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Experimental. Cognee-RS is a Rust port of the Python cognee SDK, built for on-device AI memory (phone, smartwatch, embedded) and aiming for behavioral parity with Python cognee. The source lives at github.com/topoteretes/cognee-rs. There is no pip install / hosted step — you build the CLI from source with Cargo.
Cognee-RS exposes the same four-verb memory API as Python cognee — remember, recall, improve, forget — composing the add → cognify → search pipeline. The fastest way in is the cognee-cli binary.

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Prerequisites

  • A Rust toolchain (edition 2024, MSRV 1.91) — install via rustup.
  • An OpenAI-compatible LLM API key. The CLI hard-fails at startup if no LLM key is configured. A local endpoint (e.g. Ollama) works too — you still pass a dummy key.

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Build the CLI

git clone https://github.com/topoteretes/cognee-rs
cd cognee-rs

cargo build --release -p cognee-cli   # -> target/release/cognee-cli

# put it on your PATH for the snippets below
export PATH="$PWD/target/release:$PATH"
The default feature set wires a fully embedded, no-external-service stack: SQLite (relational), Ladybug (graph), and LanceDB (embedded, persistent vector index). Nothing else to install.

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Configure the LLM

A .env file in the working directory is auto-loaded. The only required setting is the LLM API key: 
export LLM_API_KEY="sk-..."        # canonical name (OPENAI_TOKEN is an accepted alias)
# optional overrides:
export LLM_MODEL="gpt-4o-mini"     # the compiled default is openai/gpt-5-mini
export LLM_ENDPOINT="https://..."  # alias: OPENAI_URL; empty -> OpenAI's API
Embeddings need a key by default too. On desktop/server the default embedding provider is OpenAI (text-embedding-3-small), reusing LLM_API_KEY / LLM_ENDPOINT — so setting LLM_API_KEY alone is enough for the full pipeline. To run embeddings fully local, set EMBEDDING_PROVIDER=onnx (or ollama).
ollama serve &
ollama pull llama3.2:3b

export OPENAI_URL=http://localhost:11434/v1
export OPENAI_TOKEN=not-needed      # dummy value still required — startup checks for a non-empty key
export OPENAI_MODEL=llama3.2:3b
export EMBEDDING_PROVIDER=ollama    # or onnx — otherwise embeddings still call OpenAI

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Your first memory

# store, then ask — this is the whole loop
cognee-cli remember "Cognee turns raw data into a queryable knowledge graph."
cognee-cli recall   "what does cognee do?"
  • remember ingests the data, builds the knowledge graph, and runs a self-improvement pass (disable with --no-improve).
  • recall auto-routes the search type for you when --query-type is omitted.
On desktop/server, the default vector index is LanceDB and persistent. The pure-Rust brute-force vector index is in-memory and selected on Android, or when you set VECTOR_DB_URL=:memory:. For Postgres-backed vectors, build with the pgvector feature and point VECTOR_DB_PROVIDER=pgvector at a Postgres instance.

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Lower-level pipeline

remember / recall wrap the explicit stages, which exist as separate subcommands for fine-grained control: 
# 1. Ingest data into a dataset (defaults to "main_dataset")
cognee-cli add ./notes.txt "some inline text" -d my_dataset

# 2. Build the knowledge graph
cognee-cli cognify -d my_dataset

# 3. Query it (defaults --query-type to GRAPH_COMPLETION)
cognee-cli search "Alan Turing" -t GRAPH_COMPLETION -k 10 -d my_dataset
Run cognee-cli <command> --help for the full flag list.

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Language bindings

The ergonomic Cognee class — new(settings) → warm() → add() / cognify() / search() / remember() — is exposed by the bindings, which keep the component graph alive across calls in one process:
  • Python (PyO3): from cognee_py import Cognee
  • JavaScript/TypeScript (Neon): import { Cognee } from '@cognee/cognee-ts'
  • C (FFI): #include "cognee_sdk.h"

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Next Steps

Cognee-RS on GitHub

Full README, architecture docs, and the crate-by-crate workspace breakdown.

Python Quickstart

The same remember / recall loop in the Python SDK.