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Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

Subagents15.6k stars2.9k forksPythonNOASSERTIONUpdated 1mo ago
Install as a Claude Code subagent
Method: Clone
Terminal
git clone https://github.com/MemoriLabs/Memori && cp Memori/*.md ~/.claude/agents/
1. Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
2. Start a new Claude Code session to load the agents.
3. Delegate work to them with the Task/Agent tool or by name.
Use cases

Subagents overview

[![Memori Labs](https://images.memorilabs.ai/banner-dark-large.jpg)](https://memorilabs.ai/)

<p align="center">
  <strong>Memory from what agents do, not just what they say.</strong>
</p>

<p align="center">
  <i>Memori plugs into the software and infrastructure you already use. It is LLM, datastore and framework agnostic and seamlessly integrates into the architecture you've already designed.</i>
</p>

<p align="center">
  <strong>→ <a href="https://memorilabs.ai/docs/memori-cloud/">Memori Cloud</a></strong> — Zero config. Get an API key and start building in minutes.
</p>
<p align="center">
  <a href="https://trendshift.io/repositories/15435" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15435" alt="MemoriLabs%2FMemori | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>

<p align="center">
  <a href="https://badge.fury.io/py/memori">
    <img src="https://badge.fury.io/py/memori.svg" alt="PyPI version">
  </a>
  <a href="https://www.npmjs.com/package/@memorilabs/memori">
    <img src="https://img.shields.io/npm/v/@memorilabs/memori.svg" alt="NPM version">
  </a>
  <a href="https://pepy.tech/projects/memori">
    <img src="https://static.pepy.tech/badge/memori" alt="Downloads">
  </a>
  <a href="https://opensource.org/license/apache-2-0">
    <img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License">
  </a>
  <a href="https://discord.gg/abD4eGym6v">
    <img src="https://img.shields.io/discord/1042405378304004156?logo=discord" alt="Discord">
  </a>
</p>

<p align="center">
  <a href="https://github.com/MemoriLabs/Memori/stargazers">
    <img src="https://img.shields.io/badge/⭐%20Give%20a%20Star-Support%20the%20project-orange?style=for-the-badge" alt="Give a Star">
  </a>
</p>

<p align="center">
  <strong>Choose memory that performs</strong>
</p>



[![Memori Labs](https://images.memorilabs.ai/stats.jpg)](https://memorilabs.ai/benchmark)

---

## Getting Started

### Installation

<details open>
<summary><b>TypeScript SDK</b></summary>

```bash
npm install @memorilabs/memori
```
</details>

<details open>
<summary><b>Python SDK</b></summary>

```bash
pip install memori
```
</details>

### Quickstart

Sign up at [app.memorilabs.ai](https://app.memorilabs.ai), get a Memori API key, and start building. Full docs: [memorilabs.ai/docs/memori-cloud/](https://memorilabs.ai/docs/memori-cloud/).

Set `MEMORI_API_KEY` and your LLM API key (e.g. `OPENAI_API_KEY`), then:

<details open>
<summary><b>TypeScript SDK</b></summary>

```typescript
import { OpenAI } from 'openai';
import { Memori } from '@memorilabs/memori';

// Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment
const client = new OpenAI();
const mem = new Memori().llm
  .register(client)
  .attribution('user_123', 'support_agent');

async function main() {
  await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: 'My favorite color is blue.' }],
  });
  // Conversations are persisted and recalled automatically in the background.

  const response = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: "What's my favorite color?" }],
  });
  // Memori recalls that your favorite color is blue.
}
```
</details>

<details open>
<summary><b>Python SDK</b></summary>

```python
from memori import Memori
from openai import OpenAI

# Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment
client = OpenAI()
mem = Memori().llm.register(client)

mem.attribution(entity_id="user_123", process_id="support_agent")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "My favorite color is blue."}]
)
# Conversations are persisted and recalled automatically.

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What's my favorite color?"}]
)
# Memori recalls that your favorite color is blue.
```
</details>

## Explore the Memories

Use the [Dashboard](https://app.memorilabs.ai) — Memories, Analytics, Playground, and API Keys.

> [!TIP]
> Want to use your own database? Check out docs for Memori BYODB here:
> [https://memorilabs.ai/docs/memori-byodb/](https://memorilabs.ai/docs/memori-byodb/).
> For disposable BYODB development databases, see the TiDB Zero provisioning
> guide: [docs/memori-byodb/databases/tidb.mdx](docs/memori-byodb/databases/tidb.mdx).

## LoCoMo Benchmark

Memori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved **81.95% overall accuracy** while using an average of **1,294 tokens per query**. That is just **4.97% of the full-context footprint**, showing that structured memory can preserve reasoning quality without forcing large prompts into every request.

Compared with other retrieval-based memory systems, Memori outperformed Zep, LangMem, and Mem0 while reducing prompt size by roughly **67% vs. Zep** and lowering context cost by more than **20x vs. full-context prompting**.

Read the [benchmark overview](docs/memori-cloud/benchmark/overview.mdx), see the [results](docs/memori-cloud/benchmark/results.mdx), or download the [paper](https://arxiv.org/abs/2603.19935).

!["Memori's average accuracy along with the standard deviation"](https://images.memorilabs.ai/docs/memori-locomo-benchmark.webp)

## OpenClaw (Persistent Memory for Your Gateway)

By default, OpenClaw agents forget everything between sessions. The Memori plugin fixes that. It automatically captures structured memory from conversation and agent execution after each turn — including tool calls, decisions, and outcomes — and makes it available for agents to recall on demand.

No changes to your agent code or prompts are required. The plugin hooks into OpenClaw's lifecycle, so you get structured memory, agent-controlled recall, and Advanced Augmentation with a drop-in plugin.

```bash
openclaw plugins install @memorilabs/openclaw-memori
openclaw plugins enable openclaw-memori

openclaw memori init \
  --api-key "YOUR_MEMORI_API_KEY" \
  --entity-id "your-app-user-id" \
  --project-id "my-project"

openclaw gateway restart
```

For setup and configuration, see the [OpenClaw Quickstart](docs/memori-cloud/openclaw/quickstart.mdx). For architecture and lifecycle details, see the [OpenClaw Overview](docs/memori-cloud/openclaw/overview.mdx).

## Hermes Agent (Persistent Memory Provider)

Memori also ships as a Hermes Agent memory provider. It captures completed conversations in the background and gives Hermes explicit `memori_recall` and `memori_recall_summary` tools for agent-controlled recall.

```bash
pip install hermes-memori
hermes-memori install

hermes config set memory.provider memori
HERMES_HOME="${HERMES_HOME:-$HOME/.hermes}"
mkdir -p "$HERMES_HOME"
echo "MEMORI_API_KEY=YOUR_MEMORI_API_KEY" >> "$HERMES_HOME/.env"
echo "MEMORI_ENTITY_ID=your-app-user-id" >> "$HERMES_HOME/.env"
```

`MEMORI_PROJECT_ID` is optional; when omitted, the provider uses Hermes' active project context for scoping.

For setup and configuration, see the [Hermes Quickstart](docs/memori-cloud/hermes/quickstart.mdx). For architecture and lifecycle details, see the [Hermes Overview](docs/memori-cloud/hermes/overview.mdx).

## MCP (Connect Your Agent in One Command)

Your agent forgets everything between sessions. Memori fixes that. It remembers your stack, your conventions, and how you like things done so you stop repeating yourself.

Works for solo developers and teams. Your agent learns coding patterns, reviewer preferences, and project conventions over time. For teams, that means shared context that new engineers pick up on day one instead of absorbing tribal knowledge over months.

If you use Claude Code, Cursor, Codex, Warp, or Antigravity, you can connect Memori with no SDK integration needed:

```bash
claude mcp add --transport http memori https://api.memorilabs.ai/mcp/ \
  --header "X-Memori-API-Key: ${MEMORI_API_KEY}" \
  --header "X-Memori-Entity-Id: your_username" \
  --header "X-Memori-Process-Id: claude-code"
```

For Cursor, Codex, Warp, and other clients, see the [MCP client setup guide](docs/memori-cloud/mcp/client-setup.mdx).

## Attribution

To get the most out of Memori, you want to attribute your LLM interactions to an entity (think person, place or thing; like a user) and a process (think your agent, LLM interaction or program).

If you do not provide any attribution, Memori cannot make memories for you.

<details open>
<summary><b>TypeScript SDK</b></summary>

```typescript
mem.attribution("12345", "my-ai-bot");
```
</details>

<details open>
<summary><b>Python SDK</b></summary>

```python
mem.attribution(entity_id="12345", process_id="my-ai-bot")
```
</details>

## Session Management

Memori uses sessions to group your LLM interactions together. For example, if you have an agent that executes multiple steps you want those to be recorded in a single session.

By default, Memori handles setting the session for you but you can start a new session or override the session by executing the following:

<details open>
<summary><b>TypeScript SDK</b></summary>

```typescript
mem.resetSession();
// or
mem.setSession(sessionId);
```
</details>

<details open>
<summary><b>Python SDK</b></summary>

```python
mem.new_session()
# or
mem.set_session(session_id)
```
</details>

## Supported LLMs

- Anthropic
- Bedrock
- DeepSeek
- Gemini
- Grok (xAI)
- OpenAI (Chat Completions & Responses API)

_(unstreamed, streamed, synchronous and asynchronous)_

## Supported Frameworks

- Agno
- LangChain
- Pydantic AI

## Supported Platforms

- DeepSeek
- Nebius AI Studio

## Examples

For more examples and demos, check out the [Memori Cookbook](https://github.com/MemoriLabs/memori-cookbook).

## Memori Advanced Augmentation

Memories are tracked at several different levels:

- **entity**: think person, place, or thing; like a user
- **process**: think your agent, LLM interaction or program
- **ses
agentagent-memoryagenticaiaiai-memoryclaude-codeenterprisehermesllmlong-short-term-memorymemorymemory-managementopenclawpythonragstate-managementstatefultypescript

What people ask about Memori

What is MemoriLabs/Memori?

+

MemoriLabs/Memori is subagents for the Claude AI ecosystem. Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises. It has 15.6k GitHub stars and was last updated 1mo ago.

How do I install Memori?

+

You can install Memori by cloning the repository (https://github.com/MemoriLabs/Memori) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is MemoriLabs/Memori safe to use?

+

MemoriLabs/Memori has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.

Who maintains MemoriLabs/Memori?

+

MemoriLabs/Memori is maintained by MemoriLabs. The last recorded GitHub activity is from 1mo ago, with 26 open issues.

Are there alternatives to Memori?

+

Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.

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