remember

Remember

Ingest data and build the knowledge graph in a single call.

This endpoint combines the add and cognify steps. Data is ingested first, then automatically processed into a structured knowledge graph.

Request Parameters

data (List[UploadFile]): Files to upload and process.
datasetName (Optional[str]): Name of the target dataset.
datasetId (Optional[UUID]): UUID of an existing dataset.
session_id (Optional[str]): Session to attribute this memory to. When set, data is stored in the session cache and bridged into the permanent graph in the background; the session is tracked in the sessions dashboard. When omitted, data is ingested directly via add + cognify.
node_set (Optional[List[str]]): Node identifiers for graph organisation.
run_in_background (Optional[bool]): Run the cognify step asynchronously (default: False).
custom_prompt (Optional[str]): Custom prompt for entity extraction.
chunk_size (Optional[int]): Maximum tokens per chunk (default: 4096).
chunks_per_batch (Optional[int]): Chunks per cognify batch.
ontology_key (Optional[List[str]]): Reference to one or more previously uploaded ontology files to use for knowledge graph construction.
graph_model (Optional[str]): JSON-serialised graph model schema (same dict format accepted by the cognify endpoint).
content_type (Optional[str]): Set to “skills” to ingest SKILL.md files as Skill nodes; omit for normal ingestion.

Either datasetName or datasetId must be provided.

Error Codes

400 Bad Request: Neither datasetId nor datasetName provided, unsupported content_type, or invalid graph_model JSON/schema
409 Conflict: Error during processing

POST

api

remember

Remember

curl --request POST \
  --url https://api.cognee.ai/api/v1/remember \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: multipart/form-data' \
  --form 'data=<string>' \
  --form datasetName=default_dataset \
  --form datasetId= \
  --form session_id= \
  --form node_set=null \
  --form run_in_background=false \
  --form custom_prompt= \
  --form chunk_size=4096 \
  --form chunks_per_batch=36 \
  --form graph_model= \
  --form content_type= \
  --form import_mode= \
  --form skills_text= \
  --form skill_name= \
  --form data.items='@example-file'

import requests

url = "https://api.cognee.ai/api/v1/remember"

files = { "data.items": ("example-file", open("example-file", "rb")) }
payload = {
    "data": "<string>",
    "datasetName": "default_dataset",
    "datasetId": "",
    "session_id": "",
    "node_set": "null",
    "run_in_background": "false",
    "custom_prompt": "",
    "chunk_size": "4096",
    "chunks_per_batch": "36",
    "graph_model": "",
    "content_type": "",
    "import_mode": "",
    "skills_text": "",
    "skill_name": ""
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, data=payload, files=files, headers=headers)

print(response.text)

{}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

multipart/form-data

data

file[]

datasetName

string | null

Name of the target dataset (created if it does not exist). Required unless datasetId is provided.

Example:

"default_dataset"

datasetId

Example:

""

session_id

string | null

Session to attribute this memory to (e.g. claude-code-1718000000). When set, the data is stored in the session cache (and bridged into the permanent graph in the background) and the session appears in the sessions dashboard. Leave empty for a direct add+cognify.

Example:

""

node_set

string[] | null

Tags the ingested data with named node sets (e.g. per-agent or per-project groups). Extracted graph nodes are linked to these sets, and recall/search can later be restricted to them via their node_name parameter. Leave empty to skip tagging.

Example:

null

run_in_background

boolean | null

default:false

If true, the request returns immediately (status 'running' with a pipeline_run_id) while ingestion and graph building continue server-side — poll GET /v1/datasets/status to track completion. If false, the request blocks until the knowledge graph is fully built, which can take minutes for large files.

custom_prompt

string | null

default:""

Replaces the default entity-extraction prompt used during graph building. Use it to steer which entities and relationships get extracted (e.g. focus on technical concepts, people, or contracts). Leave empty for the default prompt.

chunk_size

integer | null

default:4096

Maximum tokens per text chunk during ingestion (default: 4096). Each chunk is processed by the LLM separately for entity extraction: larger chunks give more context per extraction but fewer, coarser passes; smaller chunks give finer-grained extraction at higher LLM cost.

chunks_per_batch

integer | null

default:36

Number of chunks processed per cognify task batch (default: 36). Controls ingestion parallelism/throughput; rarely needs changing.

ontology_key

string[] | null

Keys of previously uploaded ontologies (see /v1/ontologies) to ground entity extraction. Leave empty to ingest without an ontology.

Example:

[]

graph_model

string | null

JSON-serialised graph model schema (same format as the cognify endpoint), e.g. {"title": "CompanyGraph", "type": "object", "properties": {...}}. Must include a top-level 'title' key. Leave empty to use the default KnowledgeGraph model — a restrictive schema here can produce an empty graph. Invalid JSON or an unconvertible schema is rejected with 400.

Example:

""

content_type

string | null

Set to 'skills' to ingest SKILL.md files as dataset-scoped Skill nodes. Only supported value: 'skills'; leave empty for normal ingestion.

Example:

""

import_mode

string | null

COGX archive imports only: 'preserve' (default), 'hybrid', or 're-derive'.

Example:

""

skills_text

string | null

content_type='skills' only: inline SKILL.md markdown to ingest without a file upload (no-code path). When set and no files are uploaded, it is written to a temporary SKILL.md and ingested via the normal skills pipeline. Pair with skill_name to control the resulting skill name.

Example:

""

skill_name

string | null

content_type='skills' + skills_text only: name/slug for the inline skill (defaults to 'skill').

Example:

""

Response

Successful Response

The response is of type Response Remember Api V1 Remember Post · object.

Remember EntryStore a typed memory entry in the session cache. Accepts a discriminated union of ``QAEntry``, ``TraceEntry``, ``FeedbackEntry``, or ``SkillRunEntry`` and dispatches to the matching ``remember`` path. Session-backed entries require ``session_id``; ``SkillRunEntry`` can persist with or without one. ## Response The returned ``RememberResult`` includes ``entry_type`` and ``entry_id`` — the ``qa_id``/``trace_id`` returned by the cache (or the ``qa_id`` a feedback was attached to). Use this to chain feedback to a freshly stored QA.

⌘I

Remember

curl --request POST \
  --url https://api.cognee.ai/api/v1/remember \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: multipart/form-data' \
  --form 'data=<string>' \
  --form datasetName=default_dataset \
  --form datasetId= \
  --form session_id= \
  --form node_set=null \
  --form run_in_background=false \
  --form custom_prompt= \
  --form chunk_size=4096 \
  --form chunks_per_batch=36 \
  --form graph_model= \
  --form content_type= \
  --form import_mode= \
  --form skills_text= \
  --form skill_name= \
  --form data.items='@example-file'

import requests

url = "https://api.cognee.ai/api/v1/remember"

files = { "data.items": ("example-file", open("example-file", "rb")) }
payload = {
    "data": "<string>",
    "datasetName": "default_dataset",
    "datasetId": "",
    "session_id": "",
    "node_set": "null",
    "run_in_background": "false",
    "custom_prompt": "",
    "chunk_size": "4096",
    "chunks_per_batch": "36",
    "graph_model": "",
    "content_type": "",
    "import_mode": "",
    "skills_text": "",
    "skill_name": ""
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, data=payload, files=files, headers=headers)

print(response.text)

{}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}