Memory¶

Memory is injected behind the MemoryManager seam. The ContextBuilder retrieves in-scope items and injects them into the planner context (emitting memory_retrieved / memory_injected). Core ships three reference managers.

InMemoryMemory — term-overlap relevance¶

Retrieval ranks in-scope items by how many query terms appear in the content (a question surfaces a fact that shares words), not substring containment. preference items are always floored in so standing instructions surface. Ties break by item score, then recency. For tests and local examples.

EmbeddingMemory — semantic recall¶

Ranks in-scope items by cosine similarity between the query embedding and each item's content embedding. The embedder is injected — no model is bundled, so it runs in any runtime. A text-less query falls back to score/recency.

TypeScriptPython

import { EmbeddingMemory, type Embedder } from "@agentic-kernel/core";

const embed: Embedder = async (text) => myEmbeddingModel(text); // number[]
const memory = new EmbeddingMemory({ embed });
// new AgentEngine({ planner, policy, memory, ... })

from agentic_kernel.core import EmbeddingMemory

async def embed(text: str) -> list[float]:
    return await my_embedding_model(text)

memory = EmbeddingMemory(embed=embed)
# engine = create_agent_engine(planner=..., policy=..., memory=memory)

Scopes¶

Memory is tenant-namespaced: a scope is tenant/[workspace/][agent]. A seeded item's scope must be within the agent's scope prefix to be retrievable — e.g. for tenant acme, agent researcher, an item with scope acme/researcher matches.

Production & governance¶

For durable storage use memory-postgres (or any MemoryManager). Long-term memory has a governance lifecycle — proposeWrite / supersede / tombstone / explain / export — and SessionMemoryExtractor / a memory governor can extract attributable session summaries automatically on completion.