Airbyte
Move data between sources, warehouses, lakes, and AI applications through a catalog of 600+ connectors.
- Source repo
- airbytehq/airbyte
- Stars
- ★ 22k
- Last updated
- 2d ago
- License
- NOASSERTION
- Primary language
- Python
- FA score
- 52/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A data engineering team needs to consolidate records from several SaaS APIs and operational databases into BigQuery, Snowflake, or Redshift.
- Not a fit if
- Individuals wanting a one-command local run without deploying services or configuring connectors
- Teams that need fully offline operation with data never leaving their network
- Teams unwilling to pay for the managed cloud or enterprise security features
- Source review
- 52/100 · Major gaps 1 safety controls not found
What does this agent do, and when should you use it?
Airbyte is a data movement platform available as a self-hosted open-source deployment or a managed cloud service for ELT, ETL, and AI-oriented data access. This repository contains Airbyte Open Source, which synchronizes data from APIs, databases, and files into databases, warehouses, and lakes. Its catalog includes more than 600 connectors, while the no-code Connector Builder and low-code CDK cover sources that need custom integration work. Connections can be configured through the Airbyte interface and operated through the Airbyte API or orchestrated with Airflow, Dagster, and Kestra. For AI systems that need real-time business data, Airbyte separately offers the managed Airbyte Agents product and an open-source Agent SDK; neither should be confused with the open-source data movement platform in this repository.
After a user selects a source and destination, Airbyte connects to an API, database, or file source, reads its data, and synchronizes records into a database, data warehouse, data lake, or AI application. The repository topics explicitly cover PostgreSQL, MySQL, MSSQL, BigQuery, Redshift, Snowflake, S3, and change data capture. Teams can select from the catalog of 600+ connectors or build another connector with Connector Builder or the low-code CDK. Connections and syncs can be managed in the Airbyte UI, called through the Airbyte API, and scheduled within Airflow, Dagster, or Kestra workflows. Turning connector calls directly into LLM tools requires the separate airbyte-agent-sdk repository rather than this repository alone.
- A data engineering team needs to consolidate records from several SaaS APIs and operational databases into BigQuery, Snowflake, or Redshift.
- An organization that must control its own infrastructure wants to deploy Airbyte Open Source instead of using a managed data integration service.
- A data platform team needs change data capture from PostgreSQL, MySQL, or MSSQL as part of a continuously updated warehouse pipeline.
- A connector developer cannot find a required source in the catalog and needs to build one with Connector Builder or the low-code CDK.
- A team already using Airflow, Dagster, or Kestra wants Airbyte synchronization jobs to participate in its existing orchestration workflows.
- An AI application team evaluating access to business systems can compare the separate Airbyte Agents and Agent SDK offerings with this repository's pipeline-focused deployment.
How do you install or deploy this agent?
The supplied material directs users to https://docs.airbyte.com/quickstart/deploy-airbyte for deploying Airbyte Open Source, but it does not include a copyable deployment command, supported runtime versions, infrastructure requirements, or a first-start command. A complete repository installation procedure therefore cannot be reproduced from the supplied evidence alone. Airbyte Cloud is available through https://cloud.airbyte.com/signup without a self-hosted installation. For the separate open-source Agent SDK, the only documented installation command in the material is uv pip install airbyte-agent-sdk; that command does not install this repository. Source and destination credentials are connector-specific and are not enumerated in the supplied material.
How do you use this agent?
Deploy Airbyte Open Source or create an Airbyte Cloud account, then use the connections interface to select a source connector and a destination connector, provide access configuration for both systems, and create a sync. Check https://docs.airbyte.com/integrations/ or the connector registry to determine whether the required endpoints are covered; use Connector Builder or the low-code CDK when they are not. For automation, operate syncs through the Airbyte API or use the documented Airflow operator, Dagster integration, or Kestra plugin. The supplied material does not include an example API request, connector credential schema, or copyable first-sync command. To expose connector calls as LLM tools, use the separate Agent SDK, which is described as working with pydantic-ai, LangChain, OpenAI Agents, and FastMCP.
What are this agent's strengths and limitations?
- The catalog contains more than 600 connectors spanning APIs, databases, warehouses, lakes, and AI applications, reducing the number of integrations a data team must build itself.
- Teams can choose between self-hosted Airbyte Open Source and Airbyte Cloud according to their infrastructure and operational preferences.
- The no-code Connector Builder and low-code CDK provide documented paths for extending coverage beyond the existing connector catalog.
- Airbyte explicitly supports API-driven operation and orchestration through Airflow, Dagster, and Kestra.
- The supplied material does not provide this repository's copyable deployment command, minimum runtime versions, or resource requirements, so adopters must consult the deployment documentation.
- The managed AI context layer and open-source Agent SDK are separate offerings; installing this repository does not by itself provide an embedded LLM-tool interface.
- Connectors depend on network access and credentials for external source and destination systems, adding connectivity and secret-management work to deployment.
- Repository metadata reports NOASSERTION while the README displays both MIT and ELv2 badges and points to separate licensing documents, so adopters must verify which terms apply to the components they use.
How does this agent compare with similar options?
Airbyte Open Source is the self-hosted choice for teams operating ELT or ETL data movement themselves, while Airbyte Cloud is the managed alternative. Airbyte Agents is a managed data and context layer for AI agents, and the separate open-source Agent SDK embeds type-safe connectors as LLM tools. Those AI offerings are distinct from the deployment contained in this repository.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Airbyte This agent | 52 · Major gaps | Self-hosted serviceFreemium | ★ 22k | 2d ago | Python | — |
| OpenBB Open Data Platform | 37 · Major gaps | Library / SDKFreemium | ★ 74k | today | Python | — |
| Airweave Context Retrieval Layer | 46 · Major gaps | Self-hosted serviceFreemium | ★ 6.6k | 4mo ago | Python | — |
| ktx — Context layer for data agents | 64 · Some gaps | CLIFree + model costs | ★ 1.6k | 3mo ago | TypeScript | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
The README describes movement from APIs, databases, and files into warehouses, lakes, and AI applications, while the workflows disclose documentation generation, PR creation, comments, and canary rollout effects. Some Actions are commit-pinned and job permissions are declared. Deductions apply because the canary workflow still uses main or mutable version tags, and SDK documentation is regenerated daily from unpinned private sonar/main. The supplied files do not establish runtime least-privilege controls, per-operation user approval, credential isolation, or a sensitive-data lifecycle. A private vulnerability-reporting channel and a unit test for secret naming are useful but do not demonstrate secure storage, redaction, rotation, or access control. No explicit rollback or recovery procedure is shown for canary deployment. Generated PRs identify the source by short commit hash, although publisher identity remains unverified by the stated registry.
The README gives a generally coherent division between ELT, Cloud, Agents, and the Agent SDK, and asserts retry, exception translation, and output-size safeguards. Deductions arise because the repository is described as open source while the root license is ELv2 with a managed-service restriction, and the README simultaneously displays MIT and ELv2 without explaining file-level scope. The SDK implementation resides in another private repository, and documentation tracks its unpinned main branch, weakening availability and version consistency. A workflow posts a start status and run link, but the evidence contains no product-level failure taxonomy, actionable error messages, or recovery guidance.
Audience and scenario coverage is strong: data engineers can choose self-hosted or Cloud deployment, while AI developers can choose managed Agents or an SDK, with warehouses, lakes, databases, LLMs, MCP, and several frameworks named. Product-selection guidance and workflow triggers are reasonably explicit. Deductions reflect the absence of Agent SDK implementation evidence, detailed connector permission boundaries, and precise runtime triggering rules. Environment fit is supported mainly through compatibility claims and external documentation links rather than an in-repository configuration matrix or complete deployment constraints.
The README is organized into platform selection, onboarding, community, contribution, security, and licensing sections. It provides deployment and SDK-installation entry points, tutorials, an FAQ, and support channels. Deductions apply because the relationship among Open Source, Cloud, Agents, Agent SDK, and the private sonar source is complex, and the workflow explicitly says the SDK has no versioned release stream. Known limitations are only partially documented. The license text is complete, but the MIT/ELv2 badges and root ELv2 file do not clearly establish component-level scope. Release, announcement, and roadmap links offer update paths, yet no stable SDK changelog is evidenced. Maintainer channels and contribution procedures are clear, without relying on the unverified publisher identity for extra credit.
The material presents outputs usable by warehouses, lakes, databases, and AI tools, and claims a large connector catalog, type-safe tool exposure, retries, and output-size safeguards. Self-hosted, Cloud, and SDK options plausibly add value for different users. Deductions apply because no concrete output samples, quality measurements, latency figures, resource requirements, pricing, or operating-cost analysis are supplied. Claims such as 600+ connectors and real-time access are primarily README assertions, so cost-benefit support remains thin.
Many claims point to specific documentation, the connector registry, SDK repository, releases, or workflows, and generated documentation PRs record a source short hash. Workflow comments distinguish generation mechanics and design intent. Deductions apply because the supplied evidence omits the Agent SDK implementation and generated reference content; central capability claims are repeated mostly by sources under the same publisher rather than corroborated across implementation artifacts. Facts and aspirations are usually linguistically separated, but the meaning of open source, license scope, and real-time capability still leaves inference unresolved.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- The Agent SDK implementation is absent and comes from a private sonar repository; do not treat README claims about retries, exception translation, type safety, or output guards as verified by this static review.
- Documentation tracks unpinned sonar/main, and the canary workflow references main or mutable action tags; the same Airbyte revision may therefore produce different generated content or execute different dependency code later.
- The root license is ELv2 with a managed-service restriction, while the README displays both MIT and ELv2; confirm component-specific licensing before redistribution, hosting, or commercial use.
- The canary process rolls out to 5–10 connections and invokes an external Devin service, but the supplied files show no explicit per-run approval boundary, data-exposure specification, or automatic rollback procedure.
- A security contact and secret-naming test are not substitutes for reviewing credential storage, log redaction, rotation, tenant isolation, and connector permissions.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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v2.0.0 Airbyte 2.0
Release notes excerpt
We're back, fast, and furious. 🏎️ 🏁 Airbyte version 2.0 was released on October 14, 2025. This version marks a major achievement in Airbyte's platform architecture. We're excited to share new improvements and changes to the Airbyte platform. <!-- vale on --> Faster sync speed Faster sync speed is now generally available. When using faster sync speed, Airbyte syncs data 4-6 times faster, on average, while consuming resources more economically. [Learn more about faster sync speed >](https://docs.airbyte.com/integrations/speed-improvements) Data activation …View on GitHub ↗ -
v1.8.0 Airbyte 1.8
Release notes excerpt
Why don't Docker containers get lost in Kubernetes? Because Helm charts the right course! Airbyte 1.8 was released on August 11, 2025. We're excited to share new improvements and changes to the Airbyte platform. Platform changes These changes improve Airbyte for everyone. Pagination for better UI performance Airbyte has added pagination to many pages in the user interface. This provides a critical performance boost for workspaces with hundreds or thousands of connections. As you scroll, pages automatically add new data until you reach the end of the list. …View on GitHub ↗ -
v1.7.0 Airbyte 1.7
Release notes excerpt
People liked the last version of Airbyte so much that we decided to ship another one 🚢. Airbyte 1.7 was released on June 16, 2025. We're excited to share new improvements and changes to the Airbyte platform. Platform changes These changes improve Airbyte for everyone. Move files and records together Airbyte now supports moving files and records in the same connection. The combination of structured and unstructured data drives more robust knowledge systems that have more context, which is critical to successful AI systems. …View on GitHub ↗