Memrank

Memrank is a tool for reproducible, auditable evaluation of memory systems.

Status: v0.4, in active development. Interfaces still move between releases.

Quick start

Paste this to your agent, and it installs memrank and runs the evaluation below for you:

Install memrank in this project and run its smoke evaluation, following
https://github.com/atomicstrata/memrank/blob/main/docs/install.md. Check the prerequisites
that page lists before you change anything, install into this project only, and do not
install anything globally or edit my shell configuration. When the run finishes, show me the
`system:` and `evaluation:` lines it printed. Stop and ask me if any step fails.

Do it yourself

uv add memrank                  # or, into a virtualenv you already have: pip install memrank

from memrank.evaluations import SQuAD
from memrank.systems import TFIDF

evaluation = SQuAD()
result = evaluation.run(system=TFIDF())

print(result)

TFIDF is keyword search weighted by how rare each word is, and it ships with the package. SQuAD supplies 32 bundled passages and 64 questions. This measures full-passage retrieval recall, not answer-span or end-to-end answer correctness. Neither needs an engine, a key or the network. Installing memrank covers uv, Python versions and upgrading.

Use cases

Each snippet below runs on its own.

Evaluate a system of your own

Four methods, and the system is ready to evaluate. A memory engine you already run has a client that ships with memrank instead -- AtomicMemory, Hindsight, Mem0 and Supermemory take a base_url= where NoteBook() goes below, and adding a system is the rest.

from memrank import Memory, Recall
from memrank.evaluations import Demo


class NoteBook(Memory):
    name, version, engine_version = "notebook", "0.1", "0.1"

    def prepare(self, isolation_unit):
        self.notes = []

    def ingest(self, documents):
        self.notes.extend(documents)

    def retrieve(self, query, k, user_id, query_timestamp=None) -> Recall:
        wanted = set(query.lower().split())
        ranked = sorted(self.notes, reverse=True,
                        key=lambda note: len(wanted & set(note.content.lower().split())))
        return Recall(documents=ranked[:k])

    def cleanup(self):
        self.notes = []


result = Demo().run(system=NoteBook())

Ask your own questions

An evaluation you write by hand and one that ships are the same object: tasks, the measures that read them, and when the system is cleared.

from memrank import Clearing, Document, Evaluation, Expected, Task, WordMatch
from memrank.systems import WordOverlap

notes = (Document(id="t1", user_id="acme",
                  content="Acme moved to the enterprise plan in March."),)

tickets = Evaluation(
    name="tickets", version="internal@2026-09",
    tasks=(Task(id="q_plan", prompt="What plan is Acme on?", group="acme", context=notes,
                expected=Expected(required_spans=("enterprise",), evidence_doc_ids=("t1",))),),
    measures=(WordMatch(),), clearing=Clearing.PER_GROUP)

result = tickets.run(system=WordOverlap())

Find out why a value is what it is

Every value names the task it came from, and every task kept its trace -- what was asked, what the evaluation wanted, what came back.

from memrank.evaluations import Demo
from memrank.systems import WordOverlap

result = Demo().run(system=WordOverlap())

lowest = min(result.values_of("word-match"), key=lambda value: value.value or 0.0)
trace = result.traces_of(lowest.task_id)[0]

print(lowest.value, lowest.why)
print(trace.task.prompt, trace.task.expected.required_spans)
for document in trace.recalled.documents:
    print(document.id, document.content)

Compare two systems

memrank.paired refuses two results of different evaluations, then reads them task by task and says how often chance alone produces a gap that size. It never says "better".

import memrank
from memrank.evaluations import Demo
from memrank.systems import NoContext, WordOverlap

evaluation = Demo()

print(memrank.paired(evaluation.run(system=WordOverlap()),
                     evaluation.run(system=NoContext())))

Check the instrument

NoContext is the floor: it retrieves nothing. FullContext is the ceiling: it is given every document, unranked. A gap between them is what makes the evaluation worth running at all, and methodology states what a value does and does not license you to say.

from memrank.evaluations import Demo
from memrank.systems import FullContext, NoContext, WordOverlap

evaluation = Demo()

for system in (NoContext(), WordOverlap(), FullContext()):
    result = evaluation.run(system=system)
    scored = [value.value for value in result.values_of("word-match")
              if value.value is not None]
    print(result.system.name, sum(scored) / len(scored))

Where to read more

Memrank ships a memrank command as well, and it is not core: nothing above needs it, and it keeps an older vocabulary of its own -- the command line is where it lives.

Governance

Memrank is maintained by AtomicStrata under a vendor-neutral charter: anyone may submit a system, results are published as measured, and methodology changes go through public proposal and comment. The commitments are in SPEC.md section 5. AtomicStrata also ships a memory engine, AtomicMemory, which this tool evaluates and which has placed below a no-memory control arm in our own runs -- which is why the floor and the ceiling above are in the package rather than in a report of ours.

Licences

Memrank's code is Apache-2.0. The bundled SQuAD subset is CC BY-SA 4.0; its notice credits the creators and passage sources and records the selection and reformatting.

Methodology questions and disagreements: open an issue. Anything else: hello@atomicstrata.ai