Memtriage

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ memtriage-emit --version
MEMTRIAGE 1.0.0

$ memtriage-emit --help
usage: memtriage [-h] [--version] {triage} ...

MEMTRIAGE v1.0.0 — defensive memory-dump triage.

positional arguments:
  {triage}
    triage    Triage a memory-dump export.

options:
  -h, --help  show this help message and exit
  --version   show program's version number and exit

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

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

{
"Findings": [
    {
        "id": "123456",
        "title": "Suspicious Network Traffic",
        "description": "Potential malicious activity detected on port 443.",
        "mitre_attack_id": ["T1566.001"],
        "kill_chain_phases": ["Initial Access", "Persistence"],
        "created_at": "2023-02-15T14:30:00Z"
    }
]
}

Usage — step by step

memtriage performs defensive triage of a memory-dump export — extracting printable strings and flagging findings by severity. Console script: memtriage (or python -m memtriage).

1. Install from a clone:

   pip install -e .
   

2. Triage a dump export (use - to read from stdin):

   memtriage triage dump.bin
   

3. Write a shareable HTML report and tune the minimum string length:

   memtriage triage dump.bin --format html --min-len 6 -o report.html
   

4. Read the output — --format json is pipeline-friendly:

   memtriage triage dump.bin --format json | jq '.max_severity, .findings'
   

   Exit codes: 0 nothing at/above --fail-on, 2 findings present, 3 usage/IO error.
5. Automate — gate an incident pipeline on high-severity hits:

   memtriage triage dump.bin --fail-on high --format json > triage.json
   

Contents

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

Why memtriage?

Triage memory-dump artifacts: strings, IOCs, suspicious processes from a dump export — without standing up heavyweight infrastructure.

memtriage 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

• ✅ Extract Strings
• ✅ Analyze
• ✅ Render Json
• ✅ Render Table
• ✅ Render Html
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[disk / memory artifact] --> P[memtriage<br/>parse]
  P --> OUT[timeline + IOCs]

Use it from any AI stack

memtriage is interoperable with every popular way of using AI:

• MCP server — memtriage mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe memtriage 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 memtriage	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 (memtriage 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/memtriage.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/memtriage.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/memtriage.git" # uv
pip install cognis-memtriage                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/memtriage:latest --help        # Docker
brew install cognis-digital/tap/memtriage                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/memtriage/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/memtriage	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 memtriage 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.

---