Skip to main content
You are currently on a page documenting the use of Cohere models as text completion models. Many popular Cohere models are chat completion models.You may be looking for this page instead.
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
Head to the API reference for detailed documentation of all attributes and methods.

​
Overview

​
Integration details

ClassPackageLocalSerializableJS supportDownloadsVersion
Coherelangchain-community❌beta✅PyPI - DownloadsPyPI - Version

​
Setup

The integration lives in the langchain-community package. We also need to install the cohere package itself. We can install these with:

​
Credentials

We’ll need to get a Cohere API key and set the COHERE_API_KEY environment variable: 
import getpass
import os

if "COHERE_API_KEY" not in os.environ:
    os.environ["COHERE_API_KEY"] = getpass.getpass()

​
Installation

pip install -U langchain-community langchain-cohere
It’s also helpful (but not needed) to set up LangSmith for best-in-class observability 
os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

​
Invocation

Cohere supports all LLM functionality: 
from langchain_cohere import Cohere
from langchain.messages import HumanMessage

model = Cohere(max_tokens=256, temperature=0.75)

message = "Knock knock"
model.invoke(message)

" Who's there?"

await model.ainvoke(message)

" Who's there?"

for chunk in model.stream(message):
    print(chunk, end="", flush=True)

 Who's there?

model.batch([message])

[" Who's there?"]

​
API reference

For detailed documentation of all Cohere llm features and configurations head to the API reference
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.