ChatLab
ChatLab is an open-source, local-first chat-history analysis project that helps people import, search, visualize, and understand their social conversations with SQL and AI agents. It is designed to keep chat data and settings on the user's device and to send only the relevant, desensitized context to an AI when AI features are enabled.
At a Glance
- Individuals preserving and analyzing personal chat histories
- Privacy-conscious users who want local/offline data processing
- Developers and AI-agent users needing a local chat-history interface
- Researchers and analysts working with exported conversation data
- +1 more
AI Tools by ChatLab
(1)
ChatLab
Local Chat History Analyzer
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Latest News
ChatLab v0.37.5: unified right-side panels, desktop UI scaling, and fixes for session loading, data-source configuration, and proxy model downloads.
ChatLab v0.37.3: improved multilingual AI tool descriptions and language handling, fixed AI time filters and Qwen compatibility, and improved conversation continuation and context retrieval.
ChatLab v0.37.2: improved recovery of truncated/stopped AI replies, emoji recognition, skill entry points, and download safety.
ChatLab v0.37.1: added contributor attribution and voice-transcription analysis support, plus Docker and AI-tool reliability improvements.
Products & Services
Electron desktop application for importing chat exports and locally browsing, searching, analyzing, and visualizing conversations with AI capabilities.
Node.js command-line service and web UI installable as chatlab-cli; supports API and Web UI modes, headless operation for scripts and AI agents, daemon mode, authentication, and MCP/agent integration.
Container deployment option for running the local analysis service, documented alongside Desktop and CLI installation.
A standardized JSON/JSONL data format and conversion workflow that normalizes exports from different chat platforms into sessions, members, and messages.
Market Position
ChatLab positions itself as a privacy-first, local alternative to uploading complete chat exports to a general-purpose AI: it normalizes and indexes records locally, uses SQL and purpose-built tools to retrieve only relevant context, and supports both a desktop experience and agent-oriented CLI/API workflows. Its differentiators are local-first operation, broad cross-platform import normalization, large-history stream processing, and AI-agent tooling. Relevant alternatives include general-purpose chat export viewers, self-hosted/local analytics tools, and cloud AI products that analyze uploaded conversation files; ChatLab emphasizes that it does not require raw-history uploads or subscriptions.
Leadership
Founders
digua (地瓜 / hellodigua)
Independent developer and AI full-stack engineer according to the developer's GitHub profile; maintains ChatLab and other projects including code996, vue-danmaku, and UniEmoji. The ChatLab repository package metadata identifies digua as its author (hello@digua.me).
Founding Story
ChatLab was started to give people a private, secure way to analyze and revisit their own conversations. Its initial vision was that chat histories contain social connections and emotional memories that should not disappear into an unsearchable scroll; the project therefore organizes exported records locally before using SQL and AI to answer questions.
Business Model
Revenue Model
ChatLab is described by its official documentation and GitHub organization as free and open-source, with no subscriptions and no mandatory cloud service. The project distributes a free desktop app, CLI package, and Docker option under AGPL-3.0; no paid monetization model is stated in the reviewed sources.
Pricing Tiers
Desktop, CLI, and Docker deployment are available as free/open-source software; the repository is licensed AGPL-3.0.
Target Markets
- Individuals preserving and analyzing personal chat histories
- Privacy-conscious users who want local/offline data processing
- Developers and AI-agent users needing a local chat-history interface
- Researchers and analysts working with exported conversation data
- Open-source contributors building chat import adapters and integrations
- Revisiting and searching personal social memories
- Understanding relationships, conversation rhythms, activity patterns, and shared topics
- Summarizing long conversations and asking an AI questions grounded in message evidence
- Analyzing group-chat participation, rankings, trends, and key moments
- Running private offline/local analysis over exported chat histories
- Connecting external AI agents such as Codex or Claude Code to a local chat database