AI app for it and development · no coding needed
Agent memory library and stewardship console
Give agents persistent, inspectable memory they own instead of renting several memory subscriptions.
Made for: Engineering and product teams running AI agents across several tools and sessions
What it does for you
The problem
Agents restart from zero each session and cannot share context across tools, so teams rebuild the same state and lose decisions.
What it gives you
A searchable, permissioned memory library with review states
What you give it
Agent transcriptsnotespreferencesproject contexttask statedecisions
Build your own version of pumaDB, Walrus Memory and more
One app with what these 10 AI tools do, yours to keep and change: pumaDB, Walrus Memory, Actx0, Memmy Agent, Maximem Synap, CogniMemo, Mnexium AI, Kit For AI, Boost.space v5, Claude-Mem.
Everything these tools do, in one app
- Persistent cross-session memory Stores context so agents remember past interactions and do not start from zero each session.Found in pumaDB, Walrus Memory, Actx0 and 7 more
- Shared memory across tools Lets multiple agents and AI tools access the same memory so context carries across apps.Found in pumaDB, Walrus Memory, Actx0 and 4 more
- No database setup Provides memory without requiring you to set up or manage a database, vector store, or RAG stack.Found in pumaDB, Actx0, Maximem Synap and 3 more
- Simple API integration Connects through a small number of API calls so developers can add memory quickly.Found in pumaDB, Walrus Memory, Actx0 and 3 more
- MCP tool access Exposes remember and recall as native MCP tools that agents can call directly.Found in pumaDB, Kit For AI, Boost.space v5
- Stores varied context types Holds transcripts, notes, preferences, project context, task state, and decisions.Found in pumaDB, Memmy Agent, CogniMemo and 1 more
- Semantic and hybrid search Retrieves relevant memories by meaning and exact terms, then reranks results.Found in Mnexium AI, Kit For AI
- Recency-aware retrieval Uses recency signals to surface more relevant and current memories.Found in Walrus Memory, Maximem Synap
- Memory inspection and audit Shows metadata, sources, and logs so you can inspect and verify stored memories.Found in Walrus Memory, Memmy Agent, Claude-Mem
- User control and ownership Keeps memory under user control and portable rather than locked into one application.Found in Walrus Memory, Memmy Agent
- Workspace and multi-level scoping Separates memory by workspace, team, agent, user, or customer deployment.Found in Actx0, Maximem Synap
- Low-latency retrieval Returns stored memories in milliseconds or sub-15ms so retrieval stays out of the critical path.Found in Actx0, Maximem Synap
- Automatic entity resolution Tracks what is current versus stale across sessions without manual reconciliation.Found in Maximem Synap
- Intelligent forgetting Discards information that stops being relevant instead of retaining it indefinitely.Found in Maximem Synap
- File and URL ingestion Extracts and indexes content from dropped files or URLs automatically.Found in Kit For AI
- Token budget on retrieval Caps retrieved chunks by a token budget to prevent context window overload.Found in Kit For AI
- Built-in automation engine Runs automations and orchestrates workflows without external tooling.Found in Boost.space v5
- Real-time two-way sync Keeps data live and bi-directionally synced across connected apps.Found in Boost.space v5
How it works, step by step
- Store context across sessions so agents do not start from zero
- Share one memory store across multiple agents and AI tools
- Provide memory without requiring a database, vector store or RAG stack
- Connect through a small number of API calls
- Expose remember and recall as native MCP tools
- Hold transcripts, notes, preferences, project context, task state and decisions
- Retrieve by semantic and hybrid search with reranking
- Apply recency signals to surface current memories
- Show metadata, sources and logs for inspection and audit
- Keep memory under user control and portable
- Separate memory by workspace, team, agent, user or customer deployment
- Return stored memories with low latency
- Resolve entities automatically to mark what is current versus stale
- Discard information that stops being relevant
- Extract and index content from dropped files or URLs
- Cap retrieved chunks by a token budget
- Run automations and orchestrate workflows without external tooling
- Keep data live and bi-directionally synced across connected apps
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 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 Agent memory 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 links5 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 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
Give agents persistent, inspectable memory they own instead of renting several memory subscriptions. For engineering and product teams running AI agents across several tools and sessions, convert agent transcripts, notes, preferences, project context, task state and decisions into a searchable, permissioned memory library with review states. The benefit is a testable hypothesis, measured through recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent transcripts, notes, preferences, project context, task state and decisions, then follow this sequence: 1. Store context across sessions so agents do not start from zero. 2. Share one memory store across multiple agents and AI tools. 3. Retrieve by semantic and hybrid search with reranking. Resolve uncertain cases with qualified reviewers, approve the searchable, permissioned memory library with review states, and measure recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time 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 memory schema and permissioned scope set; final decisions on what is current and what is discarded remain with the owning team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, permission boundaries and data rights. The owning team approves what is current, what is discarded and who can access each scope. One fixed memory schema and permissioned scope set; final decisions on what is current and what is discarded remain with the owning team. 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 memory schema and permissioned scope set; final decisions on what is current and what is discarded remain with the owning team. Implement one approved input format, a bounded representative case set and the first two task modules: store context across sessions so agents do not start from zero; share one memory store across multiple agents and AI tools. Support the third module with operator review: retrieve by semantic and hybrid search with reranking. Include source references, corrections, basic organization access, review 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
Agent frameworks, MCP clients, chat and coding tools, and file or URL sources. Cloud storage, identity providers and existing data warehouses. Start with file exchange and validate destination specifications before promising direct sync. 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, Record detail and audit, Stewardship console. Use a searchable list of memory records with filters by workspace, agent, user and type, a central record view showing content, source, metadata and version history, and a right-hand panel for scope, retention and review state. Let users compare current and stale versions side by side. Display draft, reviewed and expired states. Provide an API and MCP access view with keys, scopes and usage. Make the task-specific outcome a searchable, permissioned memory library with review states visible beside its evidence, review state and value baseline.
