--- 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:

```bash uv venv && source .venv/bin/activate uv pip install cognee # from PyPI # or, working inside this repo: 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):

```bash 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:

```python 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"`.