Alternatives
Products that do what Hippocampus does
Openclaw AI agents forget between sessions. This fixes that.
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com
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Turn your work activity into structured AI context.
Feb 2026 · claw.toggle.pro
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One memory across OpenClaw, ChatGPT, Claude & Gemini
Feb 2026 · maximem.ai
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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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Hey HN! I'm Arindam, part of the team behind Memori (https://memori.gibsonai.com/). Memori adds a stateful memory engine to AI agents, enabling them to stay consistent, recall past work, and improve over time. With Memori, agents don’t lose track of multi-step workflows, repeat tool calls, or forget user preferences. Instead, they build up human-like memory that makes them more reliable and efficient across sessions. We’ve also put together demo apps (a personal diary assistant, a research agent, and a travel planner) so you can see memory in action. Current LLMs are stateless…
2025 · github.com
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Open Source Context Infrastructure for AI Agents
May 2026 · ravbyte-ai.github.io
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Open-source MCP server. #5 on LoCoMo. 100% local. MIT.
May 2026 · totalmemory.dev
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Hi HN, I’ve been building AI agents and copilots, and kept running into a frustrating problem: they don’t fail loudly, they forget things quietly. Users re-explain preferences, agents contradict earlier responses, and context resets without any clear visibility into why. I built Memograph CLI as a debugging tool to analyze conversation transcripts and show: - what the agent forgot - where continuity broke - contradictions and repeated context - estimated token waste due to re-prompting It works locally and supports plain text or JSON transcripts. Example: $ memograph Output: Cognitive Drift…
Feb 2026
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