Skip to main content
Noēsis can persist episode facts into a long-term memory backend. You bring the storage (SQLite, Postgres, vector store, etc.), and expose it through the MemoryPort contract. A fact is a small JSON-style record derived from an episode (task, outcome, tags, timestamps, and related metadata) that you can query later.

​
When to use

  • You want episodes to leave behind a fact you can query later (e.g., for chat history, incident recall, eval traces).
  • You need to link episodes to facts for audit/search.
  • You want a minimal, schema-light way to store summaries without wiring a new service.
If you just need artifacts on disk, you can skip memory entirely.

​
Quick start: SQLite memory port

Use the built-in SQLite adapter for a lightweight, file-backed memory: 
from pathlib import Path
from noesis.runtime.session import SessionBuilder
from noesis.infrastructure.memory.sqlite import SQLiteMemory

memory = SQLiteMemory(db_path=Path("memory/noesis.db"))

session = (
    SessionBuilder.from_env()
    .with_port("memory", memory, api=memory.__api_version__)
    .build()
)

ep = session.run("Summarize incident INC-1234")
What happens:
  • After the episode, Noēsis builds a fact from summary.json (task, episode ID, metrics, tags) and persists it into SQLite.
  • The memory event records status (persisted/skipped/error) in events.jsonl.
  • Episodes are linked to fact IDs for later lookup.
In multi-service or multi-tenant environments, consider using a separate database, schema, or table prefix per app/tenant so facts stay isolated.

​
Querying memory

You can query the memory port directly: 
from noesis.interfaces.memory import MemoryQuery

results = memory.query(MemoryQuery(text="incident"), k=5)
for fact in results:
    print(fact.id, fact.content)
Facts include id, content, and metadata (e.g., tags, artifacts, timestamps).

​
Implement your own memory port

Implement the MemoryPort protocol if you want Postgres, a vector store, or another backend: 
from dataclasses import dataclass
from typing import Sequence
from noesis.interfaces.memory import Fact, MemoryPort, MemoryQuery


@dataclass
class PostgresMemory(MemoryPort):
    __api_version__ = "memory/1.1"

    def supports(self, capability: str) -> bool:
        # Capabilities Noēsis checks today
        return capability in {"write_fact", "query", "episode_links", "long_term_memory"}

    def write_fact(self, fact: Fact) -> None:
        # persist fact.content + fact.metadata
        ...

    def query(self, query: MemoryQuery, *, k: int = 5) -> Sequence[Fact]:
        # return top-k matching facts
        ...

    def link_episode(self, episode_id: str, fact_ids: Sequence[str]) -> None:
        # link episodes to fact IDs
        ...
Then register it on your session: 
memory = PostgresMemory(...)
session = (
    SessionBuilder.from_env()
    .with_port("memory", memory, api=memory.__api_version__)
    .build()
)

​
Capabilities and behavior

  • The memory port must return True for supports("long_term_memory") to receive persisted facts.
  • On each run, Noēsis builds a fact from summary.json, calls write_fact, and then link_episode.
  • Success/errors are logged as memory events in events.jsonl.
Avoid storing sensitive payloads verbatim. Use metadata or hashed IDs when needed; Noēsis does not enforce PII handling for you.