[Blog](https://supermemory.ai/blog) · [Learning](https://supermemory.ai/blog/tag/learning)

# Estimate a Custom Memory System by Workstream, Not API Count

By Shardul Mane September 11, 2026 · 2 min read 

![Estimate a Custom Memory System by Workstream, Not API Count](https://supermemory.ai/_astro/cover.DDMSz2Hi_Z1bFVBA.webp)

Estimate a custom memory system by the work required to make it correct, observable, and maintainable. Counting endpoints or asking how quickly a prototype can store embeddings misses source processing, identity, corrections, evaluation, and operations. A useful estimate exposes assumptions and ranges so the team can decide what to build or buy.

## Break the system into deliverable work

List source ingestion, parsing, identity and authorization, storage, retrieval, context assembly, correction and deletion, evaluation, observability, and recovery. For each workstream, define a concrete completion test. “Supports deletion” should describe which representations disappear and how that is verified.

Separate prototype work from production work. A local database and one happy-path query can demonstrate feasibility, but they do not establish concurrent updates, permissions, migrations, or reliable recovery.

## Estimate uncertainty explicitly

Assign a low, expected, and high effort range to each workstream using your team's experience. Record dependencies and unknowns. A source without reliable change events may need an investigation before a connector estimate is meaningful.

Use arithmetic transparently. If an illustrative plan contains eight workstreams averaging five person-days each, the total is 40 person-days before coordination, investigation, or ongoing operations. That is an example of the calculation, not a benchmark for how long a memory system should take. Calendar duration also depends on staffing and dependencies.

## Include work after the first release

Budget for source schema changes, re-embedding, access-policy changes, incident response, evaluation maintenance, and upgrades. Estimate expected monthly operational effort separately from infrastructure charges. A component can be inexpensive to host but costly to diagnose.

Include the opportunity cost of delaying the product workflow that memory is meant to improve. Compare alternatives over the same period and scope; otherwise a complete custom system is being compared with only a vendor's base API price.

## Reduce uncertainty with a narrow spike

Choose the least-understood requirement and implement enough to test it: a difficult source, a permission transition, or a correction across derived records. Update the estimate from what you learn instead of polishing a speculative total.

If the initial architecture came from a coding assistant, use the [AI-generated proposal review](https://supermemory.ai/blog/review-ai-generated-memory-architecture/) to expose omitted requirements before estimating it.

The [self-managed decision guide](https://supermemory.ai/blog/when-self-managed-memory-is-justified/) helps identify requirements that may justify the work. To price a managed alternative against the same workload, [try Supermemory](https://console.supermemory.ai/) and record the application work that remains alongside service usage.

## 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)

## Start building with supermemory.

Memory and continual learning for any model, any harness. Available through our API, plugins, and MCP.

[Build with supermemory ](https://console.supermemory.ai/)