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Knowledge Plane

Shared memory for AI agents and teams.

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February 13, 2026

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Knowledge Plane

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Shared memory for AI agents and teams.

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Overview

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Knowledge Plane is a shared memory layer designed to streamline workflows and facilitate communication by allowing teams to work in their preferred AI toolslike Cursor, Claude, and otherswithout creating new silos.

It aims to consolidate a team's code, docs, and chats into a comprehensive and up-to-date source of truth that is auditable across existing tools. The system automatically extracts and cleans data from ingested documents or synced GitHub repos.

Knowledge Plane combines Graph capabilities that model relationships with Vector Embeddings for understanding semantics. It is designed to plug into AI agents like Claude and Cursor through the Model Context Protocol (MCP), with no custom integrations needed.

The platform auto-ingests existing documentation and codes to build knowledge cards and also offers self-hosting options for those who prefer to keep proprietary context within their infrastructure.

Knowledge Plane isnt a simple vector store but combines graph memory with vector embeddings to allow AI agents to reason about dependencies, ownership, and timelines.

Additionally, it features workspace isolation, full audit logs, and scoped API keys for security concerns.

Key Features

Knowledge Cards — Atomic Facts Extracted From Docs, Not Raw File Storage. Higher Accuracy Under Tight Context Windows • Skills — Scheduled Autonomous Jobs That Detect Drift And Update Facts Automatically (hourly/daily). This Is A Real Differentiator, Most Tools Don't Have This • Graph Memory With Typed Edges (depends_on, Decided_by, Owned_by) — Agents Can Traverse Chains Of Decisions Like A Senior Team Member Would • Hybrid Retrieval: Graph Traversal + Vector Search Combined • Auto-ingestion From Google Drive, Github Repos, Pdfs, Wikis, Tickets

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Initial release

February 13, 2026

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Initial release of Knowledge Plane.

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Danail Arapkuliev

@danail-arapkuliev

🇧🇬 Bulgaria

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Vitaliy Filipov

🙏 1 karma

Feb 12, 2026

@ initial release

Rated it

@Knowledge Plane

I'm in early beta and I'm managing to orchestrate my 3 openclaw agents with my 2 claude code sessions. It's cool, maybe this is the future.

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@ initial release

Keep building - KnowledgePlane will take care of the memory. Right now you're the orchestrator between 5 agents. As you scale, that becomes the bottleneck. We give them a shared memory layer so they stay in sync, build on each other's work, and remember across sessions without you as the middleman. You're already living the multi-agent future. We just make sure they don't forget what they learned yesterday or what each other knows today.

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Pros and Cons

Pros

Consolidates team's code, documentation, chats

Prevents context exhaustion

Exceptional long-term memory

Ability to auto-ingest

Auto extraction from documents, codebases

Knowledge cards creation

Combines Graph and Vector embeddings

Understands dependencies beyond keywords

Built on Model Context Protocol

Query with standard MCP protocol

No need for custom integrations

Offers data sovereignty

Self-hosted option available

Enhanced governance with audit logs

Provided workspace isolation

Scoped API keys for security

Trails for every query, update

Automated Github data extraction, cleaning

Integrates across platforms

Workflow management system

Facilitates inter-agent communication

Offers semantic understanding

Structured intelligence provision

Prevents fragmentation in team memory

Enables understanding of relationships and semantics

Service-dependency comprehension

Turns files to knowledge cards

Autonomous update of knowledge base

Interprets ownership, timelines in codebases

extracts, cleans data from documents

Transforms amnesic chatbots to knowledgeable agents

Automatic data from document ingestion

Combines vector store with Graph memory

Relationship modeling with semantic search

Combines graph memory with vector embeddings

Features workspace isolation

Full audit logs available

Automatically scans docs, codebases

Understands team dynamics

Structured hybrid engine for modeling relationships

Self-organizing structure

Provides team-wide synchronized knowledge

Persistent shared memory layer

View 38 more pros

Cons

Complex Graph and Vector Model

Dependent on Model Context Protocol

No clear pricing information

Requires regular source updating

Potential learning curve

Risk of information overload

Limited Workspace Isolation

Self-hosting may be complex

Limited early access availability

View 4 more cons

Q&A

What is Knowledge Plane?

Knowledge Plane is an AI-based shared memory tool designed for engineering teams. It consolidates a team's code, documentation, and chats into a unified, updated source of information to ensure consistent quality from AI tools. It bridges the infrastructural gap of fragmented memory among different AI tools, transforming amnesic chatbots into agents with long-term memory.

How does Knowledge Plane work?

The functionality of Knowledge Plane is based on automatically scanning documents and codes, extracting raw facts, cleaning data, and creating knowledge cards. It combines Graph capabilities to understand dependencies with vector embeddings to locate keywords. AI agents connect to Knowledge Plane via the Model Context Protocol (MCP), eliminating the need for custom integrations. It also features workspace isolation, scoped API keys, and maintains audit trails for all queries and updates for added security.

What is the purpose of Knowledge Plane's auto-ingestion feature?

The purpose of Knowledge Plane's auto-ingestion feature is to automatically scan documents and codebases, extract raw facts and clean data, and create knowledge cards. This functionality enables an efficient and automatic consolidation and organization of information, obviating manual effort.

What are the benefits of Knowledge Plane's Graph capabilities and vector embeddings?

Knowledge Plane's Graph capabilities and vector embeddings facilitate a profound understanding of dependencies, not just keywords. Graph capabilities model relationships between elements, while vector embeddings assist with semantic understanding. Thereby, it enables AI agents to reason about dependencies, ownership, and timelines instead of simply keyword matching.

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is a standard protocol that AI agents like Claude and Cursor can leverage to query Knowledge Plane. It is designed to eliminate the need for custom integrations.

How does Knowledge Plane use the MCP?

Knowledge Plane uses the Model Context Protocol (MCP) to allow compatible AI agents like Claude and Cursor to query the plane directly. This simplifies and streamlines its integration into existing AI ecosystems without the need for custom implementations.

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How does Knowledge Plane ensure data sovereignty?

To ensure data sovereignty, Knowledge Plane provides a self-hosted option, enabling organizations to keep proprietary information within their infrastructure. The tool extracts structured facts and relationships but does not store raw copies of files, ensuring that original documents remain in place while providing a structured and governed extraction of knowledge.

What does the self-hosted option in Knowledge Plane entail?

The self-hosted option in Knowledge Plane allows organizations to keep their proprietary information within their infrastructure by deploying Knowledge Plane within their own enterprise environment. This preserves data sovereignty as the data does not leave the safe confines of their network and remains fully under their control.

How does Knowledge Plane handle audit logs?

Knowledge Plane handles audit logs by logging every query and update. It tracks who made the request, when it was made, and backs every fact to its source, providing a comprehensive audit trail. This enhances governance and ensures transparency and accountability within the system.

What are knowledge cards in Knowledge Plane and how are they created?

In Knowledge Plane, knowledge cards are created by the system's automatic ingestion and structuring of data. The system scans documents and codebases, extracts raw facts, cleans the data, and consolidates the information into high-density knowledge cards. These knowledge cards encapsulate atomic facts and a large view of context within a structured format, facilitating better comprehension and utilization for AI agents.

How does Knowledge Plane assist in workflow management?

Knowledge Plane assists in workflow management by acting as a unified memory tool for the entire team. It consolidates all code, documentation, and chat data into a single up-to-date and auditable source of truth across various tools. This ensures a more streamlined workflow as teams don't have to switch between tools or lose context when transitioning.

What AI tools can Knowledge Plane integrate with?

Knowledge Plane can seamlessly integrate with AI tools like Cursor and Claude via the Model Context Protocol (MCP). This allows an undisturbed workflow in teams' preferred AI tools without the need to create new silos or perform custom integrations.

How does Knowledge Plane facilitate inter-agent communication?

Knowledge Plane facilitates inter-agent communication by providing a shared memory layer that persists beyond individual sessions. This feature allows one agent's learning to be instantly accessible to the entire team's agents, thus smoothing communication and knowledge transfer.

What security measures does Knowledge Plane have in place?

To secure data, Knowledge Plane employs workspace isolation and scoped API keys, providing secure and specific access to data for agents. It also ensures traceability with audit logs that track every query and update. Additionally, the self-hosted option provides data sovereignty, keeping sensitive information within the organization's infrastructure.

How does Knowledge Plane assist with data extraction and cleaning?

Knowledge Plane assists with data extraction and cleaning through its auto-ingestion feature. This feature auto-scans, extracts raw facts from documents and codebases, and cleans the data into a refined and usable format. The cleaned data is then structured into knowledge cards accessible by AI agents.

What type of data can Knowledge Plane consolidate?

Knowledge Plane can consolidate various types of data including a team's code, documentation, and chat transcripts. It transforms these sources into a unified, up-to-date, and auditable source of knowledge.

How does Knowledge Plane use Github integration?

Knowledge Plane uses GitHub integration to sync repository data, extract valuable facts, and create knowledge cards from the ingested code. This ensures that the data used for building knowledge is always up-to-date with the latest codebase.

Can Knowledge Plane read and understand context?

Yes, Knowledge Plane can read and understand context. Its combination of Graph capabilities and Vector embeddings allows it to process dependencies and semantics, rather than mere keywords. This feature ensures that interactions and knowledge are contextually aware, offering a nuanced and meaningful understanding and interaction with AI tools.

What benefits does Knowledge Plane offer for teams using AI tools?

Knowledge Plane offers several benefits for teams using AI tools. It consolidates different types of team data into a structured, current, and auditable information source. It facilitates seamless integration with AI tools such as Claude and Cursor via the Model Context Protocol (MCP), thereby avoiding custom integration hassles. The shared, persisting memory layer enhances inter-agent communication, while the robust security and self-hosting option safeguard data privacy and sovereignty.

How does Knowledge Plane stand out from other shared memory tools?

Knowledge Plane stands out from other shared memory tools because of its hybrid engine which combines vector embeddings with Graph capabilities. This unique feature allows it to understand not just keywords but dependencies among elements. Also, its provision for a self-hosting option ensures data sovereignty unlike most shared memory tools. Furthermore, it promotes workspace isolation, full audit logs, and scoped API keys, ensuring security and control over data access.

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