FIND-20260325-017 — Airweave — Open-Source Unified RAG Retrieval Layer for AI Agents

Airweave is an open-source context retrieval middleware (6,110 stars, MIT license) that unifies 50+ data sources (GitHub, Linear, Slack, Notion, Google Workspace, etc.) into a single search/RAG layer for AI agents. It provides REST API, Python and TypeScript SDKs, MCP integration, and runs fully self-hosted via Docker Compose. Tech stack: FastAPI (Python), PostgreSQL (metadata), Vespa (vector store), Temporal (orchestration), Redis (pub/sub), React/TypeScript UI. Active development with commits as recent as 2026-03-25. Shared by @James via Slack #Innovation. The tool itself is not written in Rust or Go and does not integrate with Redpanda/Kafka, but it is directly relevant to ODS as a pattern and potential internal-tooling component for cross-service knowledge retrieval — particularly as the platform grows toward 10+ microservices with disparate data in GitHub, Linear, Slack, Confluence, and PostgreSQL schemas.

MEDIUM relevance for ODS platform. Potential use cases: (1) Internal developer knowledge assistant — index ODS specs, ADRs, GitHub issues, Slack threads, Linear tickets into a searchable corpus; the Slack Knowledge Assistant template repo (airweave-ai/slack-knowledge-assistant) is a direct drop-in. (2) Pattern reference — Airweave's connector architecture (OAuth/API key per source, incremental delta sync, vector indexing) mirrors exactly what an ODS cross-service search feature would need. (3) Integration bridge — if ODS agents need to query external SaaS (Linear tasks, GitHub PRs) as context, Airweave can provide that without custom connectors. NOT a direct dependency for P0-P1 services; more relevant at P3+ when platform complexity increases. No Redpanda/Kafka integration exists today, so streaming CDC events from ODS would require a custom connector or Kafka source plugin.

Airweave — Open-Source Unified RAG Retrieval Layer for AI Agents

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