Airweave: Universal Search for AI Agents

airweave-ai/airweave

Airweave is essentially a platform or tool that allows AI agents to search any application. Think of it as a universal connector that turns the data within your various apps (productivity tools, databases, documents, etc.) into a semantically searchable knowledge base.

From a software engineering perspective, Airweave is incredibly useful for several reasons

Since Airweave is an open-source tool with a focus on agents and search, the typical integration process would involve installation, configuration (connecting data sources), and then using the Airweave API/SDK within your agent's codebase.

As a modern tool, you'll likely install it using a package manager like pip for Python or npm/yarn for JavaScript, or by cloning the repository and running a setup command.

Example (Conceptual Python Installation)

# Assuming an SDK is available via pip
pip install airweave-sdk

This is the crucial step. You need to tell Airweave which apps to connect to and how to authenticate.

Platform Setup
You would typically run a command or interact with a UI to set up the Airweave platform itself (potentially involving Docker or a dedicated server).

Connecting Apps
You'd provide API keys or OAuth credentials for the apps you want to search (e.g., Google Drive, GitHub, an internal PostgreSQL database). Airweave then starts the process of indexing and embedding the data.

Once Airweave is indexing your data, your AI agent can use the Airweave client to perform a search query. This is often done as a Tool Call within a larger Large Language Model (LLM) application.

This example shows how an agent, powered by an LLM, could use Airweave as a tool to answer a user's question by searching across all connected apps.

import airweave_sdk as airweave

# 1. Initialize the Airweave Client
# Replace with your actual Airweave server URL and API key
AIRWEAVE_URL = "http://localhost:8080"
AIRWEAVE_API_KEY = "your-secure-key"

client = airweave.Client(url=AIRWEAVE_URL, api_key=AIRWEAVE_API_KEY)

def search_knowledge_base(query: str) -> str:
    """
    Uses Airweave to perform a semantic search across all connected apps 
    (e.g., GitHub, Slack, Notion).
    """
    print(f"Agent is searching Airweave for: '{query}'...")
    
    try:
        # 2. Execute the semantic search
        # We limit to the top 5 most relevant results (sources)
        results = client.search(
            query=query, 
            limit=5, 
            sources=["github", "notion"] # Optionally restrict search
        )
        
        # 3. Format the results for the Agent/LLM
        # The LLM will use this context to formulate a final answer.
        formatted_context = []
        for result in results:
            formatted_context.append(
                f"SOURCE: {result.source_app} - URL: {result.url}\n"
                f"CONTENT: {result.snippet}\n"
                f"---"
            )
            
        return "\n".join(formatted_context)

    except airweave.AirweaveError as e:
        return f"ERROR: Could not complete search. {e}"

# --- Agent Simulation ---

user_query = "What was the final decision on the microservice migration plan?"

# The Agent calls Airweave as a tool
airweave_context = search_knowledge_base(user_query)

print("\n--- Airweave Search Results (Context for the Agent) ---")
print(airweave_context)

# 4. Agent then feeds this context to the LLM to generate the final answer:
# final_answer = llm.generate(prompt=f"Context: {airweave_context}\nUser Query: {user_query}\nAnswer:")