Cognee: Building AI Agent Memory in Five Lines of Code—A Guide

Unlock graph + vector memory for your LLM agents with Cognee’s simple E→C→L pipeline. Build, query, and scale smarter AI apps in minutes.

1. Why “memory” matters—even for stateless APIs

Large-language-model apps feel magical until users ask a follow-up question and the model stares back like a goldfish. Cognee fixes that goldfish-brain by giving your agents a first-class, queryable memory layer—documents, calls, images, audio transcripts, the lot—while keeping the ergonomics down to a handful of lines.

2. Under the hood: E → C → L pipelines

Cognee structures everything as ECL pipelines:

Step What happens
Extract Split raw text & media into chunks and metadata
Cognify Call an LLM to enrich, generate a knowledge graph, add embeddings
Load Persist to your chosen vector + graph stores

Because each step is a reusable task, you can remix them or insert custom logic without rewiring the whole stack.  github.com

3. Quick-start in 90 seconds

# 1 – install
pip install cognee

# 2 – set credentials
export LLM_API_KEY=sk-...

# 3 – code!
import cognee, asyncio

async def main():
    await cognee.add(
        "Natural language processing (NLP) is an interdisciplinary subfield of computer science."
    )
    await cognee.cognify()
    print(await cognee.search("Tell me about NLP"))

asyncio.run(main())

That’s literally the “hello world” from the repo—and yes, it prints back a tidy explanation of NLP. github.com

4. Storage & runtime options

Layer Default Other options
(just pip install 'cognee[option]')
Vector DB LanceDB Qdrant · PGVector · Weaviate
Graph DB NetworkX Neo4j
LLM provider OpenAI Anyscale · Ollama

Each backend ships as an extra, so you only pull the wheels you actually need.  github.com

5. A slightly fancier demo: adding an ontology

# examples/python/ontology_demo_example.py boiled down
from cognee.api.v1.search import SearchType
from cognee.api.v1.visualize.visualize import visualize_graph
...
await cognee.prune.prune_data()
await cognee.add([cars_text, tech_text])
await cognee.cognify(ontology_file_path="basic_ontology.owl")

graph_answer = await cognee.search(
    SearchType.GRAPH_COMPLETION,
    "What cars and their types are produced by Audi?"
)
visualize_graph()

In ~20 lines we: reset memory, load two paragraphs, fuse them with an OWL ontology, then complete a graph query. Try that in vanilla RAG and watch the boilerplate grow. github.com

6. Using Cognee inside LangChain (if you really can’t quit the Chain)

from langchain_cognee.retrievers import CogneeRetriever
retriever = CogneeRetriever(llm_api_key="KEY", dataset_name="project-x", k=3)

retriever.add_documents([
    Document(page_content="Elon Musk is the CEO of SpaceX."),
    Document(page_content="SpaceX builds rockets."),
])
retriever.process_data()          # builds the graph
for doc in retriever.invoke("Tell me about Elon Musk"):
    print(doc.page_content)

Same graph-first semantics, now drop-in compatible with the LangChain ecosystem.

7. How Cognee compares (briefly, promise)

  • Classic RAG toolkits (pure vector search) give you cosine-similarity chunks; Cognee fuses vector + graph so you can ask for explicit relationships (“Which suppliers relate to battery tech and have ISO-9001?”).
  • Knowledge-graph frameworks (Neo4j-only stacks) excel at structure but stumble on fuzzy similarity; Cognee bundles both stores and lets you choose the backend.
  • Agent libraries often leave “memory” to a key-value cache; Cognee’s memory is persistent, multi-modal, and permission-aware.

(Yes, the repo literally says it “replaces RAG systems”—we’re just reading the docs.)

8. Production pointers & best practices

  1. Start local: LanceDB + NetworkX run in-process, perfect for dev containers.
  2. Gradually externalise: swap in Qdrant/Neo4j when latency & multi-node access matter.
  3. Prune aggressively in tests (cognee.prune.*)—the graph grows fast.
  4. Split pipelines: run extraction async at ingest time, cognition lazily when queries hit spike.
  5. Visualise early: visualize_graph() saves hours of “why is my edge missing?” debugging.

9. TL;DR for the busy VP

  • 5 lines to a working, queryable memory.
  • Graph + Vector unified, pluggable backends.
  • ECL pipelines keep ops transparent and auditable.
  • Ships with LangChain integration, CLI, and a local UI.
  • Apache-2.0; latest tag v0.1.42 (Jun 7 2025)—yes, it’s alive.
“Give your agents something better than a goldfish memory—with Cognee they’ll remember the conversation and draw you a knowledge graph of it.”

Happy hacking, and may your embeddings always find the shortest path!

‍

Cohorte Team

June 10, 2025