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Cortex by SKYNETLAB Review

Cortex by SKYNETLAB is a semantic memory infrastructure that allows AI models and agents to share knowledge, decisions, and context across conversations, enhancing their cognitive capabilities.

shipped Aug 24, 2026freemium
Cortex by SKYNETLAB — product screenshot

Why it matters

1Offers a 30-day free trial with no credit card required.
2Paid plans for Cortex by SKYNETLAB start from €0.99/month.
3Rejects approximately 80% of redundant memory writes in production.
4Tracks contradictions in AI's memory rather than overwriting them.

About Cortex by SKYNETLAB

Headquarters
Bergamo, Italy

Pricing Plans

Free Trial
Free
  • • Connect in 2 minutes
  • • Test the platform

Leadership

Filippo Pilotta
GitHubOpen Source

Screenshots

overview

What is Cortex by SKYNETLAB?

Cortex by SKYNETLAB is a semantic memory infrastructure tool developed by SKYNETLAB that enables AI models and agents to share knowledge, decisions, and context across conversations. It functions as a hosted semantic memory layer that AI systems connect to over a Multi-Party Computation (MCP) protocol, designed to ensure high-quality memory by rejecting redundant writes, tracking contradictions, and enabling AI to cite its sources. The tool enhances the cognitive capabilities of AIs rather than replacing them, focusing on the 'write path' and implementing a quality gate for every memory. It was launched on Product Hunt on August 23, 2026, by founder Filippo Pilotta, featuring EU infrastructure and a patent-pending engine.

features

Key Features of Cortex by SKYNETLAB

Cortex by SKYNETLAB provides a suite of features designed to optimize and manage AI's semantic memory, ensuring data quality and cognitive enhancement. These features include advanced memory filtering, cognitive metrics for performance analysis, and a living encyclopedia for structured knowledge.

  • Model-agnostic cognitive extension for various AI architectures.
  • Memory filtering that rejects redundant writes (approximately 80% in production).
  • Cognitive metrics for monitoring and assessing AI memory performance.
  • Living encyclopedia functionality for dynamic knowledge management.
  • Epistemic self-assessment capabilities for AI models.
  • Fact extraction as typed claims and contradiction tracking.
  • Source citation for AI-generated answers, enhancing transparency.

use cases

Who Should Use Cortex by SKYNETLAB?

Cortex by SKYNETLAB is primarily designed for developers and organizations working with AI models and agents who require robust, high-quality semantic memory management. Its capabilities are particularly beneficial for scenarios where AI accuracy, source verifiability, and efficient knowledge sharing are critical.

  • LLM Developers: For providing hosted semantic memory and integrating with models like Claude.
  • AI Developers: To filter redundant AI memories through a quality gate and track contradictions.
  • AI Researchers: For enabling AI to cite sources for its answers and enhancing cognitive capabilities.
  • Organizations deploying AI agents: To allow agents to share knowledge, decisions, and context across conversations.

how to use

How to Use Cortex by SKYNETLAB

Cortex by SKYNETLAB is designed for quick setup, particularly with LLMs like Claude, leveraging custom connectors. The platform connects to AI systems over a Multi-Party Computation (MCP) protocol.

  • 1Register for a 30-day free trial on the SKYNETLAB website.
  • 2Connect your AI model or agent to Cortex via the MCP protocol.
  • 3Utilize custom connectors for specific LLMs, such as Claude, for rapid integration.
  • 4Configure memory filtering parameters to manage redundant information.
  • 5Implement source citation mechanisms for AI-generated responses.
  • 6Monitor cognitive metrics and contradiction tracking for memory integrity.

pricing

Cortex by SKYNETLAB Pricing & Plans

Cortex by SKYNETLAB operates on a freemium model, offering a free trial period before transitioning to paid subscriptions. The pricing structure is designed to be accessible, with low-cost entry points.

  • Free Trial: 30-day access with no credit card required.
  • Paid Plans: Start from €0.99/month.

Pros

  • +Ensures high-quality AI memory by rejecting approximately 80% of redundant writes.
  • +Tracks contradictions in AI's memory rather than overwriting them, providing a more robust knowledge base.
  • +Enables AI to cite sources for its answers, enhancing transparency and trustworthiness.
  • +Offers a 30-day free trial with no credit card required, allowing for easy evaluation.
  • +Integrates quickly with LLMs like Claude via custom connectors.
  • +Provides model-agnostic cognitive extension for diverse AI architectures.

Cons

  • −As a recently launched product (August 2026), extensive user reviews and community support are still developing.
  • −Requires integration over a Multi-Party Computation (MCP) protocol, which may require specific development effort.
  • −The specific benefits of its patent-pending engine compared to established vector database algorithms are still emerging.
  • −While offering a free trial, the lowest paid tier starts at €0.99/month, which may not be suitable for all hobbyist projects.

Similar Tools

Cortex by SKYNETLAB vs Competitors

Cortex by SKYNETLAB differentiates itself in the semantic memory and vector database landscape through its explicit focus on memory quality, contradiction tracking, and source citation. While other vector databases provide storage and search, Cortex by SKYNETLAB emphasizes a 'quality gate' for AI memory.

1
Chroma↗

Chroma is a lightweight, in-process vector database designed for ease of use and local development, making it simple to get started with semantic memory.

Chroma is simpler to set up and use for smaller projects or local development compared to Cortex, but might require more manual integration with complex AI agent architectures for advanced features.

2
QdrantOn Stork

Qdrant is a production-ready vector similarity search engine that can be self-hosted or used as a managed cloud service, offering robust performance for semantic memory.

Qdrant offers more advanced features and scalability for production environments than Cortex, but requires more operational overhead if you choose to self-host rather than use their cloud service.

3
WeaviateOn Stork

Weaviate is a vector database that can store data objects and vector embeddings, allowing for semantic search and graph-like connections to build rich AI memory.

Weaviate provides a more comprehensive data management solution with semantic search capabilities beyond just vector storage, potentially offering richer context sharing than Cortex, but has a steeper learning curve due to its broader feature set.

4
PineconeOn Stork

Pinecone is a fully managed vector database service, simplifying the deployment and scaling of vector search applications for AI memory with minimal operational effort.

Pinecone offers a managed service experience with high scalability and reliability, reducing operational burden compared to Cortex, but its free tier has limitations and costs can increase significantly with higher usage.

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GitHubgithub.com/FilippoPilo/cortex-connector