Datus-agent

Datus-ai/Datus-agent is ranked 5,283 among these 8 peers; abinthomasonline/repo2txt is ranked 5,290.

Datus handles SQL authoring and validation, semantic model and metric construction, and the generation of pipelines, reports, and dashboards. Every run and every correction settles into context, which steadily raises the accuracy of its output. The whole stack stays open and flexible: databases, BI, schedulers, LLMs, and your team's own tools all connect through standard interfaces. Architecture The diagram reads top to bottom: who uses Datus, what the agent is made of, and what it connects to. **Three entry points

, by role**: data engineers work in Datus-CLI to explore data and build assets; analysts ask through Datus-Chat on the web, in Slack/Feishu, or in VS Code, and their feedback flows back into the agent; other agents and applications consume Datus-API over REST and MCP. **The agent core**: subagents package curated context, tools, and rules for one business domain, and skills add packaged tools. Underneath sits the context engine: metadata, metrics, reference SQL, knowledge, and local files

, retrieved through business-domain trees plus vector search, with storage on embedded LanceDB and SQLite and PostgreSQL for teams that share context. **Connected systems**: LLM providers, data warehouses, job schedulers, BI tools, and MCP servers and clients connect through adapters and plugins. The Dosi semantic engine is built into the agent. Features Semantic layer **Automated semantic modeling**: the agent reads your database schema and SQL history, then generates OSI semantic models and metric definitions, with no hand-written YAML. **Dosi

execution engine**: compiles one semantic model into SQL for 13+ database dialects, and ships as an independent program you can also run as a CLI, REST server, or MCP server. **Metric Q&A and attribution**: AskMetrics answers business questions from metric definitions instead of improvising SQL, and when a metric moves, dimension attribution locates which dimension drove the change. Agent and context **Sharper with use**: the context engine gathers schemas, reference SQL, and business rules, and writes every correction back

, so later answers keep getting more accurate. **Subagent delivery**: curate context, tools, and rules for one domain, package them as a dedicated chatbot, and serve it to analysts over web, API, MCP, Slack/Feishu, or VS Code. **Data engineering automation**: built-in subagents handle cross-database migration, ETL job generation, and wide-table builds, with Airflow orchestration and Superset/Grafana dashboard read-write. **Report and dashboard generation**: produce self-contained HTML reports and interactive dashboards straight from chat, previewed locally

with no SaaS backend. Openness and governance **Open ecosystem**: adapters for 21 databases, 10+ LLM providers, and an MCP server and client. **External integrations**: the plugin framework connects third-party platforms and in-house tools to the agent; one manifest declares CLI commands, skills, and prompt context, with per-project activation. **Skills**: packaged tools following the agentskills. io convention, installable from a marketplace. **Enterprise governance**: tiered permission profiles, statement-level SQL authorization with AI pre-review, bash confined to an

OS-level sandbox, and traces exportable to any OTLP platform. Quickstart Linux x86_64/aarch64 (glibc 2. 28+) or Apple