AI app for it and development · no coding needed
Shared agent knowledge library and stewardship console
Give assistants and agents one governed place to store, retrieve and update knowledge so context survives tool and session changes.
Made for: Teams running multiple AI assistants and agents that need shared, governed knowledge across tools and sessions
What it does for you
The problem
Each assistant and agent keeps its own context, so knowledge is duplicated, stale or lost between tools and sessions.
What it gives you
A reviewed shared knowledge library with source pointers and access scopes
What you give it
Permitted sourcesagent write policiesaccess rules
Build your own version of Brainfork, OzBrain and more
One app with what these 8 AI tools do, yours to keep and change: Brainfork, OzBrain, Loop MCP by SimpliflowAI, AGNT.Hub, N71, Yavy, Novi Notes, Notion MCP.
Everything these tools do, in one app
- Shared knowledge base Stores knowledge in one place that multiple AI agents and humans can read from and write to.Found in Brainfork, OzBrain, N71 and 1 more
- MCP integration Connects to AI assistants and agents through the Model Context Protocol so they can access the knowledge.Found in Brainfork, OzBrain, Loop MCP by SimpliflowAI and 4 more
- Real-time read/write Lets agents both consume and update stored content in real time.Found in Notion MCP, OzBrain, N71
- Encryption Encrypts stored data at rest and in transit to keep it private.Found in Brainfork, OzBrain
- Conflict resolution Surfaces simultaneous writes to an agent or human for resolution instead of overwriting.Found in OzBrain
- Version history Keeps older entries and logs what changed and which agent made the change.Found in OzBrain
- Deprecation-based versioning Marks old information as deprecated and links to the current version instead of deleting it.Found in OzBrain
- Collection-level sharing Shares specific collections with teammates at read or read-write access levels.Found in OzBrain
- Export to markdown Exports all stored knowledge to markdown at any time without lock-in.Found in OzBrain
- Dynamic tool retrieval Fetches only the relevant tool schema at query time to keep the AI context small.Found in Loop MCP by SimpliflowAI
- Tool execution Runs the selected tool after retrieval to complete actions end-to-end.Found in Loop MCP by SimpliflowAI
- Always-on agents Runs agents in hosted containers so they keep working after local sessions end.Found in AGNT.Hub
- Skill marketplace Provides a marketplace of skills and supports custom skills to extend agent behavior.Found in AGNT.Hub
- Living knowledge graph Builds a graph of people, projects, and decisions that updates automatically as connected tools change.Found in N71
- Entity resolution Uses behavioral history to handle renames and duplicates when identifying entities.Found in N71
- Contradiction flagging Tracks fact evolution and flags when two sources present conflicting information.Found in N71
- Confidence scores Assigns confidence scores and source pointers to agent writes to prevent bad data from spreading.Found in N71
- URL crawl and index Crawls a public URL and indexes its content for AI assistants to query.Found in Yavy
- Chunk-based semantic indexing Splits content into chunks and embeds them to improve retrieval accuracy for meaning-based queries.Found in Yavy
- Crawl controls Configures scheduled refreshes, depth and concurrency caps, and respects robots.txt and sitemap directives.Found in Yavy
- Team access management Manages multiple sources, organizes projects, and shares access with teammates.Found in Yavy
- Local-first storage Stores notes locally on the device by default and allows offline access.Found in Novi Notes
- Zero-config AI integration Lets AI read, create, and edit notes directly from the app without API keys or complex setup.Found in Novi Notes
- Block-style editor Provides a block-style editor with support for daily notes, manuals, post-its, and a calendar view.Found in Novi Notes
- Workspace governance Scopes which pages or databases an agent can access with admin controls.Found in Notion MCP
- Automation templates Provides context-aware templates for creating docs, managing tasks, generating reports, and organizing knowledge.Found in Notion MCP
- Developer resources Offers guides and resources for deploying servers and handling integration details like API usage, batching, and queuing.Found in Notion MCP
How it works, step by step
- Store shared knowledge in one place for agents and humans
- Connect assistants and agents through the Model Context Protocol
- Support real-time reads and writes from agents
- Encrypt stored data at rest and in transit
- Surface simultaneous writes for human or agent resolution
- Keep version history with change and author logs
- Mark old entries deprecated and link to the current version
- Share collections at read or read-write levels
- Export all knowledge to markdown without lock-in
- Retrieve only relevant tool schemas at query time
- Execute the selected tool after retrieval
- Run always-on agents in hosted containers
- Offer a skill marketplace and custom skills
- Maintain a living knowledge graph of people, projects and decisions
- Resolve entities across renames and duplicates
- Flag contradictions between sources
- Attach confidence scores and source pointers to agent writes
- Crawl and index permitted public URLs
- Chunk and embed content for semantic retrieval
- Control crawl schedules, depth, concurrency and robots directives
- Manage team sources, projects and access
- Store notes locally with offline access
- Let AI read, create and edit notes without API keys
- Provide a block-style editor with daily notes, manuals and calendar
- Scope agent access to specific pages or databases
- Provide context-aware automation templates
- Supply developer guides for deployment, API usage, batching and queuing
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Shared agent knowledge library and stewardship console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Shared agent knowledge library and stewardship console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links6 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Give assistants and agents one governed place to store, retrieve and update knowledge so context survives tool and session changes. For teams running multiple AI assistants and agents, convert permitted sources, agent writes and human corrections into a reviewed shared knowledge library with source pointers and access scopes. The benefit is a testable hypothesis, measured through retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted sources, agent write policies and access rules, then follow this sequence: 1. Store shared knowledge in one place for agents and humans. 2. Connect assistants and agents through the Model Context Protocol. 3. Support real-time reads and writes from agents. Resolve uncertain cases with qualified reviewers, approve a reviewed shared knowledge library with source pointers and access scopes, and measure retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed MCP version and permissioned source set; final access and data-quality decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, access permissions and data rights. Named owners approve access scopes and substantive changes. One fixed MCP version and permissioned source set; final access and data-quality decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed MCP version and permissioned source set; final access and data-quality decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: store shared knowledge in one place for agents and humans; connect assistants and agents through the Model Context Protocol. Support the third module with operator review: support real-time reads and writes from agents. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned sources, authorized agent logs and permitted public URLs. Cloud storage, identity providers, MCP clients and export destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Source and collection setup, Library and graph explorer, Agent access and audit. Use a searchable list of collections, a central entry view with version and deprecation state, and a right-hand panel for sources, confidence, conflicts and access scope. Let users compare versions side by side. Display draft, reviewed, deprecated and conflicting states. Provide a client or teammate preview link with comments anchored to the relevant entry. Make the task-specific outcome a reviewed shared knowledge library with source pointers and access scopes visible beside its evidence, review state and value baseline.
