Human-first notes (Notion / Obsidian)
Page-centric and great for people; AI access depends on connectors or plugins, and structure is a human convention.
OPEN SOURCE · LOCAL-FIRST · MCP-NATIVE
A queryable, traceable knowledge base for your AI agents
hyseed is an open-source local knowledge base: it turns team decisions, lessons, APIs, and runbooks into structured facts that MCP clients like Claude Code, Cursor, and Cline can use directly — with data staying on your machine.
MIT open source · Windows x64 · VS Code 1.101+
$ hyseed search "sqlite fts5 decision"
dec/use-sqlite-fts5decision · accepted
→ kb_get(..., section="Architecture")target section only
context70–90% less on long docs
Section-level retrieval keeps whole documents out of the context window.
Notes, vector stores, and AI memory each miss a piece
They solve how humans write, how to find similar text, and how to remember a conversation — but not how AI reliably uses facts your team has reviewed.
Vector stores / RAG (Chroma / LanceDB, etc.)
Strong at similarity search, but no schema, versioning, or team collaboration; you build the retrieval pipeline.
AI memory (Mem0 / Zep, etc.)
Per-agent conversational memory, mostly auto-extracted; not designed for team review or long-term governance.
hyseed fills the gap: structured, team-reviewed facts that AI can use through MCP.
Designed to be used by AI
Not a chat box bolted onto a wiki — retrieval, structure, versioning, and collaboration live in the protocol and the data model.
MCP-native
CRUD, search, graph, versioning, and scheduling are exposed through MCP. Claude Code, Cursor, Cline, and other clients can connect directly. For a lighter integration, switch to the read-only thin mode.
Typed schema, not free text
Nine built-in document types: project, decision, lesson, glossary, person, FAQ, API, runbook, and release. Typed metadata and stable IDs let AI extract exact facts.
Section-level retrieval
kb_get can return a single heading section; when the section is missing, it returns the table of contents. Long-document context drops by 70–90%.
Versioning, audit, rollback
Every change has a version number, with field-level diffs, restore to any version, soft-delete recovery, and an audit log. AI writes become safe to trust.
Typed knowledge graph
Ten standard relation types, automatic backlinks, multi-hop traversal, impact analysis, and decision chains. Change a decision and follow the graph to affected docs.
Git-native collaboration
Knowledge lives as Markdown in Git; tags, aliases, links, and metadata are three-way merged. PR review, branches, and Code Owners keep working.
Side by side
From design center to data deployment, the differences between the four approaches are clear at a glance.
| Dimension | Human-first notes (Notion / Obsidian) | Vector stores + RAG | AI memory | hyseed |
|---|---|---|---|---|
| Design center | People and pages | Models and vectors | One agent's conversation | AI agents + people, one shared body of knowledge |
| AI access | Web / editor; connectors or plugins | SDK + custom retrieval pipeline | SDK / API tied to a framework | MCP-native + CLI + admin console + VS Code extension |
| Structure | Free-form, by convention | Vectors only, no schema | Auto-extracted memory entries | 9 document types + typed metadata + stable IDs |
| Retrieval | Keywords / plugins | Mostly vector similarity | Semantic recall | FTS5 BM25 / fuzzy / semantic (sqlite-vec) / RRF hybrid |
| Context cost | Whole page | Chunks, still need trimming | Injected memory, uncontrolled granularity | Section-level; 70–90% less on long docs |
| Versioning & audit | Page/file history; audit depends on plan or plugin | Usually none | Not the focus | Versions, field-level diffs, restore, soft delete, audit |
| Team collaboration | Notion: real-time; Obsidian: Git/sync | None | Weak, platform-bound | Git-native + three-way merge + PR review |
| Data & deployment | Notion: cloud; Obsidian: local files | Local or cloud, self-managed | Self-hosted open source or cloud | Single-file SQLite, fully local, no cloud, no telemetry, MIT |
Three steps into your workflow
Install the extension and go; no separate Python runtime required.
Install the extension
Install hyseed from the VS Code Marketplace, with support for Windows x64 and VS Code 1.101+.
code --install-extension limccn.kbmcp-vscodeOpen the admin console
Run hyseed: Open admin console from the Command Palette; the local admin console opens in your browser.
Create, convert or import knowledge
Create a document, convert an existing document or import Markdown — or let AI search directly from your MCP client. Every change is versioned.
hyseed search "sqlite fts5"Supports Markdown, Word (.docx), PowerPoint (.pptx), Excel (.xlsx / .xlsm / .xls), PDF, TXT, JSON and CSV; a local vault is created on first use.
Ready out of the box, with data staying local
hyseed is open source under MIT, currently at 0.1.1. Single-file SQLite storage, no cloud service, no telemetry; install the VS Code extension and your team's decisions and lessons are searchable and traceable. Semantic search can be enabled with the sqlite-vec extension.
Version
Marketplace 0.1.1
License
MIT · open source
Data
Local SQLite · no telemetry
Compatibility
Windows x64 · VS Code 1.101+
Runtime
Bundled, no Python required
Search
Lexical / fuzzy / semantic / RRF hybrid
Bring team knowledge into your AI workflow
Install hyseed from the VS Code Marketplace and get a local, traceable, collaborative open-source knowledge base in minutes.
MIT open source · Windows x64 · no separate runtime install