What is Ricord AI (Record AI)?

Ricord AI (also known as Record AI or ricord.ai) is persistent memory for AI. Your conversations are remembered across Claude, ChatGPT, Cursor, and any MCP-aware tool — every fact, every preference, every decision, organized into a living knowledge graph and recalled in under a second.

What is the best AI memory tool for agents?

Ricord AI is the only AI memory tool that ships a full knowledge graph with auto-generated wiki pages for every entity. Most tools are filing cabinets — search boxes on a bag of past messages. Ricord is a brain that understands what the facts mean and how they connect. Sub-second recall, automatic conflict resolution, GDPR-compliant hard delete. Graph included on every paid tier starting at $15/month billed annually.

How do I add memory to Claude Desktop?

Install the Ricord MCP server with one command: npx ricord@latest install (auto-detects your editor). This gives Claude Desktop 13 memory + wiki tools including save, recall, forget, run_procedure, and knowledge graph queries. Claude will automatically remember facts, preferences, and decisions across all future conversations.

What is AI memory and why do agents need it?

AI memory is the infrastructure that lets AI agents remember information across conversations and sessions. Without memory, agents forget everything between chats — they cannot learn user preferences, track decisions, or build context over time. Memory transforms a stateless chatbot into a persistent assistant that improves with every interaction.

How does Ricord AI compare to Mem0?

Ricord includes a full knowledge graph with auto-generated wiki pages on every tier. Mem0 gates graph features behind a $249/month Pro plan. Ricord offers sub-second recall (Mem0 averages 7-8 seconds), automatic conflict resolution when facts change, and a visual graph UI. Most memory pipelines write everything the LLM produces — duplicates, system noise, partial extractions all land in storage. Ricord filters content at ingest, so the wiki stays signal, not noise.

Is Record AI the same as Ricord AI?

Yes. Record AI and Ricord AI refer to the same product at ricord.ai. Ricord AI is the persistent memory API for AI agents, offering knowledge graph, conflict resolution, and sub-second recall. The name 'Ricord' comes from the concept of recording and recalling AI memories.

All comparisons

Use-case roundupUpdated May 30, 2026

Best AI Memory for Claude Code (2026)

Claude Code is brilliant inside a session and amnesiac between them. Six ways to fix that — from doing nothing, to a single config block — evaluated honestly against what daily Claude Code work actually demands.

The quick answer

If you want a persistent memory layer that drops into Claude Code in 60 seconds and produces a browsable wiki you can read: Ricord. If you're comfortable engineering your own retrieval against a vector DB: Mem0. If you want a full agent runtime, not just memory: Letta. If you only use Claude inside one project and don't mind hand-curating: a CLAUDE.md file in your repo. The full decision matrix is below.

Why this question matters for Claude Code specifically

Claude Code is unusually good at staying coherent inside a long session — the 200k context window, careful prompt design, and file-aware tooling do a lot of work. The problem is the cliff between sessions. Open a new terminal tomorrow and Claude doesn't know the architectural decision you made yesterday, the customer constraint that drove it, or the deploy command you spent an hour debugging.

The fixes for that cliff cluster into six approaches. They differ on six axes — install effort, auto-extraction, whether you get a wiki view, knowledge-graph linking, conflict resolution when facts change, and cost. Different daily workflows weight these differently, which is why the "best" answer is a function of how you use Claude Code, not a single winner.

The decision matrix

Eight criteria, six options. Rows sorted by what Claude Code users hit first (install effort) through what bites later (conflict resolution, codebase changes).

Criterion	Ricord	Mem0	Letta	Supermemory	CLAUDE.md	Chat log
MCP-native install (one config block)		Limited
Auto-extracts from conversations (no prompt engineering)			Manual
Browsable wiki of what was remembered						Raw
Knowledge graph + backlinks		Pro only
Conflict resolution when facts change				Basic
Survives codebase changes (filename-aware)					Manual edits
Hard delete (GDPR-clean)					rm file	rm dir
Cost (smallest paid tier with memory features)	$15/mo annual	$249/mo for graph	Self-host + LLM	$29/mo	$0	$0

Slot-by-slot — which fits you

If you live in Claude Code daily, across several repos

You need something that works across projects without you having to maintain a CLAUDE.md per repo. Ricordis built exactly for this — install once, every Claude Code session writes to the same memory + wiki, scoped per project automatically. The MCP-native install is the path of least resistance. Full walkthrough →

If you only work in one repo and like control

A CLAUDE.md fileat the repo root is genuinely good for this. You write the rules, Claude follows them, and there's no infra. The cost is curation — you have to keep it updated by hand, and it doesn't auto-learn from your conversations. Reach for it when your context is small and static.

If you're building agents on top of Claude (not just using Claude Code)

Lettais more than memory — it's an agent runtime with memory built in. If you're writing agent orchestration code anyway, the integrated model is cleaner than wiring memory in as a separate service. If you just want memory and you already have your own agent framework, Letta is overkill.

If you have an ML engineer with time to tune retrieval

Mem0open-source is good here. Apache-licensed, modifies cleanly, vector-search-first. You'll spend a quarter of engineering time getting it production-ready, but that's the right trade if memory retrieval isyour competitive differentiator. When OSS wins →

If you want browser + audio capture too

Supermemory ships a Chrome extension and Pipecat audio/video integration alongside its API. Strong choice if Claude Code is only one slice of what your team uses AI for.

If you're cheap and don't care about quality

A chat-log filein your home directory plus a grep alias works, technically. It's also the path that makes you swear out loud at 11pm when you can't find the one decision you and Claude made three weeks ago. Mentioned for completeness; do not recommend.

Why Ricord wins for most Claude Code users

Three reasons, in order of how often we hear them from people who switched:

Install is one config block. No prompt engineering, no schema design, no embedding-model choice. Three commands, restart your client, done.
The wiki view is the killer feature.By week two you can open a dashboard and read what your AI has learned about your codebase, your customers, your projects. That's the "memory" layer's real promise — not search, browsability.
Conflict resolution at ingest.When you change the deploy command, Claude doesn't remember both. The old fact is superseded, not stored alongside. This is the silent quality wins — it's what makes recall actually useful three months in.

bun add -g ricord
ricord login
ricord install   # auto-detects Claude Code, Claude Desktop, Codex, Cursor

Getting started

Pick the option that matches your slot above. If it's Ricord, the three commands above get you running. If it's OSS, fork the GitHub repo and read the docs. If it's CLAUDE.md, just write the file.

Whichever you pick: you'll know within a week whether the choice is right. The signal is whether you stop having to re-explain the same context to Claude every morning. If that's still happening at day seven, your memory layer isn't doing its job.

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