 | Been building a multi-agent system called Shadows for a few months. Nine agents collaborating on strategy work with a shared memory layer. I spent most of my time on retrieval because that's what every benchmark measures. Mem0, MemPalace, Graphiti, all of them. On LongMemEval, recall_all@5 hit 97%. Overall accuracy was 73%. So the right memories are there. The agent still picks the wrong answer. It can't aggregate across sessions, doesn't know when to abstain, and guesses which aspect of a preference the user meant. That lined up with something I've been stuck on. Most LLMs jump straight to execution when you give them a task. People don't. We filter first, check if we're even the right person, then start. Next direction: Agents that can be moved with their identity and memory! [link] [comments] |
Project Shadows: Turns out "just add memory" doesn't fix your agent
Reddit r/artificial / 4/20/2026
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Key Points
- The author describes building “Shadows,” a multi-agent system with nine agents collaborating using a shared memory layer, and reports strong retrieval performance on LongMemEval (recall_all@5 at 97%).
- Despite having the right memories retrieved, the agent still produces incorrect answers, indicating that adding memory alone does not solve agent reasoning failures.
- The write-up attributes errors to limitations such as poor cross-session aggregation, lack of calibration for when to abstain, and difficulty interpreting which part of a user preference the user intended.
- The author contrasts agent behavior with human workflows, arguing that people typically filter and verify identity/context before executing, unlike many LLM agents that jump straight to action.
- The proposed next step is to develop agents that can be moved/controlled alongside their identity and memory, aiming to better align behavior with the needed pre-filtering process.
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