AI app for it and development · no coding needed
Persistent coding-agent memory console
Reduce repeated context re-explanation while keeping memory under the team's control.
Made for: Software teams running AI coding agents across multiple tools and sessions
What it does for you
The problem
Coding agents forget past sessions, so teams re-explain context, decisions and preferences on every task.
What it gives you
Source-linked persistent memory set
What you give it
Session transcriptsrepository filesdecisionspreferences
Build your own version of ContextPool, Agentmemory and more
One app with what these 6 AI tools do, yours to keep and change: ContextPool, Agentmemory, Byterover, GPS, Atlaso, ContextsBase - Backlog for Coding Agents.
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- Memory deletion Allows removal of outdated or unwanted memories.Found in ContextPool, Byterover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
How it works, step by step
- Store knowledge from past coding sessions as structured memory entries
- Load relevant memories at session start without extra prompting
- Capture decisions, preferences and actions from sessions as you work
- Keep memory data on your machine or in your repository
- Edit, retrieve and manage stored memories
- Remove outdated or unwanted memories
- Share memories across team members
- Provide a CLI for initialization and use
- Store memories with titles, summaries, tags and optional file references
- Retrieve memories with combined keyword and vector search
- Expose memory via MCP so agents read and write directly
- Integrate with AI IDE extensions such as Cursor and Windsurf
- Star important memories to prioritize coding approaches
- Anchor memories to specific files, functions and symbols
- Let agents record their own failures and noteworthy events mid-task
- Keep memory consistent across Claude Code, Cursor and Codex
- Separate memory per project and store global preferences
- Strip API keys, tokens, passwords and credentials before sending data
- Serve features, business rules, data models and guidelines to agents in one MCP call
- Convert Given/When/Then business rules into Playwright tests that assert the rule
- Provide an ordered backlog that agents pull from one step at a time
- Publish design tokens so agents build on-brand
- Let users click elements on a live site to change text or styling via the agent
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned source-linked persistent memory set with source references and unresolved questions
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 Persistent coding-agent memory 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 Persistent coding-agent memory 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 Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data195 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
Reduce repeated context re-explanation while keeping memory under the team's control. For software teams running AI coding agents across multiple tools and sessions, convert session transcripts, repository files, decisions and preferences into source-linked persistent memory that agents load at session start. The benefit is a testable hypothesis, measured through context re-explanation minutes per task and accepted agent outputs per session; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect session transcripts, repository files, decisions and preferences, then follow this sequence: 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. Resolve uncertain cases with qualified reviewers, approve source-linked persistent memory, and measure context re-explanation minutes per task and accepted agent outputs per session 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 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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
