robots.txt
# ============================================
# Maximem.ai - Robots.txt (SEO Optimized)
# Last Updated: 2026-08-01
# ============================================
#
# POLICY
# ------
# Maximem allows all reputable crawlers — traditional search, AI training, and
# inference-time retrieval — across the whole public site. Only genuinely
# internal surfaces are withheld (see the Disallow block repeated per group).
#
# A crawler is blocked only when it is undocumented by its vendor, ignores
# robots.txt in practice, or cannot be attributed to an identifiable operator.
# Bytespider is the only agent that currently meets that bar. Any future block
# must be recorded here, with the reason, in the group itself.
#
# USAGE SIGNALS
# -------------
# Two vocabularies are declared on every allowed group. They say the same thing;
# they exist because parsers are mid-migration between them.
#
# Content-Signal (Cloudflare / draft-romm-aipref-contentsignals):
# search=yes — allow indexing for traditional search
# ai-train=yes — allow use of content for AI model training
# ai-input=yes — allow inference-time retrieval (RAG, citations)
#
# Content-Usage (IETF AIPREF — draft-ietf-aipref-attach / -vocab):
# train-ai=y — allow use in producing or refining a generative model
# search=y — allow use in applications that direct users to the source
#
# BOT TIERS
# ---------
# Most vendors run separate agents for training, for search indexing, and for
# fetching a page because a user just asked a question. They are independent:
# allowing ClaudeBot does nothing for Claude-User or Claude-SearchBot. The
# user-initiated tier is what produces citations, so all three are listed.
#
# MAINTENANCE
# -----------
# robots.txt does not inherit rules from User-agent: * once a named agent has
# its own group. Every named group therefore repeats the full Disallow block.
# When adding a new named agent, copy an existing group verbatim and change
# only the User-agent line.
User-agent: *
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── OpenAI ──────────────────────────────────────────────────────────────
# GPTBot = training · OAI-SearchBot = search index · ChatGPT-User = user-initiated
User-agent: GPTBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: OAI-SearchBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: ChatGPT-User
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Anthropic ───────────────────────────────────────────────────────────
# ClaudeBot = training · Claude-SearchBot = search index · Claude-User = user-initiated
# anthropic-ai is the legacy token, retained for older crawler builds.
User-agent: ClaudeBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Claude-SearchBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Claude-User
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: anthropic-ai
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Google ──────────────────────────────────────────────────────────────
# Google-Extended is an opt-out token, not a crawler; Google-CloudVertexBot
# and Google-NotebookLM are real fetchers.
User-agent: Google-Extended
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Google-CloudVertexBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Google-NotebookLM
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Microsoft ───────────────────────────────────────────────────────────
# Bingbot is the crawler behind Copilot. It inherited User-agent: * before
# this block, which allowed it but left it the one major crawler with no
# content signal of its own.
User-agent: Bingbot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Perplexity ──────────────────────────────────────────────────────────
# PerplexityBot = index · Perplexity-User = user-initiated retrieval
User-agent: PerplexityBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Perplexity-User
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Apple ───────────────────────────────────────────────────────────────
# Applebot is the crawler; Applebot-Extended is the AI-training opt-out token.
User-agent: Applebot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Applebot-Extended
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Meta ────────────────────────────────────────────────────────────────
# meta-externalagent = training/indexing · meta-externalfetcher = user-initiated
User-agent: meta-externalagent
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: meta-externalfetcher
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Others ──────────────────────────────────────────────────────────────
User-agent: MistralAI-User
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: DuckAssistBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: Amazonbot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: CCBot
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
User-agent: cohere-ai
Content-Signal: search=yes, ai-train=yes, ai-input=yes
Content-Usage: train-ai=y, search=y
Allow: /
Disallow: /admin
Disallow: /echo/
Disallow: /uninstall-feedback
Disallow: /font-compare
Disallow: /wrapped/share
# ── Blocked ─────────────────────────────────────────────────────────────
# Bytespider (ByteDance): undocumented by its vendor and widely reported to
# ignore robots.txt. Blocked until ByteDance publishes crawler documentation.
User-agent: Bytespider
Disallow: /
# Sitemap Locations
Sitemap: https://www.maximem.ai/sitemap.xml
Sitemap: https://www.maximem.ai/sitemap-pages.xml
Sitemap: https://www.maximem.ai/sitemap-blog.xml
Sitemap: https://www.maximem.ai/sitemap-images.xml
Sitemap: https://www.maximem.ai/sitemap-glossary.xml
Sitemap: https://www.maximem.ai/sitemap-videos.xml
Sitemap: https://docs.maximem.ai/sitemap.xml
llms.txt
# Maximem
Maximem is an AI memory and context-management company, founded in 2025 and headquartered in San Francisco with engineering in Bangalore. Maximem builds AI memory infrastructure for people and their agents. Its two core products are **Maximem Vity**, a personal AI memory assistant that syncs context across ChatGPT, Claude, Gemini, Perplexity, and other AI platforms via a Chrome extension, and **Maximem Synap**, a developer memory SDK that gives AI applications persistent, cross-session memory with enterprise-grade security. Both products solve the same problem from different angles: AI conversations today are stateless, context is scattered across tools, and no AI products share memory. Maximem provides a unified, private, encrypted memory layer.
- Website: https://www.maximem.ai
- Docs: https://docs.maximem.ai
- LinkedIn: https://www.linkedin.com/company/maximem-ai/
- X / Twitter: https://x.com/maximem_ai
- GitHub (org): https://github.com/maximem-ai
- Synap source code (GitHub): https://github.com/maximem-ai/maximem_synap_sdk
- YouTube: https://www.youtube.com/@Maximem-AI
- Crunchbase: https://www.crunchbase.com/organization/maximem-ai
- Product Hunt: https://www.producthunt.com/products/memoryplugin-for-openclaw
- Contact: gaurav@maximem.ai (general) · support@maximem.ai (support)
## Products
- [Vity - AI Memory for ChatGPT, Claude & Gemini](https://www.maximem.ai/vity): Vity is the free personal AI memory extension that remembers your context across ChatGPT, Claude, Gemini, Perplexity, and 6 more — install in 30 seconds.
- [Vity Apps — first-party apps built on your Maximem Vity vault](https://www.maximem.ai/vity/apps): Echo, WaitPro and more: first-party apps that turn your Maximem Vity memory vault into everyday actions. One vault, shared across every app.
- [Vity Integrations — where your Maximem Vity vault works](https://www.maximem.ai/vity/integrations): Maximem Vity works with the agents and clients you already use — OpenClaw, Hermes, the Chrome extension and more. Your memory vault, everywhere.
- [Maximem Synap: Memory Solution for AI Agents | Agentic Context Management](https://www.maximem.ai/synap): Maximem Synap is a memory solution for AI agents, with agentic context management built in. 92% LongMemEval, 93.2% LoCoMo, under 15ms at P75 in-conversation retrieval, native across 23 frameworks. Free tier.
- [How Maximem Synap Works | Agent Memory Architecture](https://www.maximem.ai/synap/how-it-works): How Maximem Synap turns a message into structured, scoped memory: the capture, compact, and recall trilemma, the extraction-first pipeline, entity resolution, validated compaction, hierarchical scoping, and a memory architecture per agent. 92% on LongMemEval, 93.2% on LoCoMo, under 15ms at P75.
- [Pricing - Synap](https://www.maximem.ai/synap/pricing): Synap pricing: every plan gets every capability. Free, $19 Starter (launch offer), $249 Pro, $999 Scale, and custom Enterprise. Tiers differ on volume and service level — not features.
- [AI Agent Memory Cost Calculator: Build vs Buy vs Vendors](https://www.maximem.ai/tco): Interactive TCO calculator for agent memory. Models the cost of building memory infrastructure in-house, switching from Mem0, Zep, Letta, Supermemory, Cognee or Evermind, or running Maximem Synap, over 12 to 36 months. Every input is editable and every assumption is shown, including where subscription costs come out roughly tied and the argument moves to capability rather than price.
- [Security & Privacy | Maximem Synap AI Memory](https://www.maximem.ai/synap/security): How Maximem Synap protects your data: encryption in transit and at rest, strict tenant isolation, US hosting, 15-day raw-payload retention, full deletion within 20 business days, a named subprocessor list, a DPA on request, and self-hosted or air-gapped deployment.
- [Synap Integrations - 23 AI Agent Frameworks](https://www.maximem.ai/synap/integrations): Every framework Synap ships native memory for: LangChain, LangGraph, LlamaIndex, OpenAI Agents, Pydantic AI, CrewAI, AutoGen, Google ADK, Haystack, Agno, Semantic Kernel, Microsoft Agent Framework, NeMo Agent Toolkit, LiveKit Agents, Pipecat, Claude Agent SDK, Mastra, Vercel AI SDK, Vercel eve, Strands Agents, CAMEL-AI, Smolagents, and deepagents.
- [Maximem Vity for OpenClaw - Persistent AI Memory Plugin](https://www.maximem.ai/openclaw): Maximem Vity (formerly MemoryPlugin) gives OpenClaw persistent memory across every session — and across ChatGPT, Claude, and Gemini. Install in 30 seconds.
- [Maximem Vity for Hermes - Persistent AI Memory Plugin](https://www.maximem.ai/hermes): Give your Hermes Agent long-term memory that lasts across sessions. Recalled before each turn, captured after, and bridged to ChatGPT, Claude, and Gemini.
- [Product - Vity & Synap AI Memory Tools](https://www.maximem.ai/product): Explore Maximem's AI memory products: Vity for personal AI memory, Synap developer SDK, and Maximem Vity for OpenClaw. Cross-platform, private, and secure.
- [Cross-App AI Memory - Sync Context Across ChatGPT, Claude, Gemini](https://www.maximem.ai/cross-app-memory): Give your AI apps shared memory. Vity syncs your context across ChatGPT, Claude, Gemini, Perplexity, and more: encrypted, private, and fast. One-click Chrome extension setup.
- [Download Vity Chrome Extension](https://www.maximem.ai/download-extension): Download the Vity Chrome extension for cross-app AI memory. Works with ChatGPT, Claude, Gemini, and more.
- [Synap Playground - Try AI Memory Live](https://www.maximem.ai/playground): Chat with AI agents powered by Synap memory. Watch in real time how Synap captures context, recalls past details, and builds persistent memory across your conversation.
## Comparisons
- [The Best AI Agent Memory Layers in 2026, Ranked on Axes You Can Verify](https://www.maximem.ai/blog/best-ai-agent-memory-platforms-2026): How to choose the best memory layer for an AI agent in 2026, situation by situation, and why Maximem Synap fits customer-facing, voice and multi-tenant SaaS agents. Every price, licence and benchmark condition is read from the vendor's own page.
- [AI Memory Benchmarks: LongMemEval and LoCoMo Results | Maximem Synap](https://www.maximem.ai/evals): Published AI memory benchmark results with the configuration that produced them. Maximem Synap scores 92% on LongMemEval and 93.2% on LoCoMo, category by category, on an open harness — plus what happened when we re-ran other vendors' published claims.
- [OpenClaw Memory Plugins Compared: Every Option for 2026](https://www.maximem.ai/openclaw/memory-comparison): Comprehensive comparison of every persistent memory plugin you can use with OpenClaw in 2026 — Maximem Vity, Mem0, Supermemory, ByteRover, Hindsight, Cognee, MemOS, Basic Memory, OpenMemory MCP, and OpenClaw's native markdown memory. Every claim sourced.
- [Maximem Vity vs Mem0 vs Supermemory - AI Memory Extension Comparison](https://www.maximem.ai/compare/maximem-vity-vs-mem0-vs-supermemory): Compare Maximem Vity, Mem0 OpenMemory, and Supermemory Nova side by side. Platform support, privacy, memory types, auto-sync, pricing, and reliability across the top AI memory Chrome extensions.
- [Maximem Synap vs Mem0, Zep, Letta, Supermemory, Cognee, Evermind - AI Memory Comparison](https://www.maximem.ai/compare/maximem-synap-vs-mem0-vs-zep-vs-letta-vs-supermemory-vs-cognee-vs-evermind): Compare Maximem Synap against Mem0, Zep, Letta, Supermemory, Cognee, and Evermind. LongMemEval and LoCoMo benchmarks reproduced on our open harness (Synap 92% / 93.2%), an in-conversation P75 under 15 ms retrieval, memory types, scope isolation, SDKs, and framework integrations for AI memory infrastructure.
- [Maximem Synap vs Mem0 - AI Memory for Agents Compared](https://www.maximem.ai/compare/maximem-synap-vs-mem0): Maximem Synap vs Mem0: 92% vs 73.8% on LongMemEval (same open-source harness), 15ms vs 180ms P50 retrieval latency. Compare ingestion, entity resolution, scope isolation, and SDKs for agentic context management.
- [Maximem Synap vs Zep - AI Memory for Agents Compared](https://www.maximem.ai/compare/maximem-synap-vs-zep): Maximem Synap vs Zep (Graphiti): 92% vs 71.2% on LongMemEval, an in-conversation P75 under 15 ms retrieval. Compare per-agent pipeline customization, bitemporal awareness, scope isolation, and framework integrations.
- [Maximem Synap vs Letta - AI Memory for Agents Compared](https://www.maximem.ai/compare/maximem-synap-vs-letta): Maximem Synap vs Letta: system-managed vs agent-managed memory. 92% LongMemEval, 93.2% LoCoMo, an in-conversation P75 under 15 ms retrieval, automatic entity resolution, nested scope isolation, and framework-agnostic SDK versus Letta's OS-inspired tiered memory.
- [Maximem Synap vs Supermemory - AI Memory for Agents Compared](https://www.maximem.ai/compare/maximem-synap-vs-supermemory): Maximem Synap vs Supermemory: 92% vs 71.3% on LongMemEval (same open-source harness), 15ms vs 220ms P50 retrieval latency. Compare per-agent pipelines, scope isolation, and agentic context management capabilities.
- [Maximem Synap vs Cognee - AI Memory for Agents Compared](https://www.maximem.ai/compare/maximem-synap-vs-cognee): Maximem Synap vs Cognee: extract-first multi-stage pipelines compared. 92% LongMemEval, 93.2% LoCoMo, an in-conversation P75 under 15 ms retrieval, per-agent YAML customization versus Cognee's graph-native retrieval with 14 traversal modes.
- [Maximem Synap vs Evermind (EverOS) - AI Memory for Agents Compared](https://www.maximem.ai/compare/maximem-synap-vs-evermind): Maximem Synap vs Evermind: 92% LongMemEval reproduced on our open harness versus Evermind's self-reported 83.0%, reproduced on our open harness. An in-conversation P75 under 15 ms retrieval, per-agent YAML pipelines, and production maturity versus Evermind's self-evolving skills and multimodal ingestion.
- [Mem0 vs Zep for AI Agent Memory: A 2026 Comparison, With Both Vendors' Claims Checked](https://www.maximem.ai/compare/mem0-vs-zep): Mem0 vs Zep for AI agent memory, and where Maximem Synap differs from both: the unit of memory, temporal handling, scoping, latency, pricing and compliance, worked monthly bills, and an audit of twenty-eight claims the leading comparison pages make about each other.
- [Alternatives to Zep and Graphiti: Can You Keep the Temporal Model Without the Graph?](https://www.maximem.ai/compare/zep-graphiti-alternatives): Every AI assistant answers this question by saying Zep and Graphiti own temporal reasoning. We tested that against Graphiti's source and both vendors' own published sub-scores, and the answer changes the shortlist.
- [Best Mem0 Alternatives for AI Memory (2026)](https://www.maximem.ai/mem0-alternatives): The best Mem0 alternatives for agent memory: Maximem Synap, Zep, Letta, Supermemory, Cognee, Evermind. Synap leads on the open-source LongMemEval harness (92% vs 73.8%) at an in-conversation P75 under 15 ms, with entity resolution on every tier.
- [Best Zep Alternatives for Agent Memory, by Why You Are Leaving (2026)](https://www.maximem.ai/zep-alternatives): The best Zep alternatives for agent memory in 2026, matched to why you are leaving. Maximem Synap for customer-facing, voice and multi-tenant SaaS agents, a routing table for every other case, and a migration recipe.
- [6 Best Letta Alternatives, Benchmarked (2026)](https://www.maximem.ai/letta-alternatives): The best Letta alternatives for agent memory: Maximem Synap, Mem0, Zep, Supermemory, Cognee, Evermind. Synap gives you Letta-grade memory without adopting a framework — system-managed context at 92% LongMemEval, 93.2% LoCoMo, and 15ms retrieval.
- [6 Best Supermemory Alternatives, Benchmarked (2026)](https://www.maximem.ai/supermemory-alternatives): The best Supermemory alternatives for agent memory: Maximem Synap, Mem0, Zep, Letta, Cognee, Evermind. Synap leads on the same harness (92% vs 71.3%) at an in-conversation P75 under 15 ms, with five structured memory types and nested scope isolation.
- [6 Best Cognee Alternatives, Benchmarked (2026)](https://www.maximem.ai/cognee-alternatives): The best Cognee alternatives for agent memory: Maximem Synap, Mem0, Zep, Letta, Supermemory, Evermind. Synap gives you graph-quality structure with a published, reproducible 92% LongMemEval score (93.2% on LoCoMo) and 15ms retrieval.
- [6 Best Evermind Alternatives, Benchmarked (2026)](https://www.maximem.ai/evermind-alternatives): The best Evermind (EverOS) alternatives for agent memory: Maximem Synap, Mem0, Zep, Letta, Supermemory, Cognee. Synap offers 92% reproduced on our open harness, and an in-conversation P75 under 15 ms today, where Evermind is still in public beta.
## Use Cases
- [Synap Use Cases - AI Memory for Agents](https://www.maximem.ai/use-cases/synap): Five real-world deployments for Synap's agentic memory layer: healthcare, customer support, sales, voice AI, and multi-agent workflows.
- [Healthcare & Mental Health Use Case - Synap](https://www.maximem.ai/use-cases/synap/healthcare): Continuity of care, by design. Persistent patient profiles, treatment timelines, emotional continuity, and HIPAA-ready memory for AI agents.
- [Customer Support Use Case - Synap](https://www.maximem.ai/use-cases/synap/support): Support that arrives already caught up. Cross-channel memory, sentiment tracking, and recurring-issue detection for AI support agents.
- [Sales & Revenue Intelligence Use Case - Synap](https://www.maximem.ai/use-cases/synap/sales): Your pipeline, with a memory. Synthesized deal history, pattern-aware selling, and next-best action grounded in real account context.
- [Voice AI Agents Use Case - Synap](https://www.maximem.ai/use-cases/synap/voice-ai): Real-time memory for voice AI. Retrieval inside the turn, under 15ms at P75, anticipatory loading, live correction handling, and native temporal resolution.
- [Multi-Agent Workflows Use Case - Synap](https://www.maximem.ai/use-cases/synap/multi-agent): One memory across every agent. Shared memory layer with scope isolation, durable institutional memory, and consistent facts for multi-agent systems.
- [Use Cases - Personalized AI Workflow Solutions](https://www.maximem.ai/use-cases/vity/personalized-workflow): Discover how Maximem transforms personalized workflow with private AI memory. Securely operate between LLM & AI powered apps with same context-level.
- [Use Cases - Professional Roles](https://www.maximem.ai/use-cases/vity/professional-roles): See how professionals across different roles use Maximem AI memory to boost productivity and maintain context across AI tools.
## Why AI Memory
- [The Memory Problem: Why AI Agents Need Memory | Maximem Synap](https://www.maximem.ai/the-memory-problem): The hub for the case for agent memory. Frames the problem and indexes the seven objection pages below. The nine loudest objections to AI memory, each given its fair version and answered with proof.
- [A Bigger Context Window Is Not a Memory | Maximem Synap](https://www.maximem.ai/memory-vs-context-windows): Why a bigger context window is not a memory. Bigger windows raise the ceiling on cost, attention, and cross-session continuity, but do not fix them. A memory layer decides what belongs in the prompt.
- [Memory Is Not Just RAG With a New Name | Maximem Synap](https://www.maximem.ai/memory-vs-vector-rag): Why memory is not just RAG with a new name. RAG retrieves documents; memory maintains a model of the user and the world over time. Includes the file-vs-vector experiment across 50,000 documents.
- [We do not need a memory layer. We have skills.](https://www.maximem.ai/memory-vs-agent-skills): A skill file holds facts that stay true and fails on facts that get replaced. Why a SKILL.md is the right home for procedure and the wrong home for state that changes.
- [Build vs Buy Agent Memory: Files, Databases, or a Memory Layer | Maximem Synap](https://www.maximem.ai/build-vs-buy-agent-memory): Build vs buy agent memory. Why writing to markdown files or your governed database stops short of the operations a memory layer adds: entity resolution, decay, contradiction handling, and write-time governance.
- [Measuring Agent Memory: Benchmarks You Can Inspect | Maximem Synap](https://www.maximem.ai/measuring-agent-memory): How Maximem Synap measures agent memory, with methodology you can inspect. Published runs, reported losses, and the pollution, forgetting, and concurrency tests most vendors skip. 92% LongMemEval, 93.2% LoCoMo, under 15ms at P75 in-conversation.
- [Memory Privacy and Ownership: Own Your Context Across Every Model | Maximem Synap](https://www.maximem.ai/memory-privacy-and-ownership): Why memory you own beats native platform memory, and why bad memory is worse than none. Portable across models, private by design, encrypted even from us, with conscious forgetting and write-time checks.
- [How Agent Memory Works: The Operations a Wrapper Skips | Maximem Synap](https://www.maximem.ai/how-agent-memory-works): How agent memory works and the operations a wrapper skips: entity resolution, contradiction handling, staleness and decay, scoping, and conscious forgetting. The difference between a memory system and a cache.
- [How to Reduce LLM Token Costs in Long Conversations](https://www.maximem.ai/blog/reduce-llm-token-costs-long-conversations): Why a long conversation costs more than its turn count, what prompt caching saves and where it stops, and the conversation length at which a bounded context becomes cheaper than cached full history, worked at current Claude prices with the method published.
- [Claude API Pricing in 2026](https://www.maximem.ai/blog/claude-api-pricing-2026): Every current Claude model's price per million tokens, cache and batch rates, what a multi-turn conversation costs, and how the Free, Pro and Max plans differ, checked on 26 September 2026.
## Research
- [What 35 Research Papers Say About Agent Skills](https://www.maximem.ai/research/agent-skills): Thirty-five arXiv papers on agent skills, read end to end. 299 claims in an open register, re-derived from source across five adversarial passes.
- [Can Agent Skills Replace Memory? What the Research Shows](https://www.maximem.ai/research/agent-skills/skills-vs-agent-memory): Teams route agent memory through skill files. Thirty-five papers published this month say that works for facts and fails for state.
- [Why Agent Skills Fail Even When Retrieval Works](https://www.maximem.ai/research/agent-skills/why-agent-skills-fail): The skill that breaks a task is usually the right skill for that task. Getting retrieval right does not save you, and here is the evidence.
- [Skill Retrieval at Scale: Confusability, Not Count](https://www.maximem.ai/research/agent-skills/skill-retrieval-at-scale): Fifty near-identical skills are harder to retrieve from than two hundred well-separated ones. Library size is the wrong number to watch.
- [Self-Evolving Skill Libraries and the Judge Reliability Cliff](https://www.maximem.ai/research/agent-skills/self-evolving-skill-libraries): A self-evolving skill library stops pruning once its judge scores failures as passes at 0.45. Seven measured judges sit at or past that line.
- [What Agent Skills Are Actually Good For](https://www.maximem.ai/research/agent-skills/what-agent-skills-are-good-for): Skills stabilise how an agent acts rather than supplying facts: 65.7% procedural anchoring against 4.5% knowledge injection over 528 triples.
- [The Agent Skills Ecosystem, in Numbers](https://www.maximem.ai/research/agent-skills/agent-skills-ecosystem-data): A mining run found 3.8 million SKILL.md files across 282,200 public repositories, and 50.5% of them are verbatim copies of another file.
- [How to Write a SKILL.md That Actually Gets Selected](https://www.maximem.ai/research/agent-skills/how-to-write-a-skill-md): A static-analysis census found 89.3% of 138,133 public skill files violate at least one rule in the official Agent Skills specification.
- [Agent Skill Security: An Unsigned Instruction Su
