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Airweave Search

Searches Airweave collections with natural-language queries through MCP for unified agent context retrieval.

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Install Airweave Search

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "airweave-search": {
      "command": "uvx",
      "args": [
        "airweave-sdk"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx airweave-sdk

Process exited before answering initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 18d ago.

Overview

The Airweave Search MCP server lets AI agents search Airweave collections using natural-language queries. Airweave connects to apps, databases, and documents, then syncs and indexes their data behind a unified retrieval interface that agents can access through MCP. Use it when an agent needs context from multiple connected sources without implementing separate retrieval pipelines. It can run through Airweave Cloud or a self-hosted Docker deployment.

Use cases

  • Search synchronized company knowledge across multiple applications
  • Retrieve relevant development context from connected repositories and project tools
  • Ask natural-language questions over Airweave collections
  • Centralize agent retrieval instead of integrating each source separately

Key features

  • Natural-language search over Airweave collections
  • MCP access for AI agents
  • Unified retrieval across connected apps, databases, and documents
  • Airweave Cloud or self-hosted deployment
  • Support for more than 50 source integrations

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

Not checked yetLast checked Today

We have not completed a health check for this listing yet.

5/52 checks healthy over the last 90d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 7d ago via OSV.dev · airweave-sdk (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars6.5kStargazers on the source repository.
Last commit5mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Airweave Search works

What Airweave Search MCP server does

The Airweave Search MCP server exposes Airweave’s unified retrieval layer to MCP-compatible AI agents. It is designed for searching collections that contain data synchronized from connected applications, databases, and documents. Instead of requiring an agent to query each source independently, Airweave provides one collection-oriented search surface.

Airweave supports more than 50 integrations, including services such as GitHub, GitLab, Gmail, Google Drive, Jira, Linear, Notion, Slack, Salesforce, SharePoint, and Stripe. The available sources depend on the integrations configured in Airweave. The MCP server searches the resulting Airweave collections; it is not described as a direct API client for each individual source.

How it works

Airweave sits between source systems and the agent. First, applications, databases, or documents are connected to Airweave. Airweave then handles synchronization and indexing, making the collected data available through its retrieval layer. An agent sends a natural-language request through MCP, and the Airweave Search MCP server searches the relevant collection for matching context.

This arrangement is useful when several systems contribute to the information an agent needs. It also centralizes retrieval rather than requiring a separate search implementation for every connected application. The repository describes access through MCP alongside SDKs, a REST API, and native integrations with agent frameworks.

Setup and configuration

Airweave can be used as a hosted service at app.airweave.ai or run locally from the repository. The documented self-hosted path requires Docker and Docker Compose:

bash
git clone https://github.com/airweave-ai/airweave.git
cd airweave
./start.sh

The local application is available at http://localhost:8080 after startup. The startup process creates an environment file from the example configuration, generates required secrets, starts the services with health checks, and can prompt for OpenAI or Mistral API keys. Initial startup may take several minutes while services become healthy.

The repository also provides startup options to restart services, skip the frontend for a backend-only deployment, or destroy the local setup. Docker must be running before these commands are used.

Tools and capabilities

The documented capability is natural-language search over Airweave collections through MCP. This makes the Airweave Search MCP server suitable for agents that need relevant context from synchronized business and development data.

Limitations and notes

Search quality and available results depend on which sources have been connected, synchronized, and indexed in Airweave. The MCP server does not eliminate the need to configure those sources.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Airweave Search

Airweave Search is an MCP server that connects AI agents to Airweave collections built from synced apps, databases, and documents. Its main tool is natural-language search across those collections, returning relevant context through MCP. Use it when an agent needs unified retrieval instead of querying each connected source separately.

Install it by cloning the Airweave repository and running `./start.sh` from the project directory. The self-hosted setup requires Docker and Docker Compose, and the local application is then available at `http://localhost:8080`.

Yes, Cursor is named in the repository’s setup material for Airweave. The provided instructions link to a Cursor setup prompt covering Docker, repository startup, health checks, and common local deployment issues.

Yes, it can search Airweave collections populated from multiple connected sources. Airweave synchronizes and indexes data from apps, databases, and documents, then exposes it through a unified retrieval interface for natural-language agent queries.

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