Eliza 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
An open-source TypeScript framework and app stack for building autonomous AI agents.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
The short answer
Choose Eliza if you need…
- Developers who want to build AI agents or plugins in TypeScript
- People who want a personal AI assistant they can run on their own hardware or with their own provider keys
- Teams experimenting with local inference using the Eliza-1 on-device path
- Builders who want a modular plugin system for actions, providers, and services
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
| Eliza | LangChain | |
|---|---|---|
| Type | Framework | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 19.8k | 149k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
Eliza
elizaOS is an open-source TypeScript project for running autonomous AI agents. It includes a core runtime, a user-facing Eliza app for web, desktop, and mobile, a command-line tool, cloud services, and first-party plugins that add abilities such as chat, memory, messaging connectors, and browser automation. The README describes it as model-agnostic, so it can use local models or hosted model providers.
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
Eliza
- Developers who want to build AI agents or plugins in TypeScript
- People who want a personal AI assistant they can run on their own hardware or with their own provider keys
- Teams experimenting with local inference using the Eliza-1 on-device path
- Builders who want a modular plugin system for actions, providers, and services
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
Eliza
- Open source under the MIT license, with a public repository and documentation links
- Model-agnostic design that supports local, direct-provider, or cloud model backends
- Modular plugin structure with documented component types and a clear package layout
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
Eliza
- Beginners who want a simple one-click chat app: the repository setup assumes Bun, Node versions, and command-line work
- People who need a stable, minimal library: the README describes a large monorepo with many packages and optional cloud services
- Anyone who wants to submit third-party plugins: the README says third-party plugins and registry listings are no longer accepted
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
Eliza
- Large, fast-changing monorepo; the README is truncated and some capabilities depend on OS, permissions, and installed plugins
- Contribution rules are strict and the project does not accept third-party plugins
- Some features such as live benchmarks may incur model provider costs
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
Eliza
Clone the repository
git clone --filter=blob:none https://github.com/elizaos/eliza.gitEnter the folder
cd elizaFrequently asked questions
What is the difference between Eliza and LangChain?
Eliza: An open-source TypeScript framework and app stack for building autonomous AI agents. LangChain: A Python framework for building AI agents and apps powered by language models. Eliza suits developers who want to build ai agents or plugins in typescript, while LangChain suits python developers building llm-powered applications.
Are Eliza and LangChain free?
Eliza: Free and open source. LangChain: Free and open source.
Which is easier to set up, Eliza or LangChain?
LangChain is easier to get started with (rated intermediate); Eliza is rated advanced and usually needs more setup.
Is Eliza or LangChain more popular?
LangChain has more GitHub stars (149k) than Eliza (19.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Eliza and LangChain?
Other options people compare them with include CrewAI, VoltAgent, Material for MkDocs, OpenUI, Quivr.
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.