Trendshift - Ask AI

base on Universal memory layer for AI Agents <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> <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> <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> · <a href="https://mem0.dev/openmemory">OpenMemory</a> <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> <a href="https://mem0.ai/research">📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →</a> ⚡ +26% Accuracy vs. OpenAI Memory • 🚀 91% Faster • 💰 90% Fewer Tokens > **🎉 mem0ai v1.0.0 is now available!** This major release includes API modernization, improved vector store support, and enhanced GCP integration. [See migration guide →](MIGRATION_GUIDE_v1.0.md) ## 🔥 Research Highlights - **+26% Accuracy** over OpenAI Memory on the LOCOMO benchmark - **91% Faster Responses** than full-context, ensuring low-latency at scale - **90% Lower Token Usage** than full-context, cutting costs without compromise - [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> Choose between our hosted platform or self-hosted package: ### Hosted Platform Get up and running in minutes with automatic updates, analytics, and enterprise security. 1. Sign up on [Mem0 Platform](https://app.mem0.ai) 2. Embed the memory layer via SDK or API keys ### Self-Hosted (Open Source) Install the sdk via pip: ```bash pip install mem0ai ``` Install sdk via npm: ```bash npm install mem0ai ``` ### Basic Usage Mem0 requires an LLM to function, with `gpt-4.1-nano-2025-04-14 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). 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, user_id=user_id, limit=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-4.1-nano-2025-04-14", 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) · [Twitter](https://x.com/mem0ai) - Contact: [email protected] ## 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.", Assign "at most 3 tags" to the expected json: {"id":"11194","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

AI prompts