AI app for it and development · no coding needed
Agent memory graph stewardship console
Give agents a structured, persistent memory of connected context so they retrieve accurate, relationship-aware information instead of isolated snippets.
Made for: Engineering and platform teams building AI agents that need persistent, relationship-aware memory
What it does for you
The problem
Agents retrieve isolated snippets, lose context between sessions and cannot show where a remembered fact came from.
What it gives you
A reviewed, queryable memory graph with typed claims, contradiction records and source citations
What you give it
Connected source materialdomain entity definitionsaccess rules
Build your own version of cognee, Papr and more
One app with what these 6 AI tools do, yours to keep and change: cognee, Papr, HydraDB OSS, Open Index, Jitera, Cortex by SKYNETLAB.
Everything these tools do, in one app
- Knowledge graph memory Stores information as entities and relationships in a graph so agents can understand connections between concepts.Found in cognee, Papr, HydraDB OSS and 2 more
- Persistent stateful memory Accumulates knowledge over time and across sessions so the AI retains understanding between interactions.Found in cognee, Papr, Cortex by SKYNETLAB
- Combined vector and graph retrieval Uses both vector similarity search and graph relationships to retrieve richer, more accurate context.Found in cognee, Papr
- Open-source codebase The full source code is publicly available for inspection, self-hosting, and modification.Found in cognee, Papr, HydraDB OSS and 1 more
- Multi-tenant access controls Built-in permission controls and ACLs keep data isolated for multiple customers or teams.Found in Papr
- Flexible query interfaces Lets developers and UIs query memory using GraphQL or natural language.Found in Papr
- Object storage graph backend Runs graph storage directly on object storage, removing the need for separate disk provisioning or cluster management.Found in HydraDB OSS
- Low-latency single API call Returns core graph primitives in one API call with sub-200ms latency.Found in HydraDB OSS
- Rust-based performance Uses a Rust implementation for memory efficiency and predictable latency under concurrent workloads.Found in HydraDB OSS
- Domain entity modeling Lets developers define the entities that matter in their domain and map relationships between them.Found in Open Index
- Navigable agent graph Gives agents a graph structure they can traverse instead of long unstructured prompts.Found in Open Index
- Domain-agnostic design Applies the same structured context approach across security, support, legal, insurance, sales, and other complex domains.Found in Open Index
- Human-agent co-editing Lets people and agents edit documents, specs, and notes together in the same place.Found in Jitera
- Shared team context Centralizes context in one team chat and context graph so agents draw on up-to-date team knowledge.Found in Jitera
- Pluggable middleware architecture Provides integration points for telemetry, storage, and different LLM runtimes to fit varied workflows.Found in Jitera
- Write quality gate Evaluates each memory submission and rejects redundant writes with a synchronous verdict, reason, and closest existing memory ID.Found in Cortex by SKYNETLAB
- Typed claim storage Extracts facts and stores them as typed claims rather than free-form text, giving structure to what the AI remembers.Found in Cortex by SKYNETLAB
- Contradiction tracking Stores conflicting information as a first-class object instead of overwriting the previous fact, so both sides are tracked.Found in Cortex by SKYNETLAB
- Source citation Answers can show where their information came from so the AI can prove why it said something.Found in Cortex by SKYNETLAB
- MCP connector support Connects to AI assistants over the Model Context Protocol, working with any MCP client.Found in Cortex by SKYNETLAB
How it works, step by step
- Store entities and relationships in a knowledge graph
- Accumulate memory across sessions and interactions
- Combine vector similarity search with graph traversal
- Provide an open-source, self-hostable codebase
- Enforce multi-tenant access controls and ACLs
- Query memory through GraphQL and natural language
- Run graph storage on object storage without separate clusters
- Return core graph primitives in one low-latency API call
- Use a Rust implementation for predictable latency
- Let developers define domain entities and map relationships
- Give agents a navigable graph instead of long prompts
- Apply the same approach across security, support, legal, insurance and sales
- Let people and agents co-edit documents, specs and notes
- Centralize team context in one chat and context graph
- Expose pluggable middleware for telemetry, storage and LLM runtimes
- Gate each memory write with a verdict, reason and closest existing memory ID
- Extract and store facts as typed claims
- Track contradictions as first-class objects
- Cite sources for every answer
- Connect to assistants over the Model Context Protocol
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 Agent memory graph 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 Agent memory graph 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 links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 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 agents a structured, persistent memory of connected context so they retrieve accurate, relationship-aware information instead of isolated snippets. For engineering and platform teams building AI agents that need persistent, relationship-aware memory, convert connected source material, domain entity definitions and access rules into a reviewed, queryable memory graph with typed claims, contradiction records and source citations. The benefit is a testable hypothesis, measured through retrieval accuracy on held-out questions and reviewer correction time per accepted memory; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect connected source material, domain entity definitions and access rules, then follow this sequence: 1. Store entities and relationships in a knowledge graph. 2. Accumulate memory across sessions and interactions. 3. Combine vector similarity search with graph traversal. Resolve uncertain cases with qualified reviewers, approve a reviewed, queryable memory graph, and measure retrieval accuracy on held-out questions and reviewer correction time per accepted memory against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, extract typed claims, suggest entity and relationship mappings and generate candidate answers for the stated task modules. Use deterministic code for schema validation, ACL enforcement, latency budgets and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One bounded domain schema and one approved connector set; final schema, access and contradiction decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, tenant isolation, access permissions and data rights. Named owners approve schema changes, contradiction resolutions and external actions. One bounded domain schema and one approved connector set; final schema, access and contradiction 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 bounded domain schema and one approved connector set; final schema, access and contradiction decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: store entities and relationships in a knowledge graph; accumulate memory across sessions and interactions. Support the third module with operator review: combine vector similarity search with graph traversal. Include source citations, 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 source systems, authorized document stores and permitted model runtimes. Cloud object storage, GraphQL endpoints, MCP clients and existing agent frameworks. Start with file exchange and validate destination specifications before promising direct connector support. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Memory graph explorer, Claim and contradiction review, Access and tenant settings. Use a searchable entity list, a central relationship canvas, and a right-hand panel for claims, sources, contradictions and comments. Let users compare a retrieved answer against its cited memories. Display draft, under review and approved states. Provide a query console for GraphQL and natural-language lookups. Make the task-specific outcome a reviewed, queryable memory graph visible beside its evidence, review state and value baseline.
