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Memoripy v4

Memory server

Created by Khazar Ayaz

Published in caspianmoon/memoripy. Publisher page

691 GitHub stars

What it is

Evidence-first local memory for AI agents with temporal versions, admission policies, citations, explainable recall, MCP, and audit tooling.
— the description by Khazar Ayaz

AutoGineer overview, from the brain’s own files

Memoripy is an evidence-first memory runtime designed to provide AI agents with structured, verifiable, and persistent memory. It operates by capturing evidence and managing it through a lifecycle that includes admission, deferral, rejection, or quarantine of candidate memories. The system maintains both current and historical versions of data, allowing for temporal tracking. Retrieval is handled through independent lanes and rank fusion, ensuring that agents receive a scoped and cited context pack. Additionally, Memoripy includes audit tooling to provide transparency into what the system has remembered and the reasoning behind those memory decisions.

How it works

Memory model: Hybrid

AutoGineer overview, from the brain’s own files

The system functions as a memory runtime that processes information through six core operations: capturing evidence, applying admission policies to filter candidates, preserving temporal versions, retrieving data via rank fusion, building cited context packs, and auditing memory state. It utilizes a hybrid memory model and supports integration via an official MCP server, a CLI, and a REST API, with storage capabilities provided by PostgreSQL.

Folders
FolderRoleFiles
.githubagent-config4
docsdocs20
examplesexamples4
teststests11
benchmarksfolder3
memoripyfolder32

Requirements

Runtimes: Python >=3.10

Databases: PostgreSQL

API keys (by name): MEMORIPY_API_KEY

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. This brain runs code automatically: 1 script. Review setup.py before opening the folder in Claude Code. No step below turns a hook on for you.

    • setup.py: python setup.py
  2. Not scanned by AutoGineerThe author’s README also describes installing it from a clone of its repository. Read its install section: AutoGineer did not scan that path. https://github.com/caspianmoon/memoripy/blob/42316ca5d49f7df3d91afbf93e65871b33c588ad/README.md#install-v4-from-source
  3. Not scanned by AutoGineerAutoGineer could not scan everything it runs. https://pypi.org/project/memoripy/
  4. Created by Khazar Ayaz. Published in caspianmoon/memoripy. Licence: Apache License 2.0.

Usage

AutoGineer overview, from the brain’s own files

  • Integrating memory into AI agents via the MCP server
  • Managing memory lifecycles with admission and quarantine policies
  • Auditing agent memory state for transparency
  • Retrieving cited context for agent tasks
  • Tracking historical memory versions
“Retrieve all evidence regarding project X from the last month.”
The system returns a cited context pack containing the relevant temporal versions of memories associated with project X.
“Audit the memory admission for the recent user interaction.”
The system provides an explanation of why the interaction was admitted or rejected based on the configured admission policies.

Documentation by Khazar Ayaz

Read the documentation at the source

SECURITY.md

Security policy

Supported version

Security fixes are currently developed for Memoripy v4.

Reporting a vulnerability

Do not open a public issue for a vulnerability that could expose memory contents, bypass scope isolation, corrupt a store, or turn untrusted content into trusted memory.

Report it privately through GitHub's security advisory flow for this repository. Include:

  • affected version or commit

  • reproduction steps

  • expected and actual behavior

  • impact

  • whether the issue requires a malicious source, authenticated caller, or local file access

Security boundaries

Memoripy provides memory admission controls, provenance, scope filtering, file integrity checks, and optional API-key authentication for the local service. It does not by itself provide:

  • operating-system process isolation

  • encryption at rest

  • hosted multi-tenant authorization

  • complete secret detection

  • regulatory retention compliance

  • protection from a compromised application process

Applications must authenticate identities before assigning user_id, organization_id, or other scope fields. A caller that can lie about scope can defeat isolation at the application boundary.

The bundled HTTP server should be treated as a local-development or controlled-deployment component unless the operator adds deployment-grade authentication, authorization, network policy, logging, rate limits, and secret management.

88 files · 62 not served here

Advantages

Limitations

Security

Risk level: Low risk

This brain runs

Install scripts (1)

  • setup.py: python setup.py
  • Low risk: scripts-present (setup.py)

Attribution and licence

Created by
Khazar Ayaz
Declared authors
  • Khazar Ayaz (declared in pyproject.toml)
Publisher
Khazar Ayaz (Community)
Source
caspianmoon/memoripy · commit 42316ca
Licence
Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Khazar Ayaz.

Install from source

62 of its files are not served here, so there is no ZIP. caspianmoon/memoripy at 42316ca