> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Run your first Cognee workflow with remember and recall.
After completing the [installation steps](https://docs.cognee.ai/getting-started/installation) successfully, run your first Cognee example to see AI memory in action.
## Basic Usage
This minimal example shows how to store content and retrieve it:
```python theme={null}
import cognee
import asyncio
async def main():
# Create a clean slate for cognee -- reset data and system state
await cognee.forget(everything=True)
# Store content in memory (ingests, builds knowledge graph, enriches)
text = "Cognee turns documents into AI memory."
await cognee.remember(text)
# Retrieve from memory
results = await cognee.recall(
query_text="What does Cognee do?"
)
# Print
for result in results:
print(result.text)
if __name__ == '__main__':
asyncio.run(main())
```
```text theme={null}
Cognee converts (transforms) documents into AI memory — a structured, queryable representation of document content for AI systems.
```
Output wording may vary by provider and model, but it should answer the question using the text stored with remember.
Interactive knowledge graph visualization -- drag nodes, zoom, and hover for details. Create your own visualization with 2 additional lines of code [here](/guides/graph-visualization).
Open visualization full screen
## What just happened
The code demonstrates Cognee's two primary v1.0 operations:
* **`.remember`** — Stores data in memory. Under the hood it runs ingestion, chunking, entity extraction, graph building, and a follow-up enrichment pass. The result is a fully queryable knowledge graph.
* **`.recall`** — Retrieves from memory. It auto-routes the query to the best retrieval strategy and returns contextual results from the knowledge graph.
## About `async` / `await` in Cognee
**Cognee uses asynchronous code extensively.** That means many of its functions are defined with `async` and must be called with `await`. This lets Python handle waiting (e.g. for I/O or network calls) without blocking the rest of your program.
This example uses async / await, Python’s way of doing asynchronous programming.
Asynchronous programming is used when functions may block because they are waiting for something (for example, a reply from an API call). By writing async def, you define a function that can pause at certain points.
The await keyword marks those calls that may need to pause.
To run such functions, Python provides the asyncio library. It uses a loop, called the event loop, which executes your code in order but, whenever a function is waiting, can temporarily run another one. From inside your function, though, everything still runs top-to-bottom: each line after an await only executes once the awaited call has finished.
* A good starting point is this [guide](https://realpython.com/async-io-python/).
* Official documentation is available [here](https://docs.python.org/3/library/asyncio.html).
## Next Steps
Learn about Cognee's core concepts, architecture, building blocks, and main operations.
Enrich an existing graph and bridge session memory into permanent memory.