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We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for...

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andy nguyen

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28,980 просмотров • 7 месяцев назад •via X (Twitter)

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Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup
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Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

andy nguyen

169,596 просмотров • 7 месяцев назад

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
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New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

163,155 просмотров • 7 месяцев назад

MemOS now has a plugin for OpenClaw that enables your AI agents to work on the common memory layer and cut down token usage. - Multiple agents read/write the same memory — no manual context handoff - 72% lower token costs (15.6M → 4.4M on LOCOMO dataset) - Cross-session and long-term memory that actually works Sponsored
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MemOS now has a plugin for OpenClaw that enables your AI agents to work on the common memory layer and cut down token usage. - Multiple agents read/write the same memory — no manual context handoff - 72% lower token costs (15.6M → 4.4M on LOCOMO dataset) - Cross-session and long-term memory that actually works Sponsored

🚨 AI News | TestingCatalog

41,040 просмотров • 8 месяцев назад

LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.
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LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.

Dria

170,847 просмотров • 1 год назад

Chat+Build is launching Living Brain which decouples memory from the model. One persistent brain that works across ChatGPT, Claude, Cursor, Codex, and custom agents. Write in one, retrieve in any. Models become interchangeable compute; the memory stays with you. 92.2% on LoCoMo. 464ms median retrieval. Up to 22.6x fewer tokens per question. Built around neuroplasticity principles (worthiness, placement, resonance, re-entry) with built-in health scores so memory stays current instead of going stale. A real step toward agents that don’t reset every time you switch tools.
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Chat+Build is launching Living Brain which decouples memory from the model. One persistent brain that works across ChatGPT, Claude, Cursor, Codex, and custom agents. Write in one, retrieve in any. Models become interchangeable compute; the memory stays with you. 92.2% on LoCoMo. 464ms median retrieval. Up to 22.6x fewer tokens per question. Built around neuroplasticity principles (worthiness, placement, resonance, re-entry) with built-in health scores so memory stays current instead of going stale. A real step toward agents that don’t reset every time you switch tools.

Documenting AGI

17,737 просмотров • 3 дней назад

Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬
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Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬

OriginTrail

26,198 просмотров • 8 месяцев назад

🚨 KEEP CALM AND AI - AUTONOMOUS AGENTS WITH INFINITE MEMORY We are launching the ability to create arbitrary agents that run on schedule and have access to a persistent and infinite memory The agents will be able to store, retrieve, and update information across sessions and perform repetitive tasks that require persistent memory Another step towards AGI and automating white-collar work 🚀🚀🚀
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🚨 KEEP CALM AND AI - AUTONOMOUS AGENTS WITH INFINITE MEMORY We are launching the ability to create arbitrary agents that run on schedule and have access to a persistent and infinite memory The agents will be able to store, retrieve, and update information across sessions and perform repetitive tasks that require persistent memory Another step towards AGI and automating white-collar work 🚀🚀🚀

Bindu Reddy

13,082 просмотров • 9 месяцев назад

Introducing MemCurrent, a persistent memory architecture designed to help production agents stop starting from zero. MemCurrent combines episodic memory with derived memory across three access patterns: • Plugins to log and load context automatically • Tools the agent calls on demand • An offline “dreaming” pipeline that consolidates memory in the background Agents carry forward user preferences, prior corrections, metric definitions and successful workflows instead of rediscovering them every session. On our internal benchmark built from real enterprise data, MemCurrent triples the agent's pass rate while using 31% fewer tokens and running 26% faster. It's built into Agent Bricks' Memory offering and already powers memory in Genie. Read the research:
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Introducing MemCurrent, a persistent memory architecture designed to help production agents stop starting from zero. MemCurrent combines episodic memory with derived memory across three access patterns: • Plugins to log and load context automatically • Tools the agent calls on demand • An offline “dreaming” pipeline that consolidates memory in the background Agents carry forward user preferences, prior corrections, metric definitions and successful workflows instead of rediscovering them every session. On our internal benchmark built from real enterprise data, MemCurrent triples the agent's pass rate while using 31% fewer tokens and running 26% faster. It's built into Agent Bricks' Memory offering and already powers memory in Genie. Read the research:

Databricks

44,883 просмотров • 18 дней назад

Memory Skill for OpenClaw with 26k+ users in 1 week🚀 OpenClaw's memory system is broken by default. It requires curating massive MEMORY.md files or relying on duplicate-heavy generation. Hours are wasted tuning, and massive amounts of tokens are burned. It's time to stop. So we built the memory skill to solve that prob Here is our superpower ⚔️ 🎯 Top #1 market accuracy (92.19%) after 8+ months of intense architecture iteration 🧠 The ultimate solution to keep the timeline, facts, and meaning perfectly in place ☁️ Local & Cloud + Version control ⚡ Super easy setup
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Memory Skill for OpenClaw with 26k+ users in 1 week🚀 OpenClaw's memory system is broken by default. It requires curating massive MEMORY.md files or relying on duplicate-heavy generation. Hours are wasted tuning, and massive amounts of tokens are burned. It's time to stop. So we built the memory skill to solve that prob Here is our superpower ⚔️ 🎯 Top #1 market accuracy (92.19%) after 8+ months of intense architecture iteration 🧠 The ultimate solution to keep the timeline, facts, and meaning perfectly in place ☁️ Local & Cloud + Version control ⚡ Super easy setup

andy nguyen

590,678 просмотров • 8 месяцев назад

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari
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The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari

Akash Network

22,918 просмотров • 7 месяцев назад

We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY
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We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY

Adeniyi.sui

46,316 просмотров • 5 месяцев назад

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.
10:52

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.

Akshay 🚀

57,540 просмотров • 1 год назад

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.
10:52

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.

Akshay 🚀

156,686 просмотров • 1 год назад

ByteDance just f*cked the AI world their open-source memory for AI agents cuts input tokens by up to 91% and can save you $10k a month it's OpenViking: your agent's memory, knowledge and skills stored like a file system > the agent reads short summaries first, then opens only the folders it needs > memory test on long chats: input tokens down 91% on OpenClaw, 63% on Claude Code, 34% on Hermes > accuracy goes up too: from 24-57% to 80-83% > one installer adds the memory plugin to Claude Code, Codex, Cursor, OpenCode and more > needs a server setup first, or use their cloud. there's also a browser Studio to try it spend $11k a month on input tokens? up to 91% off is $10k back 41k+ stars on github my pokemon already moved their memories in xd
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ByteDance just f*cked the AI world their open-source memory for AI agents cuts input tokens by up to 91% and can save you $10k a month it's OpenViking: your agent's memory, knowledge and skills stored like a file system > the agent reads short summaries first, then opens only the folders it needs > memory test on long chats: input tokens down 91% on OpenClaw, 63% on Claude Code, 34% on Hermes > accuracy goes up too: from 24-57% to 80-83% > one installer adds the memory plugin to Claude Code, Codex, Cursor, OpenCode and more > needs a server setup first, or use their cloud. there's also a browser Studio to try it spend $11k a month on input tokens? up to 91% off is $10k back 41k+ stars on github my pokemon already moved their memories in xd

Mnimiy

17,234 просмотров • 3 дней назад

Memory Store (Memory Store) gives your team and AI agents a shared company brain. Your team's knowledge & decisions are scattered across slack, emails, and people's heads. Memory Store turns them into a living wiki for your agents and teammates. Congrats on the launch, Ishita Jindal & Diwank Singh Tomer!
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Memory Store (Memory Store) gives your team and AI agents a shared company brain. Your team's knowledge & decisions are scattered across slack, emails, and people's heads. Memory Store turns them into a living wiki for your agents and teammates. Congrats on the launch, Ishita Jindal & Diwank Singh Tomer!

Y Combinator

247,177 просмотров • 5 месяцев назад

current OpenClaw🦞's memory system sucks. it's only hosted locally and you have to manually add memories. you can now connect Momo's memory as an OPENCLAW PLUGIN. > cloud-backed, encrypted, persistent > auto indexing, storage & memory management > shared across teams get an api key at Momo and run: openclaw plugins install Phil-memory/momo-memory
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current OpenClaw🦞's memory system sucks. it's only hosted locally and you have to manually add memories. you can now connect Momo's memory as an OPENCLAW PLUGIN. > cloud-backed, encrypted, persistent > auto indexing, storage & memory management > shared across teams get an api key at Momo and run: openclaw plugins install Phil-memory/momo-memory

Cailyn Y.

59,633 просмотров • 8 месяцев назад

5 huge AI agents, MCP, and LLM updates today: 1. Mem0 just dropped OpenMemory MCP server that adds persistent memory across MCP clients. Create a shared memory layer with style, topics, emotions, etc. that works Claude, Cursor, Windsurf, and others. 100% local and opensource.
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5 huge AI agents, MCP, and LLM updates today: 1. Mem0 just dropped OpenMemory MCP server that adds persistent memory across MCP clients. Create a shared memory layer with style, topics, emotions, etc. that works Claude, Cursor, Windsurf, and others. 100% local and opensource.

Unwind AI

63,275 просмотров • 1 год назад

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.
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ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,359 просмотров • 3 месяцев назад

Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem! It’s called Claude-Mem, and it lets Claude remember your work across sessions. ⚡ Slash token usage by up to 95% every time you start a session. 🔧 Unlock the ability to make 20× more tool calls before hitting limits. My Video:
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Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem! It’s called Claude-Mem, and it lets Claude remember your work across sessions. ⚡ Slash token usage by up to 95% every time you start a session. 🔧 Unlock the ability to make 20× more tool calls before hitting limits. My Video:

WorldofAI

28,687 просмотров • 9 месяцев назад