Org-aware memory for multi-user AI apps.
Drop-in memory infrastructure that learns at the role, team, and org level — so your AI app gets smarter as more agents and humans use it.
$ pip install memsy >>> from memsy import MemsyClient, EventPayload >>> client = MemsyClient(api_key="...") >>> >>> # Agent and human — same API, same memory plane >>> client.ingest([ >>> EventPayload(actor_id="agent:triage-bot", role_id="support", ...), >>> EventPayload(actor_id="user_42", role_id="support", ...), >>> ])
Memory at every level of your org.Surfaced when it matters.
Memsy keeps individual moments and the patterns that emerge from them. When your AI app needs cross-user context, the right tier surfaces.
Raw conversations as they arrive. The source material that patterns and shared knowledge emerge from.
Patterns within a function — what a role has come to know across everyone in it.
Knowledge that crosses roles — shared across a team or department.
What the whole org has come to know — company-wide truth.
Memory that knows your users' org. Not just their last prompt.
Other systems remember the last conversation. Memsy operates at a different layer — extracting patterns, procedures, and context that apply at the role, team, and function level. Individual conversations stay scoped to the individual.
Everything your AINeeds to remember
Four steps to org memory
# 1. Install
pip install memsy
# 2. Initialize
import os
from memsy import MemsyClient
client = MemsyClient(
base_url="https://api.memsy.io",
api_key=os.environ["MEMSY_API_KEY"],
)Tested against every major memory system.Ranked above all of them.
No benchmark exists for organizational intelligence — the cross-user patterns, role context, and structured memory that Memsy extracts. So we ran the industry's toughest individual memory benchmark instead. And topped it.
LoCoMo Answer Accuracy — Production Retrieval Depth
LoCoMo (Snap Research, ACL 2024). Cat 1–4, Adversarial Excluded Per Standard Protocol.
Now look at what it takes.
mem0 reports 91.6% on LoCoMo — the highest published score. Here are the conditions behind that number and ours.
90.19% with GPT-4.1 mini and 10 memories, vs mem0's 91.56% with GPT-5 and 200.
Memsy context tokens are per-query averages from the linked runs (k=25: 2,412 · k=10: 942). mem0 score, models, and retrieval depth are from mem0's own published results (1410/1540 = 91.56%, GPT-5, top_200, pinned to commit 4b61c5d). mem0's ~7,000 token figure is from their blog post; their results artifact carries no token field, so it is not directly comparable to our measured context tokens.
Memory performance metrics
The accuracy advantage isn't the model.
It's the Architecture.
Higher scores exist — retrieving 200 memories with GPT-5 as both answerer and judge. Memsy finds the right memory first, on 10. And then does something no other system even attempts — extracting the organizational intelligence behind it.
LoCoMo Long-Context Memory Benchmark (Snap Research, ACL 2024). Categories 1–4, Adversarial Excluded Per Standard Protocol. Memsy: Single-Pass Run, GPT-4.1 Mini, No Post-Processing. mem0 Scores From github.com/mem0ai/memory-benchmarks. CORE Score From github.com/RedPlanetHQ/core-benchmark. Zep Score From zep.ai. All Scores Reflect Each System's Own Published Evaluation Configuration.