Knowledge store

The knowledge base's tools work on a KnowledgeStore, never on files. The kit ships one over markdown files; an agent can plug in another one (a database, a vector store) and the tools, the prompt and the skills stay the same.

The kit's store

createFileKnowledgeStore(knowledgeDir, { pageTypes?, sourcesDir? }) keeps pages as markdown files in the layout the knowledge base always had (see On disk). buildSessionOptions() creates it for you, with spec.knowledgePageTypes and config.sourcesDir, and returns it as knowledgeStore, so a host can read the knowledge base too (to show it in a desktop app, say):

const { options, knowledgeStore } = await buildSessionOptions(config, runDir, spec);
const topics = (await knowledgeStore?.list())?.filter((page) => page.type === "topic");

It's usable on its own as well, in a script or a test:

import { createFileKnowledgeStore } from "@falkenslab/agent-kit";

const store = createFileKnowledgeStore("course/knowledge", { sourcesDir: "course/sources" });
console.log(await store.index());
const report = await store.check();

The interface

interface KnowledgeStore {
  types(): readonly PageType[];
  list(): Promise<PageInfo[]>; // every page but the retired ones
  read(id: string): Promise<KnowledgePage | null>; // with linkedFrom: the pages that link to it
  create(type: string, slug: string, title: string, content: string, fields?: FieldChanges): Promise<string>;
  createMany(pages: readonly NewPage[]): Promise<string[]>; // links among them allowed; all or none
  edit(id: string, oldText: string, newText: string, fields?: FieldChanges): Promise<void>;
  rewrite(id: string, content: string, fields?: FieldChanges): Promise<void>;
  supersede(id: string, by: string, reason?: string): Promise<void>;
  retire(id: string, reason: string): Promise<void>;
  search(query: string, limit?: number): Promise<SearchHit[]>;
  index(): Promise<string>;
  log(operation: string, what: string, pages?: readonly string[]): Promise<void>;
  check(): Promise<CheckReport>;
  overview(): Promise<string>;
  writeOverview(content: string): Promise<void>;
}

What a store must keep, whatever it stores the pages in:

• Ids are type/slug, and links in content are [text](type/slug): return them that way from read(), whatever you keep internally. parseId() and linkedIds() are exported to help.
• Links must resolve: create, createMany, edit, rewrite and writeOverview refuse a link to a page that doesn't exist, with a message the model can act on (the tools pass errors through as they are).
• Pages are never deleted or renamed. supersede and retire mark them (fields.status: superseded or retired); a retired page leaves list(), index() and search(), but read() still returns it.
• edit replaces exactly one occurrence, and fails if there's none or several.
• check() returns broken links, orphans, links to retired pages and summaries whose original is gone. The tool adds the sources folder's part itself.

BUILT_IN_PAGE_TYPES are the kit's four types with their templates; a store gets the agent's declared ones too, and should treat a page's type as a label, so it can hold any type.

Plugging in your own

import type { AgentSpec, KnowledgeStore } from "@falkenslab/agent-kit";
import { createPgKnowledgeStore } from "./pgStore.js"; // yours

const spec: AgentSpec<Config> = {
  // …
  knowledgeStore: (config) => createPgKnowledgeStore(process.env.DATABASE_URL!, { pageTypes: myTypes }),
};

With knowledgeStore, the kit doesn't touch knowledgeDir's files; it still needs knowledgeDir set, since that's what turns the knowledge base on (and keeps the file tools out of that folder).

A vector store usually keeps the curated pages as the source of truth and indexes them for search(): similarity search on embeddings of each page (or each section), with the same SearchHit result. Anthropic has no embeddings API; Voyage AI is the usual choice, and local options exist (LanceDB or sqlite-vec with a local model). index() and check() don't change.

Wiring the tools yourself

A host that builds its own session options gets the same tools with createKnowledgeServer(store, options):

import { createKnowledgeServer } from "@falkenslab/agent-kit";

mcpServers: {
  knowledge: createKnowledgeServer(store, { runDir, interactive: true, sourcesDir, projectDir, knowledgeDir }),
},

interactive adds knowledge_retire, which asks a person first; leave it off where there's nobody to ask. Name the server knowledge, so the plan gate, the step gate and the tool labels recognize its tools.