khive

khive

[Documentation](https://ohdearquant.github.io/khive/) · [Discord](https://discord.gg/JDj9ENhUE8)

Vector search finds similar text. A knowledge graph finds _structure_: lineages, dependencies, contradictions, gaps. khive gives your research agent a typed, queryable graph that grows as it works. Read a paper and entities and edges fall out. Make a connection and it's traversable immediately. Come back next session and the graph remembers what you built.

There's no Neo4j to run and no separate SPARQL endpoint to deploy. It's SQLite on disk, MCP over stdio, and cargo test finishes in 4 seconds.

khive also runs hosted, as memory and a knowledge graph your agents reach over MCP: https://khive.ai/?utm_source=github&utm_campaign=khive-readme

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What you get

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The three substrates

Everything in khive is one of three things:

Entities are _things_. Notes are _what you think about things_. Events are _what happened_.

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The MCP verb surface

One MCP tool: request (ADR-016 + ADR-027). Every verb is a parsed op inside it.

request(ops="verb(arg=value, arg=value)")              # single op

request(ops="[v1(...), v2(...), v3(...)]") # parallel batch (max 100) request(ops="[{\"tool\":\"v1\",\"args\":{...}}, ...]") # equivalent JSON form

All 14 packs load by default, giving 162 verbs out of the box (updated from the current handler declarations; verify again with request(ops="verbs()") before editing this table):

create, list, search take kind=entity|note (or kind=edge for list). get, update, delete, merge are UUID-only: they auto-detect the record type.

Agents reach khive via MCP stdio, using Python, TypeScript, Rust, or any MCP-compatible client, so there is no language SDK to learn.

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What typed edges add beyond vector similarity

A vector index returns "these two texts are close." It has no idea that document B introduced concept A, that concept C is a variant of concept A, or that concept D was superseded by concept E last month. Cosine distance carries no direction and no type. It can't tell you what a relationship _is_, only that something is nearby.

khive's graph carries both signals. Every edge is one of 20 closed relations across 10 categories: structure (contains, part_of, instance_of, links_to, located_in), derivation (extends, variant_of, introduced_by, supersedes), provenance (derived_from), temporal (precedes), dependency (depends_on, enables), implementation (implements), lateral (competes_with, composed_with), annotation (annotates), epistemic (supports, refutes), and ownership (owns, owner → owned).

The following example, run against a scratch database, shows the difference in practice (full transcript in demos/research-ingest.md):

request(ops="create(kind=\"entity\", entity_kind=\"concept\", name=\"FlashAttention\")")

request(ops="create(kind=\"entity\", entity_kind=\"concept\", name=\"FlashAttention-2\", description=\"Improved parallelism and work partitioning over FlashAttention\")") request(ops="link(source_id=\" \", target_id=\" \", relation=\"extends\")") request(ops="traverse(roots=[\" \"], max_depth=2)")

traverse returns the lineage directly: FlashAttention-2 reaches FlashAttention across one extends edge, with the edge id and relation name attached to the path. A vector search over the same two entities returns a similarity score between two chunks of text, with no way to tell which one came first or which direction the relationship runs.

khive runs both signals together. search combines FTS5 and vector similarity through RRF fusion to find candidates. traverse, neighbors, and GQL/SPARQL walk the typed edges to show how those candidates actually relate. Similarity surfaces what's nearby in meaning. The graph records what's connected, in which direction, and why.

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Architecture

┌──────────────────────────────────────────────────────────────┐

│ kkernel mcp: stdio MCP server (the kkernel binary) │ │ khived: persistent daemon (ADR-049): warm runtime │ │ auto-spawned on first request │ │ 1 tool: request (ADR-016 + ADR-027): parses DSL, │ │ dispatches each op through the VerbRegistry │ └──────────────────────────────────────────────────────────────┘ ↕ VerbRegistry dispatch ┌──────────────────────────────────────────────────────────────┐ │ khive-pack-kg: KG vocabulary + 31 public verbs │ │ khive-pack-gtd: task lifecycle (8 verbs) │ │ khive-pack-memory: salience + decay recall (6 ver