Knowledge Processing

These endpoints transform raw data into structured knowledge graphs and remove data from them.

Cognify

POST /api/v1/cognify — Transform datasets into structured knowledge graphs. Takes uploaded data and runs entity extraction, relationship detection, and embedding generation. This is the same pipeline described in Cognify.

curl -X POST https://your-tenant.aws.cognee.ai/api/v1/cognify \
  -H "X-Api-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{"datasets": ["my_dataset"]}'

By default the request blocks until graph building finishes. Set run_in_background=true in the JSON body to return immediately and track progress with the dataset status endpoint.

If you used remember to ingest data, cognify was already executed automatically. You only need to call cognify separately when using the add endpoint.

Error responses

Status	Body	Meaning
`400`	`{"error": "No datasets or dataset_ids provided"}`	Neither `datasets` nor `dataset_ids` was provided.
`402`	`{"error": "Token budget exhausted", "detail": "..."}`	The configured LLM provider (or LiteLLM proxy) reported that its token budget is exhausted. Treat as terminal — see the note below.
`409`	`{"error": "..."}`	A referenced `ontology_key` does not exist.
`500`	`{"error": "Pipeline run errored", "detail": "..."}`	The pipeline run failed server-side (e.g. missing LLM API key, database connection failure, or a dataset that does not exist).

POST /api/v1/cognify returns 402 Payment Required with body {"error": "Token budget exhausted", "detail": "..."} when the configured LLM provider or LiteLLM proxy reports token-budget exhaustion. This response is terminal — Cognee does not retry budget-exhaustion errors, so clients should not reattempt the request automatically. Handle 402 by surfacing a top-up / billing flow rather than retrying.

Forget

POST /api/v1/forget — Remove data from the knowledge graph. Deletes content from the graph, vector store, and associated metadata. See Forget for the underlying operation. Identify the target with the dataset field (the dataset name) — not dataset_name. There is no data field; to remove a single item, pass its data_id together with a dataset.

curl -X POST https://your-tenant.aws.cognee.ai/api/v1/forget \
  -H "X-Api-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{"dataset": "my_dataset"}'

Request body

Parameter	Type	Description
`dataset`	string	Dataset name to delete (or clear with `memoryOnly`). Use this or `datasetId`, not both.
`datasetId`	UUID	Dataset UUID, alternative to `dataset`.
`dataId`	UUID	UUID of a single data item to remove. Requires `dataset` or `datasetId` to also be set.
`memoryOnly`	boolean	When `true` with a dataset, delete only graph nodes/edges and vector embeddings and reset pipeline status — raw files are preserved so the dataset can be re-cognified. Default `false`.
`everything`	boolean	DANGER: when `true`, permanently deletes all datasets and data you own. Ignores `dataId`/`dataset`/`datasetId`. Default `false`.

Field names are shown camelCased in the schema; snake_case aliases (data_id, dataset_id, memory_only) are accepted too.

Forget behavior

dataset (or datasetId) alone → delete the entire dataset and its graph/vector data.
dataset + dataId → delete a single item, leaving the dataset intact.
dataset + memoryOnly: true → clear the dataset’s memory but keep the raw files, so you can re-run cognify.
everything: true → wipe everything you own.

Error responses

Status	When
`422`	Invalid parameter combination — e.g. both `dataset` and `datasetId`, `dataId` without a dataset, or `memoryOnly` without a dataset.
`500`	`{"error": "An error occurred during deletion."}` — the deletion failed server-side. The most common cause is naming a dataset that does not exist or that you lack delete permission on. Confirm the exact name with `GET /api/v1/datasets/`, then retry.

Calling forget (and other JSON endpoints) with curl on Windows

The single quotes around the -d body in these examples are a Unix shell convention. cmd.exe and PowerShell do not strip them, so the body is sent with literal quotes and the request fails to parse.Windows cmd.exe — wrap the body in double quotes and escape the inner double quotes with \:

curl -X POST https://your-tenant.aws.cognee.ai/api/v1/forget ^
  -H "X-Api-Key: your-key" ^
  -H "Content-Type: application/json" ^
  -d "{\"dataset\": \"my_dataset\"}"

PowerShell — escaping is fragile; passing the JSON from a file with -d "@body.json" avoids it entirely (this also works on macOS/Linux):

'{"dataset": "my_dataset"}' | Out-File -Encoding ascii body.json
curl -X POST https://your-tenant.aws.cognee.ai/api/v1/forget `
  -H "X-Api-Key: your-key" `
  -H "Content-Type: application/json" `
  -d "@body.json"

Getting Started

Local UI

Navigating the UI

Cloud Functionality

Connections

Reference

Cognify

Error responses

Forget

Request body

Forget behavior

Error responses

​Cognify

​Error responses

​Forget

​Request body

​Forget behavior

​Error responses

Cognify

Error responses

Forget

Request body

Forget behavior

Error responses