Cognee turns documents into a knowledge graph.
memco turns work into learning.

01The difference

• What it remembers

  Cognee

  What is in your documents and data, as a graph.

  memco

  What agents learned doing the work.

• What improves over time

  Cognee

  The coverage and structure of the knowledge base.

  memco

  Behavior on the next task, measured.

• Who learns from whom

  Cognee

  Whoever queries the graph.

  memco

  Everyone in the network, with standing earned from use.

02Honest overlap

Where the products genuinely overlap.

Both products can capture session traces, use feedback, improve memory, share context across coding agents, enforce permissions and preserve provenance. The clean distinction is the operating model: Cognee is a broad configurable memory platform; memco makes the governed engineering-learning workflow the packaged center of the product.

1. Capture session traces from real coding-agent work.
2. Use feedback signals to improve what is stored.
3. Improve and maintain memory over time, including forgetting.
4. Share context across coding agents and tools.
5. Enforce permissions on who can read what.
6. Preserve provenance for where knowledge came from.

03Where each product starts

Different centers of gravity.

Cognee gives technical teams infrastructure to build and operate broad agent memory. memco packages a governed engineering-learning workflow around capturing, governing and retiring reusable insights from coding-agent work.

• Cognee — Configurable agent-memory platform

  Build and operate broad, customizable agent memory across relational, vector and graph stores.

  • Primary inputs: documents, code, application data, session Q&A, prompts, tool traces, feedback and skills.
  • Outcome: a team gets a flexible agent-memory platform it can self-host or use as managed Cloud, then configure around its own data and operating model.

• memco — Packaged engineering-learning workflow

  Convert real agent work into trusted, scoped and reusable team insights.

  • Primary inputs: agent attempts, human corrections, failed paths, tests, review decisions, accepted fixes and downstream outcomes, according to released integration behavior.
  • Outcome: a later engineer or agent receives a trusted engineering insight under the right source, scope and retirement controls.
  • One insight crosses from Agent A to Agent B with source, scope, trust and later outcome attached.

04The same week, replayed

One payment-retry bug. Two product jobs.

Illustrative engineering workflow, not customer evidence — the same scenario runs on every comparison page, so what differs is the product job, not the anecdote.

1. Agent A · Cursorpayment-retry taskcalls a helper that was retired last quarter
2. Failed testci · redthe dead end costs the run its first hour
3. Senior correctionreview + passing PRthe durable fact: which path works now, and why
4. Scoped trusted insightteam scope · provenance keptsource, contributor, scope and outcome attached
5. Agent B · Claude Codenext week · different personstarts ahead of the dead end

• What they can represent

  Cognee can ingest the repository and session trace, apply feedback and distillation, connect the result in graph memory and expose it through managed Cloud or coding-agent integrations.

• What memco makes of it

  memco focuses the packaged workflow on the correction-backed insight, the passing test, trust and team scope, then whether a later agent avoided the same mistake.

INSIGHT MEM-4821
ACTION  use retry_payment_v2
AVOID   legacy_retry
SCOPE   payments-service / Payments team
SOURCE  corrected PR + passing integration test
STATE   trusted
PROOF   later task avoided the retired helper

05Practical comparison

Dimension by dimension, in words.

Rows, not scoreboards: each dimension states what each product actually does, so the difference survives scrutiny.

Scroll sideways →

06Choose honestly

Two good answers to two different questions.

• Choose Cognee if

  • The team wants an Apache-2.0 agent-memory platform it can self-host or consume as managed Cloud.
  • Custom stores, ontologies, data models, pipelines or memory APIs are central requirements.
  • The platform team wants direct control over schemas, backends, feedback and curation policy.
  • Owning the broad memory architecture and operating model is part of the product strategy.

• Choose memco if

  • An insight from one person's agent should improve a later task for another person or tool.
  • Corrections, failed paths and accepted outcomes keep getting rediscovered.
  • Shared learning needs source, standing, governance and a path to correction or retirement.
  • The learning should survive model, IDE and agent-platform changes.

If the main job is “Build and operate broad, customizable agent memory across relational, vector and graph stores”, evaluate Cognee. If the job is to make real engineering learning travel across people and tools, run the memco reuse test.

07For your customers

If you are building memory into a product

• Cognee SDK

  A knowledge-graph pipeline your application runs over its data.

• memco SDK

  A memory network per customer that learns from use, not only from ingestion, metered by use.

  Build with the memco SDK →

08Proof, not vibes

The reuse test.

Do not stop at “the agent remembered.” Cognee’s proof question is whether the configured memory and improvement workflow represented, curated and retrieved the right knowledge for the later task. memco’s is whether a source-backed insight from one run improved a later task for another person, agent, session or tool without violating scope.

1. Pick one repository and one repeated failure, correction or convention.
2. Record the baseline behavior without the shared insight.
3. Let Agent A hit the problem and capture the source-backed correction.
4. Review and scope the candidate insight using the product states actually available.
5. Give Agent B a related task in another session or tool.
6. Measure repeated-error rate, completion, agent steps, review corrections and source/scope integrity.
7. Record whether the insight helped, conflicted or should be corrected or retired.

A signup is not activation. Activation is an insight contributed from one task and correctly reused on a later task.

Run the reuse test →Read the research →

09Questions

What evaluating teams ask.