| name | cognee-install |
| description | Use when the user wants to install cognee and run their first remember → recall flow with the Python SDK — fresh setup, virtual env, extras selection, or a minimal working example. |
Install and run cognee
Install
Requires Python 3.10–3.14. Prefer uv:
uv venv && source .venv/bin/activate
uv pip install cognee
uv pip install -e .
Add extras only when needed — examples: cognee[postgres], cognee[neo4j],
cognee[docling] (office/HTML document parsing, slim), cognee[docs]
(unstructured), cognee[anthropic], cognee[ollama], cognee[aws]. The full
list is in pyproject.toml under [project.optional-dependencies].
Configure
The only required setting is an LLM API key. Create .env in the working
directory (or export the variable):
LLM_API_KEY="your_openai_api_key"
Defaults need no services: SQLite (relational), LanceDB (vector), and Ladybug
(graph), all stored locally. OpenAI is the default LLM and embedding provider —
if you configure a different LLM but not embeddings (or vice versa), the other
silently stays on OpenAI. For other providers and databases use the
cognee-integrations skill.
First run
As of cognee 1.x the memory API — remember, recall, forget, improve —
is the primary surface. All SDK functions are async. Minimal end-to-end script:
import asyncio
import cognee
async def main():
await cognee.remember("Cognee turns documents into AI memory.")
results = await cognee.recall("What does cognee do?")
print(results)
asyncio.run(main())
remember() is the whole ingestion path in one call — it runs add() +
cognify(), then improve() to index the graph (self_improvement=True by
default). It accepts text, file paths, URLs, and binary streams, with an
optional dataset_name="my_project"; pass datasets=["my_project"] to
recall() to stay inside one dataset.
recall() auto-routes the query to a search strategy by default. Pass
query_type=SearchType.CHUNKS (etc.) to pin one, or auto_route=False to
fall back to GRAPH_COMPLETION.
Session memory is the other half of the API — remember(..., session_id="chat_1")
writes to a fast session cache rather than running add+cognify inline, and
recall(..., session_id="chat_1") reads it back (session hits short-circuit the
graph search). With the default self_improvement=True it still bridges that
data into the permanent graph in the background; improve(dataset=..., session_ids=[...]) does the same explicitly. Session memory runs on the
session cache, which is on by default (CACHING=true); setting
CACHING=false disables it entirely and makes remember(session_id=...)
raise.
Start with examples/advanced_guides/remember_recall_improve_example.py, which walks
through permanent memory, session memory, and the sync between them.
The add() / cognify() / search() / memify() primitives still exist and
are what remember/recall/improve call underneath — reach for them when you
need to drive a stage in isolation (e.g. custom pipeline tasks), not for
ordinary ingestion. cognee.delete is formally deprecated (since 0.3.9);
forget() is the v1 replacement, unifying the old delete/prune/empty_dataset
paths behind one call. When to use recall() versus the low-level search()
is covered in docs/recall-vs-search.md.
Verify / troubleshoot
cognee-cli remember "hello" && cognee-cli recall "hello" exercises the same
flow from the shell.- To wipe local state during experiments:
cognee-cli forget --all (or
await cognee.forget(everything=True)).
- Reads slow or spending tokens on every query → set
AUTO_FEEDBACK=false
(keep CACHING=true); by default cognee makes one structured-output LLM
call per answered query to self-tune its memory.
- Structured LLM output errors usually mean the model/provider needs an
explicit instructor mode:
LLM_INSTRUCTOR_MODE="json_schema_mode".
