Agent memory stewardship console

For

Engineering teams running AI agents that need durable, auditable context across sessions

Solves

Agent context is scattered across conversations and tools, so teams cannot store, retrieve or audit what an agent knew and when.

Delivers

A reviewed, permissioned memory library with provenance and retention states

Built in

about 4 weeks of creation time, MVP in 5 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

Reduce token waste and lost context while keeping memory data on the buyer's own infrastructure.

1. Store and retrieve durable context beyond a single conversation.
2. Keep memory data on the buyer's own machine without external infrastructure.
3. Let developers inspect, modify and self-host the system.
4. Find related memories by following graph connections, not only keyword matches.
5. Age out low-value context automatically to keep memory focused.
6. Mark certain memories as important so they are not pruned accidentally.
7. Cut unnecessary context sent to the model to lower runtime cost.
8. Store multiple data types together so related writes commit atomically.
9. Record per-fact provenance with source references and trust levels.
10. Track when a fact was recorded, first believed and true in the world.
11. Combine vector similarity, graph relations, full-text search and keyword filters.
12. Separate memory data by tenant or project to prevent cross-contamination.
13. Expose memory operations through the Model Context Protocol.
14. Capture tool calls, execution paths, decisions and outcomes as structured memory.
15. Store facts, decisions and patterns as discrete items with entity, project and timestamp metadata.
16. Retrieve memory scoped by project, session, entity or time range.
17. Build memory after interactions so response latency is not affected.
18. Show memory creation and retrieval activity plus generated summaries of priorities and issues.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Agent interactions, tool traces, structured facts and permissioned sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent interactions

4. 3

   Tool traces

5. 4

   Structured facts and permissioned sources

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A reviewed, permissioned memory library with provenance and retention states

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, memory versions, scope boundaries, review states, importance rules, decay settings, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

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

   5 days

   One buyer segment, one recurring use case; first modules: store and retrieve durable context beyond a single conversation; find related memories by following graph connections, not only keyword matches. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 teams running AI agents that need durable, auditable context across sessions use it to solve "agent context is scattered across conversations and tools, so teams cannot store, retrieve or audit what an agent knew and when"?
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: Relevant-memory retrieval rate and context tokens per completed task.
4. Measure, then decide. Track relevant-memory retrieval rate and context tokens per completed task; 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 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.

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 a reviewed, permissioned memory library. Retain the explicit scope boundary: One fixed agent framework and permissioned data scope; final fact approval and retention decisions remain with the buyer's stewards.

What the build depends on. Memory upload and preview, asynchronous memory jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed agent framework and permissioned data scope; final fact approval and retention decisions remain with the buyer's stewards.

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 scope. Offer a monthly production allowance after repeat demand. Quote complex multi-tenant or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, permissioned memory library. 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

Reduce token waste and lost context while keeping memory data on the buyer's own infrastructure. Demonstrate a concrete reviewed, permissioned memory library using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample reviewed, permissioned memory library from a small authorized input set, with a transparent calculation of relevant-memory retrieval rate and context tokens per completed task and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams running AI agents that need durable, auditable context across sessions and inspect a recent example of agent context scattered across conversations and 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 relevant-memory retrieval rate and context tokens per completed task, 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: Relevant-memory retrieval rate and context tokens per completed task. 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

Relevant-memory retrieval rate and context tokens per completed task; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, permissioned memory library. 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, retention 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 teams running AI agents that need durable, auditable context across sessions. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

YourMemory, Spectron and Memori, plus custom in-house memory stores and generic vector databases. Compare this product with the buyer's present method on relevant-memory retrieval rate and context tokens per completed task. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a reviewed, permissioned memory library. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.