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Context-Harbor
Self-hosted knowledge layer for AI teams and agents
ContextHarbor helps teams build AI applications without rebuilding document ingestion, indexing, and retrieval infrastructure. Deploy it on infrastructure you control, organize knowledge into access-controlled projects, and search it through a Web UI, REST API, CLI, or MCP-connected coding agents. It combines semantic search with keyword matching for exact terms and supports optional AI answers, contextual enrichment, and remote embedding providers.
Hi Product Hunt 👋
I built ContextHarbor after repeatedly seeing the same knowledge infrastructure rebuilt for every AI project: document ingestion, semantic chunking, indexing, retrieval, access control, and integration with AI agents.
ContextHarbor brings these capabilities together in a self-hosted platform. Teams can deploy it with Docker, keep documents and vectors on infrastructure they control, organize knowledge into access-controlled projects, and use it through a Web UI, REST API, CLI, or remote MCP.
The search pipeline combines semantic retrieval with keyword matching, which helps it understand meaning while still finding exact terms such as error codes, class names, product terminology, and requirement identifiers.
I’m currently looking for engineering teams to pilot ContextHarbor in real-world environments. I’d especially value feedback on deployment, retrieval quality, MCP workflows, and the types of internal knowledge your teams need to make accessible to AI applications.
I’d love to hear what you’re building and what you currently use for your knowledge layer.
About Context-Harbor on Product Hunt
“Self-hosted knowledge layer for AI teams and agents”
Context-Harbor was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #135 on the daily leaderboard. ContextHarbor helps teams build AI applications without rebuilding document ingestion, indexing, and retrieval infrastructure. Deploy it on infrastructure you control, organize knowledge into access-controlled projects, and search it through a Web UI, REST API, CLI, or MCP-connected coding agents. It combines semantic search with keyword matching for exact terms and supports optional AI answers, contextual enrichment, and remote embedding providers.
On the analytics side, Context-Harbor competes within Productivity, API and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Context-Harbor performed against the three products that launched closest to it on the same day.
Who hunted Context-Harbor?
Context-Harbor was hunted by Pradeep Gudipati. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Context-Harbor including community comment highlights and product details, visit the product overview.
Pradeep Gudipati