Mem0, Open Source Giving Memory to AI Agents [2026]

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Mem0: Adding Long-Term Memory to AI Agents

  • GitHub Stars: 46,900+
  • Language: Python (66.4%), TypeScript (20.7%)
  • License: Apache 2.0

Why This Project is Trending

Mem0 is an open-source memory layer that gives AI agents long-term memory. LLMs forget context after a conversation ends, and Mem0 solves this problem.[GitHub]

It recorded 26% higher accuracy compared to OpenAI Memory in the LOCOMO benchmark.[Mem0 Research] Response speed is 91% faster, and token consumption is reduced by 90%.

3 Key Features

  • Multi-Layered Memory: Separately stores memories by user, session, and agent.
  • Hybrid Search: Combines vector and graph search. Supports 25+ vector DBs.[Mem0 Docs]
  • LLM Auto-Tidying: LLMs handle fact extraction, conflict resolution, and memory merging.

Quick Start

# Python
pip install mem0ai

# JavaScript
npm install mem0ai

The default LLM is OpenAI gpt-4.1-nano. It can be replaced with Anthropic, Ollama, etc.

Where Can You Use It?

Apply it to customer support chatbots to remember previous inquiries. In healthcare, it can be used to track patient history. Companies like Netflix and Lemonade have already adopted it.[Mem0]

It’s a Y Combinator alum and has raised $24 million in funding.[YC]

Things to Note

  • Self-hosting requires vector DB setup. If you don’t have infrastructure experience, the cloud is easier.
  • v1.0.3 is the latest. Test thoroughly before applying to production.

Frequently Asked Questions (FAQ)

Q: What is the difference between Mem0 and general RAG?

A: General RAG retrieves documents to provide context, but Mem0 automatically extracts facts from conversations, resolves conflicts, and updates memories. It differs in that it combines vector and graph search to provide more accurate context, and it can manage user-specific memories separately.

Q: Which LLMs are compatible?

A: It is compatible with over 50 LLM providers, including OpenAI, Anthropic, and Ollama. The default is OpenAI gpt-4.1-nano, but it can be changed in the settings. It supports over 25 vector DBs, including Qdrant, Pinecone, and ChromaDB.

Q: Is it free to use?

A: The open-source version is completely free under the Apache 2.0 license. You need to build your own infrastructure. There is also a managed cloud platform, which has a separate pricing plan. If you have a small project, the open-source version is sufficient.

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References

Meta Unveils Meta Omnilingual ASR, Opening the Era of Speech Recognition for 1600 Languages

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Meta has unveiled ‘Omnilingual ASR’, a speech recognition AI model that recognizes over 1600 languages. Unlike existing speech recognition technologies that focused on a few dozen major languages, this model covers even the world’s minority languages. It’s an attempt to fundamentally change the accessibility of speech AI technology.

Meta revealed the technical details of Omnilingual ASR through its official blog (2026-02-04). This model processes over 1600 languages with a single system. This is a fundamentally different approach from existing multilingual speech recognition models that required separate modules for each language. The key is a training method that combines large-scale unsupervised learning with a small amount of labeled data. It is noteworthy that it achieved a practical level of recognition rate even in low-resource languages with insufficient data.

According to a VentureBeat report (2026-02-05), Meta has released this model as open source. This aligns with Meta’s open-source AI strategy. It means that researchers and developers can use and improve the model. In particular, it can provide practical benefits to speakers of minority languages in Africa, Southeast Asia, and the Pacific Islands. The language barrier for voice-based services such as medical consultations, administrative services, and educational content is expected to be significantly lowered.

The competitive landscape is also interesting. According to a MarkTechPost report (2026-02-04), Mistral AI has also entered the multilingual speech recognition market by launching Voxtral Transcribe 2. A Medium analysis (2026-02-03) predicted that voice AI in 2026 will expand beyond simple dictation to real-time interpretation and emotion analysis. Meta’s Omnilingual ASR has secured the basic physical strength of language coverage in this trend.

The real meaning of Omnilingual ASR lies in inclusiveness rather than the technology itself. Of the approximately 7,000 languages ​​worldwide, only a very small number of languages ​​benefit from digital technology. Supporting 1600 languages ​​is the first step in narrowing that gap by nearly half. If community-based improvements continue with the open-source release, the universalization of speech recognition technology may come sooner than expected.

FAQ

Q: Does Omnilingual ASR support Korean?

A: Since it supports more than 1600 languages, Korean is naturally included. However, the recognition accuracy for each language may vary depending on the amount of training data.

Q: What is the difference from existing speech recognition services?

A: Existing services such as Google and Amazon mainly support less than 100 major languages. Omnilingual ASR processes more than 1600 languages ​​with a single model, so the scale itself is different.

Q: Can general developers also use it?

A: Since Meta has released it as open source, anyone can download and use it. It can be applied not only for research purposes but also for commercial service development.

AI Video Generator Comparison: Sora vs Runway vs Pika – 2026 Complete Analysis

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The AI video generation market in 2026 has officially entered a three-way race. OpenAI’s Sora, Runway, and Pika Labs are each vying for creators’ attention with their distinct strengths. Here’s a breakdown of the core features and differences between these three tools.

First, Sora is the most talked-about tool in the text-to-video space. According to PXZ AI’s comparative analysis, Sora can generate high-resolution videos up to 60 seconds long and is highly rated for the naturalness of its physical movements. It also delivers stable results in complex scene transitions and camera work. However, it’s noted that its generation speed is relatively slow and its accessibility is still limited.

Runway aims to be an all-in-one platform that combines video editing and generation, based on the Gen-3 Alpha model. LovArt’s review analyzes that Runway excels in editing functions using existing video sources. It provides detailed control tools like motion brushes and inpainting, making it practical for professional video creators. It’s also considered a well-balanced choice in terms of functionality for the price.

Pika Labs is popular among individual creators due to its low barrier to entry. According to IPFoxy’s in-depth review, Pika’s strengths are its intuitive interface and fast generation speed. In particular, its image-to-video conversion function is excellent and optimized for short clip production. It’s often praised as being suitable for creating short-form content for social media.

Magic Prompt’s guide emphasizes that the quality of the output from these three tools varies greatly depending on how the prompt is written. Sora excels at detailed scene descriptions, Runway at style specification, and Pika responds well to concise prompts. Richly AI advises that choosing the right tool for the job is key.

The AI video generation tool market is expected to become even more competitive in the second half of 2026. As each platform begins to aggressively upgrade its models and compete on price, creators will have a wider range of options. The ability to choose the right tool for the purpose will become increasingly important.

FAQ

Q: Which tool is best for beginners: Sora, Runway, or Pika?

A: Pika Labs offers the most intuitive interface and has a low learning curve, making it suitable for beginners. Its fast generation speed is also a plus.

Q: Which tool has the best professional video editing features?

A: Runway provides detailed editing tools such as motion brushes and inpainting, making it the most advantageous in a professional production environment.

Q: Which tool is best for generating long videos?

A: Sora supports videos up to 60 seconds long and delivers the most stable results in maintaining consistency between scenes.

Big Tech AI Infrastructure Investment Exceeds $65 Billion, The Reality of the 2026 Data Center War

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In 2026, Big Tech’s AI infrastructure investments have surpassed $65 billion. Major players like Microsoft, Google, Meta, and Amazon are pouring astronomical sums into expanding their data centers. The AI race is evolving beyond simple model development into a full-blown infrastructure arms race.

According to Bloomberg’s report, Big Tech’s total AI computing investment for 2026 will reach $65 billion, a more than 40% increase year-over-year. Notably, Google’s parent company, Alphabet, announced a capital expenditure plan of $80 billion for 2026, significantly exceeding Wall Street’s expectations. Yahoo Finance reported an immediate drop in Alphabet’s stock price following this announcement, reflecting investor concerns about short-term profitability. However, Big Tech executives are all singing from the same hymn sheet: the risk of *not* investing in AI infrastructure outweighs the risk of investing. The competition for GPU supply remains fierce, with long-term contracts being signed left and right to secure Nvidia chips. Securing data center locations has also become a new battleground, with massive data center complexes popping up across the US Midwest and Southeast Asia.

TechCrunch has diagnosed 2026 as the year AI transitions from hype to pragmatism. The core question is whether these massive infrastructure investments will translate into actual sales and profits. Failure to recoup these investments could significantly strain Big Tech’s performance. Conversely, if demand for AI services explodes as predicted, the companies that made preemptive investments will dominate the market. Ultimately, this infrastructure investment race will likely be the decisive variable that separates the winners from the losers in the AI ecosystem.

FAQ

Q: What is the scale of Big Tech’s AI infrastructure investment in 2026?

A: According to Bloomberg, the total investment in AI computing by major Big Tech companies is approximately $65 billion. Alphabet alone is planning capital expenditures of $80 billion.

Q: Why are Big Tech companies investing so much money in AI infrastructure?

A: Because the computational power required for AI model training and inference is increasing exponentially. Securing GPUs and data centers is now directly linked to AI competitiveness, making preemptive investment essential.

Q: Is there a possibility that these investments will fail?

A: Yes, there is. If AI service sales don’t grow enough to justify the scale of the investment, profitability could be significantly impaired. Alphabet’s stock price drop is an example of this market concern.

DoNotNotify Open Source Transition, A New Choice for Notification Management [2026]

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DoNotNotify: Rule-Based Android Notification Blocker App

  • GitHub Stars: 92
  • Language: Kotlin
  • License: MIT

Why Open Source?

DoNotNotify is an app that blocks unwanted notifications on Android based on rules. Developer Anuj Jain emphasizes privacy as a core value and has released the source code.[DoNotNotify] The intention is to “allow you to directly verify that the app only does what it says.”

The fact that it’s a completely offline app with no network permissions adds to its trustworthiness. By opening the code, anyone can verify this fact.[GitHub]

Key Features Overview

  • Rule-Based Blocking: Filter notifications by setting up blacklists and whitelists. Regular expression pattern matching is also supported.
  • Time-Based Scheduling: Rules can be activated only at specific times. You can use this to block SNS notifications only during work hours.
  • 40+ Preset Rules: Includes pre-defined rules for popular apps. These are automatically applied when the app is installed.
  • Notification History: Logs blocked notifications. You can track which rule blocked which notification.

Quick Start

# Clone the source and build
git clone https://github.com/anujja/DoNotNotify.git
cd DoNotNotify
./gradlew assembleDebug
./gradlew installDebug

Where Can You Use It?

Useful for users who find the focus mode lacking. It allows for more granular control than Android’s basic Do Not Disturb mode. For example, you can block only advertising notifications from a delivery app and still receive order status notifications.[GitHub]

It supports JSON-based rule export/import, so you can use the same settings on multiple devices. It is also possible to distribute it in bulk to company work phones.

Things to Note

  • Only works on Android 7.0 (API 24) or higher.
  • Requires NotificationListenerService permission. This is similar to accessibility permissions, so it should only be granted to trusted apps.
  • The community is still small with only 92 stars. Issue response speed remains to be seen.

Frequently Asked Questions (FAQ)

Q: Which apps’ notifications can DoNotNotify block?

A: It can filter notifications from any app displayed on the Android system through NotificationListenerService. You can set rules using a blacklist or whitelist method, and use text inclusion search or regular expression matching for notification titles and bodies. Preset rules are also provided for over 40 popular apps.

Q: Is there a risk of privacy breaches?

A: DoNotNotify is a completely offline app that does not request network permissions. The structure prevents notification data from leaving the device. The open-source transition allows anyone to verify the source code, and it is released under the MIT license, allowing for free code auditing.

Q: What is the difference between Android’s basic Do Not Disturb mode?

A: The basic Do Not Disturb mode only controls notifications on an app-by-app basis. DoNotNotify filters based on notification content, so you can selectively block only certain patterns of notifications from the same app. Much finer control is possible with time-based scheduling and rule combinations.

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References

LocalGPT: A 27MB Local AI Assistant Made with Rust [2026]

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LocalGPT: A 27MB Local AI Assistant Made with Rust

  • GitHub Stars: 280
  • Language: Rust (93.1%)
  • License: Apache-2.0

Why This Project is Trending

LocalGPT is an AI assistant that runs locally. Your data doesn’t leave your machine. It’s gaining attention as privacy concerns around cloud AI grow.[GitHub]

It operates as a single 27MB binary, without Node.js, Docker, or Python. The fact that a developer completed it in just 4 nights is also a hot topic.[GitHub]

What Can It Do?

  • Persistent Memory: Stores long-term memory in MEMORY.md. Searches using SQLite FTS5 and sqlite-vec.
  • Autonomous Tasks: Automatically processes task queues via HEARTBEAT.md.
  • Diverse Interfaces: Supports CLI, web UI, desktop GUI, and HTTP API.
  • Multi LLM: Can connect to various providers like Claude, OpenAI, and Ollama.

Quick Start

# Installation
cargo install localgpt

# Interactive Chat
localgpt chat

# Daemon Mode (Web UI + API)
localgpt daemon

Where Would It Be Useful?

Suitable for developers handling sensitive data. Useful in situations where you’re hesitant to upload company code to the cloud.[GitHub]

Also good as a personal knowledge management tool. Markdown-based, so it’s easy to integrate with existing notes.

Things to Keep in Mind

  • Requires a Rust build environment (cargo). This could be a barrier to entry.
  • As an early-stage project with 280 stars, long-term maintenance remains to be seen.
  • Ollama provides a completely local experience, but using Claude/OpenAI means API calls go externally.

Frequently Asked Questions (FAQ)

Q: Does using LocalGPT ensure my data doesn’t go outside?

A: Memory and search data are stored in a local SQLite database. However, if you use Claude or OpenAI as the LLM, the conversation content is sent to their servers. For completely local execution, you should use a local LLM like Ollama. The level of privacy depends on the provider you choose.

Q: How does persistent memory work?

A: It’s based on Markdown files. Long-term memories are stored in MEMORY.md, and structured information in the knowledge directory. Keyword searches are performed with SQLite FTS5, and semantic searches with sqlite-vec. It automatically loads the previous context even when the session changes.

Q: What are the advantages compared to existing AI tools?

A: It can be executed as a single 27MB binary without dependencies. Just one line: `cargo install`. Markdown memory is transparent because you can directly read and edit it. HEARTBEAT autonomous tasks are a rare feature in other local AI tools.

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References

Super Bowl LX Ads: 3 Reasons Why AI Companies Dominated [2026]

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AI Companies Dominate Super Bowl LX Ads

  • OpenAI, Anthropic, Google, Meta, and others heavily participated in Super Bowl advertising.
  • The advertising battle between Anthropic and OpenAI is the biggest topic.
  • A 30-second ad costs over $10 million, a testament to the financial power of the AI industry.

2026 Super Bowl: A Battleground for AI Companies

The biggest trend of this year’s Super Bowl LX is AI. Following the Dot-com Bowl in 2000 and the Crypto Bowl in 2022, 2026 is the AI Bowl. Sixteen tech companies ran ads.[The Verge]

Anthropic vs OpenAI: Competition Extends to Advertising

The hottest topic is the feud between Anthropic and OpenAI. Anthropic created a satirical ad where an AI chatbot suddenly starts advertising insoles. The tagline was “Ads are coming to AI. But not to Claude.”[TechCrunch]

Sam Altman retorted, “They’re selling expensive products to rich people.” OpenAI also aired an ad with the message “Anyone can create anything.”[CNN]

Big Tech Also Deploys AI Across the Board

Google released an emotional ad, ‘New Home,’ utilizing Gemini. Meta aired two ads for AI smart glasses, and Amazon showcased a humorous ad for the Alexa+ chatbot.[Axios]

Even vodka brand Svedka featured an AI-generated ad. The influx of AI companies into the Super Bowl, where a 30-second spot costs over $10 million, is a prime example of the industry’s financial muscle.[TechCrunch]

Frequently Asked Questions (FAQ)

Q: Which companies ran AI ads during the Super Bowl?

A: OpenAI, Anthropic, Google, Meta, Amazon, Svedka, Wix, GenSpark, Base44, and others participated. According to NBCUniversal, tech and AI showed the biggest growth in advertising this year. In total, more than 16 tech companies ran Super Bowl ads.

Q: What was the content of Anthropic’s Super Bowl ad?

A: It was a satirical ad that parodied a scene where an AI chatbot suddenly starts advertising during a conversation. It targeted ChatGPT’s plans to introduce advertising. They ran a 60-second pre-game ad and a 30-second main game ad, emphasizing that Claude is an ad-free model.

Q: How much does a Super Bowl ad cost?

A: The cost of a 30-second ad for the 2026 Super Bowl is over $10 million (approximately ₩14.5 billion). With the potential to reach over 120 million viewers, AI companies are actively using it to secure brand recognition.

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References

Claude Code v2.1.37 Fast Mode Bug Fix — 3 Things to Know [2026]

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Claude Code v2.1.37 Update: Key Takeaways

  • Fixed a bug where /fast wasn’t immediately applied after enabling /extra-usage in Fast mode.
  • Follow-up patch to the Opus 4.6 Fast mode introduced in v2.1.36.
  • Focus on improving developer workflow stability.

Fast Mode Bug: What Exactly Was It?

Anthropic released Claude Code v2.1.37 on February 7th. This update is a patch focused on a single bug fix.[GitHub Release]

It fixed an issue where the /fast command wouldn’t work immediately after activating /extra-usage. Fast mode is a new feature added for Opus 4.6 in v2.1.36.[v2.1.36 Release]

Recent Updates Are Coming in Hot

Eight versions, from v2.1.30 to v2.1.37, have been released in just 5 days. Opus 4.6 and agent team features were added in v2.1.32, and a sandbox security vulnerability was patched in v2.1.34.[Claude Code Releases]

It seems Anthropic is pushing Claude Code to become a terminal-based AI development platform. It currently has over 65,000 stars on GitHub.[GitHub Repository]

How to Apply the Update

Just re-run the installation script, or if you’re a Homebrew user, update with brew upgrade. If you’re using Fast mode, we recommend checking if /fast works immediately after switching /extra-usage.

Frequently Asked Questions (FAQ)

Q: What is Claude Code Fast Mode?

A: It’s a feature introduced in v2.1.36. It increases the response speed in the Opus 4.6 model, making it suitable for simple tasks. It’s activated with the /fast command, allowing users to choose between cost and speed.

Q: What is Extra Usage?

A: It’s a feature that allows you to continue using Claude Code even after exceeding the basic usage allowance. You can turn it on and off with the /extra-usage command. This patch ensures Fast mode is applied immediately after activation.

Q: How do I update to the latest version?

A: For macOS and Linux, re-run the installation script. For Homebrew, use brew upgrade, and for Windows, use winget upgrade Anthropic.ClaudeCode. NPM installation is no longer recommended.

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References

New York State Bill to Halt Data Center Construction for 3 Years: Impact on AI Infrastructure

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New York State Data Center 3-Year Construction Moratorium Bill: Impact on AI Infrastructure

  • New York State legislators have proposed a 3-year construction moratorium on new data centers exceeding 20MW.
  • US electricity demand is projected to increase by 60-80% within 25 years, with data centers being a major contributor.
  • Similar bills are in progress in at least 6 states, including Maryland and Virginia.

Regulations Targeting Big Tech’s Massive Facilities

New York State Senator Liz Krueger and Assemblymember Anna Kelles have introduced bill S9144. It proposes a minimum 3-year moratorium on the construction of new data centers using more than 20 megawatts of power.[TechCrunch]

The target is big tech’s massive facilities like those of Amazon, Meta, and Google. Buffalo’s public research project ‘Empire AI’ is an exception.[Common Dreams]

Driven by Power Shortages and Environmental Concerns

US electricity demand is projected to increase by 60-80% over the next 25 years. Data centers could account for more than half of this increase by 2030. New York State’s power grid already faces a 1.6-gigawatt shortfall.[Common Dreams]

There’s also a potential 19-29% increase in carbon emissions. Senator Krueger pointed out that “data centers have little positive impact on local economies.”[TechCrunch]

Environmental Impact Assessments During the Moratorium

For three years, the New York State Department of Environmental Conservation (DEC) will analyze water usage, greenhouse gas emissions, and noise levels. The Public Service Commission (PSC) will also investigate the impact on electricity rates.[The Hill]

The key is to make companies, not consumers, bear the infrastructure costs.

A Regulatory Trend Spreading Across the US

New York is at least the 6th state to consider a moratorium. Similar bills have emerged in Maryland, Georgia, and Virginia.[Common Dreams]

While the prospect of passage is uncertain, this trend raises important questions about the expansion of AI infrastructure. Hope this is helpful!

Frequently Asked Questions (FAQ)

Q: Does this bill apply to all data centers?

A: No. It only targets large facilities using 20 megawatts or more. Smaller facilities or the public project Empire AI are excluded. It’s a regulation aimed at big tech’s massive data centers.

Q: What will happen during the moratorium?

A: The Department of Environmental Conservation will prepare environmental impact statements on water usage, greenhouse gas emissions, and noise. The Public Service Commission will also investigate the impact on electricity rates and develop new regulatory standards.

Q: Is New York the only state pursuing similar legislation?

A: No. Similar bills have been introduced in Maryland, Georgia, Oklahoma, Virginia, and Vermont. There have been similar movements in Michigan and Wisconsin as well.

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References

Claude Code v2.1.36 Fast Mode Release — 3 Things to Know About Speed and Cost [2026]

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Claude Code v2.1.36, Opus 4.6 Fast Mode: 3 Key Takeaways

  • Anthropic has released Fast mode for Opus 4.6 in Claude Code v2.1.36
  • Offers faster response times with the same model quality
  • Priced at $30 per input token MTok, with a 50% discount until February 16th

What is Fast Mode?

Anthropic deployed Claude Code v2.1.36 on February 7th. The key feature is Fast mode support for Opus 4.6.[GitHub Release] Fast mode isn’t a separate model. It’s the same Opus 4.6, but with API settings tweaked for speed.

You can toggle it on/off by typing /fast in the CLI. When activated, a lightning bolt icon appears next to the prompt.[Claude Code Docs]

The Trade-off: Cost vs. Speed

For under 200K tokens, it costs $30 per input MTok and $150 per output MTok. Above 200K, it increases to $60 for input and $225 for output. A 50% discount applies to all plans until February 16th.[Claude Code Docs]

Subscription plan users can only access it via Extra Usage. It’s not included in the existing usage allowance.

When Should You Use It?

It’s suitable for real-time debugging or rapid code iteration. Regular mode is better for CI/CD or batch jobs. Fast mode is separate from effort level. Fast mode only reduces latency while maintaining quality, while effort level can affect quality by reducing thinking time.[Claude Code Docs]

It’s currently in research preview, so pricing and features may change. Hope this helps!

Frequently Asked Questions (FAQ)

Q: Is Fast mode a different model?

A: No. It uses the same Opus 4.6 model. Only the API settings are different, which speeds things up. There’s no difference in model quality or features, only a reduction in response latency. The trade-off is a higher cost per token.

Q: Is the fee included in my existing subscription?

A: No. Fast mode usage is charged as Extra Usage from the start. It’s billed separately from the subscription plan’s base usage, and Extra Usage must be enabled. A 50% discount applies to all plans until February 16th.

Q: What happens if I hit the rate limit?

A: It automatically switches to regular Opus 4.6 mode. The lightning bolt icon turns gray to indicate a cooldown state. It will automatically reactivate once the cooldown is over. To turn it off manually, type /fast again.

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References