All 206 Dependencies
Every package Trieve depends on, ranked by repo health score.
Trieve is an open-source, API-first platform that unifies semantic search, recommendation engines, retrieval-augmented generation (RAG), and search analytics into a single deployable system. Built for developers and AI teams who are tired of stitching together a vector database, an embedding service, a reranking model, and a RAG framework separately, Trieve ships all of it as one cohesive stack with no mandatory cloud dependency.
The backend is written in Rust using Actix-web and Diesel, with Qdrant handling vector storage and PostgreSQL managing metadata and structured data. Hybrid search combines dense semantic vectors (via OpenAI or Jina embeddings), sparse neural vectors (via SPLADE), and BM25 keyword matching, with optional BAAI/bge-reranker-large cross-encoder reranking for maximum precision. Search results can be further tuned with SemanticBoost — an algebraic vector manipulation technique that shifts a chunk's embedding toward a concept at index time.
The ecosystem extends well beyond search: a TypeScript SDK, Python client, MCP server for AI agents, Docusaurus and VitePress plugins, a Shopify extension, and an n8n node integration mean Trieve plugs into existing development workflows rather than demanding a dedicated integration effort. Built-in analytics via ClickHouse track queries, click-through rates, and RAG responses, closing the feedback loop between user behavior and search quality.
Deployment targets include Docker Compose for local development, Helm charts for Kubernetes, and documented paths for AWS and GCP. PDF-to-markdown conversion, web crawling via Firecrawl, and a hallucination detection module round out a platform that covers the full AI retrieval stack without requiring a managed cloud subscription.