> ## 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.

# Cognee CLI Overview

> Command line interface for Cognee AI memory operations

The `cognee-cli` command lets you run Cognee from the terminal so you can remember data, enrich memory, and ask questions without opening a Python file. The commands are designed to be short, use friendly defaults, and are safe for people who are just starting out.

## Setup

Before using the CLI, you need to configure your API key. The recommended approach is to store it in a `.env` file:

```bash theme={null}
# Create a .env file in your project root
echo "LLM_API_KEY=your_openai_api_key" > .env
```

Alternatively, you can export it in your terminal session:

```bash theme={null}
export LLM_API_KEY=your_openai_api_key
```

<Note>
  Use the `cognee-cli config set` command only for temporary tweaks during a long-running session. For persistent configuration, use `.env` files or environment variables.
</Note>

## Quick Tour of Commands

* `cognee-cli remember <data>` ingests data and builds retrieval-ready memory in one step
* `cognee-cli recall "question"` retrieves answers from the graph or session memory
* `cognee-cli improve` enriches an existing dataset
* `cognee-cli forget` removes stored data when you no longer need it
* `cognee-cli config` reads and updates saved settings
* `cognee-cli -ui` launches the local web app

Add `--help` after any command (for example, `cognee-cli recall --help`) to see every option.

<Note>
  The CLI still includes lower-level legacy commands such as `add`, `cognify`, `search`, and `delete`, but for new workflows the v1.0 `remember` / `recall` / `improve` / `forget` commands are the preferred interface.
</Note>

## Remember Data

Start by loading something the graph can learn from. You can remember files, folders, URLs, S3 paths, or even plain text.

```bash theme={null}
# Remember a single file into the default dataset
cognee-cli remember docs/company-handbook.pdf

# Pick a dataset name so you can separate topics later
cognee-cli remember docs/policies.docx --dataset-name onboarding

# Remember multiple files at once
cognee-cli remember docs/policies.docx docs/faq.md --dataset-name onboarding

# Remember a short text note (wrap the note in quotes)
cognee-cli remember "Kickoff call notes: customer wants faster onboarding" --dataset-name sales_calls
```

<Accordion title="Remember Command Options">
  * `data`: One or more file paths, URLs, S3 paths, or text strings. Mix and match as needed
  * `--dataset-name` (`-d`): Defaults to `main_dataset`. Use clear names so the team remembers what each dataset holds
  * `--chunk-size`: Token limit for each chunk. Leave blank to let Cognee choose
  * `--chunker`: `TextChunker` (default), `CsvChunker`, or `LangchainChunker`
  * `--background` (`-b`): Ingests data, then keeps graph-building running in the background
  * `--chunks-per-batch`: Number of chunks to process per task batch
</Accordion>

## Improve Memory

Use `improve` when you want to enrich an existing dataset after ingestion. This is especially useful for session-bridging or an explicit post-processing pass over memory you already stored.

```bash theme={null}
# Improve the default dataset
cognee-cli improve

# Improve a named dataset
cognee-cli improve --dataset-name onboarding

# Improve a dataset and bridge selected session histories
cognee-cli improve --dataset-name onboarding --session-ids chat_1 chat_2

# Kick off a long job and return immediately
cognee-cli improve --dataset-name onboarding --background
```

<Accordion title="Improve Command Options">
  * `--dataset-name` (`-d`): Dataset to improve. Defaults to `main_dataset`
  * `--dataset-id`: Dataset UUID (alternative to `--dataset-name`)
  * `--node-name`: Narrow the improvement pass to specific named entities
  * `--session-ids` (`-s`): Session IDs whose Q\&A and feedback should be bridged into the permanent graph
  * `--feedback-alpha`: Learning rate for feedback-based weighting updates
  * `--background` (`-b`): Handy for large datasets; the CLI exits while the job keeps running
</Accordion>

## Recall Memory

Once `remember` finishes, you can question the graph. Start with a simple natural-language question, then experiment with search types. The CLI exposes a **subset** of the available retrieval types; see [Recall](/core-concepts/main-operations/recall) for the memory-oriented workflow and [Search](/core-concepts/main-operations/legacy-operations/search) for the lower-level search type reference.

```bash theme={null}
# Default recall (GRAPH_COMPLETION)
cognee-cli recall "Who owns the rollout plan?"

# Limit the scope to one dataset
cognee-cli recall "What is the onboarding timeline?" --datasets onboarding

# Return three answers at most
cognee-cli recall "List the key risks" --top-k 3

# Save a JSON response for another tool
cognee-cli recall "Which documents mention security?" --output-format json
```

<Accordion title="Recall Types">
  Try these quick examples to feel the differences:

  ```bash theme={null}
  # Conversational answer with reasoning (default)
  cognee-cli recall "Give me a summary of onboarding" --query-type GRAPH_COMPLETION

  # Shorter answer based on chunks
  cognee-cli recall "Show the onboarding steps" --query-type RAG_COMPLETION

  # Raw text passages you can copy
  cognee-cli recall "Find security requirements" --query-type CHUNKS --top-k 5

  # Summaries only (great for reviews)
  cognee-cli recall "Summarise the onboarding handbooks" --query-type SUMMARIES

  # Advanced graph query (requires Cypher skills)
  cognee-cli recall "MATCH (n) RETURN COUNT(n)" --query-type CYPHER
  ```

  <Note>
    The CLI supports a **subset** of search types: `GRAPH_COMPLETION`, `RAG_COMPLETION`, `CHUNKS`, `SUMMARIES`, and `CYPHER`. Other search types (like `GRAPH_SUMMARY_COMPLETION`, `CODING_RULES`, and `TEMPORAL`) are available in the Python API.
  </Note>
</Accordion>

<Accordion title="Recall Command Options">
  * `--query-type`: Subset of search types (e.g. GRAPH\_COMPLETION, RAG\_COMPLETION, CHUNKS, SUMMARIES, CYPHER). See [Search](/core-concepts/main-operations/legacy-operations/search) for the full list.
  * `--datasets`: Limit search to specific datasets
  * `--top-k`: Maximum number of results to return
  * `--system-prompt`: Point to a custom prompt file for LLM-backed modes
  * `--session-id` (`-s`): Search session memory directly when used by itself, or add session history to graph-backed recall
  * `--output-format` (`-f`): `pretty` (friendly layout), `simple` (minimal text), or `json` (structured output for scripts)
</Accordion>

## Forget Data

Clean up when a dataset is outdated or when you reset the environment.

```bash theme={null}
# Remove one dataset
cognee-cli forget --dataset onboarding

# Remove a single item from a dataset
cognee-cli forget --dataset onboarding --data-id 123e4567-e89b-12d3-a456-426614174000

# Wipe everything for the current user
cognee-cli forget --everything
```

<Accordion title="Forget Command Options">
  * `--dataset`: Dataset name or UUID to remove
  * `--data-id`: Remove a single item from the specified dataset
  * `--everything`: Remove all datasets and data for the current user
</Accordion>

<Note>
  `forget` is the v1.0 deletion interface. If you still need the older `delete` flow, it remains available as a lower-level legacy command.
</Note>

## Manage Configuration

The CLI stores its settings so you do not have to repeat them. Configuration updates line up with the Python API.

```bash theme={null}
# See the list of supported keys
cognee-cli config list

# Check one value (if implemented)
cognee-cli config get llm_model

# Update your LLM provider and model
cognee-cli config set llm_provider openai
cognee-cli config set llm_model gpt-4o-mini

# Store an API key (quotes are optional)
cognee-cli config set llm_api_key sk-yourkey

# Reset a key back to its default value
cognee-cli config unset chunk_size
```

<Accordion title="Config Command Options">
  * `list`: Print the common keys
  * `get [key]`: Show the saved value; omit the key to list everything
  * `set <key> <value>`: Save a new value. JSON strings such as `{}` or `true` are parsed automatically
  * `unset <key>`: Reset to the default. Add `--force` to skip confirmation
  * `reset`: Placeholder for a future "reset everything" command
</Accordion>

<Accordion title="Useful Configuration Keys">
  * Language model: `llm_provider`, `llm_model`, `llm_api_key`, `llm_endpoint`
  * Storage: `graph_database_provider`, `vector_db_provider`, `vector_db_url`, `vector_db_key`
  * Chunking: `chunk_size`, `chunk_overlap`
</Accordion>

## Launch the UI

Prefer a browser view? Launch the UI with one flag.

```bash theme={null}
cognee-cli -ui
```

The CLI starts the backend on `http://localhost:8000` and the React app on `http://localhost:3000`. Leave the window open and press `Ctrl+C` to stop everything.

## Next Steps

<CardGroup cols={2}>
  <Card title="Installation Guide" href="/getting-started/installation" icon="download">
    **Set up your environment**

    Install Cognee and configure your environment to start using the CLI.
  </Card>

  <Card title="Quickstart Tutorial" href="/getting-started/quickstart" icon="play">
    **Run your first example**

    Get started with Cognee by running your first knowledge graph example.
  </Card>
</CardGroup>