LLM-Free Classification

How it works

Every memory is automatically classified into a category when it is stored. Instead of calling an LLM to categorize, MemWire uses cosine similarity to anchor embeddings:

Each category has one or more example sentences (anchors) that define its meaning
At startup, anchor embeddings are computed as the centroid of all examples for that category
When a new memory arrives, its embedding is compared to every anchor centroid
The category with the highest cosine similarity wins — as long as it clears classification_threshold

This is fully local, runs in microseconds, and requires no API key.

Default categories

Category	Example anchors
`fact`	”This is a factual statement”, “The capital of France is Paris”
`preference`	”I prefer dark mode over light mode”, “I like my coffee black”
`instruction`	”Always respond in formal English”, “Never share my personal information”
`event`	”We had a team meeting yesterday”, “The product launched last week”
`entity`	”This is about a company called Acme”, “John is the head of engineering”

Code example

from memwire import MemWire, MemWireConfig

config = MemWireConfig(qdrant_path="./memwire_data")
memory = MemWire(config=config)

records = memory.add(
    user_id="alice",
    messages=[
        {"role": "user", "content": "I always prefer bullet points over long paragraphs."},
        {"role": "user", "content": "Our Q1 planning session is scheduled for next Monday."},
        {"role": "user", "content": "Water freezes at 0 degrees Celsius."},
    ],
)

for record in records:
    print(f"{record.category}: {record.content}")
# preference: I always prefer bullet points...
# event:      Our Q1 planning session...
# fact:       Water freezes at...

Filtering search by category

results = memory.search(
    "formatting preferences",
    user_id="alice",
    category="preference",
    top_k=5,
)
for record, score in results:
    print(f"[{score:.2f}] {record.content}")

Adding custom categories

Define domain-specific categories for your application:

memory.add_anchor(
    name="bug_report",
    text="There is a crash or error in the application",
    user_id="alice",
)

memory.add_anchor(
    name="feature_request",
    text="The user is asking for a new capability to be added",
    user_id="alice",
)

Multiple calls to add_anchor with the same name accumulate examples — the centroid is recomputed each time, sharpening the category boundary.

Customising anchors at config level

config = MemWireConfig(
    qdrant_path="./memwire_data",
    classification_threshold=0.05,   # lower = more memories get classified
    default_anchors={
        "goal": [
            "This is something the user wants to achieve",
            "I want to finish the project by end of quarter",
        ],
        "constraint": [
            "This is a limitation or restriction",
            "We only have a budget of $10,000",
        ],
    }
)

Setting default_anchors replaces the built-in set entirely. Include any categories you want to keep from the defaults.

Configuration reference

Parameter	Default	Description
`classification_threshold`	`0.05`	Minimum cosine similarity to assign a category. Below this the memory is tagged `unknown`.
`default_anchors`	5 built-in categories	Dict of `category_name → list[example_sentences]`.

Getting started

Features

Configuration

LLM-Free Classification

How it works

Default categories

Code example

Filtering search by category

Adding custom categories

Customising anchors at config level

Configuration reference

​How it works

​Default categories

​Code example

​Filtering search by category

​Adding custom categories

​Customising anchors at config level

​Configuration reference

How it works

Default categories

Code example

Filtering search by category

Adding custom categories

Customising anchors at config level

Configuration reference