Memori
Memory framework
Created by Memori Labs Team
Published in MemoriLabs/Memori. Publisher page
17,548 GitHub stars
What it is
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.
AutoGineer overview, from the brain’s own files
Memori is an LLM-agnostic memory layer designed to provide persistent state for AI agents. It captures interactions, tool calls, and execution traces, transforming them into structured memory that agents can recall on demand. The system is designed to integrate with existing data infrastructure and supports various deployment environments, including managed cloud and on-premises setups. By maintaining structured memory, it aims to reduce the need for large context windows in subsequent interactions.
How it works
Memory model: Hybrid
AutoGineer overview, from the brain’s own files
Memori functions as a memory layer that hooks into agent lifecycles. It uses SDKs for Python and TypeScript to attribute interactions to specific entities, processes, and sessions. It captures data in the background and stores it in a configured backend, which can be a managed cloud service or a user-provided database. The system provides tools for agents to perform recall operations, allowing them to retrieve relevant facts, preferences, or past execution outcomes without requiring full-context prompts.
| Folder | Role | Files |
|---|---|---|
| .github | agent-config | 16 |
| docs | docs | 86 |
| examples | examples | 38 |
| images | attachments | 3 |
| tests | tests | 153 |
| benchmarks | folder | 5 |
| core | folder | 66 |
| integrations | folder | 61 |
| memori | folder | 136 |
| memori-ts | folder | 91 |
Requirements
Runtimes: Python >=3.10
Databases: FAISS, MongoDB, PostgreSQL
API keys (by name): ANTHROPIC_API_KEY, GEMINI_API_KEY, MEMORI_API_KEY, NEBIUS_API_KEY, OPENAI_API_KEY, XAI_API_KEY, YOUR_MEMORI_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
This brain runs code automatically: 2 scripts. Review integrations/hermes/src/memori_hermes/install.py, setup.py before opening the folder in Claude Code. No step below turns a hook on for you.
integrations/hermes/src/memori_hermes/install.py:python integrations/hermes/src/memori_hermes/install.pysetup.py:python setup.py
- Not scanned by AutoGineerAutoGineer could not scan everything it runs. https://pypi.org/project/memori/
Created by Memori Labs Team. Published in MemoriLabs/Memori. Licence: Apache License 2.0.
Usage
AutoGineer overview, from the brain’s own files
- Registering an LLM client with the Memori SDK to enable automatic persistence.
- Attributing interactions to specific users and processes for scoped memory.
- Using the MCP interface to connect Memori to existing agent clients.
- Configuring custom database backends for BYODB deployments.
- Managing memory sessions to group related agent execution steps.
- “What is my favorite color?”
- The agent recalls the fact 'favorite color is blue' from previous persisted conversation history.
- “Connect Memori to Claude Code via MCP.”
- The agent gains persistent access to project conventions and coding patterns across sessions.
Documentation by Memori Labs Team
<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>
<hr>
Getting Started
Installation
<details> <summary><b>TypeScript SDK</b></summary>
npm install @memorilabs/memori</details>
<details> <summary><b>Python SDK</b></summary>
pip install memori</details>
Quickstart
Sign up at app.memorilabs.ai, get a Memori API key, and start building. Full docs: memorilabs.ai/docs/memori-cloud/.
Set MEMORI_API_KEY and your LLM API key (e.g. OPENAI_API_KEY), then:
<details> <summary><b>TypeScript SDK</b></summary>
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> <summary><b>Python SDK</b></summary>
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 — 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/. For disposable BYODB development databases, see the TiDB Zero provisioning guide: docs/memori-byodb/databases/tidb.mdx.
LoCoMo Benchmark
Memori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved 87% overall accuracy while using an average of 721 tokens per query. That is just 2.8% 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 36x vs. full-context prompting.
Read the benchmark overview, see the results, or download the paper.
Image: "Memori's average accuracy along with the standard deviation"
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.
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 restartFor setup and configuration, see the OpenClaw Quickstart. For architecture and lifecycle details, see the OpenClaw Overview.
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.
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. For architecture and lifecycle details, see the Hermes Overview.
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:
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.
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> <summary><b>TypeScript SDK</b></summary>
mem.attribution("12345", "my-ai-bot");</details>
<details> <summary><b>Python SDK</b></summary>
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> <summary><b>TypeScript SDK</b></summary>
mem.resetSession();
// or
mem.setSession(sessionId);</details>
<details> <summary><b>Python SDK</b></summary>
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.
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
session: the current interactions between the entity, process and the LLM
Memori's Advanced Augmentation enhances memories at each of these levels with:
attributes
events
facts
people
preferences
relationships
rules
skills
Memori knows who your user is, what tasks your agent handles and creates unparalleled context between the two. Augmentation occurs in the background incurring no latency.
By default, Memori Advanced Augmentation is available without an account but rate-limited. When you need increased limits, sign up for Memori Advanced Augmentation or use the Memori CLI:
# Install the CLI via pip to manage your account
python -m memori sign-up <email_address>Memori Advanced Augmentation is always free for developers!
Once you've obtained an API key, set the following environment variable (used by both Python and TypeScript SDKs):
export MEMORI_API_KEY=[api_key]The Memori CLI uses your exported environment first, then fills missing values from a .env file in the directory where you run the command.
Managing Your Quota
At any time, you can check your quota using the Memori CLI (works for both SDKs):
python -m memori quotaOr by checking your account at https://app.memorilabs.ai/. If you have reached your IP address quota, sign up and get an API key for increased limits.
If your API key exceeds its quota limits, we will email you and let you know.
Command Line Interface (CLI)
The Memori CLI is the unified tool for managing your account, keys, and quotas across all SDKs. To use it, execute the following from the command line:
# Requires Python installed
python -m memoriThis will display a menu of the available options. For more information about what you can do with the Memori CLI, please reference Command Line Interface.
Contributing
We welcome contributions from the community! Please see our Contributing Guidelines for details on:
Setting up your development environment
Code style and standards
Submitting pull requests
Reporting issues
Support
Memori Enterprise
Memori Enterprise is built for any enterprise running production agents, particularly high-volume ones such as internal SDLC.
Recently, a major project management platform automating QA with AI agents faced severe inefficiency as its agents suffered from session amnesia, repeatedly burning excessive tool calls.
By integrating Memori's agent-native memory layer in a secure private VPC, the platform captured execution traces to stop redundant discoveries and improve run over run.
This deployment delivered:
$2.1M/year reduction in token spend on just one QA automation workflow
Material soft ROI including increases in accuracy, reduction in latency, and reduction in human in the loop review
Following this success, this enterprise has already identified four additional high-priority agentic workflows for Memori integration.
Please email us at e-mail address on the upstream page if you'd like a preview.
License
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Advantages
- Reduces prompt size and context costs by using structured memory instead of full-context prompting. evidence: README.md
- Integrates with existing agent code and infrastructure without requiring a complete rewrite. evidence: README.md
Limitations
- Requires explicit attribution of interactions to entities and processes to function. evidence: README.md
- Relies on external API keys for LLM and Memori cloud services. evidence: README.md
Security
Risk level: Medium risk
This brain runs
Git hooks (2)
.pre-commit-config.yaml:pre-commit hooks from https://github.com/pre-commit/pre-commit-hooks at v4.5.0 (contributor tooling: runs only after pre-commit install)· uses the network.pre-commit-config.yaml:pre-commit hooks from https://github.com/astral-sh/ruff-pre-commit at v0.15.1 (contributor tooling: runs only after pre-commit install)· uses the network
Agent instructions that run or fetch something (22)
docs/memori-cloud/mcp/skills/memori-mcp/SKILL.md:Routine session progress such as tests passed, commands run, files edited, or commit messagesdocs/memori-cloud/mcp/skills/memori-mcp/SKILL.md:Once they provide an email, run `memori_signup` with that email.integrations/claude-code/SKILL.md:Run commands from the Claude Code integration root:integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts <command> [--flags ...]integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts recall [--projectId ID] [--sessionId ID] [--dateStart ISO] [--dateEnd ISO] [--source SOURCE --signal SIGNAL]integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts recall.summary [--projectId ID] [--sessionId ID] [--dateStart ISO] [--dateEnd ISO]integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts advanced-augmentation --userMessage "$USER_MESSAGE" --assistantMessage "$ASSISTANT_MESSAGE" [--sessionId ID] [--projectId ID] [--model MODEL] [--summary "$SUMMARY"] [--trace "$TRACE_JSON"] [--processId ID]integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts compaction [--projectId ID] [--sessionId ID] [--numMessages 5]integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts feedback --content "feedback text"integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts quotaintegrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts signup --email "[e-mail address: https://github.com/MemoriLabs/Memori]"integrations/claude-code/SKILL.md:Run `recall` before drafting any substantive response.integrations/claude-code/SKILL.md:Always run `recall` before any external information lookup (WebSearch, WebFetch, or any other tool that fetches facts from outside the repo).integrations/claude-code/SKILL.md:After drafting the final response for a non-trivial turn, run `advanced-augmentation` with that final response.integrations/claude-code/SKILL.md:For prompts like "what did we discuss last session about X?", run `recall` before answering even though the topic text cannot be passed as `--query`.integrations/claude-code/SKILL.md:Run after drafting the final assistant response for every non-trivial turn:integrations/claude-code/SKILL.md:bun .claude/skills/memori/index.ts advanced-augmentation \integrations/openclaw/skills/clawhub/SKILL.md:**Contextual Recall**: The agent can run a `memori_recall` search to retrieve relevant details if context is missing regarding user preferences.integrations/openclaw/skills/clawhub/SKILL.md:Run `memori quota` to check usageintegrations/openclaw/skills/memori/SKILL.md:**Execution:** Once they provide an email, run `memori_signup` with that email.integrations/openclaw/skills/memori/SKILL.md:Instead, immediately tell them that their CLI installation is broken or missing, and explicitly instruct them to run `npm install -g @memorilabs/memori` in their terminal to fix it.integrations/openclaw/skills/memori/SKILL.md:Instead, immediately tell them that their CLI installation is broken or missing, and explicitly instruct them to run `npm install -g @memorilabs/memori` in their terminal to fix it.
Install scripts (2)
integrations/hermes/src/memori_hermes/install.py:python integrations/hermes/src/memori_hermes/install.pysetup.py:python setup.py
- Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:2)
- Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:10)
- Low risk: scripts-present (examples/sqlite/rust_core_main.py)
- Low risk: scripts-present (integrations/hermes/src/memori_hermes/__init__.py)
- Low risk: scripts-present (integrations/hermes/src/memori_hermes/_paths.py)
- Low risk: scripts-present (integrations/hermes/src/memori_hermes/client.py)
- Low risk: scripts-present (integrations/hermes/src/memori_hermes/install.py)
- Low risk: scripts-present (integrations/hermes/src/memori_hermes/tools.py)
- Low risk: autonomy-phrase (integrations/openclaw/skills/memori/SKILL.md:324)
- Low risk: network-instructions (integrations/openclaw/skills/memori/SKILL.md:324)
- Low risk: network-instructions (pyproject.toml:43)
- Low risk: scripts-present (setup.py)
- Low risk: scripts-present (tests/build/mongodb.py)
- Low risk: scripts-present (tests/build/mysql.py)
- Low risk: scripts-present (tests/build/oceanbase.py)
- Low risk: scripts-present (tests/build/oracle.py)
- Low risk: scripts-present (tests/build/postgresql.py)
- Low risk: scripts-present (tests/build/sqlite.py)
- Low risk: scripts-present (tests/database/init_db.py)
Attribution and licence
- Created by
- Memori Labs Team
- First committed by
- @Boburmirzo
- Declared authors
- Memori Labs Team (declared in pyproject.toml)
- Publisher
- Memori Labs (Community)
- Source
- MemoriLabs/Memori · commit 574b1ea
- Licence
- Apache License 2.0 (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Memori Labs Team.
Install from source
505 of its files are not served here, so there is no ZIP. MemoriLabs/Memori at 574b1ea