PAI: Persistent Memory and Structured Workflows for Claude Code
PAI (Personal AI Infrastructure) is an open-source layer on top of Claude Code. It provides persistent memory across sessions, custom reusable skills, and a structured seven-step algorithm that governs how the AI approaches tasks. The intent is to turn a stateless AI assistant into a context-aware collaborator that retains knowledge about your projects and preferences.
The statelessness problem
Every Claude Code session starts fresh. If you have an architectural decision from yesterday, a coding standard you established last week, or a complex WebSocket design you spent an hour explaining, none of it carries over.
The result is a repetitive onboarding loop: paste the repo structure, restate the architecture, remind the AI of past decisions, provide the style guide again. This overhead shifts mental load from the actual task to maintaining the AI's short-term memory manually.
PAI's architecture
PAI organizes the AI's context into several interconnected components.
PAI is the core layer managing memory, skills, algorithms, and purpose (called Telos).
Pulse is a local life dashboard for viewing goals and project state.
The DA (Digital Assistant) is the personalized AI with a name, voice, and personality you define.
Persistent memory
Memory is the foundational feature. PAI builds a persistent knowledge base of past sessions, key decisions, and project files that the AI consults before responding. Starting a new session on a known project requires no context re-introduction.
Custom skills
Skills are reusable, project-specific workflows invoked with slash commands. Examples:
/review-next-js-component: reviews a component against your team's specific standards for state management and accessibility/plan-database-migration: outlines migration steps including your preferred schema tools, testing strategies, and rollback procedures/security-audit: analyzes code against a set of rules you define
Unlike generic prompts, skills encode your specific way of working and produce consistently aligned output.
The Algorithm
The Algorithm is a seven-step process that governs how the AI handles any task.
- Observe: gather information from the prompt and persistent memory
- Think: identify the core problem and consider approaches
- Plan: formulate a step-by-step plan
- Build: generate the code, documentation, or other artifacts
- Execute: write files, run commands, or produce output
- Verify: suggest tests or verification steps
- Learn: update persistent memory with outcomes from the interaction
This structure ensures responses include a plan, a risk analysis, and a verification strategy rather than just a code snippet.
Practical example
Given this prompt:
"Help me plan the architecture for adding real-time WebSocket notifications and rate limiting to my TaskFlow backend using my current project context, past decisions, and coding standards."
PAI consults its memory of the project's file structure, existing architecture, and established patterns before responding.
The response includes:
- Architecture plan with specific files and modules to create or modify (e.g.,
src/realtime/,SocketGateway.ts,NotificationService.ts) - Core design decisions with reasoning (for example, making
NotificationServicetransport-agnostic) - Rate limiting approach with a configurable factory, pluggable backend, and sliding-window algorithm
- Risks and potential issues: "WS connection state is per-instance" and "In-memory rate-limit counters don't share across instances," with mitigations for each
- Assumptions: "Single-instance now, but design must survive horizontal scaling"
- Verification steps: end-to-end tests, fallback purity checks, message loss validation
The entire response came from a single prompt with no manual context setup. The decisions made in this session are saved to memory, making subsequent interactions more informed.
When to use PAI
PAI is most valuable for developers already using Claude Code who work on long-running projects where context accumulates over time. The benefits compound: the longer it runs on a project, the more useful the memory becomes.
Custom skills pay off when you find yourself writing the same complex prompt instructions repeatedly. If your workflow involves consistent patterns (component reviews, migration plans, security audits), encapsulating them in skills saves significant time.
PAI requires comfort with the terminal, Git, and editing configuration files. Initial setup is an investment: defining your Telos, structuring your initial memory, and creating your first skills takes time before it pays back.
Tradeoffs to consider:
The Algorithm's thorough analysis uses more tokens than a simple prompt-response cycle. For usage-based API plans, this is a meaningful cost factor.
Maintaining PAI configuration as projects and preferences evolve requires ongoing attention. Heavy customization also means careful management when upstream PAI updates are released.
For simple, one-off questions, PAI adds overhead without proportional benefit. Its value is concentrated in complex, multi-step, context-dependent work.
Final thoughts
PAI's core contribution is persistence: memory that survives session boundaries and skills that encode consistent working patterns. This addresses the most common friction in using AI for serious development work, where context depth matters more than any single response.
The seven-step Algorithm is the more distinctive feature. It produces outputs that include not just code but plans, risk assessments, and assumptions, which is more useful for architectural decisions than for quick questions. Whether the additional token cost and setup investment is justified depends on how much of your AI usage falls into the complex, long-running category versus the quick and one-off.
Source code and documentation are at github.com/danielmiessler/PAI.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.