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# Agent Memory Architecture: Types, Schemas, and Data Flow

Design agent memory around explicit data contracts, retrieval, corrections, and application state. Use memory types to clarify decisions, not multiply services.

By Shardul Mane March 21, 2026 · 5 min read 

![Agent Memory Architecture: Types, Schemas, and Data Flow](https://supermemory.ai/_astro/cover.CP6NmW2k_1PNLM2.webp)

Agent memory architecture is the set of storage, retrieval, and update decisions that lets an application use earlier information in later work. It connects durable records to the agent's current context while preserving identity, provenance, and the distinction between a past observation and current truth.

The useful starting point is a data flow: what enters memory, how it becomes available, what qualifies it for retrieval, and what happens when it is corrected or removed. Use memory types to organize those decisions within the storage and services your workload needs.

## Use memory types to describe the workload

| Type                 | Example                                | Design question                                        |
| -------------------- | -------------------------------------- | ------------------------------------------------------ |
| Working context      | The open task and last tool result     | What must the next model call see?                     |
| Episodic records     | A customer asked to postpone a rollout | Can we recover the event, date, and source?            |
| Semantic facts       | The customer prefers email updates     | What supports this fact, and is it still applicable?   |
| Procedural knowledge | A repository's release checklist       | Who can change the procedure, and how is it validated? |

These are useful descriptions, not a compulsory product checklist. A document assistant might need only source retrieval and a small task record. A support agent may need preferences, prior events, and live account data. A release agent needs versioned procedures whose authority is stronger than an incidental conversation suggestion.

## Design the write path before the search index

For every candidate memory, establish its scope, source, intended lifetime, and how much confidence the application can place in it. An explicit preference is different from an inference based on one interaction. A request inside a quoted example is different from the user's instruction.

Consider “Use Python for this migration, but TypeScript remains our default.” Extracting only “prefers Python” changes the meaning. A useful representation keeps the project exception and the general default separately, each tied to the original message.

For example, your application can represent a scoped preference with this record:

```
{
  "id": "decision-104",
  "tenantId": "acme",
  "subjectId": "migration-project",
  "kind": "decision",
  "statement": "Use Python for the migration worker",
  "scope": "migration-project",
  "sourceId": "meeting-37#turn-18",
  "observedAt": "2026-09-10T15:00:00Z",
  "status": "active",
  "supersedes": null
}
```

Your database can enforce required fields and permitted status values. The extraction model cannot grant permission to read a tenant or silently promote a project decision to a company policy. Those are application rules.

## Separate the record from its representations

One source may produce searchable chunks, embeddings, a summary, and extracted facts. Give those derived records a path back to the source version. Otherwise, a deleted document can survive as an orphaned summary, or a corrected fact can remain in a cached profile.

Keep stable source identity separate from content identity. The same document can have multiple revisions; two documents can have identical text but different permissions. A content hash helps detect repeated content but is not a substitute for a source ID or an access policy.

This distinction also makes reprocessing safer. When an extraction prompt changes, you can identify the affected version, build new derived records, inspect them, and switch retrieval to the accepted representation.

## Make retrieval a constrained selection step

A practical read path is:

1. Authenticate the request and derive allowed scopes.
2. Determine the question's entity, time frame, and task.
3. Retrieve permitted candidates from the relevant sources.
4. Resolve or expose version conflicts.
5. Select a bounded evidence set and preserve its citations.
6. Generate the answer with uncertainty visible where evidence is incomplete.

Similarity is one input to selection. It cannot by itself establish authorization, freshness, or whether a remembered preference overrides a current explicit request. Apply those requirements deliberately.

For a user's shipping address, a past conversation might explain an earlier issue, while the order system controls the current shipment. Returning both without labeling their roles can create a convincing but incorrect answer.

## Define operations that can be tested independently

| Operation | Contract to establish                                                               |
| --------- | ----------------------------------------------------------------------------------- |
| Write     | Repeating an event does not create unintended duplicates                            |
| Read      | Only authorized, applicable evidence reaches the answer                             |
| Correct   | Current questions use the correction; supported historical questions retain history |
| Expire    | Expired context stops participating in the intended retrieval path                  |
| Delete    | Source, derived records, and application caches follow the agreed deletion behavior |
| Export    | Records retain identity, provenance, and version information                        |

Map these operations to your chosen service and implement any remaining lifecycle behavior in the application. For Supermemory's documented relationships between memories, see the [graph-memory documentation](https://supermemory.ai/docs/concepts/graph-memory). The application contract still needs to cover its own records and caches.

## Keep procedures under stronger control

An agent may observe that a workaround helped once. That does not make the workaround an approved procedure. Store the observation with its context, then promote it to a shared instruction only through your chosen review and testing process.

For example, “skip this failing test” during a local investigation should not become a permanent release rule. A memory system that faithfully repeats the wrong instruction can cause more damage than one that forgets it.

## Choose the smallest architecture that passes the workload

Begin with a relational record or a versioned file when the facts are few and exact lookup is enough. Add [vector or hybrid retrieval](https://supermemory.ai/blog/ai-memory-vs-vector-databases-complete-guide/) when the questions require semantic matching. Add graph paths when explicit relationships solve a demonstrated gap.

Use the [three implementation approaches](https://supermemory.ai/blog/3-ways-to-build-llms-with-long-term-memory/) to choose what to build or buy. Then test one complete lifecycle: capture a decision, retrieve it in another session, correct it, verify isolation, and remove it. That sequence reveals more about the architecture than a diagram containing every possible memory type.

For framework-specific implementations, see the guides for [LangGraph](https://supermemory.ai/blog/supermemory-with-langgraph/), [Microsoft Agent Framework](https://supermemory.ai/blog/supermemory-with-microsoft-agent-framework/) and [CrewAI](https://supermemory.ai/blog/supermemory-with-crewai/).

If a managed memory layer fits your design, [build a small pilot with Supermemory](https://console.supermemory.ai/). Bring one decision and two fictional users, then walk through the lifecycle above to see how it fits your application.

## 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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