Remember and Recall in One Script

A minimal guide to the smallest end-to-end Cognee script. Use it as the starting point for a new project: one passage of text goes in with remember(), one question comes back answered with recall(), and nothing else is configured.

Before You Start

Complete Quickstart to understand basic operations
Ensure you have LLM Providers configured — the answer is generated by a language model
Read Remember and Recall for what the two operations do
No data is required up front — the script ingests its own passage, but it starts with cognee.forget(everything=True), which wipes all existing Cognee data; run it against a setup you can afford to reset

Code in Action

import asyncio

import cognee
from cognee import SearchType
from cognee.shared.logging_utils import ERROR, setup_logging


async def main():
    # Start clean, then remember knowledge with the v1.0 memory API.
    await cognee.forget(everything=True)
    text = """
    Natural language processing (NLP) is an interdisciplinary
    subfield of computer science and information retrieval.
    """

    await cognee.remember(text, self_improvement=False)

    query_text = "Tell me about NLP"
    print(f"Searching cognee for insights with query: '{query_text}'")
    search_results = await cognee.recall(
        query_type=SearchType.GRAPH_COMPLETION, query_text=query_text
    )

    for result_text in search_results:
        print(result_text)


if __name__ == "__main__":
    logger = setup_logging(log_level=ERROR)
    asyncio.run(main())

What Just Happened

Step 1: Start From a Clean Slate

await cognee.forget(everything=True)

forget(everything=True) deletes every dataset, graph node, embedding, and session-cache entry the current user owns, so the run starts with an empty graph and the answer can only come from the passage below. Leave this line out of your own application — you rarely want to erase everything before storing something new.

Step 2: Remember the Passage

text = """
Natural language processing (NLP) is an interdisciplinary
subfield of computer science and information retrieval.
"""

await cognee.remember(text, self_improvement=False)

remember() takes raw text and runs the full ingestion workflow on it: the passage is chunked, entities and relationships are extracted, and the result is written to the graph as memory. self_improvement=False keeps the run to plain ingestion instead of also running improve().

Step 3: Recall an Answer

query_text = "Tell me about NLP"
print(f"Searching cognee for insights with query: '{query_text}'")
search_results = await cognee.recall(
    query_type=SearchType.GRAPH_COMPLETION, query_text=query_text
)

for result_text in search_results:
    print(result_text)

SearchType.GRAPH_COMPLETION retrieves the graph triplets relevant to query_text, builds a context from them, and asks a language model to answer from that context. recall() returns a list, so the loop prints each result — with this small a graph, expect a single answer describing NLP.

Step 4: Run the Script

if __name__ == "__main__":
    logger = setup_logging(log_level=ERROR)
    asyncio.run(main())

Both operations are asynchronous, so main() runs under asyncio.run(). The setup_logging(log_level=ERROR) call no longer changes the console level: logging is configured once per process, on the first call, and import cognee already made it. To keep Cognee’s own progress logging out of the output, set LOG_LEVEL=ERROR in your .env or in the environment before importing Cognee — see Setting the Log Level.

Remember

Every way to get data into Cognee memory, and what each option changes.

Recall

Understand recall()‘s full parameter surface and auto-routing behavior.

Inspecting Graph Completion Context

See the triplets GRAPH_COMPLETION retrieved before it answered.

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

OSS

Before You Start

Code in Action

What Just Happened

Step 1: Start From a Clean Slate

Step 2: Remember the Passage

Step 3: Recall an Answer

Step 4: Run the Script

Remember

Recall

Inspecting Graph Completion Context

​Before You Start

​Code in Action

​What Just Happened

​Step 1: Start From a Clean Slate

​Step 2: Remember the Passage

​Step 3: Recall an Answer

​Step 4: Run the Script

Remember

Recall

Inspecting Graph Completion Context

Before You Start

Code in Action

What Just Happened

Step 1: Start From a Clean Slate

Step 2: Remember the Passage

Step 3: Recall an Answer

Step 4: Run the Script