langchain-cohere
An integration package connecting Cohere and LangChain
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, and low installation friction. Install it if you are building LangChain applications that need Cohere's models or embeddings. The MIT license poses no restrictions. Verify that your environment can set the COHERE_API_KEY before deploying.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a Cohere API key set as the COHERE_API_KEY environment variable; Python 3.10 or later.
- Low friction installation with a pure Python wheel.
- The package is actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions. You may use, modify, and distribute this package freely provided you retain the license notice.
last release 2026-06-01 (74 days) · last repo commit 2026-08-05 · 50 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 960,330 downloads/mo, #4,635 on PyPI
Alternatives
Verify before relying
pip install langchain-cohere
from langchain_cohere import ChatCohere
from langchain_core.messages import HumanMessage
llm = ChatCohere()
messages = [HumanMessage(content="Hello, can you introduce yourself?")]
print(llm.invoke(messages))- Whether Cohere SDK v5+ is automatically installed or must be managed separately in your environment.
- Performance characteristics and rate limits when used with large document sets or high-volume embedding requests.
What it is and what it does
langchain-cohere is a bridge package that exposes Cohere's language models and retrieval tools through LangChain's standard interfaces. It provides four main capabilities: a chat interface for building conversational applications, text embeddings for semantic search, a retrieval-augmented generation (RAG) retriever for connecting to external data sources, and a reranking tool for sorting results by relevance. The package also includes a ReAct agent for multi-step reasoning with tool use.
The package was migrated from langchain-community starting at version 0.0.30, consolidating Cohere integrations into a dedicated namespace. It requires Python 3.10 or later and depends on cohere (the Cohere Python SDK), langchain-core (LangChain's base abstractions), pydantic (for data validation), and types-pyyaml (type hints). You authenticate by setting a COHERE_API_KEY environment variable before instantiating any Cohere component.
Use it for
- Build chatbots and conversational agents using Cohere's chat models through LangChain's message interface.
- Embed documents and queries into vector space for semantic search and similarity matching.
- Implement retrieval-augmented generation pipelines that fetch and rank external documents before generating responses.
- Rerank search or retrieval results by relevance without building a full RAG pipeline.
- Create multi-step reasoning agents that use tools and external data sources to answer complex questions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, and low installation friction. Install it if you are building LangChain applications that need Cohere's models or embeddings. The MIT license poses no restrictions. Verify that your environment can set the COHERE_API_KEY before deploying.
Install
langchain-cohere on PyPI
Before you install
Low friction installation with a pure Python wheel. The package is actively maintained with a recent release and no known vulnerabilities. Four runtime dependencies (cohere, langchain-core, pydantic, types-pyyaml) are all standard ecosystem packages.
Requires a Cohere API key set as the COHERE_API_KEY environment variable; Python 3.10 or later.
License in practice
MIT license permits commercial and private use with minimal restrictions. You may use, modify, and distribute this package freely provided you retain the license notice.
Quickstart
pip install langchain-cohere
from langchain_cohere import ChatCohere
from langchain_core.messages import HumanMessage
llm = ChatCohere()
messages = [HumanMessage(content="Hello, can you introduce yourself?")]
print(llm.invoke(messages))Verify before relying
- Whether Cohere SDK v5+ is automatically installed or must be managed separately in your environment.
- Performance characteristics and rate limits when used with large document sets or high-volume embedding requests.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagescoherelangchain-corepydantictypes-pyyaml |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 960,330 / month, #4,635 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: langchain_cohere-0.6.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “langchain cohere integration”
- langchain-cohereConnects LangChain applications to Cohere's language models,…
- apache-airflow-providers-cohereIntegrates Cohere's language model APIs into Apache Airflow workflows…
- opentelemetry-instrumentation-cohereAutomatically traces calls to Cohere's API endpoints when using the…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also cohere · langchain-qdrant · langchain-nvidia-ai-endpoints · langchain-cerebras · langchain-oci · langchain-chroma · langchain-ibm · langchain-sambanova · langchain-baseten · langchain-groq