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|
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# Mem0 Python SDK
|
||||
|
||||
<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">
|
||||
<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;">
|
||||
@@ -24,187 +22,166 @@
|
||||
<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">
|
||||
<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="PyPI package version">
|
||||
<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>
|
||||
|
||||
Mem0 gives AI assistants and agents persistent memory. It extracts useful facts from conversations, scopes them to a user, agent, or run, and retrieves the relevant facts for later interactions. The Python package includes `MemoryClient` for the hosted Mem0 Platform and `Memory` for open-source, in-process memory.
|
||||
<p align="center">
|
||||
<a href="https://mem0.ai/research"><strong>📄 Benchmarking Mem0's token-efficient memory algorithm →</strong></a>
|
||||
</p>
|
||||
|
||||
## Requirements
|
||||
## New Memory Algorithm (April 2026)
|
||||
|
||||
- Python 3.10 or later
|
||||
- Hosted Platform: `MEM0_API_KEY` from the [Mem0 dashboard](https://app.mem0.ai/dashboard/api-keys)
|
||||
- Open source with the default providers: `OPENAI_API_KEY`
|
||||
| Benchmark | Old | New | Tokens | Latency p50 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| **LoCoMo** | 71.4 | **92.5** | 7.0K | 0.88s |
|
||||
| **LongMemEval** | 67.8 | **94.4** | 6.8K | 1.09s |
|
||||
| **BEAM (1M)** | — | **64.1** | 6.7K | 1.00s |
|
||||
| **BEAM (10M)** | — | **48.6** | 6.9K | 1.05s |
|
||||
|
||||
## Install
|
||||
All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top_200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect directionally similar gains but not identical numbers.
|
||||
|
||||
**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
|
||||
- **92.5 on LoCoMo** -- +21 points over the previous algorithm
|
||||
- **94.4 on LongMemEval** -- +27 points, with 98.2 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>
|
||||
|
||||
### Sign up as an agent
|
||||
|
||||
AI agents can mint a working Mem0 API key in under five seconds — no email, no dashboard, no OTP. Four commands end-to-end:
|
||||
|
||||
```bash
|
||||
# 1. Install
|
||||
npm install -g @mem0/cli # or: pip install mem0-cli
|
||||
|
||||
# 2. Sign up as an agent (replace `claude-code` with your name)
|
||||
mem0 init --agent --agent-caller claude-code
|
||||
|
||||
# 3. Add a memory
|
||||
mem0 add "I am using mem0"
|
||||
|
||||
# 4. Search
|
||||
mem0 search "am I using mem0"
|
||||
```
|
||||
|
||||
The human owner can claim the account later with `mem0 init --email <their-email>` — same key, memories preserved. Full guide: [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup).
|
||||
|
||||
| | 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:
|
||||
For enhanced hybrid search with BM25 keyword matching and entity extraction, install with NLP support:
|
||||
|
||||
```bash
|
||||
pip install "mem0ai[nlp]"
|
||||
pip install mem0ai[nlp]
|
||||
python -m spacy download en_core_web_sm
|
||||
```
|
||||
|
||||
## Platform or open source
|
||||
|
||||
| | Platform (`MemoryClient`) | Open source (`Memory`) |
|
||||
|---|---|---|
|
||||
| Import | `from mem0 import MemoryClient` | `from mem0 import Memory` |
|
||||
| Where memories live | Mem0's hosted API | Your configured vector store |
|
||||
| Required key | `MEM0_API_KEY` | `OPENAI_API_KEY` with the defaults, or keys for your chosen providers |
|
||||
| Extraction | Managed and asynchronous | Runs synchronously against your configured LLM |
|
||||
| Best for | Zero-ops production use | Local development and custom infrastructure |
|
||||
|
||||
## Platform quickstart
|
||||
|
||||
Set `MEM0_API_KEY`, then add a conversation:
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key=os.environ["MEM0_API_KEY"])
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "I am vegetarian and allergic to nuts."},
|
||||
{"role": "assistant", "content": "I will remember that."},
|
||||
]
|
||||
result = client.add(messages, user_id="alex")
|
||||
print(result)
|
||||
```
|
||||
|
||||
Hosted `add()` queues extraction and usually returns an `event_id` with `status: "PENDING"`. Do not search immediately after `add()`. Wait for processing to finish in the dashboard, or use a [`memory_add` webhook](https://docs.mem0.ai/platform/features/webhooks), then search:
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key=os.environ["MEM0_API_KEY"])
|
||||
results = client.search(
|
||||
"What does Alex eat?",
|
||||
filters={"user_id": "alex"},
|
||||
top_k=5,
|
||||
)
|
||||
print(results["results"])
|
||||
```
|
||||
|
||||
`search()` and `get_all()` take entity IDs inside `filters`. `add()` and `delete_all()` take `user_id`, `agent_id`, or `run_id` as top-level keyword arguments.
|
||||
|
||||
## Open-source quickstart
|
||||
|
||||
Set `OPENAI_API_KEY` before using the default OpenAI LLM and embedder:
|
||||
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
memory = Memory()
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "I am vegetarian and allergic to nuts."},
|
||||
{"role": "assistant", "content": "I will remember that."},
|
||||
]
|
||||
memory.add(messages, user_id="alex")
|
||||
|
||||
results = memory.search(
|
||||
"What does Alex eat?",
|
||||
filters={"user_id": "alex"},
|
||||
top_k=5,
|
||||
)
|
||||
print(results["results"])
|
||||
```
|
||||
|
||||
The default `Memory` configuration uses OpenAI `gpt-5-mini`, OpenAI `text-embedding-3-small`, local Qdrant storage, and a SQLite history database. Pass a `MemoryConfig` or use `Memory.from_config()` to change the LLM, embedder, vector store, history path, or reranker.
|
||||
|
||||
## Configuration and features
|
||||
|
||||
| Feature | Documentation |
|
||||
|---|---|
|
||||
| Memory operations: `add`, `search`, `get`, `get_all`, `update`, `delete`, `delete_all`, `history` | [Python quickstart](https://docs.mem0.ai/open-source/python-quickstart) |
|
||||
| Entity scoping with `user_id`, `agent_id`, and `run_id` | [Entity-scoped memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) |
|
||||
| Metadata and filters | [Metadata filtering](https://docs.mem0.ai/open-source/features/metadata-filtering) |
|
||||
| Async clients: `AsyncMemory` and `AsyncMemoryClient` | [Async memory](https://docs.mem0.ai/open-source/features/async-memory) |
|
||||
| LLMs, embedders, vector stores, and rerankers | [Components](https://docs.mem0.ai/components/llms/overview) |
|
||||
| Graph memory | [Graph memory](https://docs.mem0.ai/platform/features/graph-memory) |
|
||||
| Custom instructions | [Custom instructions](https://docs.mem0.ai/open-source/features/custom-instructions) |
|
||||
| Multimodal input | [Multimodal support](https://docs.mem0.ai/open-source/features/multimodal-support) |
|
||||
| Platform webhooks, export, feedback, expiration, and custom categories | [Platform features](https://docs.mem0.ai/platform/features) |
|
||||
|
||||
## Benchmarks
|
||||
|
||||
<p align="center">
|
||||
<a href="https://mem0.ai/research"><strong>Benchmarking Mem0's token-efficient memory algorithm</strong></a>
|
||||
</p>
|
||||
|
||||
| Benchmark | Old | New | Tokens | Latency p50 |
|
||||
|---|---:|---:|---:|---:|
|
||||
| **LoCoMo** | 71.4 | **92.5** | 7.0K | 0.88s |
|
||||
| **LongMemEval** | 67.8 | **94.4** | 6.8K | 1.09s |
|
||||
| **BEAM (1M)** | n/a | **64.1** | 6.7K | 1.00s |
|
||||
| **BEAM (10M)** | n/a | **48.6** | 6.9K | 1.05s |
|
||||
|
||||
All benchmarks use the same production-representative model stack, single-pass retrieval, and a top-200 retrieval budget. Scores reflect the managed Platform, which includes proprietary optimizations not available in the open-source SDK. Open-source results should show similar directional gains, but may not match these scores.
|
||||
|
||||
The current algorithm uses single-pass ADD-only extraction, first-class agent facts, entity linking, multi-signal retrieval, and temporal reasoning. Read the [research paper](https://mem0.ai/research), the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3), or the open-source [evaluation framework](https://github.com/mem0ai/memory-benchmarks).
|
||||
|
||||
## Self-hosted server
|
||||
|
||||
Run Mem0 as a FastAPI service with PostgreSQL, pgvector, and Neo4j:
|
||||
Install sdk via npm:
|
||||
|
||||
```bash
|
||||
# Recommended: start the stack, create an admin, and issue the first API key.
|
||||
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, then finish setup in the browser wizard.
|
||||
cd server && docker compose up -d
|
||||
# Manual: start the stack and finish setup via the browser wizard.
|
||||
cd server && docker compose up -d # http://localhost:3000
|
||||
```
|
||||
|
||||
Self-hosted authentication is enabled by default. See the [self-hosted documentation](https://docs.mem0.ai/open-source/overview) and [upgrade notes](https://docs.mem0.ai/open-source/setup#upgrade-notes).
|
||||
See the [self-hosted docs](https://docs.mem0.ai/open-source/overview) for configuration.
|
||||
|
||||
## CLI
|
||||
### Cloud Platform
|
||||
|
||||
Manage hosted memories from your terminal:
|
||||
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
|
||||
3. Using hosted Qdrant vectors? See the [Platform migration guide](https://docs.mem0.ai/migration/oss-to-platform) to import them into Mem0 Platform.
|
||||
|
||||
### CLI
|
||||
|
||||
Manage memories from your terminal:
|
||||
|
||||
```bash
|
||||
pip install mem0-cli
|
||||
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
|
||||
```
|
||||
|
||||
AI agents can create an account without email or a dashboard:
|
||||
See the [CLI documentation](https://docs.mem0.ai/platform/cli) for the full command reference.
|
||||
|
||||
```bash
|
||||
mem0 init --agent --agent-caller claude-code
|
||||
```
|
||||
### Agent Skills
|
||||
|
||||
The human owner can claim the account later with `mem0 init --email <their-email>`. The API key and memories remain unchanged. See the [CLI documentation](https://docs.mem0.ai/platform/cli) and [agent signup guide](https://docs.mem0.ai/platform/agent-signup).
|
||||
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:
|
||||
|
||||
## Agent skills
|
||||
|
||||
Install reference skills to give compatible coding assistants Mem0 context:
|
||||
**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
|
||||
```
|
||||
|
||||
Install pipeline skills for end-to-end workflows:
|
||||
**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
|
||||
@@ -212,30 +189,72 @@ npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
|
||||
```
|
||||
|
||||
See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding).
|
||||
Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. Use `/mem0-oss-to-platform` to migrate an existing project from Mem0 OSS to the hosted Platform SDK. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
|
||||
|
||||
## Integrations and demos
|
||||
### Basic Usage
|
||||
|
||||
- [ChatGPT with Memory demo](https://mem0.dev/demo)
|
||||
- [Browser extension](https://chromewebstore.google.com/detail/onihkkbipkfeijkadecaafbgagkhglop?utm_source=item-share-cb)
|
||||
- [LangGraph integration](https://docs.mem0.ai/integrations/langgraph)
|
||||
- [CrewAI integration](https://docs.mem0.ai/integrations/crewai)
|
||||
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).
|
||||
|
||||
## Documentation and help
|
||||
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.
|
||||
|
||||
- [Python quickstart](https://docs.mem0.ai/open-source/python-quickstart)
|
||||
- [Platform quickstart](https://docs.mem0.ai/platform/quickstart)
|
||||
- [API reference](https://docs.mem0.ai/api-reference)
|
||||
- [Discord](https://mem0.dev/DiG)
|
||||
- [GitHub issues](https://github.com/mem0ai/mem0/issues)
|
||||
- Email: founders@mem0.ai
|
||||
First step is to instantiate the memory:
|
||||
|
||||
## Contributing
|
||||
```python
|
||||
from openai import OpenAI
|
||||
from mem0 import Memory
|
||||
|
||||
Read [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) before opening an issue or pull request.
|
||||
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},
|
||||
@@ -245,6 +264,6 @@ Read [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md)
|
||||
}
|
||||
```
|
||||
|
||||
## License
|
||||
## ⚖️ License
|
||||
|
||||
Apache 2.0. See [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE).
|
||||
Apache 2.0 — see the [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE) file for details.
|
||||
|
||||
+196
@@ -1315,6 +1315,202 @@
|
||||
{
|
||||
"source": "/api-reference/entities/get-entities",
|
||||
"destination": "/api-reference/entities/get-users"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/event/get-events",
|
||||
"destination": "/api-reference/events/get-events"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/entities/delete-users",
|
||||
"destination": "/api-reference/entities/delete-user"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/organization/update-organization-member",
|
||||
"destination": "/api-reference/organization/update-org-member"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/organization/get-organizations",
|
||||
"destination": "/api-reference/organization/get-orgs"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/entities/get-user",
|
||||
"destination": "/api-reference/entities/get-users"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/export/create-memory-export",
|
||||
"destination": "/api-reference/memory/create-memory-export"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/mcp",
|
||||
"destination": "/platform/mem0-mcp"
|
||||
},
|
||||
{
|
||||
"source": "/platform/api-reference",
|
||||
"destination": "/api-reference"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memory/add-memory",
|
||||
"destination": "/api-reference/memory/add-memories"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/overview",
|
||||
"destination": "/api-reference"
|
||||
},
|
||||
{
|
||||
"source": "/platform/reference/python-sdk",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/platform/python",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/memory",
|
||||
"destination": "/platform/features/memory-decay"
|
||||
},
|
||||
{
|
||||
"source": "/platform/api-reference/python",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/webhooks",
|
||||
"destination": "/api-reference/webhook/create-webhook"
|
||||
},
|
||||
{
|
||||
"source": "/platform/python-quickstart",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/organization/delete-org-project",
|
||||
"destination": "/api-reference/organization/delete-org"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/scoping",
|
||||
"destination": "/platform/features/entity-scoped-memory"
|
||||
},
|
||||
{
|
||||
"source": "/platform/quickstart/python",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memory/history",
|
||||
"destination": "/api-reference/memory/history-memory"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/entities/get-user-memories",
|
||||
"destination": "/api-reference/entities/get-users"
|
||||
},
|
||||
{
|
||||
"source": "/platform/api-reference/webhooks/create",
|
||||
"destination": "/api-reference/webhook/create-webhook"
|
||||
},
|
||||
{
|
||||
"source": "/platform/python/quickstart",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memory/batch-update-memory",
|
||||
"destination": "/api-reference/memory/batch-update"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memories/add",
|
||||
"destination": "/api-reference/memory/add-memories"
|
||||
},
|
||||
{
|
||||
"source": "/python-quickstart",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/webhook/list-webhooks",
|
||||
"destination": "/api-reference/webhook/get-webhook"
|
||||
},
|
||||
{
|
||||
"source": "/platform/reference/memory/delete-memory",
|
||||
"destination": "/api-reference/memory/delete-memory"
|
||||
},
|
||||
{
|
||||
"source": "/platform/webhooks",
|
||||
"destination": "/platform/features/webhooks"
|
||||
},
|
||||
{
|
||||
"source": "/platform/api-reference/memories/add",
|
||||
"destination": "/api-reference/memory/add-memories"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/organization/organizations",
|
||||
"destination": "/api-reference/organization/create-org"
|
||||
},
|
||||
{
|
||||
"source": "/sdk/python/quickstart",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/platform/export",
|
||||
"destination": "/platform/features/memory-export"
|
||||
},
|
||||
{
|
||||
"source": "/open-source/python-memory",
|
||||
"destination": "/open-source/python-quickstart"
|
||||
},
|
||||
{
|
||||
"source": "/platform/reference",
|
||||
"destination": "/api-reference"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memory/get-all-memories",
|
||||
"destination": "/api-reference/memory/get-memories"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/user/delete-users",
|
||||
"destination": "/api-reference/entities/delete-user"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memories/search-memories",
|
||||
"destination": "/api-reference/memory/search-memories"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/organization/update-members",
|
||||
"destination": "/api-reference/project/update-project-member"
|
||||
},
|
||||
{
|
||||
"source": "/platform/api-reference/memory/batch-update",
|
||||
"destination": "/api-reference/memory/batch-update"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memory/search",
|
||||
"destination": "/api-reference/memory/search-memories"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/batch/update-memories",
|
||||
"destination": "/api-reference/memory/batch-update"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/organization/update-organization-member-role",
|
||||
"destination": "/api-reference/project/update-project-member"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/webhooks/delete",
|
||||
"destination": "/api-reference/webhook/delete-webhook"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/memory/delete-all-memories",
|
||||
"destination": "/api-reference/memory/delete-memories"
|
||||
},
|
||||
{
|
||||
"source": "/platform/reference/overview",
|
||||
"destination": "/api-reference"
|
||||
},
|
||||
{
|
||||
"source": "/api-reference/mem0/add-memories",
|
||||
"destination": "/api-reference/memory/add-memories"
|
||||
},
|
||||
{
|
||||
"source": "/platform/mcp/overview",
|
||||
"destination": "/platform/mem0-mcp"
|
||||
},
|
||||
{
|
||||
"source": "/openmemory/mcp/introduction",
|
||||
"destination": "https://mem0.ai/blog/introducing-openmemory-mcp"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -13,6 +13,7 @@ Agent and editor integrations. Each subdirectory is self-contained: its own `pac
|
||||
| `n8n-nodes-mem0/` | `@mem0/n8n-nodes-mem0` | tsc | ESLint (n8n-nodes-base) | none |
|
||||
| `zapier-mem0/` | `@mem0/zapier` | tsc | none | offline unit tests + `zapier validate` |
|
||||
| `mem0-strands/` | `mem0-strands` (PyPI) | hatch | Ruff + mypy | pytest |
|
||||
| `minimax-mem0/` | MiniMax Marketplace plugin | none (declarative) | none | JSON/manifest validation |
|
||||
|
||||
pnpm everywhere except `.opencode-plugin/` (Bun) and `mem0-strands/` (Python: pip / hatch). Never npm, never yarn.
|
||||
|
||||
@@ -44,6 +45,7 @@ Run the type check after every TypeScript change: `pnpm run typecheck` or `tsc -
|
||||
- **`openclaw/`**, **`pi-agent-plugin/`**, **`deepseek-plugin/`** are editor and agent plugins with the same shape. `deepseek-plugin/` registers Mem0 search/add tools as a native DeepSeek Harness (Cordis) plugin.
|
||||
- **`n8n-nodes-mem0/`** is an n8n community node: add, search, get, update, delete.
|
||||
- **`zapier-mem0/`** is a Zapier Platform CLI app: add, search, get, delete. It deploys to Zapier, not npm, so it is **not** in the release router. Deploy it with `gh workflow run zapier-mem0-cd.yml --ref main` (needs the `ZAPIER_DEPLOY_KEY` secret).
|
||||
- **`minimax-mem0/`** is a declarative MiniMax Marketplace plugin (no build step): a `.minimax-plugin/plugin.json` manifest plus an MCP capability pointing at the hosted Mem0 MCP server (`https://mcp.mem0.ai/mcp/`) and a memory Skill. It ships to the MiniMax Marketplace as a subdirectory, not through the release router or npm/PyPI.
|
||||
- **`mem0-strands/`** is a native Strands `MemoryStore` (Python, published to PyPI as `mem0-strands`). It plugs into the Strands `MemoryManager` for automatic recall and server-side extraction, over the hosted Mem0 platform or self-hosted Mem0 OSS. The package lives under `mem0-strands/python/`.
|
||||
|
||||
## Adding an integration
|
||||
|
||||
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"schemaVersion": 1,
|
||||
"name": "mem0",
|
||||
"displayName": "Mem0 Memory",
|
||||
"version": "0.1.0",
|
||||
"description": "Persistent long-term memory for your agent: remember preferences, decisions, and project context across sessions, and recall them on demand.",
|
||||
"author": "Mem0",
|
||||
"icon": "icon.png",
|
||||
"category": "Productivity",
|
||||
"exampleQueries": [
|
||||
"Remember that I prefer TypeScript and pnpm",
|
||||
"What do you remember about my project?",
|
||||
"Update what you know about my deployment setup"
|
||||
],
|
||||
"apps": [],
|
||||
"mcpServers": ["mem0.mcp.json"],
|
||||
"skills": ["skills/memory/SKILL.md"]
|
||||
}
|
||||
@@ -0,0 +1,201 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
and distribution as defined by Sections 1 through 9 of this document.
|
||||
|
||||
"Licensor" shall mean the copyright owner or entity authorized by
|
||||
the copyright owner that is granting the License.
|
||||
|
||||
"Legal Entity" shall mean the union of the acting entity and all
|
||||
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|
||||
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|
||||
"control" means (i) the power, direct or indirect, to cause the
|
||||
direction or management of such entity, whether by contract or
|
||||
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
||||
outstanding shares, or (iii) beneficial ownership of such entity.
|
||||
|
||||
"You" (or "Your") shall mean an individual or Legal Entity
|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
"Work" shall mean the work of authorship, whether in Source or
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|
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implied, including, without limitation, any warranties or conditions
|
||||
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
|
||||
PARTICULAR PURPOSE. You are solely responsible for determining the
|
||||
appropriateness of using or redistributing the Work and assume any
|
||||
risks associated with Your exercise of permissions under this License.
|
||||
|
||||
8. Limitation of Liability. In no event and under no legal theory,
|
||||
whether in tort (including negligence), contract, or otherwise,
|
||||
unless required by applicable law (such as deliberate and grossly
|
||||
negligent acts) or agreed to in writing, shall any Contributor be
|
||||
liable to You for damages, including any direct, indirect, special,
|
||||
incidental, or consequential damages of any character arising as a
|
||||
result of this License or out of the use or inability to use the
|
||||
Work (including but not limited to damages for loss of goodwill,
|
||||
work stoppage, computer failure or malfunction, or any and all
|
||||
other commercial damages or losses), even if such Contributor
|
||||
has been advised of the possibility of such damages.
|
||||
|
||||
9. Accepting Warranty or Additional Liability. While redistributing
|
||||
the Work or Derivative Works thereof, You may choose to offer,
|
||||
and charge a fee for, acceptance of support, warranty, indemnity,
|
||||
or other liability obligations and/or rights consistent with this
|
||||
License. However, in accepting such obligations, You may act only
|
||||
on Your own behalf and on Your sole responsibility, not on behalf
|
||||
of any other Contributor, and only if You agree to indemnify,
|
||||
defend, and hold each Contributor harmless for any liability
|
||||
incurred by, or claims asserted against, such Contributor by reason
|
||||
of your accepting any such warranty or additional liability.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
APPENDIX: How to apply the Apache License to your work.
|
||||
|
||||
To apply the Apache License to your work, attach the following
|
||||
boilerplate notice, with the fields enclosed by brackets "[]"
|
||||
replaced with your own identifying information. (Don't include
|
||||
the brackets!) The text should be enclosed in the appropriate
|
||||
comment syntax for the file format. We also recommend that a
|
||||
file or class name and description of purpose be included on the
|
||||
same "printed page" as the copyright notice for easier
|
||||
identification within third-party archives.
|
||||
|
||||
Copyright [2023] [Taranjeet Singh]
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
@@ -0,0 +1,35 @@
|
||||
# minimax-mem0
|
||||
|
||||
The [Mem0](https://mem0.ai) plugin for the MiniMax Marketplace (MiniMax Code desktop and MiniMax Agent cloud).
|
||||
|
||||
It gives a MiniMax agent persistent long-term memory: it remembers the user's preferences, decisions, and project context across sessions and recalls them on demand.
|
||||
|
||||
## What's in the package
|
||||
|
||||
This is a declarative MiniMax plugin (no build step). It follows the MiniMax plugin spec:
|
||||
|
||||
Built to the [Agent Plugins 1.0](https://agent-plugins.org/specification) standard, which the MiniMax runtime imports and validates against.
|
||||
|
||||
```
|
||||
minimax-mem0/
|
||||
plugin.json # manifest (Agent Plugins 1.0, at plugin root)
|
||||
mcp.json # remote MCP: https://mcp.mem0.ai/mcp/ (auto-discovered at root)
|
||||
icon.png # square Mem0 icon
|
||||
skills/
|
||||
memory/
|
||||
SKILL.md # when to recall / save / update memory
|
||||
```
|
||||
|
||||
Two capabilities:
|
||||
|
||||
- **MCP** (`mcp.json`) points at Mem0's hosted MCP server (`https://mcp.mem0.ai/mcp/`), which exposes the memory tools (`add_memory`, `search_memories`, `update_memory`, `delete_memory`, and more). No secrets are stored in the package.
|
||||
- **Skill** (`skills/memory/SKILL.md`) tells the agent when to search, save, update, and delete memory so recall happens automatically.
|
||||
|
||||
## Authentication
|
||||
|
||||
Mem0's MCP server requires authentication. Per the MiniMax plugin spec, no credentials are placed in this package; the MiniMax client authorizes the user's Mem0 account through the MCP OAuth flow on first use. Users get a Mem0 account from the [Mem0 dashboard](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-minimax).
|
||||
|
||||
## Links
|
||||
|
||||
- Mem0: https://mem0.ai
|
||||
- Mem0 MCP docs: https://docs.mem0.ai/platform/mem0-mcp
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 29 KiB |
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"$schema": "https://agent-plugins.org/schemas/1.0.0/mcp.schema.json",
|
||||
"mcpServers": {
|
||||
"mem0": {
|
||||
"type": "streamable-http",
|
||||
"url": "https://mcp.mem0.ai/mcp/",
|
||||
"headers": {
|
||||
"Authorization": "Token ${MEM0_API_KEY}"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"schemaVersion": 1,
|
||||
"mcpServers": {
|
||||
"mem0": {
|
||||
"type": "streamable-http",
|
||||
"url": "https://mcp.mem0.ai/mcp/",
|
||||
"headers": {
|
||||
"Authorization": "Token ${MEM0_API_KEY}"
|
||||
},
|
||||
"description": "Mem0 long-term memory: add, search, update, and delete memories so the agent remembers context across sessions.",
|
||||
"timeout": 30000
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
|
||||
"name": "mem0",
|
||||
"version": "0.1.0",
|
||||
"description": "Persistent long-term memory for your agent: remember preferences, decisions, and project context across sessions, and recall them on demand.",
|
||||
"author": { "name": "Mem0", "email": "support@mem0.ai", "url": "https://mem0.ai" },
|
||||
"homepage": "https://mem0.ai",
|
||||
"repository": "https://github.com/mem0ai/mem0",
|
||||
"license": "Apache-2.0",
|
||||
"keywords": ["memory", "long-term-memory", "personalization", "mcp"]
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
---
|
||||
name: memory
|
||||
description: Use Mem0 to persist and recall long-term memory across sessions - the user's preferences, decisions, project context, and durable facts.
|
||||
---
|
||||
|
||||
You have access to Mem0 long-term memory through the `mem0` MCP server. Use it so the
|
||||
user does not have to repeat themselves across sessions.
|
||||
|
||||
## When to recall (search memory)
|
||||
|
||||
At the start of a task, and whenever the user references something they expect you to
|
||||
already know ("my project", "like last time", "the usual"), search memory before asking.
|
||||
|
||||
- Call the memory search tool with a short natural-language query describing what you
|
||||
need (e.g. "preferred language and package manager", "deployment process").
|
||||
- Use the returned memories as context. Do not announce that you searched; just apply
|
||||
what you found.
|
||||
- If nothing relevant comes back, proceed and ask the user only for what is missing.
|
||||
|
||||
## When to save (add memory)
|
||||
|
||||
Save durable facts as they come up, without being asked:
|
||||
|
||||
- Stated preferences ("I use pnpm", "always use TypeScript", "no comments in code").
|
||||
- Decisions and conventions ("we deploy on Fridays", "the API base is X").
|
||||
- Stable facts about the user, their team, or their project (stack, roles, goals).
|
||||
|
||||
Do not save transient or trivial details (a one-off value, a temporary path, secrets,
|
||||
or anything the user would not want remembered). Save one clear fact per memory, phrased
|
||||
so it makes sense on its own later.
|
||||
|
||||
## When to update or delete
|
||||
|
||||
If a new statement contradicts something you recalled, update that memory rather than
|
||||
adding a duplicate. If the user says to forget something, delete it.
|
||||
|
||||
## Boundaries
|
||||
|
||||
- Never store secrets, credentials, API keys, or sensitive personal data.
|
||||
- Memory is scoped to the connected Mem0 account; keep one user's memories to that user.
|
||||
- Prefer recalling silently over interrupting the user to confirm what you already know.
|
||||
+36
-207
@@ -1,235 +1,64 @@
|
||||
# Mem0 TypeScript SDK
|
||||
# Mem0 - The Memory Layer for Your AI Apps
|
||||
|
||||
<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>
|
||||
Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. We offer both cloud and open-source solutions to cater to different needs.
|
||||
|
||||
<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>
|
||||
See the complete [OSS Docs](https://docs.mem0.ai/open-source/node-quickstart).
|
||||
See the complete [Platform API Reference](https://docs.mem0.ai/api-reference).
|
||||
|
||||
<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://www.npmjs.com/package/mem0ai">
|
||||
<img src="https://img.shields.io/npm/dm/mem0ai" alt="Mem0 npm 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://www.npmjs.com/package/mem0ai" target="blank">
|
||||
<img src="https://img.shields.io/npm/v/mem0ai?color=%2334D058&label=npm%20package" alt="npm package version">
|
||||
</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>
|
||||
## 1. Installation
|
||||
|
||||
Mem0 gives AI assistants and agents persistent memory. It extracts useful facts from conversations, scopes them to a user, agent, or run, and retrieves the relevant facts for later interactions. The TypeScript package, `mem0ai` on npm, includes `MemoryClient` for the hosted Mem0 Platform, imported from `mem0ai`, and `Memory` for open-source, in-process memory, imported from `mem0ai/oss`.
|
||||
|
||||
## Requirements
|
||||
|
||||
- Node.js 20 or later
|
||||
- Hosted Platform: `MEM0_API_KEY` from the [Mem0 dashboard](https://app.mem0.ai/dashboard/api-keys)
|
||||
- Open source with the default providers: `OPENAI_API_KEY`
|
||||
|
||||
## Install
|
||||
For the open-source version, you can install the Mem0 package using npm:
|
||||
|
||||
```bash
|
||||
npm install mem0ai
|
||||
npm i mem0ai
|
||||
```
|
||||
|
||||
## Platform or open source
|
||||
## 2. API Key Setup
|
||||
|
||||
| | Platform (`MemoryClient`) | Open source (`Memory`) |
|
||||
| ------------------- | --------------------------------------- | --------------------------------------------------------------------- |
|
||||
| Import | `import { MemoryClient } from "mem0ai"` | `import { Memory } from "mem0ai/oss"` |
|
||||
| Where memories live | Mem0's hosted API | Your configured vector store |
|
||||
| Required key | `MEM0_API_KEY` | `OPENAI_API_KEY` with the defaults, or keys for your chosen providers |
|
||||
| Extraction | Managed and asynchronous | Runs against your configured LLM |
|
||||
| Best for | Zero-ops production use | Local development and custom infrastructure |
|
||||
For the cloud offering, sign in to [Mem0 Platform](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-ts-readme) to obtain your API Key.
|
||||
|
||||
## Platform quickstart
|
||||
## 3. Client Features
|
||||
|
||||
Set `MEM0_API_KEY`, then add a conversation:
|
||||
### Cloud Offering
|
||||
|
||||
```ts
|
||||
import { MemoryClient, type Message } from "mem0ai";
|
||||
The cloud version provides a comprehensive set of features, including:
|
||||
|
||||
const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
|
||||
- **Memory Operations**: Perform CRUD operations on memories.
|
||||
- **Search Capabilities**: Search for relevant memories using advanced filters.
|
||||
- **Memory History**: Track changes to memories over time.
|
||||
- **Error Handling**: Robust error handling for API-related issues.
|
||||
- **Async/Await Support**: All methods return promises for easy integration.
|
||||
|
||||
const messages: Message[] = [
|
||||
{ role: "user", content: "I am vegetarian and allergic to nuts." },
|
||||
{ role: "assistant", content: "I will remember that." },
|
||||
];
|
||||
await client.add(messages, { userId: "alex" });
|
||||
```
|
||||
### Open-Source Offering
|
||||
|
||||
Hosted `add()` queues extraction and usually returns an `eventId` with `status: "PENDING"`. Do not search immediately after `add()`. Wait for processing to finish in the dashboard, or use a [`memory_add` webhook](https://docs.mem0.ai/platform/features/webhooks), then search:
|
||||
The open-source version includes the following top features:
|
||||
|
||||
```ts
|
||||
import { MemoryClient } from "mem0ai";
|
||||
- **Memory Management**: Add, update, delete, and retrieve memories.
|
||||
- **Vector Store Integration**: Supports various vector store providers for efficient memory retrieval.
|
||||
- **LLM Support**: Integrates with multiple LLM providers for generating responses.
|
||||
- **Customizable Configuration**: Easily configure memory settings and providers.
|
||||
- **SQLite Storage**: Use SQLite for memory history management.
|
||||
|
||||
const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
|
||||
const results = await client.search("What does Alex eat?", {
|
||||
filters: { user_id: "alex" },
|
||||
topK: 5,
|
||||
});
|
||||
console.log(results.results);
|
||||
```
|
||||
## 4. Memory Operations
|
||||
|
||||
`search()` and `getAll()` take entity IDs inside `filters` with snake_case keys. `add()` and `deleteAll()` take `userId`, `agentId`, or `runId` as top-level camelCase options.
|
||||
Mem0 provides a simple and customizable interface for performing memory operations. You can create long-term and short-term memories, search for relevant memories, and manage memory history.
|
||||
|
||||
## Open-source quickstart
|
||||
## 5. Error Handling
|
||||
|
||||
Set `OPENAI_API_KEY` before using the default OpenAI LLM and embedder:
|
||||
The MemoryClient throws errors for any API-related issues. You can catch and handle these errors effectively.
|
||||
|
||||
```ts
|
||||
import { Memory } from "mem0ai/oss";
|
||||
## 6. Using with async/await
|
||||
|
||||
const memory = new Memory();
|
||||
All methods of the MemoryClient return promises, allowing for seamless integration with async/await syntax.
|
||||
|
||||
const messages = [
|
||||
{ role: "user", content: "I am vegetarian and allergic to nuts." },
|
||||
{ role: "assistant", content: "I will remember that." },
|
||||
];
|
||||
await memory.add(messages, { userId: "alex" });
|
||||
## 7. Testing the Client
|
||||
|
||||
const results = await memory.search("What does Alex eat?", {
|
||||
filters: { user_id: "alex" },
|
||||
topK: 5,
|
||||
});
|
||||
console.log(results.results);
|
||||
```
|
||||
To test the MemoryClient in a Node.js environment, you can create a simple script to verify the functionality of memory operations.
|
||||
|
||||
The default `Memory` configuration uses OpenAI `gpt-5-mini`, OpenAI `text-embedding-3-small`, a SQLite-backed vector store at `~/.mem0/vector_store.db`, and a SQLite history database at `memory.db`. Pass a config object to `Memory` to change the LLM, embedder, vector store, history path, or reranker.
|
||||
## Getting Help
|
||||
|
||||
## Configuration and features
|
||||
If you have any questions or need assistance, please reach out to us:
|
||||
|
||||
| Feature | Documentation |
|
||||
| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
|
||||
| Memory operations: `add`, `search`, `get`, `getAll`, `update`, `delete`, `deleteAll`, `history`, all async and Promise-based | [Node quickstart](https://docs.mem0.ai/open-source/node-quickstart) |
|
||||
| Entity scoping with `userId`, `agentId`, and `runId` | [Entity-scoped memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) |
|
||||
| Metadata and filters | [Metadata filtering](https://docs.mem0.ai/open-source/features/metadata-filtering) |
|
||||
| LLMs, embedders, vector stores, and rerankers | [Components](https://docs.mem0.ai/components/llms/overview) |
|
||||
| Graph memory | [Graph memory](https://docs.mem0.ai/platform/features/graph-memory) |
|
||||
| Custom instructions | [Custom instructions](https://docs.mem0.ai/open-source/features/custom-instructions) |
|
||||
| Multimodal input | [Multimodal support](https://docs.mem0.ai/open-source/features/multimodal-support) |
|
||||
| Platform webhooks, export, feedback, expiration, and custom categories | [Platform features](https://docs.mem0.ai/platform/features) |
|
||||
|
||||
## Benchmarks
|
||||
|
||||
<p align="center">
|
||||
<a href="https://mem0.ai/research"><strong>Benchmarking Mem0's token-efficient memory algorithm</strong></a>
|
||||
</p>
|
||||
|
||||
| Benchmark | Old | New | Tokens | Latency p50 |
|
||||
| --------------- | ---: | -------: | -----: | ----------: |
|
||||
| **LoCoMo** | 71.4 | **92.5** | 7.0K | 0.88s |
|
||||
| **LongMemEval** | 67.8 | **94.4** | 6.8K | 1.09s |
|
||||
| **BEAM (1M)** | n/a | **64.1** | 6.7K | 1.00s |
|
||||
| **BEAM (10M)** | n/a | **48.6** | 6.9K | 1.05s |
|
||||
|
||||
All benchmarks use the same production-representative model stack, single-pass retrieval, and a top-200 retrieval budget. Scores reflect the managed Platform, which includes proprietary optimizations not available in the open-source SDK. Open-source results should show similar directional gains, but may not match these scores.
|
||||
|
||||
The current algorithm uses single-pass ADD-only extraction, first-class agent facts, entity linking, multi-signal retrieval, and temporal reasoning. Read the [research paper](https://mem0.ai/research), the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3), or the open-source [evaluation framework](https://github.com/mem0ai/memory-benchmarks).
|
||||
|
||||
## Self-hosted server
|
||||
|
||||
Run Mem0 as a FastAPI service with PostgreSQL, pgvector, and Neo4j:
|
||||
|
||||
```bash
|
||||
# Recommended: start the stack, create an admin, and issue the first API key.
|
||||
cd server && make bootstrap
|
||||
|
||||
# Manual: start the stack, then finish setup in the browser wizard.
|
||||
cd server && docker compose up -d
|
||||
```
|
||||
|
||||
Self-hosted authentication is enabled by default. See the [self-hosted documentation](https://docs.mem0.ai/open-source/overview) and [upgrade notes](https://docs.mem0.ai/open-source/setup#upgrade-notes).
|
||||
|
||||
## CLI
|
||||
|
||||
Manage hosted memories from your terminal:
|
||||
|
||||
```bash
|
||||
npm install -g @mem0/cli
|
||||
|
||||
mem0 init
|
||||
mem0 add "Prefers dark mode and vim keybindings" --user-id alice
|
||||
mem0 search "What does Alice prefer?" --user-id alice
|
||||
```
|
||||
|
||||
AI agents can create an account without email or a dashboard:
|
||||
|
||||
```bash
|
||||
mem0 init --agent --agent-caller claude-code
|
||||
```
|
||||
|
||||
The human owner can claim the account later with `mem0 init --email <their-email>`. The API key and memories remain unchanged. See the [CLI documentation](https://docs.mem0.ai/platform/cli) and [agent signup guide](https://docs.mem0.ai/platform/agent-signup).
|
||||
|
||||
## Agent skills
|
||||
|
||||
Install reference skills to give compatible coding assistants Mem0 context:
|
||||
|
||||
```bash
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
|
||||
```
|
||||
|
||||
Install pipeline skills for end-to-end workflows:
|
||||
|
||||
```bash
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
|
||||
```
|
||||
|
||||
See the [skills catalog](../skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding).
|
||||
|
||||
## Integrations and demos
|
||||
|
||||
- [ChatGPT with Memory demo](https://mem0.dev/demo)
|
||||
- [Browser extension](https://chromewebstore.google.com/detail/onihkkbipkfeijkadecaafbgagkhglop?utm_source=item-share-cb)
|
||||
- [LangGraph integration](https://docs.mem0.ai/integrations/langgraph)
|
||||
- [CrewAI integration](https://docs.mem0.ai/integrations/crewai)
|
||||
|
||||
## Documentation and help
|
||||
|
||||
- [Node quickstart](https://docs.mem0.ai/open-source/node-quickstart)
|
||||
- [Platform quickstart](https://docs.mem0.ai/platform/quickstart)
|
||||
- [API reference](https://docs.mem0.ai/api-reference)
|
||||
- [Discord](https://mem0.dev/DiG)
|
||||
- [GitHub issues](https://github.com/mem0ai/mem0/issues)
|
||||
- Email: founders@mem0.ai
|
||||
|
||||
## Contributing
|
||||
|
||||
Read [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) before opening an issue or pull request.
|
||||
|
||||
## Citation
|
||||
|
||||
```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 [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE).
|
||||
- [Join our discord community](https://mem0.ai/discord)
|
||||
- GitHub Issues: [Report bugs or request features](https://github.com/mem0ai/mem0/issues)
|
||||
|
||||
Reference in New Issue
Block a user