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Agent Memory & RAG

Everos

Everos is a Python library and local-first memory runtime for AI agents. It uses Markdown as a portable memory layer and local SQLite and LanceDB indexes for retrieval.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Everos — OpenViST project profile in Agent Memory & RAG.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Everos when an agent needs inspectable, editable and portable memory that can be shared across applications, tools or workflows while retaining local indexes for retrieval.

Who it is for

  • Python developers building AI agents
  • Agent makers who need long-term memory
  • Teams or individuals using local-first, self-hosted or CLI workflows
  • Developers interested in Markdown-native memory and MCP-compatible tooling

Documented uses

  • Add persistent, user-owned memory to a Python-based AI agent.
  • Store conversations, files and agent trajectories as portable Markdown.
  • Create a local-first long-term memory layer for chat or agent systems.
  • Search memory across user, agent, application, project and session identifiers.
  • Use offline reflection to merge episode clusters and refine profiles and skills.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented full quick start requires Python 3.12 or later and an OpenRouter API key; the standalone demo is documented without an API key or server setup.

  • Markdown is the canonical source of truth, making memory readable, editable, diffable and Git-versionable.
  • The local stack uses Markdown, SQLite and LanceDB without the documented need for MongoDB, Elasticsearch or Redis.
  • User and agent memory are represented as separate first-class surfaces: episodes/profiles and cases/skills.
  • The project documents a Knowledge Wiki with editable, source-backed Markdown pages.
  • Offline reflection is documented for memory evolution between sessions.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Everos and run the standalone memory demo

Install the package and observe Everos store and recall a memory through its documented standalone demo.

Before you start

  • Python 3.12 or later
  • Everos installed with uv pip or pip, or a cloned repository with uv available

Steps

  1. Install Everos with the command below or the command below.

    Documented command
    uv pip install everos
    Documented command
    pip install everos

  2. Run everos demo after package installation, or the command below from a cloned or forked repository without an activated virtual environment.

    Documented command
    uv run everos demo

  3. Enter information for Everos to remember, then ask a related question to observe the ingest, extract, index and recall flow.

Expected outcome

A standalone Everos demo stores an entered memory and recalls it in response to a related question.

Guide limitations

  • The standalone demo does not require an API key or server setup; the documented full setup uses an OpenRouter API key.
  • This recipe covers the standalone demo, not integration with another agent or workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
EverMind-AI/EverOS · Official homepage title · retained in its source language
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