I built Enzyme to help agents and people compound knowledge, together. This week I decided to open-source several years of my work (it began as an Obsidian plugin, then became the first MCP for Obsidian).
Enzyme is like an LSP for ideas; it orients agents by emulating your working memory from a local knowledge base (sqlite or .md). I decided that a language was the best way to represent several design elements of agent memory at once: append-only log locations, temporal distribution config, profiles for "dreaming", and more. Your agent can setup Enzyme, compile the Enzyme program, and maintain a local-first, self-updating lens over its wiki. It's very fast.
I use it daily to maintain my 8000+ note Zettelkasten that also acts as a personal CRM (meeting transcriptions), Readwise collection, journal, and home for agent rollout summaries.
Core to its design are questions that are generated from recent material and projected over the corpus. Instead of "dreaming" .md files over fragments, it generates questions to connect their ideas using embedding similarity (static embeddings developed by Flower Computer). What these questions notice is configurable.
The binary, including the embedding weights baked in, sits under 30 mb.
How is it ensured that the AI agent will actually search for the given information vs ignoring the instruction as its always been the case with any skill or system prompt?
Agents respond well to keeping instructions simple -- I've noticed that elaborate knowledge bases are hard for agents to consistently grok, so by simplifying the agent abstraction Enzyme improves its ability to navigate
QMD is great! Concept is similar, relies on having an existing Markdown store for your agent's memory.
Enzyme is 100x smaller and precomputes questions that help agents to write better queries (which theyre getting great at these days), instead of relying on a finetuned model. So it is overall faster
Enzyme produces local artifacts, so technically it could easily run on each team member's machine (it's cheap enough), provided there's another way to sync the knowledge base. Markdown + team kb's is an interesting area and I am also curious to see what standards will take shape -- probably an adjacent problem
Yeah! You're highlighting something interesting in that taste is observed over compounded judgment (ie design decisions). The reason this works well is by observing questions, Enzyme turns compounded curiosity into an agent lens
Hey all!
I built Enzyme to help agents and people compound knowledge, together. This week I decided to open-source several years of my work (it began as an Obsidian plugin, then became the first MCP for Obsidian).
Enzyme is like an LSP for ideas; it orients agents by emulating your working memory from a local knowledge base (sqlite or .md). I decided that a language was the best way to represent several design elements of agent memory at once: append-only log locations, temporal distribution config, profiles for "dreaming", and more. Your agent can setup Enzyme, compile the Enzyme program, and maintain a local-first, self-updating lens over its wiki. It's very fast.
I use it daily to maintain my 8000+ note Zettelkasten that also acts as a personal CRM (meeting transcriptions), Readwise collection, journal, and home for agent rollout summaries.
Core to its design are questions that are generated from recent material and projected over the corpus. Instead of "dreaming" .md files over fragments, it generates questions to connect their ideas using embedding similarity (static embeddings developed by Flower Computer). What these questions notice is configurable.
The binary, including the embedding weights baked in, sits under 30 mb.
How is it ensured that the AI agent will actually search for the given information vs ignoring the instruction as its always been the case with any skill or system prompt?
Agents respond well to keeping instructions simple -- I've noticed that elaborate knowledge bases are hard for agents to consistently grok, so by simplifying the agent abstraction Enzyme improves its ability to navigate
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This seems really cool. Does tobi/qmd solve a similar problem?
Would you use one or the other or could you combine both?
How easy would it be to make shareable across teams?
QMD is great! Concept is similar, relies on having an existing Markdown store for your agent's memory.
Enzyme is 100x smaller and precomputes questions that help agents to write better queries (which theyre getting great at these days), instead of relying on a finetuned model. So it is overall faster
Enzyme produces local artifacts, so technically it could easily run on each team member's machine (it's cheap enough), provided there's another way to sync the knowledge base. Markdown + team kb's is an interesting area and I am also curious to see what standards will take shape -- probably an adjacent problem
This is awesome and already very useful for the design agents I’m building!
Yeah! You're highlighting something interesting in that taste is observed over compounded judgment (ie design decisions). The reason this works well is by observing questions, Enzyme turns compounded curiosity into an agent lens
So cool!