Open-source search engine with typo-tolerant keyword, faceted, and hybrid search
Meilisearch documents an open-source search engine at meilisearch.com/docs for fast, typo-tolerant full-text search, faceting, filtering, and sorting. Meilisearch Cloud offers hosted deployment; self-hosted options include Docker and native binaries with REST API and official SDKs (JavaScript, Python, Rust, PHP, Java, .NET, Dart, Go). Recent docs and blog posts describe semantic and hybrid search capabilities, AI-powered search experiences, and the official meilisearch-mcp server for LLM clients. Meilisearch positions itself as a developer-friendly alternative focused on sub-50ms search experiences with simple index/document APIs.
Use cases
- Site and app search with instant-as-you-type experiences
- E-commerce catalogs with faceted filters and sorting
- Hybrid keyword + semantic search prototypes before production tuning
- Self-hosted search replacing heavier Elasticsearch stacks for mid-size catalogs
- Agent-driven index ops via meilisearch-mcp during development
Key features
- Typo-tolerant full-text search with configurable ranking rules
- Faceting, filtering, and geo search on indexed documents
- REST API and multi-language SDKs for index and document management
- Meilisearch Cloud with analytics and team features
- Official meilisearch-mcp for natural-language index management in dev workflows
Who Is It For?
- Developers wanting fast search without operating complex clusters
- Teams migrating from basic SQL LIKE queries to dedicated search
- Startups evaluating Meilisearch Cloud versus self-hosted Docker
Frequently Asked Questions
- Is Meilisearch the same as meilisearch-mcp?
- Meilisearch is the search engine; meilisearch-mcp is a separate MCP control layer documented on meilisearch.com/blog.
- Can Meilisearch run self-hosted?
- Yes—Meilisearch documents Docker and binary installs alongside Meilisearch Cloud.
- Does it support vector or hybrid search?
- Meilisearch blog and docs describe semantic/hybrid search features in recent releases; verify your version's docs for exact APIs.
Related
Related
3 Indexed items
Typesense
Typesense documents an open-source search engine at typesense.org/docs for fast typo-tolerant keyword search, faceting, and vector retrieval. Vector search docs at typesense.org/docs/30.2/api/vector-search describe KNN search on imported embeddings or auto-generated embeddings via OpenAI, Google PaLM API, or built-in Hugging Face models in huggingface.co/typesense/models (use the `ts` namespace prefix). Features include semantic search, hybrid search with rank fusion and adjustable `alpha` weighting, similar-document queries by ID, HNSW approximate search with optional `flat_search_cutoff` brute-force mode, and cosine `vector_distance` scoring. Deploy via Typesense Cloud or self-hosted Docker/binaries with REST API and official client libraries.
turbopuffer
turbopuffer documents a fast search engine at turbopuffer.com/docs built natively on object storage (S3, GCS, Azure Blob) with NVMe/memory caching for compute. Architecture docs describe a write-ahead log on object storage, SPFresh centroid-based ANN indexes for vectors, inverted BM25 indexes for full-text, exact metadata indexes with native filtering, and branching copy-on-write namespaces. The API supports vector ANN queries, BM25 full-text, hybrid multi-queries, regex/trigram search, filters, and encryption with customer keys per turbopuffer.com/docs/index and turbopuffer.com/docs/architecture. Docs cite production-scale limits observed (4T+ documents, 10M+ writes/s, 25k+ queries/s) with tradeoffs: higher write latency from object-storage durability and occasional cold queries on uncached namespaces.
Chroma
Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.