Your chat history, finally yours.

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ChatLab is an open-source desktop app for understanding your social conversations. It combines a flexible SQL engine with AI agents so you can explore patterns, ask better questions, and extract insights from chat data, all on your own machine.

Currently supported: WhatsApp, LINE, WeChat, QQ, Discord, Instagram, and Telegram. Coming next: iMessage, Messenger, and KakaoTalk.

Core Features

• 🚀 Built for large histories: Stream parsing and multi-worker processing keep imports and analysis responsive, even at million-message scale.
• 🔒 Private by default: Your chat data and settings stay local. No mandatory cloud upload of raw conversations.
• 🤖 AI that can actually operate on data: Agent + Function Calling workflows can search, summarize, and analyze chat records with context.
• 📊 Insight-rich visual views: See trends, time patterns, interaction frequency, rankings, and more in one place.
• 🧩 Cross-platform normalization: Different export formats are mapped into a unified model so you can analyze them consistently.

Usage Guides

• Download Guide
• Chat Record Export Guide
• Standardized Format Specification
• Troubleshooting Guide

Preview

For more previews, please visit the official website: chatlab.fun

System Architecture

Architecture Principles

• Local-first by default: Raw chat data, indexes, and settings remain on-device unless you explicitly choose otherwise.
• Streaming over buffering: Stream-first parsing and incremental processing keep large imports stable and memory-efficient.
• Composable intelligence: AI features are assembled through Agent + Tool Calling, not hard-coded into one model path.
• Schema-first evolution: Import, query, analysis, and visualization share a consistent data model that scales with new features.

Runtime Architecture

• Main Process (control plane): apps/desktop/main/index.ts handles lifecycle and windows. apps/desktop/main/ipc/ defines domain-scoped IPC. Core AI, query, NLP, import, and merge logic lives in shared packages (packages/core, packages/node-runtime, packages/tools); apps/desktop/main/ai/ and apps/desktop/main/i18n/ are thin Electron adaptation layers.
• Worker Layer (compute plane): apps/desktop/main/worker/ runs import, indexing, and query tasks via workerManager, delegating core logic to @openchatlab/core and @openchatlab/node-runtime.
• Renderer Layer (interaction plane): Vue 3 + Nuxt UI + Tailwind CSS drive management, private chat, group chat, and analysis interfaces. apps/desktop/preload/index.ts exposes tightly scoped APIs for secure process boundaries.

Data Pipeline

1. Ingestion: parser/ detects file format and dispatches to the matching parser module.
2. Persistence: Stream-based writes populate core local entities: sessions, members, and messages.
3. Indexing: Session- and time-oriented indexes are built for timeline navigation and retrieval.
4. Query & Analysis: worker/query/* powers activity metrics, interaction analysis, SQL Lab, and AI-assisted exploration.
5. Presentation: The renderer turns query output into charts, rankings, timelines, and conversational analysis flows.

Extensibility & Reliability

• Pluggable parser architecture: Adding a new import source is mostly an extension in parser/formats/*, without reworking downstream query logic.
• Full + incremental import paths: streamImport.ts and incrementalImport.ts support both first-time onboarding and ongoing updates.
• Modular IPC boundaries: Domain-based IPC segmentation reduces cross-layer coupling and limits permission spread.
• Unified i18n evolution: Main and renderer processes share an i18n system that can evolve with product scope.

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Local Development

Requirements

• Node.js >= 20
• pnpm

Setup

# install dependencies
pnpm install

# run electron app in dev mode
pnpm dev

If Electron encounters exceptions during startup, you can try using electron-fix:

npm install electron-fix -g
electron-fix start

Privacy Policy & User Agreement

Before using this software, please read the Privacy Policy & User Agreement.

Community

Please follow these principles before submitting a Pull Request:

• Obvious bug fixes can be submitted directly.
• For new features, please submit an Issue for discussion first; PRs submitted without prior discussion will be closed.
• Keep one PR focused on one task; if changes are extensive, consider splitting them into multiple independent PRs.

Thanks to all contributors:

License

AGPL-3.0 License