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Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.
Open-source AI search and vector database platform for building large-scale search, RAG, and recommendation systems.
No public pricing
Free trial available
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦in-editor chat with OpenAI assistants
- ✦workspace source-code context sharing
- ✦support for custom, user-defined assistants
- ✦secure management of the user's OpenAI account
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- ✦Combined vector, text, and structured search
- ✦Distributed machine-learned ranking at query time
- ✦Streaming search mode for cost-efficient personal/private data
- ✦Support for retrieval-augmented generation pipelines
- ✦Continuous deployment and automated scaling
- ✦Open-source core with a managed cloud option
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →getting coding help without switching out of VS Code
- →using a personalized OpenAI assistant tuned to a project
- →quick in-editor Q&A while writing code
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- →Building large-scale enterprise search engines
- →Powering RAG pipelines that need strong retrieval relevance
- →Building recommendation and ad-targeting systems
- →Search over personal/private data at lower indexing cost