Botpress vs LangChain
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source tools to build and deploy chatbots and LLM agents, centred on Botpress Cloud.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
The short answer
Choose Botpress if you need…
- Developers who want to write integrations and bots as TypeScript code using the Botpress CLI and SDK
- Teams already using Botpress Cloud who want to publish or deploy their own integrations
- Developers who want to read example bots built only with the SDK and CLI
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Side-by-side
| Botpress | LangChain | |
|---|---|---|
| Type | Dev tool | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 15k | 149k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 6 days ago | 4 days ago |
What each one is
Botpress
This repository holds the open-source parts of Botpress Cloud: integrations, developer tools (a CLI, an SDK and an API client), and some example bots written as code. Botpress is described as a platform for building chatbots and assistants powered by OpenAI. The README says the visual Botpress Studio is the recommended way to build bots, and the code-based approach is mainly for experienced developers.
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
Best for
Botpress
- Developers who want to write integrations and bots as TypeScript code using the Botpress CLI and SDK
- Teams already using Botpress Cloud who want to publish or deploy their own integrations
- Developers who want to read example bots built only with the SDK and CLI
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Strengths
Botpress
- Released under the MIT license, so the code can be reused and modified freely
- Uses TypeScript and a typed SDK and API client, which helps catch errors early
- Includes a CLI for the full integration workflow: create, deploy and publish
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
Not ideal for
Botpress
- Non-programmers who want a no-code chatbot builder: the README says the Studio is recommended over building bots as code
- People looking for the on-premise Botpress v12 product: the README sends those users to a separate v12 repository
- Anyone needing a self-contained local chatbot: the README focuses on Botpress Cloud workflows and does not describe a standalone self-hosted setup
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
Trade-offs
Botpress
- The README marks plugins as coming soon, so that part is not yet available
- Code-first bot building is described by the authors as not the recommended route for most users
- Setup and terminology depend on the Botpress Cloud service, so the project is tied to that platform
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
Getting started
Botpress
Install the Botpress CLI (npm)
npm install -g @botpress/cliCreate a new integration
bp initFrequently asked questions
What is the difference between Botpress and LangChain?
Botpress: Open-source tools to build and deploy chatbots and LLM agents, centred on Botpress Cloud. LangChain: A Python framework for building AI agents and apps powered by language models. Botpress suits developers who want to write integrations and bots as typescript code using the botpress cli and sdk, while LangChain suits python developers building llm-powered applications.
Are Botpress and LangChain free?
Botpress: Free and open source. LangChain: Free and open source.
Is Botpress or LangChain more popular?
LangChain has more GitHub stars (149k) than Botpress (15k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Botpress and LangChain?
Other options people compare them with include Prompt flow, Composio, Portkey AI Gateway, claude-context, Desktop Commander.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.