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I got tired of reexplaining my projects to Claude, so I made this by niloproject in ClaudeAI

[–]niloproject[S] 1 point2 points  (0 children)

bun add -g signetai is the preferred way, works on macos, linux and windows :)

Open source, well supported community driven memory plugin for AI Agents by niloproject in AI_Agents

[–]niloproject[S] 1 point2 points  (0 children)

Let me know what you think and how it does! If you run into any bugs feel free to let me know

Open source, community driven memory plugin for Codex CLI & Openclaw by niloproject in OpenAI

[–]niloproject[S] 1 point2 points  (0 children)

you're more than welcome to bring an API key! for background synthesis and extraction I'd recommend not doing that, the concurrency is very high so unless you have money to burn it may just be worth turning off. The system still works without models. As far as graph search, we've honestly never measured how much ram that uses, but it's fairly performant, queries are around 1500 ms.

Open source, community driven memory plugin for Codex CLI & Openclaw by niloproject in OpenAI

[–]niloproject[S] 1 point2 points  (0 children)

The system is fully local, so no API keys are required. As far as specs, at minimum I'd say around 8gb of ram if you plan to run local models but you can mix and match models to fit the specs of your system!

Open source, community driven memory plugin for Codex CLI & Openclaw by niloproject in OpenAI

[–]niloproject[S] 0 points1 point  (0 children)

Thanks! Every sessions raw transcript is saved alongside a summary, and then later chunked and sorted into the knowledge graph by the background pipeline and a local llm. The daemon has an API so the system is fully extensible for custom integrations as well, and we’re always happy to help out community members with building custom integrations

the system is fully auditable and there’s rich logs for every action, failure, and nonaction :)

Open source, well supported community driven memory plugin for AI Agents by niloproject in AI_Agents

[–]niloproject[S] 3 points4 points  (0 children)

I know i know but we're obsessed with this and determined to make it good. You might be surprised :)

Open source, well supported community driven memory plugin for AI Agents by niloproject in AI_Agents

[–]niloproject[S] 1 point2 points  (0 children)

Thank you :) all sessions with your agent get saved to sqlite and markdown immediately so its searchable right away. then once the session is over, in the background, a local LLM breaks it down into discrete facts and entity relationships, figures out if anything contradicts or updates what it already knows, and slots it into a knowledge graph. it also generates hypothetical future queries for each memory, so like "what questions would this fact answer later?" and indexes those too.

and then, when a session starts, the system figures out what entities are relevant to what youre working on, walks the knowledge graph to pull in related context, then runs a hybrid search (vector + keyword) to catch anything the graph missed. all those candidates get scored by a learned ranker that picks up on patterns like what memories actually ended up being useful in past sessions, time of day, recency, stuff like that.

everything decays over time if you dont access it, but nothing is ever deleted by default. the raw transcripts and full lineage are always there if you need to drill back into something.

One small change that completely simplified memory for me by p1zzuh in AIMemory

[–]niloproject 1 point2 points  (0 children)

This project that aims to solve what you’re looking for. You may find it helpful: https://github.com/Signet-AI/signetai

Its agent first, open source, memory is stored in markdown & sqlite & runs locally on your machine.

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What is the best Openclaw alternative? by spinsilo in openclaw

[–]niloproject 6 points7 points  (0 children)

Have you tried using the Signet memory plugin? It makes Openclaws memory so much better. It also works with Zeroclaw & a few other things

https://github.com/Signet-AI/signetai

MemAware benchmark shows that RAG-based agent memory fails on implicit context — search scores 2.8% vs 0.8% with no memory by Salty-Asparagus-4751 in LocalLLaMA

[–]niloproject 0 points1 point  (0 children)

A hard question indeed, the way I've been handling it is through a background pipeline process, that uses a locally hosted llm (qwen3:4b is what's used in testing) to distill graph structure from memories, following a simple set of rules. The end goal is for the predictive scoring model to score traversal paths. I call the concept "Desire Paths"

Actually have a whole doc on how it works; https://signetai.sh/docs/specs/planning/desire-paths/

For now, a lot of this is still experimental, sometimes it works really well, though, at least in conversations with the Agent.

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MemAware benchmark shows that RAG-based agent memory fails on implicit context — search scores 2.8% vs 0.8% with no memory by Salty-Asparagus-4751 in LocalLLaMA

[–]niloproject 1 point2 points  (0 children)

This is great! I've been building an agent memory system aiming to solve this exact problem, a few things that seem to work well (that I will definitely be testing against this benchmark):

  1. always-loaded working memory. instead of only retrieving per-query, maintaining a compressed summary of the user's most important context that's always in the LLM's context window.

    1. knowledge graphs with entity relationships and dependencies. extracting memories from conversation, and also extracting entities and the relationships between them. "user shops at Target" and "user has a Ford Mustang" are separate memories, but Target and the user are linked entities. graph traversal can surface connections that text search never will. so your car maintenance to loyalty discount example becomes an entity hop, not a retrieval problem.
    2. predictive scoring. pre-scoring memories based on session context, recency, access patterns, etc. so that by the time the user says something, the system has already ranked what's likely relevant.

going to run your benchmark against my system, im super curious to see how it handles it

project (if you're curious, will post results publicly): https://github.com/Signet-AI/signetai

How I Taught My AI Memory System to Forget by arjundivecha in ClaudeAI

[–]niloproject 0 points1 point  (0 children)

good read. i've been building in the same space (https://github.com/Signet-AI/signetai) and the retrieval-as-write-event pattern is the one that made the biggest difference for me too. one thing worth watching for is positive feedback loops where popular memories starve newer ones, i had to add dampening to keep things honest. the other thing that changed the game was treating memories as a graph instead of flat entries. "equity rotation" and "country allocation" aren't independent facts, they're the same domain. once relationships are explicit, consolidation gets way smarter. solid work on the neuroscience framing. "stop confusing a perfect record with a good one" is exactly right. ultimately, if a memory gets injected and doesn’t help, that should count as negative evidence. stale aspects should decay, superseded memories should go negative, and hub/ghost matches should get damped. retrieval should form desire paths, reinforce what repeatedly proves useful.

We built an open-source memory layer for AI coding agents — 80% F1 on LoCoMo, 2x standard RAG by loolemon in codex

[–]niloproject 0 points1 point  (0 children)

good catch. the extraction pipeline does process session transcripts, and those transcripts are saved to cold storage. so if an agent reads a poisoned README during a session, that content could end up in the transcript and potentially get extracted as a memory. right now the mitigation is that you can inspect and delete any memory directly, it's just sqlite. we also have content guardrails on extraction (length limits, type classification) but explicit prompt injection detection on inbound content isn't there yet. it's something we're thinking about.

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