Disco

# Disco > Not another AI data analyst. A discovery pipeline that finds novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships you wouldn't think to look for. ## What It Does - Finds complex patterns — interactions, thresholds, subgroup effects — without requiring prior hypotheses - Validates every pattern on hold-out data with FDR-corrected p-values - Checks each finding against academic literature for novelty, with citations - Returns structured, machine-readable results agents can reason over directly ## Links - SDK: https://pypi.org/project/discovery-engine-api/ - Full docs (LLM-friendly): https://disco.leap-labs.com/llms-full.txt - MCP server: https://disco.leap-labs.com/.well-known/mcp.json - API spec: https://disco.leap-labs.com/.well-known/openapi.json - Visualization spec: https://disco.leap-labs.com/visualization-spec - API keys: https://disco.leap-labs.com/docs - Interactive reports: https://disco.leap-labs.com/discover ## Getting an API Key **Programmatic (for agents):** Two-step signup — `POST /api/signup` sends a verification code to the email, then `POST /api/signup/verify` submits the code and returns a `disco_` API key. No auth required. **Manual (for humans):** Sign up at https://disco.leap-labs.com/sign-up, create key at https://disco.leap-labs.com/docs. Free tier active immediately (10 credits/month, unlimited public runs). No credit card required. ## Quick Start ```bash pip install discovery-engine-api ``` ```python from discovery import Engine engine = Engine(api_key="disco_...") result = await engine.discover(file="data.csv", target_column="outcome") for pattern in result.patterns: if pattern.p_value < 0.05 and pattern.novelty_type == "novel": print(f"{pattern.description} (p={pattern.p_value:.4f})") ``` ## Cost - Public runs: Free (results published, depth=1) - Private runs: 1 credit/MB/depth × 5 with LLM explanations ($0.10/credit) - Free tie