ES|QL

BTQL is Braintrust's SQL-like query language and API for querying agent traces, logs, experiments, and datasets. It supports real-time queries over incoming trace data and long-running analysis of large, deeply nested semi-structured JSON payloads. The documentation describes standard SQL syntax alongside a legacy pipe-delimited BTQL syntax, with filtering, grouping, aggregation, full-text matching, sorting, pagination, and limits; unsupported SQL features can be expressed using BTQL's native syntax. Queries can run in Braintrust's SQL sandbox, the `bt sql` CLI, or the `/btql` API endpoint.

Document Query Engine is an open-source workshop project from Unblocked that turns natural-language questions about GitHub pull requests and issues into validated MongoDB aggregation pipelines. It provides structured access where semantic retrieval cannot reason about time, relationships, or precise filters, using a six-step pipeline covering schema discovery, identity resolution, query synthesis, validation, error-feedback retries, and full-text search. The project includes a web query playground and chat agent, with Bun and TypeScript, MongoDB, GitHub data ingestion, and an LLM SDK for Anthropic or OpenAI. The repository starts with the query engine stubbed and guides users through implementing it.

QMD is an on-device command-line search engine for Markdown notes, meeting transcripts, documentation, knowledge bases, and other organized information. It combines BM25 full-text search, vector semantic search, and LLM reranking locally through node-llama-cpp and GGUF models. Its hybrid query pipeline expands the original query into lexical, vector, and HyDE variants, routes them to keyword or vector search, combines the results with Reciprocal Rank Fusion, and applies an LLM reranker to produce final ranked results. Collections can be indexed and enriched with hierarchical context; documents can be retrieved individually or in batches, and JSON and file-oriented output formats support agent workflows. QMD also exposes query, retrieval, batch retrieval, and index-status operations through an MCP server.

jq is a lightweight command-line JSON processor for slicing, filtering, mapping, and transforming structured data. It can compose and pipe JSON output, including results from MCP clients such as mcpc, using an expression language modeled in part on tools such as sed, awk, and grep. jq is written in portable C, has no runtime dependencies, and is distributed as a standalone binary for multiple platforms.

Elasticsearch is a distributed search and analytics engine, scalable data store, and vector database developed by Elastic. It stores and indexes structured or unstructured text, numerical, geospatial, log, and metric data as JSON documents, which clients submit and query through REST APIs or language clients. Documents are organized in indexes and data streams and become searchable in near real time; supported search modes include full-text, vector, and hybrid retrieval for applications such as logs, metrics, application performance monitoring, security analysis, and retrieval-augmented generation. It can be used as a managed deployment through Elastic Cloud or installed and operated independently. The repository also documents a Docker-based local setup with Elasticsearch and Kibana for development and testing, and Kibana provides tools for exploring indexed data, filtering it, and creating visualizations and dashboards.