Agent memory graph stewardship console

For

Engineering and platform teams building AI agents that need persistent, relationship-aware memory

Solves

Agents retrieve isolated snippets, lose context between sessions and cannot show where a remembered fact came from.

Delivers

A reviewed, queryable memory graph with typed claims, contradiction records and source citations

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Give agents a structured, persistent memory of connected context so they retrieve accurate, relationship-aware information instead of isolated snippets.

1. Store entities and relationships in a knowledge graph.
2. Accumulate memory across sessions and interactions.
3. Combine vector similarity search with graph traversal.
4. Provide an open-source, self-hostable codebase.
5. Enforce multi-tenant access controls and ACLs.
6. Query memory through GraphQL and natural language.
7. Run graph storage on object storage without separate clusters.
8. Return core graph primitives in one low-latency API call.
9. Use a Rust implementation for predictable latency.
10. Let developers define domain entities and map relationships.
11. Give agents a navigable graph instead of long prompts.
12. Apply the same approach across security, support, legal, insurance and sales.
13. Let people and agents co-edit documents, specs and notes.
14. Centralize team context in one chat and context graph.
15. Expose pluggable middleware for telemetry, storage and LLM runtimes.
16. Gate each memory write with a verdict, reason and closest existing memory ID.
17. Extract and store facts as typed claims.
18. Track contradictions as first-class objects.
19. Cite sources for every answer.
20. Connect to assistants over the Model Context Protocol.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Connected source material, domain entity definitions and access rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected source material

4. 3

   Domain entity definitions and access rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A reviewed, queryable memory graph with typed claims, contradiction records and source citations

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, schema versions, tenant boundaries, reviewer assignments, approval states, connector allowances, query limits, 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

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.

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 entities and relationships in a knowledge graph; accumulate memory across sessions and interactions. 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 platform teams building AI agents that need persistent, relationship-aware memory use it to solve "agents retrieve isolated snippets, lose context between sessions and cannot show where a remembered fact came from"?
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: Retrieval accuracy on held-out questions and reviewer correction time per accepted memory.
4. Measure, then decide. Track retrieval accuracy on held-out questions and reviewer correction time per accepted memory; accepted-memory 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 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.

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, queryable memory graph. Retain the explicit scope boundary: One bounded domain schema and one approved connector set; final schema, access and contradiction decisions remain human.

What the build depends on. Source upload and preview, asynchronous extraction jobs, editable version history, reviewer access and tested export formats. High-fidelity memory requires specialist schema QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One bounded domain schema and one approved connector set; final schema, access and contradiction decisions remain human.

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 graph package. 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, queryable memory graph. 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 a structured, persistent memory of connected context so they retrieve accurate, relationship-aware information instead of isolated snippets. Demonstrate a concrete reviewed, queryable memory graph using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and platform teams building 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 memory graph from a small authorized input set, with a transparent calculation of retrieval accuracy on held-out questions and reviewer correction time per accepted memory and no promised savings.

The first 30 days

1. Week 1: interview five engineering and platform teams building AI agents that need persistent, relationship-aware memory and inspect a recent example of agents retrieving isolated snippets, losing context between sessions and failing to show where a remembered fact came from.
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 retrieval accuracy on held-out questions and reviewer correction time per accepted memory, 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: Retrieval accuracy on held-out questions and reviewer correction time per accepted memory. 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

Retrieval accuracy on held-out questions and reviewer correction time per accepted memory; accepted-memory rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, queryable memory graph. 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 schemas, entity mappings 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 platform teams building AI agents that need persistent, relationship-aware memory. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

cognee, Papr, HydraDB OSS, Open Index, Jitera and Cortex by SKYNETLAB, plus generic vector stores and prompt-stuffing. Compare this product with the buyer's present method on retrieval accuracy on held-out questions and reviewer correction time per accepted memory. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, embedding and graph storage, 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 a reviewed, queryable memory graph. Track cost per accepted memory, including correction work, unsuccessful cases and support.

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.