AI app for it and development · no coding needed
Agent memory stewardship console
Reduce token waste and lost context while keeping memory data on the buyer's own infrastructure.
Made for: Engineering teams running AI agents that need durable, auditable context across sessions
What it does for you
The problem
Agent context is scattered across conversations and tools, so teams cannot store, retrieve or audit what an agent knew and when.
What it gives you
A reviewed, permissioned memory library with provenance and retention states
What you give it
Agent interactionstool tracesstructured factspermissioned sources
Build your own version of YourMemory, Spectron and more
One app with what these 3 AI tools do, yours to keep and change: YourMemory, Spectron, Memori.
Everything these tools do, in one app
- Long-term agent memory Stores and retrieves durable context for AI agents beyond a single conversation.Found in YourMemory, Spectron, Memori
- Local-first storage Keeps all memory data on the user's own machine without requiring external infrastructure.Found in YourMemory
- Open-source availability Lets developers inspect, modify, and self-host the memory system freely.Found in YourMemory, Memori
- Graph-based retrieval Finds related memories by following connections between items, not just keyword matches.Found in YourMemory, Spectron
- Decay-based pruning Automatically ages out low-value context to keep memory focused and reduce token waste.Found in YourMemory
- Configurable importance Lets users mark certain memories as important so they are not pruned accidentally.Found in YourMemory
- Token waste reduction Cuts the amount of unnecessary context sent to the model, lowering runtime costs.Found in YourMemory, Memori
- Single ACID substrate Stores multiple data types together so related writes commit atomically in one transaction.Found in Spectron
- Per-fact provenance Records where each fact came from, including source references and trust levels, for audit trails.Found in Spectron
- Tri-temporal facts Tracks when a fact was recorded, when it was first believed, and when it was true in the world.Found in Spectron
- Hybrid retrieval Combines vector similarity, graph relations, full-text search, and keyword filters to find relevant memories.Found in Spectron
- Multi-tenant scopes Separates memory data by tenant or project to prevent cross-contamination.Found in Spectron, Memori
- MCP support Exposes memory operations through the Model Context Protocol for easy agent integration.Found in YourMemory, Spectron
- Trace-based memory Captures tool calls, execution paths, decisions, and outcomes as structured memory primitives.Found in Memori
- Structured knowledge layer Stores facts, decisions, and patterns as discrete items with metadata like entity, project, and timestamp.Found in Memori
- Agent-controlled recall Lets the agent retrieve memory scoped by project, session, entity, or time range to avoid irrelevant context.Found in Memori
- Asynchronous memory updates Builds memory after interactions so agent response latency is not affected.Found in Memori
- Observability and briefs Provides visibility into memory creation and retrieval, plus generated summaries of priorities and issues.Found in Memori
How it works, step by step
- Store and retrieve durable context beyond a single conversation
- Keep memory data on the buyer's own machine without external infrastructure
- Let developers inspect, modify and self-host the system
- Find related memories by following graph connections, not only keyword matches
- Age out low-value context automatically to keep memory focused
- Mark certain memories as important so they are not pruned accidentally
- Cut unnecessary context sent to the model to lower runtime cost
- Store multiple data types together so related writes commit atomically
- Record per-fact provenance with source references and trust levels
- Track when a fact was recorded, first believed and true in the world
- Combine vector similarity, graph relations, full-text search and keyword filters
- Separate memory data by tenant or project to prevent cross-contamination
- Expose memory operations through the Model Context Protocol
- Capture tool calls, execution paths, decisions and outcomes as structured memory
- Store facts, decisions and patterns as discrete items with entity, project and timestamp metadata
- Retrieve memory scoped by project, session, entity or time range
- Build memory after interactions so response latency is not affected
- Show memory creation and retrieval activity plus generated summaries of priorities and issues
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 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 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 links4 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 data196 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
Reduce token waste and lost context while keeping memory data on the buyer's own infrastructure. For engineering teams running AI agents that need durable, auditable context across sessions, convert agent interactions, tool traces and structured facts into a searchable, permissioned memory library with provenance and review states. The benefit is a testable hypothesis, measured through relevant-memory retrieval rate and context tokens per completed task; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent interactions, tool traces, structured facts and permissioned sources, then follow this sequence: 1. Store and retrieve durable context beyond a single conversation. 2. Find related memories by following graph connections, not only keyword matches. 3. Record per-fact provenance with source references and trust levels. Resolve uncertain cases with qualified reviewers, approve a reviewed, permissioned memory library, and measure relevant-memory retrieval rate and context tokens per completed task against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate memory items and retrieval results for the 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 agent framework and permissioned data scope; final fact approval and retention decisions remain with the buyer's stewards. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, fact accuracy and usage permissions. Buyers approve substantive memory changes and retention scope. One fixed agent framework and permissioned data scope; final fact approval and retention decisions remain with the buyer's stewards. 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 agent framework and permissioned data scope; final fact approval and retention decisions remain with the buyer's stewards. Implement one approved input format, a bounded representative case set and the first two task modules: store and retrieve durable context beyond a single conversation; find related memories by following graph connections, not only keyword matches. Support the third module with operator review: record per-fact provenance with source references and trust levels. 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
Buyer-owned agent frameworks, tool-call logs and permitted data sources. Cloud or local storage, design-file import/export and agent runtime destinations. Start with file exchange and validate destination specifications before promising direct integration. 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 library and search, Fact and provenance detail, Stewardship and retention console. Use a filterable list of memory items, a detail panel showing source, trust level and tri-temporal dates, and a right-hand panel for scopes, importance and decay rules. Let users compare retrieval results side by side. Display draft, reviewed and pruned states. Provide an audit export link with references anchored to the relevant memory item. Make the task-specific outcome a reviewed, permissioned memory library visible beside its evidence, review state and value baseline.
