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Documentation Index

Fetch the complete documentation index at: https://docs.agentium.in/llms.txt

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Learned Knowledge

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In plain terms

Learnings are reusable insights that apply across many conversations — “Vendor X invoices always have line-item drift,” “customs holds explain 80% of ‘lost’ international shipments.” They’re recalled by meaning (semantic search), so the right insight surfaces even when the wording differs.
The analogy: the team wiki of hard-won lessons. One person figures something out once; everyone benefits forever.
Unlike User Facts (about a person) or Entities (about things), learnings are about how to do the work well.

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When to use it

  • Domain knowledge that accrues over time — support playbooks, troubleshooting patterns, gotchas.
  • Team-shared know-how — promote an insight to agent or tenant scope so the whole team sees it (see Scope Hierarchy).
  • Anything you want recalled by similarity, not exact match.
import { InMemoryVectorStore, OpenAIEmbedding } from "@agentium/core";

memory: {
  storage,
  learnings: {
    vectorStore: new InMemoryVectorStore(new OpenAIEmbedding()),
    topK: 3,
  },
}

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When NOT to use it

  • Facts about the user → User Facts.
  • Exact-match lookups (an order by ID) → that’s a tool call, not memory.
  • Static reference docs → use a Knowledge Base / RAG for large document corpora; Learnings is for short, agent-discovered insights.

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Configuration

PropertyTypeRequiredDefaultWhat it controls
vectorStoreVectorStoreYes—Where learnings are indexed for semantic search
collectionstringNo"agentium_learnings"The named bucket inside the vector store
topKnumberNo3How many of the most relevant learnings to inject per run
Understanding the options:
  • vectorStore is required because learnings are recalled by meaning, which needs a vector index (Qdrant, Pinecone, in-memory, etc.). This is why learnings takes an object, not just true — there’s no default backend.
  • collection — change only if multiple agents should keep separate learning sets inside one vector database.
  • topK — higher means the agent considers more accumulated knowledge but spends more tokens and risks losing focus. 3 is a solid default; 5–8 for knowledge-heavy agents, 1–2 for tight budgets.

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Scope and sharing

Learnings support a four-level scope so insights aren’t trapped in one user’s silo: 
global   ← built-in defaults, everyone
tenant   ← organization-wide (requires memory.tenantId)
agent    ← shared across all users of this agent
user     ← personal (default)
The save_learning tool exposes a scope parameter; reads union every scope the caller belongs to. Auto-extracted learnings always save as user scope — the framework never auto-promotes to a shared scope. See Multi-User Isolation for the full contract. 
// Share an insight with the whole team using this agent
save_learning({
  title: "Vendor X line-item drift",
  content: "Vendor X invoices consistently show $0.10–$0.50 drift per line.",
  context: "invoice reconciliation",
  scope: "agent",
})

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Tools

ToolWhat it does
save_learningSave an insight (with optional scope)
search_learningsSemantic search across every scope the caller can see

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Cross-references