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# Claude Code Memory: Native Features and the Supermemory Plugin

Understand CLAUDE.md, auto memory, and the Supermemory plugin. Check setup, project scope, corrections, and cross-session recall for your repository.

By Shardul Mane September 16, 2026 · 4 min read 

![Claude Code Memory: Native Features and the Supermemory Plugin](https://supermemory.ai/_astro/cover.CNLcs6Br_Z236g7L.webp)

Claude Code already has persistent context mechanisms: `CLAUDE.md` files for written project instructions and auto memory for learned notes. An external memory plugin is an additional option when you need another way to capture, organize, or retrieve context across coding sessions. Start by understanding what the native features already retain.

The practical question is where a particular fact belongs. Build commands belong in maintained repository instructions. The reason a migration avoided a certain approach may belong in an architecture decision record. Temporary debugging state needs a clear scope and an expiry or correction path.

## Native memory and an external plugin serve different needs

| Mechanism                   | Good use                                         | Check before relying on it                                              |
| --------------------------- | ------------------------------------------------ | ----------------------------------------------------------------------- |
| CLAUDE.md and scoped rules  | Team conventions and known workflows             | Correct file scope, conflicting rules, and stale instructions           |
| Claude Code auto memory     | Useful learnings and preferences                 | What was saved and whether it applies to this repository                |
| Versioned project documents | Reviewed architecture decisions                  | Source revision and whether the decision is still active                |
| Supermemory plugin          | Additional capture and retrieval across sessions | Authentication, project scope, capture settings, and retrieval behavior |

Anthropic documents [native memory and its management commands](https://code.claude.com/docs/en/memory). These files provide context; they are not a substitute for enforcement of access controls or release rules.

## Diagnose the missing context before installing more tools

When an assistant repeats a rejected approach, locate the decision first. If it exists only in an old conversation, write a concise record with the rationale and source. If it exists in a project file, check whether the current session loaded or retrieved that file. If the file is loaded, inspect whether contradictory instructions or an outdated summary compete with it.

For a large repository, avoid pasting the whole codebase into memory. Keep durable architectural facts and references, then inspect current source when implementation details matter. A remembered function signature can become stale after a refactor.

The existing [SMFS engineering article](https://supermemory.ai/blog/smfs-making-agentic-retrieval-55-cheaper-and-more-accurate/) explores a filesystem-oriented retrieval approach. It is useful background when deciding how an assistant should find relevant files in a large repository.

## Install and verify the Supermemory plugin

The [current plugin documentation](https://supermemory.ai/docs/integrations/claude-code) specifies Node.js 18 or later and these commands inside Claude Code:

```
/plugin marketplace add supermemoryai/claude-supermemory
/plugin install supermemory
```

Configure the documented `SUPERMEMORY_CC_API_KEY` through your local credential mechanism, restart the relevant session if needed, and check:

```
/supermemory:status
/supermemory:project-config
```

Keep keys out of committed repository files. If you have the older plugin name installed, use the migration instructions in the same documentation rather than assuming it updates in place.

The documented plugin provides recall decisions, capture, and separate project and personal scope controls. Inspect those settings before indexing a repository or enabling capture of work conversations. After setup, run the two-session test below to check authentication and recall for your project.

## Give project decisions enough context to survive reuse

A useful record includes the decision, the reason, the repository or project, and the source revision. For example:

```
Project: billing-worker
Decision: keep invoice reconciliation in the existing queue worker.
Reason: the rollout must preserve the current retry and audit path.
Source: architecture/adr-014.md at revision abc123.
Revisit when: the new queue has passed the replay test.
Status: accepted for the current migration.
```

This example is fictional. Its value is the structure: the assistant can recover the rationale without interpreting an old preference as a permanent constraint. After the queue changes, update the record and its source rather than accumulating contradictory “remember this” notes.

Separate personal style preferences from team decisions. “I prefer compact comments” may belong to one developer. “All releases require migration replay checks” belongs to the team's maintained workflow. A shared memory scope should not silently mix the two.

## Run a two-session acceptance test

Use a harmless fictional decision in a disposable project. Save it through the configured mechanism, close the conversation, and start another session for the same project. Ask a question whose answer depends on that decision and inspect the evidence retrieved.

Then test a different project, a corrected decision, and a question that does not require memory. Confirm that the old decision is not applied globally and that an irrelevant memory does not distort the answer. Record whether the evidence came from native files, auto memory, the plugin, or a direct source read.

Add one test after a source-code change. The assistant should inspect the current implementation before asserting that an old file path or API still exists. Memory should shorten the search for context, not exempt the agent from checking current code.

## Compare plugins using your repository workload

Evaluate whether you can inspect captured content, control scope, correct records, and understand what gets sent to a hosted service. Measure retrieval relevance and context overhead on the same tasks. A benchmark for general conversational recall is not a benchmark for navigating your repository.

A remote [MCP connection](https://supermemory.ai/blog/how-to-make-your-mcp-clients-share-context-with-supermemory-mcp/) exposes tools to a client; a plugin can add client-specific behavior. Neither label alone proves exactly when information is saved or recalled.

Begin with one repeated failure, such as forgetting why a dependency was rejected. Make its source and scope explicit, test recovery in a new session, and expand only after that works reliably. For OpenClaw-specific behavior, use the [existing OpenClaw article](https://supermemory.ai/blog/why-everyone-is-complaining-about-openclaws-memory-it-sucks-and-why-supermemory-fixes-it/) rather than assuming Claude Code instructions transfer unchanged.

Try this on a recurring repository task: [set up the Supermemory Claude Code plugin](https://supermemory.ai/docs/integrations/claude-code), save a fictional project decision, and check whether a fresh session can recover it in the right scope. Use that result to decide where the plugin adds value alongside native memory.

## Other posts.

1. [We're open sourcing the company brain. Here's how we designed the multiplayer harness Company Brain is now open source. A walkthrough of the multiplayer harness behind its Slack experience, from proactivity and memory boundaries to approvals and recovery. NewsSep 25, 2026 ](https://supermemory.ai/blog/open-sourcing-company-brain)
2. [Jev changes a lot in memory & context engineering. Here's exactly how. We tested Jev across reranking, chunking, observation, and harness decisions. Here is where fast decision models help memory systems, where they cost more, and where they still fall short. EngineeringSep 24, 2026 ](https://supermemory.ai/blog/jev-memory-context-engineering)
3. [I reverse-engineered Instinct's memory. Here's exactly how it works Instinct keeps its memory as git-tracked markdown files, found with grep rather than vectors. Here is the whole system as far as black-box probing can reconstruct it, and how to rebuild it on supermemory in about 60 lines. EngineeringSep 20, 2026 ](https://supermemory.ai/blog/reverse-engineering-instinct-memory)
4. [An update to supermemory We've discontinued the supermemory company brain and Nova. Everyone who was charged has been refunded, our MCP and plugins continue to run, and we're going all in on the memory engine. NewsSep 10, 2026 ](https://supermemory.ai/blog/an-update-to-supermemory)
5. [Scaling Conversations: How Adapta Grew Usage Without Losing Context Adapta added Supermemory as a persistent memory layer so every conversation keeps its context — letting the team scale usage without losing the thread. Case StudyJun 12, 2026 ](https://supermemory.ai/blog/adapta-scaling-conversations)
6. [How Chatarmin Ditched RAG and Went Memory-Only with Supermemory Chatarmin replaced a heavy RAG pipeline with Supermemory's memory layer — cutting average AI response time from 40s to 12s and token usage by 40–50%. Case StudyJun 10, 2026 ](https://supermemory.ai/blog/chatarmin-memory-only)
7. [SMFS: making agentic retrieval 55% cheaper AND more accurate We launched SMFS.ai (Supermemory Filesystem) a few weeks ago, with a simple bet: We can redesign the filesystem specifically for agents, with special files, structures, and commands that it can use for it's tasks. Today, SMFS is used by hundreds of companies to power their agents. EngineeringMay 28, 2026 ](https://supermemory.ai/blog/smfs-making-agentic-retrieval-55-cheaper-and-more-accurate)
8. [Introducing Dynamic Dreaming: supermemory now connects the dots, for you. Dreaming is magical. TLDR: We're launching Dynamic Dreaming in supermemory today, which automatically works if you're using supermemory in any way - API, OpenClaw, Hermes agent, etc. EngineeringMay 25, 2026 ](https://supermemory.ai/blog/introducing-dynamic-dreaming-supermemory-now-connects-the-dots-for-you)
9. [Dear reader, we just made supermemory insanely cheap... the Context Cloud When I first started building supermemory, I had one goal: To build the best memory system for AI. I would talk to customers, and find out that memory was not the only thing they needed - They were all setting up 7-8 different vendors at the same time. EngineeringMay 18, 2026 ](https://supermemory.ai/blog/dear-reader-we-just-made-supermemory-insanely-cheap-the-context-cloud)
10. [Introducing @supermemory/tools v2.0.0 Today we're releasing v2.0.0\. This release unifies the API across all agents sdk integrations from AI SDK to Mastra, makes conversation identity a first-class concept, and ships with memory saving on by default. EngineeringApr 27, 2026 ](https://supermemory.ai/blog/introducing-supermemory-tools-v2-0-0)
11. [Solving the Precision-Recall Tradeoff: Search Result Aggregation When you're building memory for AI, search is your foundational layer. The way search generally works is straightforward: the user defines a query, and then sets a limit (top-K) on how many search results they want returned. Usually, this is set to 10 or 20. EngineeringApr 5, 2026 ](https://supermemory.ai/blog/solving-the-precision-recall-tradeoff-search-result-aggregation)
12. [OpenClaw Memory Problems: Why It Forgets and How to Fix It (2026) TLDR: Today, we are releasing a new version of our openclaw plugin - https://github.com/supermemoryai/openclaw-supermemory. This post is going to be a bit technical, so bear with me (or bookmark for later!) In this post, I will talk about what we do about OpenClaw memory, and how we fix it. EngineeringFeb 19, 2026 ](https://supermemory.ai/blog/why-everyone-is-complaining-about-openclaws-memory-it-sucks-and-why-supermemory-fixes-it)
13. [Stateful Coding Agents with Memory: Build Long-Running Agents (2026) We built a plugin for Claude Code and OpenCode that gives your coding agent persistent memory. It remembers your preferences, learns your codebase, and never loses context mid-conversation. The result is an agent you can run for months without starting over. EngineeringFeb 18, 2026 ](https://supermemory.ai/blog/infinitely-running-stateful-coding-agents)
14. [Clawd / Molt bot's memory SUCKS. We gave it supermemory. I'm the founder of supermemory. Clawd/Molt bot is blowing up right now, with many, many use cases. I set it up, too, and have been using it through telegram. TLDR: just go to https://supermemory.ai/docs/integrations/clawdbot to set up supermemory for your clawd bot. EngineeringJan 28, 2026 ](https://supermemory.ai/blog/clawd-molt-bots-memory-sucks-we-gave-it-supermemory)
15. [Catch up with our UNFORGETTABLE Launch Week Over the last year, one belief has guided almost everything we’ve built at Supermemory AI becomes meaningfully useful only when it remembers. Memory shouldn’t be something developers rebuild from scratch. It shouldn’t be fragile, expensive, or trapped inside a single tool. NewsJan 4, 2026 ](https://supermemory.ai/blog/catch-up-with-our-unforgettable-launch-week)
16. [Empowering the Next Generation of Founders: Supermemory Startup Program If there’s one thing we’ve learned while building Supermemory, it’s that most startups don’t fail because they didn't build features; they fail when infrastructure slows them down, or they built too slow. NewsDec 31, 2025 ](https://supermemory.ai/blog/empowering-the-next-generation-of-founders-supermemory-startup-program)
17. [Building code-chunk: AST Aware Code Chunking At Supermemory, we're building context engineering infrastructure for AI. A huge part of that is dealing with code: ingesting repos, understanding structure, and making it searchable. The problem is that most code chunking solutions are terrible. We built code-chunk to fix this. EngineeringDec 29, 2025 ](https://supermemory.ai/blog/building-code-chunk-ast-aware-code-chunking)
18. [Supermemory raises $3 million with the best memory engine for LLMs Today, I am excited to announce our first funding round to accelerate our mission of building an interoperable, scalable and reliable memory for LLMs and agents. Memory is one of the hardest challenges in AI right now. NewsOct 6, 2025 ](https://supermemory.ai/blog/supermemory-raises-3-million-and-building-the-best-memory-engine-for-llms)
19. [Mem0 vs Supermemory: Why Scira Switched Scira AI moved its production memory layer from Mem0 to Supermemory. This is what failed, what improved, and how the team evaluated the two systems. Case StudyOct 2, 2025 ](https://supermemory.ai/blog/why-scira-ai-switched)
20. [Never Record Again: How Montra Uses Supermemory to Rethink Video Creation Campbell Baron, the founder of Montra, has been making videos since he was twelve. By thirteen, he was already doing brand work. Today, he’s betting on a very different future for creators: a world where recording is the exception, and most videos are generated from scratch. Case StudyAug 21, 2025 ](https://supermemory.ai/blog/never-record-again-how-montra-uses-supermemory-to-rethink-video-creation)
21. [Unified Memory That Works Where You Work: Your Second Brain With Supermemory Hi everyone, I’m Dhravya, the founder of Supermemory. I want to start with a little story behind why this product means so much to me. You can also skip straight to what it is and how it works below. EngineeringJul 25, 2025 ](https://supermemory.ai/blog/unified-memory-that-works-where-you-work-your-second-brain-with-supermemory)
22. [Supermemory just got faster on PlanetScale What is Supermemory? Supermemory completes the missing part of the LLM puzzle: memory. Just as memory is crucial for human intelligence, it's essential for truly intelligent AI systems. EngineeringJul 18, 2025 ](https://supermemory.ai/blog/supermemory-just-got-faster-on-planetscale)
23. [Faster, smarter, reliable infinite chat: Supermemory IS context engineering. People are obsessed with prompts and prompt engineering. Sure, what you say is important, but what the model knows when you say it is the difference between a stateless text generator and an intelligent AI system. In short, context is the most crucial component. NewsJul 9, 2025 ](https://supermemory.ai/blog/faster-smarter-reliable-infinite-chat-supermemory-is-context-engineering)
24. [We solved AI API interoperability One API to rule them all, One spec to find them, One library to bring them all and in the TypeScript, bind them. When we were building the Infinite Chat API, initially, we only supported the OpenAI format. This was fine, until a lot of our customers started asking for more. EngineeringJul 7, 2025 ](https://supermemory.ai/blog/we-solved-ai-api-interoperability)
25. [The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier Products Overview: Flow is a note-taking app built around a bold vision: to create a more personal, context-aware writing experience powered by AI. At the heart of this mission is memory. Case StudyJun 14, 2025 ](https://supermemory.ai/blog/the-wow-factor-of-memory-how-flow-used-supermemory-to-build-smarter-stickier-products)
26. [The UX and technicalities of awesome MCPs Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up. To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. EngineeringJun 8, 2025 ](https://supermemory.ai/blog/the-ux-and-technicalities-of-awesome-mcps)
27. [Architecting a memory engine inspired by the human brain Language is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel. EngineeringJun 5, 2025 ](https://supermemory.ai/blog/memory-engine)

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