Blog·Learning

Entity Resolution for Agent Memory: Avoid Connecting the Wrong Facts

By Shardul Mane·2 min read

Entity Resolution for Agent Memory: Avoid Connecting the Wrong Facts

Entity resolution determines whether two references describe the same person, organization, project, or other subject. In a memory graph, a mistaken merge can attach correct facts to the wrong entity and contaminate many later answers. Resolve identity with provenance and uncertainty rather than relying on similar names alone.

Separate labels from identity

Two employees can share a name, a project can be renamed, and one organization can have several aliases. Keep stable source identifiers where available and preserve the system they came from. A display name is useful for reading but usually insufficient as the only identity key.

Do not merge across tenant or workspace boundaries just because names and descriptions match. Authorization scope should constrain candidate matches before semantic similarity is considered.

Record why a match was made

Useful evidence may include a shared verified identifier, an explicit source statement, or an application-owned mapping. An inferred match should remain distinguishable from a confirmed one. Store enough provenance to revisit the decision when new evidence appears.

Choose conservative behavior for ambiguous cases. Keeping two unresolved candidates can be preferable to confidently combining them. The application can ask for clarification or retrieve source-specific evidence without asserting an identity match.

Plan for splits and corrections

A graph must be able to recover when an entity was merged incorrectly. Identify which relationships and derived memories came from each source so they can be reassigned or invalidated. A simple rename is different from a split: changing the label does not repair facts attached to the wrong subject.

Test an organization rename, two people with the same name, a reused email address, and contradictory source records. Inspect downstream answers after correcting the identity mapping, not only the graph's visual appearance.

Evaluate the questions affected by identity

Use questions that require connecting records across sources and questions that should remain separate. Measure incorrect merges and missed matches independently; they have different consequences. Include access changes and deletion in the lifecycle test.

The relationship-semantics guide explains how relationships affect memory. To understand a managed implementation before evaluating it, read Supermemory's graph-memory concepts and test your own ambiguous-identity examples against the documented behavior.

  1. An update to supermemoryWe'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.
  2. Scaling Conversations: How Adapta Grew Usage Without Losing ContextAdapta added Supermemory as a persistent memory layer so every conversation keeps its context — letting the team scale usage without losing the thread.
  3. How Chatarmin Ditched RAG and Went Memory-Only with SupermemoryChatarmin 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%.
  4. SMFS: making agentic retrieval 55% cheaper AND more accurateWe 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.
  5. 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.
  6. Dear reader, we just made supermemory insanely cheap... the Context CloudWhen 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.
  7. Introducing @supermemory/tools v2.0.0Today 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.
  8. Solving the Precision-Recall Tradeoff: Search Result AggregationWhen 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.
  9. 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.
  10. 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.
  11. 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.
  12. Catch up with our UNFORGETTABLE Launch WeekOver 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.
  13. Empowering the Next Generation of Founders: Supermemory Startup ProgramIf 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.
  14. Building code-chunk: AST Aware Code ChunkingAt 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.
  15. Supermemory raises $3 million with the best memory engine for LLMsToday, 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.
  16. Mem0 vs Supermemory: Why Scira SwitchedScira AI moved its production memory layer from Mem0 to Supermemory. This is what failed, what improved, and how the team evaluated the two systems.
  17. Never Record Again: How Montra Uses Supermemory to Rethink Video CreationCampbell 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.
  18. Unified Memory That Works Where You Work: Your Second Brain With SupermemoryHi 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.
  19. Supermemory just got faster on PlanetScaleWhat 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.
  20. 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.
  21. We solved AI API interoperabilityOne 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 the Infinite Chat API, initially, we only supported the OpenAI format. This was fine, until a lot of our customers started asking, asking for more.
  22. The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier ProductsOverview: 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.
  23. The UX and technicalities of awesome MCPsLast 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.
  24. Architecting a memory engine inspired by the human brainLanguage 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.