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# OpenCode Memory with the Supermemory Plugin

Add external capture and recall alongside OpenCode rules. Verify repository scope, cross-client access, privacy controls and lifecycle behavior.

By Dhravya Shah July 10, 2026 · 5 min read 

![Persistent memory for OpenCode](https://supermemory.ai/_astro/cover.DZI8w4BI_24B81z.webp)

OpenCode supports persistent project instructions through [rules and AGENTS.md](https://opencode.ai/docs/rules/). Supermemory adds external memory capture and retrieval for context you want to reuse across sessions.

Use the plugin when a controlled test exposes a continuity gap in your existing workflow. Check what was saved, which repository scope it used, and what was retrieved before relying on the final answer.

## The two-command install

```
bunx opencode-supermemory@latest install
bunx opencode-supermemory@latest login
```

That's it. The installer registers the plugin in `~/.config/opencode/opencode.jsonc` and adds a `/supermemory-index` command (`/supermemory-init` remains an alias). Check the connection any time with `bunx opencode-supermemory@latest status`. ([GitHub](https://github.com/supermemoryai/opencode-supermemory))

Or skip the terminal entirely: paste the README link into OpenCode and let the agent install it for you.

## What you get out of the box

The current [plugin README](https://github.com/supermemoryai/opencode-supermemory) documents these behaviors. They depend on configuration and successful service calls:

* **Context injection** \- on your first message of a session, the agent silently receives your personal profile, the project's memories, and relevant past memories with confidence scores. Inspect the injected records; a displayed similarity score is not a calibrated probability that a fact is correct.
* **Reasoned recall** \- every turn, the agent decides whether recalling memory would actually help this message, and only searches when it's worth it. Trivial messages don't burn a network call.
* **Automatic capture** \- completed conversation turns are saved in batches, and flushed when the session ends. A successful capture must still be verified; shutdown failures and unavailable services can interrupt the path.
* **Keyword detection** \- say "remember that..." or "don't forget..." and it's saved. Add your own trigger phrases in config.
* **Preemptive compaction** \- when context hits 80%, the plugin feeds your project memories into OpenCode's summarization and saves the session summary as a memory. This can preserve useful context but is not lossless capture.
* **`/supermemory-init`** \- point the agent at your codebase and it explores, then memorizes the architecture, patterns, and conventions.

For example, a cross-session exchange might look like this:

```
You: "add rate limiting to the ingest endpoint"
OpenCode: [recalls the sliding-window Redis limiter you built
for payments-api last month, and that you prefer
middleware over decorators - writes it that way]
```

## Memory scoped to the repository

Here's the thing I actually love about this plugin.

Every supermemory coding-agent plugin - OpenCode, Claude Code, Codex - writes to the **same shared container for a repository**, derived from the repo's git origin remote. Personal memories and project memories stay separate inside it (`sm_scope` metadata), but the container is one.

With compatible credentials and access to the same records, you can save a project decision in Claude Code and retrieve it in OpenCode. The repository remote identifies the project; your account's permissions determine who can read its memory.

Repositories with the same folder name but different remotes stay isolated. No origin remote? It falls back to the real filesystem path. And if you used an older version of any of the plugins, the legacy containers are still read - upgrading needs no migration.

## The `supermemory` tool

The agent gets one tool with five modes, auto-approved where it matters:

| Mode    | What it does                                                                                                    |
| ------- | --------------------------------------------------------------------------------------------------------------- |
| add     | Store a memory (typed: project-config, architecture, error-solution, preference, learned-pattern, conversation) |
| search  | Semantic search across memories                                                                                 |
| profile | View your user profile                                                                                          |
| list    | List memories by scope                                                                                          |
| forget  | Invoke the plugin’s removal operation for the selected record; verify source and derived-memory effects         |

Scopes are `user` (your personal memories for this project) and `project` (shared project knowledge, the default).

## Knobs worth turning

Everything lives in `~/.config/opencode/supermemory.jsonc`, all optional:

```
{
  // point at a self-hosted instance
  "baseUrl": "https://api.supermemory.ai",

  // retrieval tuning
  "similarityThreshold": 0.55,
  "maxMemories": 5,
  "maxProjectMemories": 10,

  // capture cadence: save every N turns (0 = session end only)
  "captureEveryNTurns": 3,

  // when compaction kicks in (0-1)
  "compactionThreshold": 0.8,

  // your own "save this" triggers (regex)
  "keywordPatterns": ["log\\s+this", "write\\s+down"],
}
```

`SUPERMEMORY_API_KEY` takes precedence over the config file, which keeps keys out of dotfiles if you prefer. Running Oh My OpenCode? Disable its built-in auto-compact hook so supermemory owns compaction - one line in its config, covered in the README.

## Privacy and self-hosting

The plugin documents redaction of `<private>` content from its capture path. That does not remove the same content from the model provider, tool logs, existing history or unrelated integrations. Test the exact capture path with synthetic data and keep credentials in supported secret configuration.

A self-hosted memory endpoint can be configured with `baseUrl`. For an offline workflow, the answering model, memory-service model providers and tools must also stay local. Pointing one URL at localhost does not establish that nothing leaves the machine. Review the [self-hosting options](https://supermemory.ai/docs/self-hosting/overview).

The coding plugin is free to use; hosted operations consume usage under [current pricing](https://supermemory.ai/pricing/).

## Why not just use the supermemory MCP?

The MCP works in OpenCode, but it has one structural limit: we can't control when the agent decides to call the tools. With MCP alone, capture depends on the client or agent invoking the save tool.

The plugin supplies host-specific hooks beyond simply exposing MCP tools:

* **Context injection** \- your profile arrives on session start, automatically
* **Automatic capture** \- conversation turns are stored whether or not the agent thinks to do it
* **Compaction integration** \- saved project context can help the agent resume after the active conversation is summarized

Use both if you want. Check account and scope if both paths are enabled, and avoid duplicate capture.

---

Start with one explicit save, retrieve it in a new session, then verify correction and removal in the same repository scope.

Install it: `bunx opencode-supermemory@latest install` \- or read the [docs](https://supermemory.ai/docs/integrations/opencode).

## Frequently asked questions

### Does OpenCode have any native persistent context?

Yes. It supports maintained project instructions and rules. Supermemory adds external capture and retrieval alongside those native controls.

### Does the same Git remote give every teammate access to the same memories?

No. Repository identity helps select a container, but compatible credentials and authorization to the stored records are still required.

### Do private tags keep content away from every service?

No. The documented redaction applies to the plugin capture path. It does not erase provider inputs, logs or copies in other integrations.

## 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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