Airweave Context Retrieval Layer
Open-source context retrieval layer for AI agents and RAG systems.
Evidence shows: security policy exists (SECURITY.md) but explicitly states no versions are currently supported for security updates; CI workflows include Trivy and Grype vulnerability scans, but only run on release tags; encryption keys (ENCRYPTION_KEY, STATE_SECRET) are generated, but no user confirmation mechanism is provided; data flow transparency is limited, README describes data sync and retrieval but does not detail data flow and permissions; sensitive data handling has key generation but lacks storage and access control details; external effects include start.sh creating .env and generating keys, but no rollback mechanism; source attribution is not clearly provided. Deductions: missing user confirmation, rollback, insufficient data flow transparency, and lack of sensitive data handling details.
Evidence shows: code quality workflows (ruff, mypy, import-linter) run in CI, indicating some consistency; dependency management uses Poetry and lock files, but no dependency availability guarantee; failure messages are covered in tests for sync failure events, but no user-facing error messages. Deductions: dependency availability not explicit, failure messages only internal, no user-visible error handling.
Evidence shows: README clearly identifies target audience (AI agents and RAG systems) and use cases; capability boundaries are described via integration list and SDK docs; trigger precision is limited, CLI and SDK provide search interfaces but no explicit trigger conditions; environment fit supports Docker Compose and Kubernetes, but detailed deployment configs are missing. Deductions: trigger precision insufficient, environment fit details limited.
Evidence shows: information architecture is clear, README provides quickstart, integration list, SDK and CLI instructions; install notes are detailed, including Docker and start.sh; naming stability is clear for SDK and CLI, but no version compatibility notes; examples and FAQ: example notebooks and docs links provided, but no FAQ; known limitations: SECURITY.md mentions security updates not supported, but no other limitations; license is MIT, clear; versioning and changelog: GitHub Actions build tags exist, but no CHANGELOG; maintenance responsibility: contributing guide exists, but maintainers not explicit. Deductions: missing FAQ, CHANGELOG, incomplete known limitations, unclear maintenance responsibility.
Evidence shows: output usability is good, SDK and CLI provide search interfaces with JSON and interactive output; marginal value is high, providing unified retrieval layer reducing rebuild effort; cost-benefit: self-hosting requires Docker and multiple services, but no cost analysis. Deductions: cost-benefit analysis missing.
Evidence shows: claims in README (e.g., 50+ integrations) lack specific sources; cross-source corroboration: CI tests and docs exist, but no independent verification; fact-inference separation: README distinguishes features and examples, but inferences not clearly labeled. Deductions: claims lack traceability, cross-source verification insufficient, fact-inference separation unclear.
- Security policy explicitly states no versions are currently supported for security updates, posing security risks.
- CI vulnerability scans only run on release tags, potentially missing daily development coverage.
- Lack of user confirmation and rollback mechanisms may affect data security.
- Data flow transparency is insufficient; data flow and permissions are not detailed.
- No CHANGELOG or FAQ provided; known limitations are incomplete.
What does this agent do, and when should you use it?
Airweave is an open-source context retrieval layer that connects your apps, tools, and databases, continuously syncs their data, and exposes it through a unified, LLM-friendly search interface. It handles authentication, ingestion, syncing, indexing, and retrieval, so agents don't need to rebuild fragile pipelines for every integration. Provides Python and TypeScript SDKs, REST API, CLI, and MCP support, with 50+ integrations. Tech stack includes React/TypeScript frontend, FastAPI backend, PostgreSQL for metadata, Vespa for vectors, Temporal for orchestration, and Redis for pub/sub. Can be self-hosted via Docker Compose or used as a cloud-hosted service.
Airweave performs the following: 1) Connects apps (like Slack, Notion, Google Drive, etc., with 50+ integrations) for data syncing; 2) Handles authentication and incremental sync, ingesting data into the system; 3) Stores vectors in Vespa and metadata in PostgreSQL; 4) Exposes a unified search interface via Python/TypeScript SDKs (e.g., AirweaveSDK's client.collections.search.instant method), REST API, CLI (e.g., airweave search), or MCP; 5) Responds to queries from AI agents, returning relevant context. It also provides a CLI for managing sources and triggering syncs.
- Developers building RAG applications who want to retrieve context from multiple SaaS tools (e.g., Notion, Slack, Google Drive) without writing custom connectors for each source.
- Data teams needing AI agents to query internal databases like Salesforce or HubSpot for customer information, using Airweave's search API for real-time retrieval.
- Platform engineers who want to self-host retrieval infrastructure to keep data within their own infrastructure, deploying via Docker Compose.
- AI agent developers looking to integrate Airweave with popular agent frameworks, querying context via SDKs or REST API.
- Enterprises that need semantic search over internal docs (Confluence, SharePoint) using Airweave's indexing and retrieval capabilities.
- Data analysts who want to trigger syncs and query multiple sources with a unified search interface via CLI or SDK.
What are this agent's strengths and limitations?
- Supports 50+ integrations, including popular SaaS tools, reducing integration work.
- Provides a unified retrieval layer with multiple interfaces (SDK, REST, CLI, MCP) for easy integration into different agent systems.
- Open-source (MIT license) with self-hosting option, giving you control over your data.
- Uses mature technologies like Vespa and Temporal for performance and scalability.
- Self-hosting requires Docker and docker-compose, and managing components like PostgreSQL, Vespa, Redis, and Temporal increases operational complexity.
- Initial setup may require configuring multiple environment variables and secrets; first run can be time-consuming.
- Relies on external API keys (e.g., OpenAI/Mistral) for some features, incurring potential costs.
- Documentation and community support may be less mature than commercial alternatives.
How do you install or deploy this agent?
Self-hosted: Requires Docker and docker-compose.
git clone https://github.com/airweave-ai/airweave.git
cd airweave
./start.shThe script creates .env, generates secrets, and starts all services. First run may take 2-3 minutes. Cloud-hosted: go to [app.airweave.ai](https://app.airweave.ai).
How do you use this agent?
Install SDK: pip install airweave-sdk or npm install @airweave/sdk.
Using Python SDK:
from airweave import AirweaveSDK
client = AirweaveSDK(api_key="YOUR_API_KEY")
results = client.collections.search.instant(readable_id="my-collection", query="Find recent failed payments")Using CLI: pip install airweave-cli, then airweave auth login, airweave search "quarterly revenue figures" --collection finance-data.