Product Launch

Introducing Memory Policies: Controlled Memory Extraction for AI

You already control response behavior with system prompts. Now you can control memory extraction behavior with memory policies, scoped by project, subject, or chat.

Marius Ndini

Founder · Feb 18, 2026

Why Memory Policies?

Not every app wants to memorize everything. Some teams need strict extraction rules for compliance, quality, or cost. Others need per-workflow behavior, like high-signal extraction in support chats and minimal extraction in casual chats.

Memory Policies let you define those rules once, then apply them automatically with scope-aware resolution.

What You Can Control

Kinds with include/exclude controls (fact, preference, context, note, event, trait).
Quality thresholdsvia minimum confidence and importance.
Volume caps with max memories per turn.
Scope precedenceacross project, subject, and chat.

API Endpoints

Memory Policies are now available on the v1 API surface:

GET    /api/v1/memory/policies
POST   /api/v1/memory/policies
GET    /api/v1/memory/policies/:id
PATCH  /api/v1/memory/policies/:id
DELETE /api/v1/memory/policies/:id
GET    /api/v1/memory/policies/resolve

Request-Level Override with mnx.memory_policy

Like mnx.system_prompt, memory policy can be controlled per request:

{
  "model": "gpt-4o-mini",
  "messages": [{ "role": "user", "content": "Remember I prefer concise weekly summaries." }],
  "mnx": {
    "subject_id": "user_123",
    "chat_id": "550e8400-e29b-41d4-a716-446655440000",
    "learn": true,
    "memory_policy": "mem_pol_support_assistant"
  }
}

memory_policy accepts a policy ID, false to disable, or omitted for scoped default resolution.

Mnexium SDK Examples (JavaScript + Python)

The same memory_policy override works directly in the Mnexium SDKs:

// npm: @mnexium/sdk
import { Mnexium } from "@mnexium/sdk";

const mnx = new Mnexium({
  apiKey: process.env.MNX_KEY,
  openai: { apiKey: process.env.OPENAI_API_KEY },
});

const alice = mnx.subject("user_123");
const response = await alice.process({
  content: "Remember that I prefer concise weekly summaries.",
  model: "gpt-4o-mini",
  learn: true,
  recall: true,
  memory_policy: "mem_pol_support_assistant",
});

console.log(response.content);

# Python: mnexium
import os
from mnexium import Mnexium, ProviderConfig, ProcessOptions

mnx = Mnexium(
    api_key=os.environ["MNX_KEY"],
    openai=ProviderConfig(api_key=os.environ["OPENAI_API_KEY"]),
)

alice = mnx.subject("user_123")
response = alice.process(ProcessOptions(
    content="Remember that I prefer concise weekly summaries.",
    model="gpt-4o-mini",
    learn=True,
    recall=True,
    memory_policy="mem_pol_support_assistant",
))

print(response.content)

Native SDK Header Fallback

For provider-native SDK routes that rely on headers, you can also pass: x-mnx-memory-policy.

x-mnx-memory-policy: mem_pol_support_assistant
# or
x-mnx-memory-policy: false

What This Enables

Memory Policies give teams a practical control plane for extraction quality. You can tune behavior once and keep it consistent across chat/completions, responses, messages, and Gemini routes.

The result: cleaner memory, lower noise, and more predictable assistant behavior over time.

Get Started

Memory Policies are available now in both SDKs and the REST API. Create a policy, set scoped defaults, or pass memory_policy per request. Your assistant will start extracting cleaner, higher-signal memory immediately.

npm install @mnexium/sdk    # JavaScript
pip install mnexium        # Python

Memory Policies are live

Control extraction quality with scoped defaults and request-level overrides. Keep memory relevant, reduce noise, and make behavior predictable across routes.

npm: @mnexium/sdk →PyPI: mnexium →Read the docs