# Mem0 Python SDK
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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.
## Requirements
- 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`
## Install
```bash
pip install mem0ai
```
For enhanced hybrid search with BM25 keyword matching and entity extraction:
```bash
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
Benchmarking Mem0's token-efficient memory algorithm
| 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
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:
```bash
mem0 init --agent --agent-caller claude-code
```
The human owner can claim the account later with `mem0 init --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
- [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
## 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).