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MEMORYBANK

Portable long-term memory store for agents, exposed over MCP

PyPI CI License: COCL 1.0 Suite

AI Agents & LLMOps โ€” build, route, evaluate, and secure agents.

pip install cognis-memorybank
memorybank scan .            # โ†’ prioritized findings in seconds

๐Ÿ”Ž Example output

Real, reproducible output from the tool โ€” runs offline:

$ memorybank-emit --version
memorybank 0.1.0
$ memorybank-emit --help
usage: memorybank [-h] [--version] [--format {table,json}] [--path PATH]
                  {remember,recall,forget,list,stats} ...

Portable agent memory store.

positional arguments:
  {remember,recall,forget,list,stats}
    remember            store a new memory
    recall              retrieve memories ranked by a query
    forget              delete a memory by id
    list                list every memory
    stats               show bank statistics

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
  --path PATH           path to the JSONL memory bank
$ memorybank-emit stats
{
  "count": 0,
  "halflife_days": 14.0,
  "path": "C:\\Users\\user\\cognis-demo\\memorybank\\memorybank.jsonl",
  "tags": {},
  "total_accesses": 0
}

Blocks above are real memorybank output โ€” reproduce them from a clone.

Related MCP server: Clark MCP Server

Usage โ€” step by step

  1. Install (Python 3.8+, stdlib only):

    pip install memorybank

    The store is a single JSONL file (default memorybank.jsonl, override with --path or MEMORYBANK_PATH).

  2. Remember a fact, optionally tagged and weighted:

    memorybank remember "User prefers metric units" --tag prefs --importance 2.0
  3. Recall the most relevant memories for a query (ranked, recency-aware):

    memorybank recall "what units?" --limit 5 --tag prefs

    Add --no-touch to retrieve without updating recency.

  4. Read the output โ€” every command emits JSON by default; switch to a human table:

    memorybank --format table list
    memorybank stats                 # bank-wide counts/metrics
    memorybank forget <id>           # delete one memory by id
  5. Drive it from an agent loop / CI โ€” point each session at its own bank file:

    export MEMORYBANK_PATH=./agent_state/memory.jsonl
    memorybank recall "$TASK" --limit 8 | jq -r '.[].text'

    Exits non-zero on error so callers can detect failures.

Contents

Why memorybank?

agent-memory niche

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

Features

  • โœ… Fast, single-purpose CLI

  • โœ… JSON / SARIF output for pipelines

  • โœ… CI fail-gate (--fail-on)

  • โœ… MCP server for AI agents

  • โœ… Runs on Linux/macOS/Windows ยท Docker ยท devcontainer

  • โœ… Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings ยท risk score 5 ยท 38ms

Architecture

flowchart LR
  IN[MCP server] --> P[memorybank<br/>inspect]
  P --> OUT[findings / policy]

Use it from any AI stack

memorybank is interoperable with every popular way of using AI:

  • MCP server โ€” memorybank mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)

  • OpenAI-compatible / JSON โ€” pipe memorybank scan . --format json into any agent or LLM

  • LangChain ยท CrewAI ยท AutoGen ยท LlamaIndex โ€” wrap the CLI/JSON as a tool in one line

  • CI / scripts โ€” exit codes + SARIF for non-AI pipelines

How it compares

Cognis memorybank

agent memory

Self-hostable, no account

โœ…

varies

Single command, zero config

โœ…

โš ๏ธ

JSON + SARIF for CI

โœ…

varies

MCP-native (AI agents)

โœ…

โŒ

Polyglot ports (JS/Go/Rust)

โœ…

โŒ

Open license

โœ… COCL

varies

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

Integrations

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

Install โ€” every way, every platform

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

DEPLOY.md (AWS/Azure/GCP/k8s)

  • agentsmith โ€” Config-first scaffolding and orchestration for multi-agent workflows

  • skillhub โ€” Local skill registry and installer for AI agents

  • toolguard โ€” Runtime allowlist and policy for agent tool-calls

  • evalbench โ€” Offline LLM / agent eval harness with regression gates

  • ragkit โ€” Batteries-included local RAG pipeline โ€” ingest, index, serve

  • promptpack โ€” Versioned prompt / template registry with A/B and rollbacks

Explore the suite โ†’ ๐Ÿ—‚๏ธ all 170+ tools ยท โญ awesome-cognis ยท ๐Ÿ”— cognis-sources ยท ๐Ÿค– uncensored-fleet ยท ๐Ÿง  engram

Contributing

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

โญ If memorybank saved you time, star it โ€” it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite โ€” 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 โ€” free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.


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