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Tools et toolkits LangChain

Tool : A precise action the AI can call · requires langchain

Over 300 integrations usable as tools by a LangChain agent.

The historical catalogue; each tool carries its own risk.

GitHub · 148,400 ★ Docs

Overall score

7.7 /10

Security: one caveat, see the card

Utility

8.0

Reliability

10.0

Security

One caveat

Ease

5 min · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Building an agent capable of acting on the real world requires providing it with concrete tools: web search, code execution, database access, email sending, and more. LangChain centralises over 300 such integrations under a common interface, avoiding the need to recode each connector from scratch.

In practice, each tool or toolkit is installed via the Python or TypeScript package corresponding to the provider (for example langchain-tavily for web search, langchain-google-community for Google services). The LangChain agent receives the list of available tools and decides on its own which ones to call depending on the task.

This catalogue is aimed at developers building agents or LLM-powered applications who want to quickly connect a model to external data sources or third-party systems, without starting from scratch for each integration.

However, it is not a ready-to-use tool for non-developers: it requires writing Python or TypeScript code, managing API keys for each third-party service, and independently assessing the risks associated with each activated tool.

What it offers

·Over 300 available integrations covering varied categories: web search, databases, file storage, messaging, and many more.
·Common interface for all tools, allowing them to be connected to a LangChain or LangGraph agent without adapting the agent's code.
·Dedicated provider packages (e.g. langchain-tavily, langchain-exa, langchain-neo4j) installable independently according to needs.
·Python and TypeScript/JavaScript support, with explicit indication of JS/TS availability for each provider.
·Compatibility with the full LangChain ecosystem: LangGraph for orchestration, LangSmith for observability and debugging.
·Continuously expanding catalogue thanks to contributions from the open source community and partner providers.
·Ability to combine multiple tools in a single agent, with each tool remaining independent and replaceable.

Our tips

Install only the provider packages you actually need (e.g. uv add langchain-tavily) rather than installing everything at once, to limit the attack surface and unnecessary dependencies.
Check for each tool whether a TypeScript equivalent exists (the JS/TS support column in the documentation) before choosing your development language.
Use LangSmith to observe which tools are called by the agent and in what order, making it easier to debug unexpected behaviours.
Read the documentation page specific to each tool before activating it in production: prerequisites, required API keys, and limitations vary significantly from one tool to another.
For complex agents requiring fine-grained orchestration, combine LangChain tools with LangGraph rather than using simple chains.

Watch out

Each activated tool introduces its own security risks and terms of use: a poorly configured agent may call a tool in an unintended way and cause real side effects (sending emails, modifying data, etc.).
The large number of available integrations can give a false impression of uniformity: maintenance quality, update frequency, and support level vary across packages and providers.

In practice

Price: Free
Commercial use: Commercial use: yes
Available on: sdk-python, sdk-ts
Host: langchain
Licence: MIT
FR interface: No

Security and data

Rating: Security: one caveat, see the card
Capabilities: readswritesexecutesnetwork
Your data: Local or cloud depending on use
Maintenance: Active
Last release: 22 October 2026

Links and repository checked on 25 October 2026 · added on 1 January 2023

How to install Tools et toolkits LangChain

1. Install Python 3.9+ and set up a virtual environment. 2. Install the base package: uv add langchain (or pip install langchain). 3. Install only the provider package(s) you need, for example: uv add langchain-tavily. 4. Retrieve the API keys required by each third-party service and store them in environment variables, never hard-coded. 5. Read the documentation for each tool to understand its specific prerequisites before activating it in an agent.

uv add langchain

Official documentation ↗

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