RECALL

Privacy-first local RAG over personal data — encrypted, audit-logged

Privacy / Personal — put individuals back in control of their data.

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ recall-emit --version
recall 0.1.0

$ recall-emit --help
usage: recall [-h] [--version] [--format {table,json}] [--vault VAULT]
              [--passphrase PASSPHRASE]
              {relevant,add,audit} ...

Privacy-first local RAG over personal data.

positional arguments:
  {relevant,add,audit}
    relevant            retrieve most relevant documents
    add                 add a document to the vault
    audit               show / verify the audit log

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

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

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

{
"findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Traffic",
        "description": "Potential malicious activity detected on port 443.",
        "created_by": "John Doe",
        "created_at": "2023-02-16T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Unusual File Access",
        "description": "An unknown process accessed a sensitive file.",
        "created_by": "Jane Smith",
        "created_at": "2023-02-17T10:45:00Z"
    }
]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install recall            # or: pipx install recall

2. Add documents to your encrypted vault. Provide a title plus inline text or a file, and optional tags. The vault path defaults to recall.vault (override with --vault or RECALL_VAULT):

   recall add "Q1 notes" --file notes.md --tag work --tag planning

3. Retrieve the most relevant documents for a query (-k controls how many):

   recall relevant "runway and burn rate" -k 5

4. Get machine-readable output for piping into another tool:

   recall --format json relevant "runway and burn rate" -k 3

5. Read the result. relevant returns the top-k matching documents ranked by relevance; everything stays local and encrypted in the vault.

6. Verify integrity via the tamper-evident audit log:

   recall audit

Contents

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

Why recall?

Privacy-first local RAG over personal data — encrypted, audit-logged — without standing up heavyweight infrastructure.

recall 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.

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Features

• ✅ Derive Key
• ✅ Add Document
• ✅ Relevant
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

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

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Example

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

  2 findings · risk score 5 · 38ms

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Architecture

flowchart LR
  IN[target / manifest] --> P[recall<br/>checks + rules]
  P --> OUT[findings (JSON / SARIF)]

Loading

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Use it from any AI stack

recall is interoperable with every popular way of using AI:

• MCP server — recall mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe recall 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

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How it compares

	Cognis recall	ollama
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

Built in the spirit of ollama/ollama, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

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

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/recall.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/recall.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/recall.git" # uv
pip install cognis-recall                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/recall:latest --help        # Docker
brew install cognis-digital/tap/recall                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/recall/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/recall	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• optout — Automated data-broker opt-out engine — top 50 brokers, CCPA/GDPR letters
• vaultmap — Personal asset & account inventory — estate-planning-grade encrypted
• breachwatch — Personal breach aggregator — HIBP + DeHashed + stealer-log triage
• piicomb — Local PII discovery in your own files — SSN/CC/passport/DL/email/phone/DOB
• trackblock — Family phone stalkerware audit — MVT-class iOS/Android forensics
• privacyshell — Hardened browser profile generator — Firefox / LibreWolf / Brave

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

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Contributing

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

⭐ If recall 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 (licensing@cognis.digital). See LICENSE.

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_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}