airbytehq/airbyte

Open-source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications. Both self-hosted and Cloud.

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Summary Information

Updated 36 minutes ago
Added to GitGenius on January 5th, 2025
Created on July 27th, 2020
Open Issues & Pull Requests: 2,370 (+0)
Number of forks: 5,322
Total Stargazers: 21,945 (+1)
Total Subscribers: 182 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 8.0 days
Mean response time: 197.4 days
90th percentile: 670.3 days
Tracked items: 3,547

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 98% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "team/use" is answered fastest, typically in about 7 days, while "team/connectors-python" waits about 4 months. 27% of tracked open issues have had no activity in three months. Only 2% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 662
New in 7 days: 6
Closed in 7 days: 8
Avg open age: 479 days
Stale 30+ days: 397
Stale 90+ days: 359

Recent activity

Opened in 7 days: 3
Closed in 7 days: 6
Comments in 7 days: 8
Events in 7 days: 31

Top labels

  • type/bug (5,133)
  • community (4,923)
  • area/connectors (4,718)
  • type/enhancement (4,082)
  • autoteam (4,003)
  • team/connectors-python (2,344)
  • team/use (1,678)
  • team/extensibility (1,640)

Detailed Description

Airbyte is an open-source platform for data movement designed to support ELT pipelines and AI agents, enabling users to move data from APIs, databases, and files to warehouses, lakes, and AI applications. The project is available both as a self-hosted solution and through Airbyte Cloud. Written primarily in Python, the repository provides a comprehensive catalog of over 600 connectors that cover a wide range of data sources and destinations including popular databases like PostgreSQL, MySQL, and MSSQL, as well as cloud platforms such as Snowflake, BigQuery, and Redshift.

The platform addresses two distinct use cases. For traditional data integration, Airbyte enables users to deploy either the open-source version or Airbyte Cloud to centralize data movement into warehouses, lakes, and databases. For AI applications, Airbyte provides both a managed Airbyte Agents product and an open-source Agent SDK that gives AI agents, LLMs, and MCP clients real-time access to business data from CRMs, support tools, SaaS APIs, and databases. The Agent SDK integrates with frameworks like pydantic-ai, LangChain, OpenAI Agents, and FastMCP, offering built-in retry logic, exception translation, and output-size guardrails.

Connector development is streamlined through multiple approaches. Users can create connectors in minutes using the no-code Connector Builder or leverage the low-code Connector Development Kit. The platform supports incremental sync and change-data-capture capabilities, enabling efficient data replication. Airbyte syncs can be orchestrated with popular tools including Airflow, Dagster, and Kestra, or directly through the Airbyte API.

The repository demonstrates substantial community engagement and active maintenance.

The project is licensed under both MIT and ELv2 licenses, with Airbyte Enterprise offering additional security features beyond the open-source version. The platform maintains a publicly viewable roadmap on GitHub and fosters community participation through the Airbyte Community Slack, GitHub Discussions forum, and a contributor program with designated good first issues for newcomers. Contributors are encouraged to enable maintainer write access to their branches to expedite the review process, and security vulnerabilities should be reported directly to [email protected] rather than through public channels.