Alternatives
Products that do what YourMemory 2.0 does
Your AI has the memory of a goldfish. Not anymore
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This is a project that I have been building for a while now, YourMemory is a solution to agentic memory which focuses on pruning of noise rather than hoarding of data. In the current state of agentic memory most of the context is stored in the form of a MD file or is derived through a RAG model where you store each and everything. Both of the solution leads to bloated context which does not optimize the usage of any tokens. In this system we only keep relevant data in our memory and prune all the unnecessary data. The relevance of a data is derived through multiple factors such as recall…
Jun 2026 · yourmemoryai.vercel.app
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It's an biological inspired decay system for our memories with extended support of temporal reasoning. Created a CLI command to infer knowledge from the context stored in memory system without any token utilization or llm call. It comes with a memory dashboard to monitor and manage your memories it can be extended as audit trail for agents as well !
May 2026
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Hey HN! We're Taranjeet and Deshraj, the founders of Mem0 (https://mem0.ai). Mem0 adds a stateful memory layer to AI applications, allowing them to remember user interactions, preferences, and context over time. This enables AI apps to deliver increasingly personalized and intelligent experiences that evolve with every interaction. There’s a demo video at https://youtu.be/VtRuBCTZL1o and a playground to try out at https://app.mem0.ai/playground. You'll need to sign up to use the playground – this helps ensure responses are more tailored to you by…
2024 · github.com
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Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
Apr 2026 · github.com
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I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
2025 · github.com
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An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents. - fellowgeek/mcp-memory
24d ago · github.com
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Hi HN, There’s been a lot of discussion lately around context graphs, decision traces, and how AI systems reason. One thing we kept running into: when AI agents make real decisions, the why behind those decisions often disappears. The context is scattered across prompts, tools, policies, and approvals. Logs show what happened, but not why it was allowed. TraceMem is an attempt to make decision context durable. It records the reasoning, authority, and context behind AI actions as a system of record, not as monitoring data, but as memory. Happy to share more details or answer questions. - Tommi
Jan 2026 · tracemem.com
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