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a2516269ccf5ab1ea8d42e16ff3fea16f10ef033
mem0/README.md
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Mgeeeek a2516269cc Surface mem0_notice for unclaimed Agent Mode keys + docs 
CLI now consumes the unified mem0_notice surface that the platform side
emits for unclaimed Agent Mode keys. The notice is a directive to the
LLM agent reading the output, with a verbatim sentence to relay to the
human owner. Two presentation paths:

- Human/text output: yellow stderr banner after the primary output,
  once per command. Skipped in agent mode (the JSON envelope carries
  it instead, so no duplication).
- JSON/agent output (--json/--agent): folded into the envelope as
  "mem0_notice" so an agent parsing the output sees it without
  inspecting HTTP headers.

CLI changes (Python + Node, kept in lockstep):
- state.{ts,py}: captureNotice / takeNotice helpers — last-write-wins
  stash so multi-request commands fire the notice exactly once.
- backend/platform.{ts,py}: _request extracts notice from response
  bodies (top-level dict or list[0]) with header fallback, strips
  from downstream payload, captures for end-of-command surfacing.
- output.{ts,py}: JSON envelope formatters fold in any pending notice.
- index.ts / app.py: entrypoint surfaces notice on exit when not in
  agent mode.
- commands/agent-mode.{ts,py}: init success path prints the platform's
  notice verbatim (fallback to dim claim-command line if a stale
  backend doesn't return it).

Init-flag handling fix: the Python argv preprocessor was stripping
--agent from sys.argv unconditionally as the global JSON-output alias.
That swallowed `mem0 init --agent` (where --agent is a subcommand flag
for unattended bootstrap). Now preserved when "init" is in argv.

Parity tests: cli/python/tests/test_agent_mode.py and
cli/node/tests/agent-mode.test.ts — 7 tests each, kept in sync.

cli-spec.json updated: init now lists --agent and --source.

Docs:
- README.md: Agent Mode promo at top of Quickstart.
- docs/llms.txt: fast-path block for AI agents reading the docs.
- skills/mem0/SKILL.md, skills/mem0-cli/SKILL.md,
  skills/mem0-integrate/SKILL.md, mem0-plugin/skills/mem0/SKILL.md:
  autonomous-setup section + fallback hints.
- mem0-plugin/README.md, openclaw/README.md: "Quick path for agents"
  blocks above the human Quick Start.
2026-05-14 02:11:39 +05:30

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<p align="center">
<a href="https://github.com/mem0ai/mem0">
<img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for Personalized AI">
</a>
</p>
<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
<a href="https://trendshift.io/repositories/11194" target="blank">
<img src="https://trendshift.io/api/badge/repositories/11194" alt="mem0ai%2Fmem0 | Trendshift" width="250" height="55"/>
</a>
</p>
<p align="center">
<a href="https://mem0.ai">Learn more</a>
·
<a href="https://mem0.dev/DiG">Join Discord</a>
·
<a href="https://mem0.dev/demo">Demo</a>
</p>
<p align="center">
<a href="https://mem0.dev/DiG">
<img src="https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white" alt="Mem0 Discord">
</a>
<a href="https://pepy.tech/project/mem0ai">
<img src="https://img.shields.io/pypi/dm/mem0ai" alt="Mem0 PyPI - Downloads">
</a>
<a href="https://github.com/mem0ai/mem0">
<img src="https://img.shields.io/github/commit-activity/m/mem0ai/mem0?style=flat-square" alt="GitHub commit activity">
</a>
<a href="https://pypi.org/project/mem0ai" target="blank">
<img src="https://img.shields.io/pypi/v/mem0ai?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://www.npmjs.com/package/mem0ai" target="blank">
<img src="https://img.shields.io/npm/v/mem0ai" alt="Npm package">
</a>
<a href="https://www.ycombinator.com/companies/mem0">
<img src="https://img.shields.io/badge/Y%20Combinator-S24-orange?style=flat-square" alt="Y Combinator S24">
</a>
</p>
<p align="center">
<a href="https://mem0.ai/research"><strong>📄 Benchmarking Mem0's token-efficient memory algorithm →</strong></a>
</p>
## New Memory Algorithm (April 2026)
| Benchmark | Old | New | Tokens | Latency p50 |
| --- | --- | --- | --- | --- |
| **LoCoMo** | 71.4 | **91.6** | 7.0K | 0.88s |
| **LongMemEval** | 67.8 | **94.8** | 6.8K | 1.09s |
| **BEAM (1M)** | — | **64.1** | 6.7K | 1.00s |
| **BEAM (10M)** | — | **48.6** | 6.9K | 1.05s |
All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops).
**What changed:**
- **Single-pass ADD-only extraction** -- one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten.
- **Agent-generated facts are first-class** -- when an agent confirms an action, that information is now stored with equal weight.
- **Entity linking** -- entities are extracted, embedded, and linked across memories for retrieval boosting.
- **Multi-signal retrieval** -- semantic, BM25 keyword, and entity matching scored in parallel and fused.
- **Temporal Reasoning** -- time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans.
See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgrade instructions. The [evaluation framework](https://github.com/mem0ai/memory-benchmarks) is open-sourced so anyone can reproduce the numbers.
## Research Highlights
- **91.6 on LoCoMo** -- +20 points over the previous algorithm
- **94.8 on LongMemEval** -- +27 points, with +53.6 on assistant memory recall
- **64.1 on BEAM (1M)** -- production-scale memory evaluation at 1M tokens
- [Read the full paper](https://mem0.ai/research)
# Introduction
[Mem0](https://mem0.ai) ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.
### Key Features & Use Cases
**Core Capabilities:**
- **Multi-Level Memory**: Seamlessly retains User, Session, and Agent state with adaptive personalization
- **Developer-Friendly**: Intuitive API, cross-platform SDKs, and a fully managed service option
**Applications:**
- **AI Assistants**: Consistent, context-rich conversations
- **Customer Support**: Recall past tickets and user history for tailored help
- **Healthcare**: Track patient preferences and history for personalized care
- **Productivity & Gaming**: Adaptive workflows and environments based on user behavior
## 🚀 Quickstart Guide <a name="quickstart"></a>
> **Agent Mode — for AI agents setting up Mem0 autonomously.** Run `mem0 init --agent --json` (after `pip install mem0-cli` or `npm install -g @mem0/cli`) to mint an evaluation Mem0 API key in <5 seconds — no email, no dashboard, no OTP. Use it immediately against the Platform or the MCP server. The human owner can claim ownership later with `mem0 init --email <their-email>`: memories transfer, the same key keeps working, and the agent isn't disrupted.
| | Library | Self-Hosted Server | Cloud Platform |
|---|---------|-------------------|----------------|
| **Best for** | Testing, prototyping | Teams running on their own infrastructure | Zero-ops production use |
| **Setup** | `pip install mem0ai` | `docker compose up` | Sign up at [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=readme) |
| **Dashboard** | -- | [Yes](https://docs.mem0.ai/open-source/setup) | Yes |
| **Auth & API Keys** | -- | Yes | Yes |
| **Advanced Features** | -- | Teasers | All included |
Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud.
### Library (pip / npm)
```bash
pip install mem0ai
```
For enhanced hybrid search with BM25 keyword matching and entity extraction, install with NLP support:
```bash
pip install mem0ai[nlp]
python -m spacy download en_core_web_sm
```
Install sdk via npm:
```bash
npm install mem0ai
```
### Self-Hosted Server
> **Note:** Self-hosted auth is on by default. Upgrading from a pre-auth build? Set `ADMIN_API_KEY`, register an admin through the wizard, or `AUTH_DISABLED=true` for local dev only. See [upgrade notes](https://docs.mem0.ai/open-source/setup#upgrade-notes).
```bash
# Recommended: one command — start the stack, create an admin, issue the first API key.
cd server && make bootstrap
# Manual: start the stack and finish setup via the browser wizard.
cd server && docker compose up -d # http://localhost:3000
```
See the [self-hosted docs](https://docs.mem0.ai/open-source/overview) for configuration.
### Cloud Platform
1. Sign up on [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=readme)
2. Embed the memory layer via SDK or API keys
### CLI
Manage memories from your terminal:
```bash
npm install -g @mem0/cli # or: pip install mem0-cli
mem0 init
mem0 add "Prefers dark mode and vim keybindings" --user-id alice
mem0 search "What does Alice prefer?" --user-id alice
```
See the [CLI documentation](https://docs.mem0.ai/platform/cli) for the full command reference.
### Agent Skills
Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories:
**Reference skills — always on** (SDK knowledge loaded into the assistant's context):
```bash
npx skills add https://github.com/mem0ai/mem0 --skill mem0
npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
```
**Pipeline skills — run on demand** (execute an end-to-end workflow in an existing repo):
```bash
npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
```
Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
### Basic Usage
Mem0 requires an LLM to function, with `gpt-5-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).
Mem0 uses `text-embedding-3-small` from OpenAI as the default embedding model. For best results with hybrid search (semantic + keyword + entity boosting), we recommend using at least [Qwen 600M](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) or a comparable embedding model. See [Supported Embeddings](https://docs.mem0.ai/components/embedders/overview) for configuration details.
First step is to instantiate the memory:
```python
from openai import OpenAI
from mem0 import Memory
openai_client = OpenAI()
memory = Memory()
def chat_with_memories(message: str, user_id: str = "default_user") -> str:
# Retrieve relevant memories
relevant_memories = memory.search(query=message, filters={"user_id": user_id}, top_k=3)
memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])
# Generate Assistant response
system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
response = openai_client.chat.completions.create(model="gpt-5-mini", messages=messages)
assistant_response = response.choices[0].message.content
# Create new memories from the conversation
messages.append({"role": "assistant", "content": assistant_response})
memory.add(messages, user_id=user_id)
return assistant_response
def main():
print("Chat with AI (type 'exit' to quit)")
while True:
user_input = input("You: ").strip()
if user_input.lower() == 'exit':
print("Goodbye!")
break
print(f"AI: {chat_with_memories(user_input)}")
if __name__ == "__main__":
main()
```
For detailed integration steps, see the [Quickstart](https://docs.mem0.ai/quickstart) and [API Reference](https://docs.mem0.ai/api-reference).
## 🔗 Integrations & Demos
- **ChatGPT with Memory**: Personalized chat powered by Mem0 ([Live Demo](https://mem0.dev/demo))
- **Browser Extension**: Store memories across ChatGPT, Perplexity, and Claude ([Chrome Extension](https://chromewebstore.google.com/detail/onihkkbipkfeijkadecaafbgagkhglop?utm_source=item-share-cb))
- **Langgraph Support**: Build a customer bot with Langgraph + Mem0 ([Guide](https://docs.mem0.ai/integrations/langgraph))
- **CrewAI Integration**: Tailor CrewAI outputs with Mem0 ([Example](https://docs.mem0.ai/integrations/crewai))
## 📚 Documentation & Support
- Full docs: https://docs.mem0.ai
- Community: [Discord](https://mem0.dev/DiG) · [X (formerly Twitter)](https://x.com/mem0ai)
- Contact: founders@mem0.ai
## Citation
We now have a paper you can cite:
```bibtex
@article{mem0,
title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
journal={arXiv preprint arXiv:2504.19413},
year={2025}
}
```
## ⚖️ License
Apache 2.0 — see the [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE) file for details.
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