We’re build a swarm of agents for all data tasks. That anyone can use for free, open-source community version. [![MseeP.ai Security Assessment Badge](https://mseep.net/pr/dataworkersproject-dataworkers-claw-community-badge.png)](https://mseep.ai/app/dataworkersproject-dataworkers-claw-community)

Data Workers - Open-Source Community Edition

Open-source autonomous AI agents for data engineering

Stop writing boilerplate pipelines. Stop debugging data incidents manually.
Describe what you need in natural language. The agents handle execution.

11 AI agents · 160+ MCP tools · 15 connectors · 2,900+ tests · Zero config to start

--- ## What is Data Workers? Data Workers is a coordinated swarm of AI agents that automate the full spectrum of data engineering workflows. Each agent is a standalone [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) server that exposes domain-specific tools to Claude Code, OpenCode, Cursor, VS Code, and any MCP-compatible client. **The problem:** Data engineers spend 60%+ of their time on undifferentiated work -- writing pipeline boilerplate, debugging data incidents at 2am, chasing schema changes across teams, manually cataloging assets, and fighting governance paperwork. **The solution:** 11 autonomous agents that understand your data stack end-to-end. They build pipelines, detect anomalies, manage catalogs, enforce governance, track ML experiments, and more -- all through natural language via the MCP protocol your AI tools already speak. Everything runs locally with in-memory stubs by default. No external services required. No data leaves your machine. BYO model -- use any LLM provider. Read more: [Why We Open-Sourced Data Workers](https://dataworkers.io/blog/why-we-open-sourced-14-autonomous-data-engineering-agents) ## Get Started ### Fastest path (2 commands) No clone required — runs straight from npm: ```bash npx dw-claw init claude mcp add data-workers -- npx -y dw-claw ``` That's it. Open Claude Code and start asking questions. Everything works instantly with in-memory seed data. ### Connect to your data (optional) ```bash npx dw-claw setup # Interactive: choose Snowflake/BigQuery/Databricks → enter creds → verified ``` ### Clone-based setup If you prefer to clone the repo and run from source (required for contributing or dev): ```bash git clone https://github.com/DataWorkersProject/dataworkers-claw-community.git cd dataworkers-claw-community npm install # full install (~3min, includes optional warehouse SDKs) # or: npm install --ignore-optional # fast install (~30s, skips heavy warehouse SDKs) ``` Then add agents to Claude Code (run from inside the cloned repo): ```bash claude mcp add dw-pipelines -- "$(pwd)/start-agent.sh" dw-pipelines && \ claude mcp add dw-incidents -- "$(pwd)/start-agent.sh" dw-incidents && \ claude mcp add dw-catalog -- "$(pwd)/start-agent.sh" dw-context-catalog && \ claude mcp add dw-schema -- "$(pwd)/start-agent.sh" dw-schema && \ claude mcp add dw-quality -- "$(pwd)/start-agent.sh" dw-quality && \ claude mcp add dw-governance -- "$(pwd)/start-agent.sh" dw-governance && \ claude mcp add dw-usage -- "$(pwd)/start-agent.sh" dw-usage-intelligence && \ claude mcp add dw-observability -- "$(pwd)/start-agent.sh" dw-observability && \ claude mcp add dw-connectors -- "$(pwd)/start-agent.sh" dw-connectors && \ claude mcp add dw-ml -- "$(pwd)/start-agent.sh" dw-ml ``` Start Claude Code and ask: - *"Search the catalog for customer-related tables"* - *"Show me the full lineage for the orders table"* - *"Why did the orders table row count drop 40% yesterday?"* - *"Scan the customer schema for PII and suggest masking policies"* - *"Compare the last two ML experiments and explain the accuracy difference"* Everything works instantly with in-memory seed data — no infrastructure required. ### Client configuration Each agent can be started via the `start-agent.sh` script, which handles working directory and dependency resolution. Replace `/path/to/dataworkers-claw-community` with your clone location. **Claude Code** (`.mcp.json` in your project root): ```json { "mcpServers": { "dw-pipelines": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-pipelines"] }, "dw-catalog": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-context-catalog"] }, "dw-quality": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-quality"] } } } ``` **Cursor** (`.cursor/mcp.json`) — same format: ```json { "mcpServers": { "dw-pipelines": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-pipelines"] }, "dw-incidents": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-incidents"] } } } ``` **OpenCode** (`opencode.json` in your project root): ```json { "mcp": { "dw-pipelines": { "type": "local", "command": ["/path/to/dataworkers-claw-community/start-agent.sh", "dw-pipelines"], "enabled": true }, "dw-catalog": { "type": "local", "command": ["/path/to/dataworkers-claw-community/start-agent.sh", "dw-context-catalog"], "enabled": true } } } ``` --- ## Agents | Agent | Package | Description | Tools | |-------|---------|-------------|:-----:| | **Pipelines** | `dw-pipelines` | NL-to-pipeline generation, template engine, Iceberg MERGE INTO, Kafka events, Airflow deployment. Write tools (`generate_pipeline`, `deploy_pipeline`) require Pro. | 4 | | **Incidents** | `dw-incidents` | Statistical anomaly detection, graph-based root cause analysis, playbook execution | 5 | | **Catalog** | `dw-context-catalog` | Hybrid search (vector + BM25 + graph), lineage traversal, Iceberg crawler | 35 | | **Schema** | `dw-schema` | INFORMATION_SCHEMA diffs, rename detection, Iceberg snapshot evolution | 9 | | **Quality** | `dw-quality` | Weighted 5-dimension scoring, z-score anomaly detection, 14-day baselines | 6 | | **Governance** | `dw-governance` | Priority-based policy engine, 3-pass PII scanner (regex + values + LLM) | 6 | | **Usage Intelligence** | `dw-usage-intelligence` | Practitioner analytics, workflow patterns, adoption dashboards, heatmaps (zero LLM) | 26 | | **Observability** | `dw-observability` | SHA-256 audit trail, drift detection, agent metrics (p50/p95/p99), health monitoring | 6 | | **Connectors** | `dw-connectors` | Unified MCP gateway to 15 catalog connectors | 56 | | **Orchestration** | `dw-orchestration` | Priority scheduler, heartbeat monitor, agent registry, event choreography | internal (not MCP) | | **MLOps & Models** | `dw-ml` | Experiment tracking, model registry, feature pipelines, SHAP explainability, drift detection, A/B testing. Write tools (`train_model`, `deploy_model`, `create_experiment`, `log_metrics`, `register_model`, `create_feature_pipeline`, `ab_test_models`) require Pro. | 16 | --- ## Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ MCP Clients │ │ Claude Code · OpenCode · Cursor · VS Code · Any MCP Client │ └────────────────────────────┬────────────────────────────────────┘ │ MCP Protocol (JSON-RPC 2.0 / stdio) │ ┌────────────────────────────▼────────────────────────────────────┐ │ 11 AI Agents (160+ tools) │ │ │ │ pipelines · incidents · catalog · schema · quality · governance│ │ usage-intelligence · observability · connectors · orchestration│ │ ml │ └────────────────────────────┬────────────────────────────────────┘ │ Factory-injected dependencies │ ┌────────────────────────────▼────────────────────────────────────┐ │ Core Platform (9 packages) │ │ MCP Framework · Context Layer · Agent Lifecycle · Validation │ │ Conflict Resolution · Enterprise · Orchestrator · Platform │ │ Medallion (Bronze → Silver → Gold lakehouse management) │ └────────────────────────────┬────────────────────────────────────┘ │ ┌────────────────────────────▼────────────────────────────────────┐ │ Infrastructure Adapters (auto-detect) │ │ Redis · Kafka · PostgreSQL · Neo4j · pgvector · PG FTS │ │ LLM Bridge · Warehouse Bridge · Airflow │ │ (falls back to InMemory stubs when services unavailable) │ └────────────────────────────┬────────────────────────────────────┘ │ ┌────────────────────────────▼────────────────────────────────────┐ │ 15 Catalog Connectors │ │ Snowflake · BigQuery · Databricks · dbt · Iceberg · Glue │ │ Hive · DataHub · OpenMetadata · Purview · Dataplex · Nessie │ │ Polaris · OpenLineage · Lake Formation │ └─────────────────────────────────────────────────────────────────┘ ``` --- ## Connectors Data Workers includes 15 catalog connectors out of the box. Additional enterprise connectors are available in Pro/Enterprise editions.

Catalog Connectors (15)

| Connector | Description | |-----------|-------------| | Snowflake | Databases, tables, DDL, usage stats | | BigQuery | Datasets, tables, schema, cost estimation | | Databricks | Unity Catalog, tables, query history | | AWS Glue | Databases, tables, partitions | | Lake Formation | Permissions, grants, resource listing | | Hive Metastore | Thrift-based database/table/partition access | | dbt | Models, lineage, test results, run history | | DataHub | Entity search, metadata, lineage, usage stats | | OpenMetadata | Entity search, lineage, tags, glossary | | Purview | Catalog search, entity metadata, classifications | | Dataplex | Lakes, zones, assets, data quality, discovery | | Nessie | Git-like branching, commits, merges, content versioning | | Apache Iceberg | REST Catalog, time travel, schema evolution, statistics | | Apache Polaris | Multi-catalog federation, OAuth2, permission policies | | OpenLineage | Lineage graphs, job runs, column lineage, event emission |

Enterprise Connectors (35) -- available in Pro/Enterprise editions

| Category | Connectors | |----------|------------| | **Orchestration** (11) | Airflow, Dagster, Prefect, AWS Step Functions, Azure Data Factory, dbt Cloud, Cloud Composer, Temporal, Mage, Kestra, Argo | | **Alerting** (5) | PagerDuty, Slack, Microsoft Teams, OpsGenie, New Relic | | **Quality** (6) | Great Expectations, Soda, Monte Carlo, Anomalo, Bigeye, Elementary | | **BI** (5) | Looker, Tableau, Metabase, Sigma, Superset | | **Observability** (2) | OpenTelemetry, Datadog | | **Identity** (2) | Okta, Azure AD | | **ITSM** (2) | ServiceNow, Jira Service Management | | **Cost** (1) | AWS Cost Explorer | | **Streaming** (1) | Kafka Schema Registry | Community Edition includes up to 3 enterprise connectors. See [pricing](https://dataworkers.io/pricing) for details.

--- ## Project Structure ``` dataworkers-claw-community/ ├── agents/ # 11 agent MCP servers │ ├── dw-pipelines/ # Write tools (generate, deploy) require Pro │ ├── dw-incidents/ │ ├── dw-context-catalog/ │ ├── dw-schema/ │ ├── dw-quality/ │ ├── dw-governance/ │ ├── dw-usage-intelligence/ │ ├── dw-observability/ │ ├── dw-connectors/ │ ├── dw-orchestration/ │ └── dw-ml/ # Write tools require Pro ├── core/ # 9 shared platform packages │ ├── mcp-framework/ # Base MCP server class │ ├── infrastructure-stubs/ # 9 interfaces + InMemory stubs + real adapters │ ├── llm-provider/ # Multi-provider LLM abstraction │ ├── medallion/ # Bronze/Silver/Gold lakehouse management │ ├── enterprise/ # Enterprise middleware shim (no-op in Community Edition) │ ├── orchestrator/ # Multi-agent coordination │ ├── context-layer/ # Shared context for cross-agent communication │ └── ... ├── connectors/ # 15 catalog connectors ├── packages/ # CLI (dw-claw) and VS Code extension ├── tests/ # Contract, integration, e2e, and eval tests ├── docker/ # Dockerfiles and compose └── docs/ # Architecture specs and guides ``` --- ## Development ```bash npm test # Run all tests (2,900+, no external services required) npm run build # Build all packages npm run lint # Lint npm run typecheck # Type-check cd agents/dw-pipelines && npm run dev # Run a single agent in dev mode ``` --- ## Troubleshooting **Agent fails to start:** Ensure you're using `start-agent.sh` (not `node` directly). The script sets the working directory correctly for tsx module resolution. See [docs/MCP-STARTUP-BUG-REPORT.md](docs/MCP-STARTUP-BUG-REPORT.md) for details. **Module not found errors:** Run `npm install` from the repo root. The monorepo uses npm workspaces — all dependencies are hoisted. **Tests fail on fresh clone:** Make sure Node.js >= 20 is installed. Run `npm install` before `npm test`. --- ## Known Limitations - **Some advanced features require the cloned repo.** The `npx dw-claw` one-liner works for most workflows. For development or contributing, use the clone-based setup. - **dw-orchestration is an internal service**, not an MCP agent. It provides task scheduling and agent coordination APIs used by other agents. - **Write operations require Pro.** Tools like `generate_pipeline`, `deploy_model`, and `train_model` return upgrade prompts in the Community Edition. --- ## Contributing We welcome contributions. See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on reporting bugs, setting up your dev environment, submitting PRs, and code style. Join the [Data Workers Community on Discord](https://discord.com/invite/b8DR5J53) to ask questions and connect with other contributors. --- ## Further Reading | Topic | Link | |-------|------| | Infrastructure details | [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) | | Configuration (env vars) | [.env.example](.env.example) | | Tiers & Pricing | [dataworkers.io/pricing](https://dataworkers.io/pricing) | | Security | [SECURITY.md](SECURITY.md) | | License | [LICENSE](LICENSE) (Apache 2.0) | | LLM Data Disclosure | [docs/LLM-DATA-DISCLOSURE.md](docs/LLM-DATA-DISCLOSURE.md) | | API Reference | [docs/API.md](docs/API.md) | ---

Built by Data Workers · Discord · Twitter · Website

Source: https://github.com/DataWorkersProject/dataworkers-claw-community

@dataworkersproj

Data Workers