The Cogwit SDK is the recommended way to interact with Cogwit programmatically. It is open source and provides a clean Python interface for working with the Cogwit platform. In particular, it makes it easy to perform core Cognee operations—add, cognify, memify, and search—that power Cogwit. You’ll need an API key to get started.
While you can call Cogwit API endpoints directly, the SDK is recommended for its error handling, type safety, and overall developer experience.

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Install and configure

# 1. Create a virtual environment (optional)
uv venv && source .venv/bin/activate

# 2. Install the SDK
uv pip install cogwit-sdk

# 3. Store your key and base URL
export COGWIT_API_KEY="<your key>"

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Complete example

Let’s jump straight in with a full code example of Cogwit in action. We’ll then go over the code piece by piece and explain all the relevant parts in detail. 
import asyncio
import os
from cogwit_sdk import cogwit, CogwitConfig
from cogwit_sdk.responses import (
    AddResponse,
    CognifyResponse,
    CombinedSearchResult,
    SearchResult,
)

# Create configuration with your API key
cogwit_config = CogwitConfig(
    api_key=os.getenv("COGWIT_API_KEY", ""),
)

# Create the client instance
cogwit_instance = cogwit(cogwit_config)

async def main():
    # Add data to your dataset
    result = await cogwit_instance.add(
        data="Cogwit automates knowledge graph creation in the cloud.",
        dataset_name="demo_dataset",
    )
    print(f"Added data: {result.status}")
    dataset_id = result.dataset_id

    # Transform data into knowledge graph
    cognify_result = await cogwit_instance.cognify(
        dataset_ids=[dataset_id],
    )
    print(f"Cognify status: {cognify_result[str(dataset_id)].status}")

    # Search with graph completion
    search_results = await cogwit_instance.search(
        query_text="What does Cogwit automate?",
        query_type=cogwit_instance.SearchType.GRAPH_COMPLETION,
    )
    for result in search_results:
        print(f"  {result.search_result}")

    # Search for raw chunks
    chunk_results = await cogwit_instance.search(
        query_text="What does Cogwit automate?",
        query_type=cogwit_instance.SearchType.CHUNKS,
    )
    for result in chunk_results:
        print(f"  {result.search_result[0]['text']}")

asyncio.run(main())

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What just happened

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Client setup

cogwit_config = CogwitConfig(
    api_key=os.getenv("COGWIT_API_KEY", ""),
)
cogwit_instance = cogwit(cogwit_config)
The SDK uses a client pattern to manage your connection to Cogwit. The client handles authentication, request formatting, and response parsing for you. All SDK operations are async, so we wrap everything in async def main() and run it with asyncio.run(main()).

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Adding data

result = await cogwit_instance.add(
    data="Cogwit automates knowledge graph creation in the cloud.",
    dataset_name="demo_dataset",
)
The add operation uploads text to Cogwit and schedules preprocessing. All data is organized by dataset for proper isolation. It returns an AddResponse with:
  • status - Whether the operation completed successfully
  • dataset_id - Unique identifier for your dataset (save this!)
  • dataset_name - The name you provided

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Cognifying data

cognify_result = await cogwit_instance.cognify(
    dataset_ids=[dataset_id],
)
The cognify operation transforms your data into a knowledge graph. It returns a CognifyResponse that maps dataset IDs to their processing status. Wait for PipelineRunCompleted before searching.

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Searching your data

# Graph completion search
search_results = await cogwit_instance.search(
    query_text="What does Cogwit automate?",
    query_type=cogwit_instance.SearchType.GRAPH_COMPLETION,
)
The search operation queries your knowledge graph. By default it uses GRAPH_COMPLETION, but you can use any of the Cognee search types. See also Search Basics for detailed parameter explanations.

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Next steps

Cogwit architecture

Design end-to-end add → cognify → memify → search automations.

Cogwit UI

Learn to manage datasets and upload files through the UI.