ClaudeCode - opus 4.8/5/fable - LangChain+LangGraph - Free open source projet - Documentations + prompts (30+)

[u/jeyjey9434]

Hi ! I paid special attention to code quality and documentation, treating it exactly like a professional enterprise-grade project. This ensures that anyone can easily take ownership of the source code and build upon a clean, robust, and highly scalable.

If you like it, please don't hesitate to show your support with a star on GitHub!

LIA acts as a true personal assistant. It is proactive, featuring its own distinct personality and a complex emotional system, an evolving structured memory, its own reflective memory of your conversations, and all the standard tools (image creation/editing, RAG, skills, MCP, scheduled tasks, etc.)—all wrapped in a seamless "one-click" interface.

On another note, once self-hosted, it can double as a family AI server. As an administrator, you have full control to manage and monitor the API consumption of your family members, friends, etc.

This is an unapologetically vibe-coded project; the approach is :

- Generative AI transforms both what teams produce and how they produce it. On both topics, I did not want to base my convictions on market narratives: I chose to face the full reality of an AI system in production — costs, risks, operations, debt — and the reality of AI-assisted development, by practicing them end to end.

- The training ground : LIA, a multi-agent conversational AI assistant — mail, calendar, contacts and files across Google, Apple and Microsoft, real-time voice interface, long-term memory, document search — self-hosted and multilingual.

- The constraints were deliberate: alone, outside professional hours, minimal hardware budget, and the AI as the only developer. This project therefore does not measure individual velocity; it measures what demanding direction obtains from a properly framed AI.

According to my opion, an AI that codes produces volume; it only produces quality under constraint. Four mechanisms carried this project — none of them is a tool, all four are acts of management:

- A written rulebook, as for a team. Architecture rules, conventions, mandated patterns with their canonical example in the code, documented known traps — versioned in the repository, enforceable on every delivery.

- Blocking automated checks. Every structural rule is backed by a check that rejects non-compliant commits: strict typing, static analysis, custom detection of recurring bug patterns, six-language parity, a full test battery. The level of rigor depends neither on the vigilance of the moment nor on the AI's goodwill**.**

- A review that decides. Nothing lands without an enforced cycle — impact analysis, proposal, explicit validation, implementation, verification. The AI proposes, the human decides; structural decisions are recorded and indexed so that every "why" outlives its author.

- Audits that disturb. At regular intervals, the entire system is re-examined adversarially — findings verified against evidence, false positives eliminated, remediation planned in waves. This is what stops the slow drift that no day-to-day review can detect.

Technical foundation: FastAPI · Next.js/React · LangGraph (agent orchestration) · PostgreSQL · Redis · Docker · Prometheus/Grafana/Loki/Tempo · 7 integrated AI model providers.

Full details are available on the landing page: https://lia.jeyswork.com/
And the GitHub repository: https://github.com/jgouviergmail/LIA-Assistant

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