Persistent coding-agent memory console

For

Software teams running AI coding agents across multiple tools and sessions

Solves

Coding agents forget past sessions, so teams re-explain context, decisions and preferences on every task.

Delivers

Source-linked persistent memory set

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce repeated context re-explanation while keeping memory under the team's control.

1. Store knowledge from past coding sessions as structured memory entries.
2. Load relevant memories at session start without extra prompting.
3. Capture decisions, preferences and actions from sessions as you work.
4. Keep memory data on your machine or in your repository.
5. Edit, retrieve and manage stored memories.
6. Remove outdated or unwanted memories.
7. Share memories across team members.
8. Provide a CLI for initialization and use.
9. Store memories with titles, summaries, tags and optional file references.
10. Retrieve memories with combined keyword and vector search.
11. Expose memory via MCP so agents read and write directly.
12. Integrate with AI IDE extensions such as Cursor and Windsurf.
13. Star important memories to prioritize coding approaches.
14. Anchor memories to specific files, functions and symbols.
15. Let agents record their own failures and noteworthy events mid-task.
16. Keep memory consistent across Claude Code, Cursor and Codex.
17. Separate memory per project and store global preferences.
18. Strip API keys, tokens, passwords and credentials before sending data.
19. Serve features, business rules, data models and guidelines to agents in one MCP call.
20. Convert Given/When/Then business rules into Playwright tests that assert the rule.
21. Provide an ordered backlog that agents pull from one step at a time.
22. Publish design tokens so agents build on-brand.
23. Let users click elements on a live site to change text or styling via the agent.
24. Compare the reviewed result with the recorded baseline and value assumptions.
25. Capture corrections and named-owner approval before consequential use.
26. Export a versioned source-linked persistent memory set with source references and unresolved questions.

Everything these tools do, in one app

• Persistent session memory Stores knowledge from past coding sessions so agents retain context across sessions.Found in ContextPool, Agentmemory, Byterover and 3 more
• Automatic context loading Loads relevant memories at session start without extra prompting.Found in ContextPool, Agentmemory, Atlaso
• Automatic memory capture Captures decisions, preferences, and actions from sessions as you work.Found in ContextPool, Agentmemory, Atlaso
• Local-first storage Keeps memory data on your machine or in your repository.Found in ContextPool, Agentmemory, GPS
• Memory editing and management Lets you edit, retrieve, and manage stored memories.Found in ContextPool, Byterover
• Memory deletion Allows removal of outdated or unwanted memories.Found in ContextPool, Byterover
• Team memory sharing Shares memories across team members for collective knowledge.Found in ContextPool, Byterover
• CLI setup and workflow Provides a command-line interface for initialization and use.Found in ContextPool, Agentmemory, GPS
• Structured memory summaries Stores memories as structured entries with titles, summaries, tags, and optional file references.Found in ContextPool, Agentmemory
• Hybrid search retrieval Uses combined keyword and vector search to find relevant memories.Found in Agentmemory, Atlaso
• MCP integration Exposes memory via MCP so agents can read and write directly.Found in Agentmemory, Atlaso, ContextsBase - Backlog for Coding Agents
• IDE integration Integrates with popular AI IDE extensions like Cursor and Windsurf.Found in Byterover
• Memory prioritization Lets you star important memories to prioritize certain coding approaches.Found in Byterover
• File and symbol anchoring Ties memories to specific files, functions, and symbols for precise context.Found in GPS
• Agent-recorded failures Allows agents to record their own failures and noteworthy events mid-task.Found in GPS
• Cross-tool memory Keeps memory consistent across multiple AI tools like Claude Code, Cursor, and Codex.Found in Atlaso
• Project and global scoping Separates memory per project and stores global preferences.Found in Atlaso
• Secret stripping Removes API keys, tokens, passwords, and credentials before sending data.Found in Atlaso
• Structured specs over MCP Serves features, business rules, data models, and guidelines to agents in one call.Found in ContextsBase - Backlog for Coding Agents
• Business rule tests Converts Given/When/Then business rules into Playwright code that asserts the rule.Found in ContextsBase - Backlog for Coding Agents
• Iteration queue Provides an ordered backlog that agents pull from one step at a time.Found in ContextsBase - Backlog for Coding Agents
• Theme designer tokens Publishes design tokens so agents build on-brand.Found in ContextsBase - Backlog for Coding Agents
• Live-site element selection Lets users click elements on a live site to change text or styling via the agent.Found in ContextsBase - Backlog for Coding Agents

What goes in, what comes out

The workflow

1. In
   Start with

   Session transcripts, repository files, decisions and preferences

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect session transcripts

4. 3

   Repository files

5. 4

   Decisions and preferences

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked persistent memory set

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 agent tool set and repository layout; final code review and security checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Memory library, Session capture review, Agent console. Use a searchable list of memory entries with titles, summaries, tags and file anchors, a detail view showing source session and linked files, and a right-hand panel for scope, priority and sharing. Let users compare a memory entry against its source session. Display draft, reviewed and shared states. Provide an agent-facing MCP endpoint view with read and write logs. Make the task-specific outcome source-linked persistent memory visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, memory versions, team comments, approval states, usage allowances, retention limits, download 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

Team-owned repositories, session logs and permitted research sources. Cloud storage, IDE extensions and agent MCP endpoints. Start with file exchange and validate destination specifications before promising direct publishing. 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 knowledge from past coding sessions as structured memory entries; load relevant memories at session start without extra prompting. 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 software teams running AI coding agents across multiple tools and sessions use it to solve "coding agents forget past sessions, so teams re-explain context, decisions and preferences on every task"?
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: Context re-explanation minutes per task and accepted agent outputs per session.
4. Measure, then decide. Track context re-explanation minutes per task and accepted agent outputs per session; 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 tool set and repository layout; final code review and security checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: store knowledge from past coding sessions as structured memory entries; load relevant memories at session start without extra prompting. Support the third module with operator review: capture decisions, preferences and actions from sessions as you work. 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 source-linked persistent memory. Retain the explicit scope boundary: One fixed agent tool set and repository layout; final code review and security checks remain human.

What the build depends on. Repository access, session capture, asynchronous indexing 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 tool set and repository layout; final code review and security checks 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 repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repo or enterprise integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked persistent memory set. 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 repeated context re-explanation while keeping memory under the team's control. Demonstrate a concrete source-linked persistent memory set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample source-linked persistent memory set from a small authorized input set, with a transparent calculation of context re-explanation minutes per task and accepted agent outputs per session and no promised savings.

The first 30 days

1. Week 1: interview five software teams running AI coding agents across multiple tools and sessions and inspect a recent example of coding agents forget past sessions, so teams re-explain context, decisions and preferences on every task.
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 context re-explanation minutes per task and accepted agent outputs per session, 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: Context re-explanation minutes per task and accepted agent outputs per session. 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

Context re-explanation minutes per task and accepted agent outputs per session; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked persistent memory. 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, repository conventions 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 software teams running AI coding agents across multiple tools and sessions. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ContextPool, Agentmemory, Byterover, GPS, Atlaso, ContextsBase - Backlog for Coding Agents, and manual note-taking. Compare this product with the buyer's present method on context re-explanation minutes per task and accepted agent outputs per session. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, vector 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 source-linked persistent memory. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, secret stripping and usage permissions. Named owners approve substantive memory changes and sharing scope. One fixed agent tool set and repository layout; final code review and security checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.