Coral is a Rust-based SQL runtime that provides agents and users with a unified query interface over APIs, files, and live data sources. Rather than requiring agents to make individual tool calls to different services, Coral translates SQL queries into API calls or file reads and returns consolidated result sets, enabling complex cross-source reasoning through a single interface.
The core value proposition centers on reducing friction in agent workflows. Traditional agent architectures that access company data through individual tools create inefficiencies: repeated authentication, pagination, and retry logic across multiple calls; poor reasoning across data sources; high token consumption; and brittle integration code. Coral addresses these problems by exposing all connected data sources as SQL schemas and tables, allowing agents to query multiple sources in a single statement and join data across them locally.
Coral's architecture revolves around source specifications, which are YAML files declaring how to reach an API or dataset and which tables and columns it exposes. When users run commands like `coral source add github`, Coral installs the source and makes it queryable as a SQL schema, so tables like `github.issues` and `github.pulls` become immediately accessible. The system handles authentication by reading variables and secrets from environment variables or interactive prompts, storing credentials locally and ensuring they never leave the user's machine. Coral includes bundled sources for popular services including Datadog, GitHub, Linear, Sentry, Stripe, and support for local JSONL and Parquet files. Users can also write custom source specifications for APIs not included in the bundle.
The performance benefits are substantial according to benchmarking conducted against direct provider MCPs from Datadog, Sentry, Linear, Slack, and GitHub across 82 real-world AI tasks using Claude Opus 4.6. Across all tasks, Claude achieved 20 percent higher accuracy and 2x greater cost efficiency using Coral compared to direct provider MCPs, with 42 percent lower latency. For complex coding agent tasks involving multi-hop queries and post-processing, the improvements were more dramatic: 31 percent higher accuracy and 3.4x better cost efficiency. Simpler tasks like raw fact retrieval showed more modest gains of 6 percent accuracy improvement and 2 percent cost efficiency improvement.
Coral is designed as a read-only layer optimized for production use. It handles pagination automatically, returns tabular rows instead of sprawling JSON responses, and allows queries to select only needed columns. Query pushdown and caching mechanisms keep performance responsive while reducing unnecessary API traffic. The system exposes itself to agents through an MCP server, allowing tools like Claude Desktop, Cursor, and VS Code to query Coral without requiring bespoke tool integration. Coral also ships with skills that teach agents a discovery-first SQL workflow, enabling them to explore available tables and schemas before constructing queries.
Local state is stored in platform-specific configuration directories, with source metadata and non-secret variables in config.toml and source secrets stored separately within the same local trust boundary. Bundled source specifications are resolved from the current binary at query time, allowing upgrades to pick up newer manifests without requiring users to re-add sources. The project is licensed under Apache License 2.0 and welcomes contributions, particularly bug fixes, tests, documentation improvements, and source enhancements. Security issues should be reported through the security policy rather than public channels.
