Agent memory library and stewardship console

For

Engineering and product teams running AI agents across several tools and sessions

Solves

Agents restart from zero each session and cannot share context across tools, so teams rebuild the same state and lose decisions.

Delivers

A searchable, permissioned memory library with review states

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Give agents persistent, inspectable memory they own instead of renting several memory subscriptions.

1. Store context across sessions so agents do not start from zero.
2. Share one memory store across multiple agents and AI tools.
3. Provide memory without requiring a database, vector store or RAG stack.
4. Connect through a small number of API calls.
5. Expose remember and recall as native MCP tools.
6. Hold transcripts, notes, preferences, project context, task state and decisions.
7. Retrieve by semantic and hybrid search with reranking.
8. Apply recency signals to surface current memories.
9. Show metadata, sources and logs for inspection and audit.
10. Keep memory under user control and portable.
11. Separate memory by workspace, team, agent, user or customer deployment.
12. Return stored memories with low latency.
13. Resolve entities automatically to mark what is current versus stale.
14. Discard information that stops being relevant.
15. Extract and index content from dropped files or URLs.
16. Cap retrieved chunks by a token budget.
17. Run automations and orchestrate workflows without external tooling.
18. Keep data live and bi-directionally synced across connected apps.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Agent transcripts, notes, preferences, project context, task state and decisions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent transcripts

4. 3

   Notes

5. 4

   Preferences

6. 5

   Project context

7. 6

   Task state and decisions

8. 7

   Then follow this sequence: 1

9. Out
   Finish with

   A searchable, permissioned memory library with review states

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Workspace ownership, memory record versions, scope assignments, review states, retention rules, API keys, MCP access scopes, usage caps, export logs and a rights record for supplied material. Add organization access boundaries, named reviewers, data retention controls and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: store context across sessions so agents do not start from zero; share one memory store across multiple agents and AI tools. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering and product teams running AI agents across several tools and sessions use it to solve "agents restart from zero each session and cannot share context across tools, so teams rebuild the same state and lose decisions"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time.
4. Measure, then decide. Track recall precision on held-out queries, context reuse across sessions and tools and and reviewer correction time; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the searchable, permissioned memory library with review states. Retain the explicit scope boundary: One fixed memory schema and permissioned scope set; final decisions on what is current and what is discarded remain with the owning team.

What the build depends on. Memory record upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed memory schema and permissioned scope set; final decisions on what is current and what is discarded remain with the owning team.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined memory package. Offer a monthly production allowance after repeat demand. Quote complex multi-tenant or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, permissioned memory library with review states. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Give agents persistent, inspectable memory they own instead of renting several memory subscriptions. Demonstrate a concrete searchable, permissioned memory library with review states using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and product teams running AI agents across several tools and sessions professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, permissioned memory library with review states from a small authorized input set, with a transparent calculation of recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time and no promised savings.

The first 30 days

1. Week 1: interview five engineering and product teams running AI agents across several tools and sessions and inspect a recent example of agents restarting from zero each session and failing to share context across tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a searchable, permissioned memory library with review states. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved memory schemas, scope rules and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and product teams running AI agents across several tools and sessions. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

pumaDB, Walrus Memory, Actx0, Memmy Agent, Maximem Synap, CogniMemo, Mnexium AI, Kit For AI, Boost.space v5 and Claude-Mem. Compare this product with the buyer's present method on recall precision on held-out queries, context reuse across sessions and tools, and reviewer correction time. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Storage, embedding and retrieval compute, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the searchable, permissioned memory library with review states. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.