership against a geofence — either a polygon or a center+radius circle — and fail when points fall outside a required AOI:

   geoaoi geofence track.csv --center 38.89,-77.03 --radius 500 --require-inside
   geoaoi geofence track.csv --polygon "38.9,-77.1;38.9,-77.0;38.8,-77.0"
   

4. Diff two snapshots for enter/exit/move events past a movement threshold:

   geoaoi diff before.csv after.csv --threshold 50 --fail-on-change
   

5. Read the output: tables print per-point/per-event rows; add --format json (a top-level flag) for machine-readable output. geofence --require-inside and diff --fail-on-change exit 1 when a breach/change is detected — use that in CI or alerting:

   geoaoi --format json geofence track.csv --center 38.89,-77.03 --radius 500 \
       --require-inside > fence.json
   

Contents

• Why geoaoi? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why geoaoi?

Area-of-interest geospatial helper: bounding boxes, geofence checks, and change-event diffs from coordinate logs. — without standing up heavyweight infrastructure.

geoaoi is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Haversine M
• ✅ Point In Polygon
• ✅ Compute Bbox
• ✅ Geofence Check
• ✅ Diff Events
• ✅ Parse Points Csv
• ✅ Parse Polygon
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-geoaoi
geoaoi --version
geoaoi scan .                       # scan current project
geoaoi scan . --format json         # machine-readable
geoaoi scan . --fail-on high        # CI gate (non-zero exit)

Example

$ geoaoi scan .
  [HIGH    ] GEO-001  example finding             (./src/app.py)
  [MEDIUM  ] GEO-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[image / coordinates] --> P[geoaoi<br/>extract + geolocate]
  P --> OUT[location estimate]

Use it from any AI stack

geoaoi is interoperable with every popular way of using AI:

• MCP server — geoaoi mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe geoaoi scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis geoaoi	typical tools
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (geoaoi mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/geoaoi.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/geoaoi.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/geoaoi.git" # uv
pip install cognis-geoaoi                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/geoaoi:latest --help        # Docker
brew install cognis-digital/tap/geoaoi                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/geoaoi/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/geoaoi	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If geoaoi saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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