LEANN — Long-Term Vision

The Best Personal Data Management Platform

LEANN's ultimate goal is to be the unified personal knowledge layer that lives on your machine. Not a cloud service. Not a SaaS product. A local-first system that understands everything you've ever worked on — code, documents, emails, chats, browser history, images — and makes it all instantly searchable and usable.

1. Continuous Learning from Your Context

LEANN should get smarter over time by continuously ingesting and indexing your data as you produce it:

Always-on indexing: Watch your filesystem, email, browser, and chat sources. Incrementally update indexes as new data arrives — no manual rebuilds.
Cross-source connections: Surface relationships across data sources. A Slack conversation about a bug should link to the relevant code change, the related email thread, and the document that describes the feature.
Temporal awareness: Understand when things happened. "What was I working on last Tuesday?" should be a searchable query.
Personalized ranking: Learn from your search patterns and usage to rank results by relevance to you, not just semantic similarity.

2. The Best MCP for Code Retrieval

AI coding assistants are only as good as the context they receive. LEANN aims to be the best context provider:

AST-aware chunking: Understand code structure — functions, classes, modules — not just raw text blocks. Retrieve semantically meaningful units.
Dynamic index updates: The index stays current as you edit. No stale results. No manual rebuilds. Save a file, and the index reflects the change within seconds.
Cross-file understanding: Resolve symbols across files. When you search for a function, also surface its callers, its tests, and the types it depends on.
Dead-simple interface: One command to build (leann build), automatic incremental updates, MCP server that any AI assistant can plug into with zero configuration.
Repository-scale search: Handle monorepos and large codebases without breaking a sweat. The storage efficiency (97% reduction vs. traditional vector DBs) makes this feasible on a laptop.

3. Multimodal Knowledge Base

Text is just the beginning:

Image search: CLIP-based retrieval for screenshots, diagrams, photos. "Find the architecture diagram from last month."
Video retrieval: Index video content — lectures, meetings, screen recordings — and search by what was said or shown.
OCR pipeline: Extract and index text from scanned documents, handwritten notes, whiteboard photos.
Unified search: One query searches across all modalities. The answer might be in a PDF, a screenshot, a code comment, or a Slack message.

4. Platform for Personal AI

Looking further ahead, LEANN becomes the memory and retrieval layer for personal AI agents:

Agent memory: AI agents that remember your preferences, past decisions, and project context across sessions.
Deep research: Agents that can search your entire personal knowledge base to answer complex questions, synthesize information, and generate insights.
Proactive suggestions: Surface relevant context before you ask for it — "You discussed this exact problem with a colleague 3 months ago, here's what you decided."

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