Cognee Integration with Langfuse

What is Cognee? Cognee is an open-source AI memory that turns your data into a searchable, reasoning-ready knowledge graph. By pairing Cognee with Langfuse you gain production-grade tracing, evaluation, and analytics for every pipeline step, and search query. Check out the GitHub repo or the docs for details.

What is Langfuse? Langfuse is the open-source LLM engineering platform. It helps teams trace applications, debug issues, evaluate quality, and monitor costs in production.

Quick Start Guide

Step 1: Install Cognee (includes Langfuse)

pip install cognee  # langfuse is declared as a dependency and will be installed automatically

Step 2: Create a Langfuse Project

Sign up at Langfuse Cloud.
Create a new project and copy your public and secret API keys.

Step 3: Configure Environment Variables

Create a .env file or export the variables directly in your shell:

LANGFUSE_PUBLIC_KEY=<your public key>
LANGFUSE_SECRET_KEY=<your secret key>
LANGFUSE_HOST=https://cloud.langfuse.com   # 🇪🇺 EU region
# LANGFUSE_HOST=https://us.cloud.langfuse.com   # 🇺🇸 US region

Step 4: Trace Cognee Functions

cognee ships with a tiny wrapper around Langfuse. Import get_observe() and decorate any function you want to monitor.

from cognee.modules.observability.get_observe import get_observe
 
observe = get_observe()
 
@observe(as_type="generation")  # optional label
async def acreate_structured_output(...):
    ...  # your business logic

Every time the function runs, the decorator automatically opens a span in Langfuse and streams metrics such as duration, token usage, and custom metadata.

Step 5: Start Cognifying & Watch Traces

Run your regular Cognee workflows:

import cognee
import asyncio
from cognee.modules.observability.get_observe import get_observe
 
observe = get_observe()
 
@observe(name="simple_example_run", as_type="example")
async def main():
    await cognee.add("Natural language processing (NLP) is ...")
    await cognee.cognify()
    results = await cognee.search("Tell me about NLP")
    for r in results:
        print(r)
 
asyncio.run(main())

Open the Langfuse UI – traces for any @observe-decorated helper functions will appear.

Adding Your Own Spans

You can instrument any function in your codebase – not just cognee internals:

from cognee.modules.observability.get_observe import get_observe
 
observe = get_observe()
 
@observe(as_type="my_tool", metadata={"foo": "bar"})
def my_helper(arg1, arg2):
    ...

Resources

Trubrics Firecrawl

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