See llms.txt for all machine-readable content.
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Created by
Cognee
Last update
a day ago
Cognee integration
is built and maintained by our partners at Cognee and verified by n8n. That means it’s solid, safe, and ready to help you tap into some great capabilities.
Add
Add text_data to a Cognee dataset to "cognify" later in the Cognee memory engine
Cognify
After adding text data to a Cognee dataset, trigger cognify to build a knowledge graph based memory from it
Memify
Run Cognee enrichment tasks over an existing dataset graph (or over custom data with custom extraction/enrichment tasks)
Create
Create a dataset by name (returns the existing one if the name is already taken)
Get Data Items
List the documents/files stored in a dataset, including their UUIDs for Forget, Update and Delete Data
Get Many
List the datasets you have access to
Get Progress
Pipeline status together with in-flight progress (files completed / total, current stage), one item per dataset
Get Status
Pipeline status per dataset (pending, running, completed, failed). Use it to poll after Run in Background.
Delete Dataset
Permanently delete a dataset and all its associated data
Delete Data
Remove a specific data item from a dataset while keeping the dataset intact
Forget
Delete a dataset, a single data item, or everything you own. Optionally clear only the graph and vector memory while keeping raw files.
Recall
Query memory with any Cognee search type, optionally combining knowledge-graph hits with session Q&A and trace entries
Remember
Ingest text or a binary file into a dataset and build the knowledge graph in one call (add + cognify)
Remember Entry
Store a typed session memory entry: a question/answer turn, an agent trace step, or feedback on an earlier answer
Update
Replace an existing data item with new text or a file; the old version is deleted and the new one is ingested into the graph
Search
Run a search query in Cognee memory engine
Get
Get one session with its Q&A and trace entries, usage and cost
Get Many
List sessions with activity, status and cost, newest first
Apply Improvement
Apply a previously created proposal to the skill (writes the new procedure)
Delete Skill
Delete one skill (graph node and embeddings) from a dataset
Get Many
List the skills ingested into a dataset
Get Proposal
Fetch a proposal with its before/after procedures, rationale and confidence
Get Skill
Fetch one skill (including its full procedure) by ID
Ingest Skill
Ingest inline SKILL.md markdown as a dataset-scoped skill (no file upload)
Propose Improvement
Record a low-scoring skill run and create a skill-improvement proposal (not applied)
Review Skill
Run an AGENTIC_COMPLETION search that loads the given skill to review a task
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