{"adoption": {"forks": 602, "observed_at": "2026-08-28T04:07:00.468011+00:00", "stars": 2550}, "canonical_url": "https://ross.abutalabs.com/products/dataagent", "card": {"archived": false, "artifact_type": "application", "description": "Spring AI Alibaba DataAgent", "domain": ["artificial-intelligence", "data-science", "databases", "analytics", "large-language-models"], "enriched": true, "function": ["agent-framework", "rag", "llm-inference", "mcp", "data-visualization", "database", "chatbot"], "health_score": 100, "homepage": null, "language": "Java", "license": "Apache-2.0", "license_family": "permissive", "maturity": "active", "member_repos": ["spring-ai-alibaba/DataAgent"], "name": "spring-ai-alibaba/DataAgent", "platform": ["jvm", "self-hosted"], "pushed_at": "2026-08-24T05:45:32+00:00", "repo": "spring-ai-alibaba/DataAgent", "stars": 2550, "tags": ["text-to-sql", "nl2sql", "spring-ai", "data-analysis", "echarts", "human-in-the-loop", "python-sandbox", "openai-compatible", "ai-agents", "retrieval-augmented-generation", "docker", "web-server", "nodejs"], "topics": [], "urls": [], "use_cases": ["convert natural language questions into SQL queries", "build an AI data analyst for my company database", "generate charts and reports from database queries automatically", "run generated Python analysis code in a sandbox", "expose text-to-SQL as an MCP tool server", "improve SQL generation accuracy with a business glossary via RAG"], "what_it_is": "An enterprise-grade AI data analyst agent built on Spring AI Alibaba Graph that converts natural language to SQL, runs Python deep analysis in sandboxed containers, and generates chart-rich HTML/Markdown reports. It supports RAG over business metadata, pluggable vector databases, OpenAI-compatible models, and can serve as an MCP server.", "when_to_avoid": ["you need a lightweight library to embed rather than a full self-hosted application", "you're outside the Java/Spring ecosystem and don't want to run JDK 17+, MySQL, and Docker", "you only need simple single-table NL2SQL without agent workflows"], "when_to_choose": ["you're on the JVM/Spring stack and want an LLM-powered analytics agent", "you need text-to-SQL plus Python deep analysis and report generation in one system", "you want MCP integration and pluggable vector databases with OpenAI-compatible models"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/dataagent", "repo": "spring-ai-alibaba/DataAgent", "role": "main", "score": 83}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.468011+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T02:23:57.644339+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "05b2c6dc7053b9bd537ca0833f72bd8cb173fee1de68b292ac03f0fad049c74f", "fetched_at": "2026-08-28T04:07:00.468011+00:00", "kind": "readme", "missing": false, "url": "https://github.com/spring-ai-alibaba/DataAgent"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 99, "longevity": 25, "rhythm": 95}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 355, "days_push": 9, "days_rel": 35, "gap_med": 19.0, "n_releases_24m": 7}, "score": 83, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}