AI Frameworks · airweave-ai

airweave

Airweave is an open-source retrieval layer that connects AI agents to enterprise data sources via 50+ integrations. It handles data synchronization, indexing, and unified search, allowing agents to retrieve relevant context from multiple systems in a single query.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/airweave-ai/airweave

6.5k

GitHub stars

813

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	airweave-ai/airweave
Owner	airweave-ai
Primary language	Python
License	MIT — OSI-approved
Stars	6.5k
Forks	813
Open issues	132
Latest release	v0.9.73 (2026-06-05)
Last updated	2026-06-05
Source	https://github.com/airweave-ai/airweave

What airweave is

Python-based context retrieval infrastructure using Docker/docker-compose deployment. Exposes data through REST API, SDKs, MCP, and agent framework integrations with semantic search and RAG capabilities across connected applications and databases.

Quickstart

Get the airweave source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/airweave-ai/airweave.git2cd airweave3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-source AI agent context retrieval

Agents querying across dispersed enterprise systems (CRM, documentation, knowledge bases, project management) to ground responses with current, authoritative data without rebuilding connectors per agent.

Centralized RAG infrastructure

Teams building multiple RAG pipelines can delegate ingestion, indexing, and sync orchestration to Airweave, reducing duplicate connectors and maintenance burden across projects.

Automated data synchronization from SaaS applications

Organizations needing real-time or scheduled syncing of data from Salesforce, HubSpot, Slack, Jira, and other platforms without custom ETL logic or polling infrastructure.

Implementation considerations

Docker and docker-compose are required; ensure infrastructure supports containerized deployment with sufficient port availability (8080, 8001, 5432, 6333, 6379, 7233, 8081, 8088).
Setup generates encryption keys and state secrets on first run; establish secure backup and rotation practices for production environments.
Connector authentication requires API keys or OAuth tokens for each integrated system; implement credential management and vetting in accordance with your security policies.
Syncing frequency and volume affect database and vector store load; configure batch schedules and retention policies to match your data change velocity and cost constraints.
Initial sync and indexing can take minutes to hours depending on data volume; plan deployment windows and communicate expected delays to end users.

When to avoid it — and what to weigh

Single local data source — If your data is primarily in one system or local files, simpler single-connector solutions or direct embedding pipelines may be more efficient than introducing a retrieval layer.
Low-latency real-time requirements — Airweave's architecture involves ingestion and indexing cycles; extreme real-time sync (sub-second) across many sources is not the primary design goal.
Highly regulated environments without self-hosted clarity — If data residency, audit trails, and compliance governance are strict, ensure self-hosted deployment, network isolation, and encryption practices are documented and meet your compliance regime before deployment.
Pre-built, vendor-locked agentic platforms — If you are committed to a specific AI platform with proprietary retrieval, adding Airweave may introduce architectural complexity or duplication.

License & commercial use

MIT License: permissive OSI-approved license allowing modification, distribution, and commercial use with attribution and no liability or warranty.

MIT permits commercial use of the code. However, verify that your use of integrated third-party APIs (OpenAI, Stripe, Salesforce connectors, etc.) complies with their terms. Self-hosted deployments are fully under your control; cloud-hosted offering (app.airweave.ai) may have separate commercial terms—requires review.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Self-hosted deployments give full data control and network isolation. Credential storage for integrated systems requires secure vaulting (practices not detailed in provided data). Encryption keys and state secrets are auto-generated on startup—establish rotation and backup practices. Ingestion of data from external SaaS systems introduces trust boundary; validate connector permissions and data classification. No security audit or CVE history provided; assess risk per your threat model. Production deployments should include network segmentation, access controls, and audit logging.

Alternatives to consider

LangChain integrations + custom vector DB

Lower-level framework requiring manual connector wiring but avoids a separate retrieval layer and may suit smaller, simpler data flows.

Pinecone, Weaviate, Milvus (standalone vector DBs)

Purpose-built vector search without multi-source orchestration; better for teams handling ingestion and sync internally or with external ETL.

Proprietary AI platform retrieval (e.g., Azure Cognitive Search, AWS Kendra)

Vendor-integrated retrieval with tighter support and compliance alignment if already committed to that ecosystem; trade-off is reduced flexibility and portability.

Software development agency

Build on airweave with DEV.co software developers

Start with self-hosted deployment (Docker) or cloud-hosted at app.airweave.ai. Connect your enterprise data sources and enable agents to retrieve grounded, current context on demand.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

AutoRAG Airtest UltraRAG

Related on DEV.co

Explore the category and the services that help you build with it.

AI Frameworks AI Development API Development Enterprise Software Talk to DEV.co

airweave FAQ

Can I run Airweave on-premises or in a private cloud?

Yes, self-hosted deployment via Docker/docker-compose is supported. Cloud-hosted option (app.airweave.ai) is also available; check terms for data residency requirements.

How often does Airweave sync data from connected sources?

Sync frequency is configurable per connector. Exact sync intervals and scheduling options not detailed in provided data; requires review of documentation or configuration examples.

What is the license cost for production use?

MIT license itself has no cost. Self-hosted deployment is free but requires infrastructure. Cloud platform (app.airweave.ai) pricing unknown from provided data; requires review of pricing page.

Does Airweave support real-time streaming ingestion?

Supported integrations appear to be pull-based (scheduled syncs). Streaming/push-based ingestion capability not mentioned; requires documentation review.

Software development & web development with DEV.co

From first prototype to production, DEV.co delivers software development services around tools like airweave. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

Deploy Airweave for Your AI Infrastructure

Start with self-hosted deployment (Docker) or cloud-hosted at app.airweave.ai. Connect your enterprise data sources and enable agents to retrieve grounded, current context on demand.

Talk to DEV.co Schedule a Consultation