Top 12 LangChain Alternatives for AI Development

Published on
Mar 1, 2024

LangChain is a popular Large Language Model (LLM) orchestration framework because:

  • It’s a good way to learn concepts and get hands-on experience with natural language processing tasks and building LLM applications.
  • Its system of chaining modules together in different ways lets you build complex use cases. LangChain modules offer different functionalities such as interfacing with LLMs or retrieving data from them.
  • Its framework is broad and expanding: it offers hundreds of integrations, as well as LangChain Expression Language (LCEL) and other tools for managing aspects like debugging, streaming, and output parsing.
  • It has a large and active following on Twitter and Discord, and especially on GitHub. In fact, according to their blog, over 2,000 developers contributed to their repo in their first year of existence.

But despite its flexibility and wide range of uses, LangChain might not be the best fit for every project. Here are a few reasons:

  • LangChain is relatively new and somewhat experimental, which may lead to instability in some cases.
  • Creating dependencies on LangChain might require you to wait when AI providers like OpenAI release new tools and updates.
  • Like any framework, LangChain embodies a certain approach to LLM development that you might not universally agree with. For instance, in LangChain you have to wrap everything, even f-strings, into their LCEL and chaining functionality.


If you’re looking for alternatives in certain areas, we’ve organized this list into the following categories:

LangChain Alternatives for Prompt Engineering

Prompt engineering tools play the most decisive role in developing LLM applications because they directly influence the quality, relevance, and accuracy of LLM responses.

Mirascope

Mirascope homepage: An intuitive approach to building with LLMs

Mirascope is a toolkit for working with LLMs for lightning-fast, high-quality development. Building with Mirascope feels like writing the Python code you’re already used to writing.


We designed Mirascope with simplicity and reliability in mind:

  • Our library uses Pythonic conventions throughout, which makes the code intuitive and easier to understand.
  • We offer a structured and modular approach to LLM prompting and calls, reducing complexity and enabling you to develop with efficiency and clarity.
  • We’ve built data validation and editor support into your prompting and calls via Python and Pydantic, helping you eliminate bugs from the start.


These benefits make prompt development overall more accessible, less error prone, and more productive for developers.


Our three core beliefs around prompt engineering have influenced our approach in building Mirascope:

  1. Prompt engineering is engineering because prompting is a complex endeavor that defies standardization. LLM outputs aren’t entirely predictable and require, for instance, different prompt tuning techniques. Also, LLM tasks are diverse and require varying steps and approaches. Prompting demands a more manual, hands-on approach than other tools and frameworks might acknowledge or recognize. For all this, you need developer tools that help you build complex prompts as easily as possible.
  2. Prompts should live in your codebase, not outside of it. Separating prompting from engineering workflows limits what you can build with LLMs. The code inside of prompts should be co-located with the codebase, since changing variables or classes within a prompt might generate errors if these changes aren’t also tracked in the codebase. The same goes for functionality that can impact the quality of a prompt (e.g. temperature, model, tools, etc.) so that you don't have to worry about a code change external to the prompt changing its quality or behavior. Tools like GitHub's Codespaces allow non-technical roles to create and edit prompts that remain in engineering workflows.
  3. Generalized wrappers and complex abstractions aren’t always necessary. You can accomplish a lot with vanilla Python and OpenAI’s API. Why create complexity when you can leverage Python’s inherent strengths?


For us, prompt engineering deserves the same level of tooling and consideration as any other aspect of software engineering. This means providing features to free you of cumbersome and repetitive tasks so you can focus on creativity and effectiveness.


Mirascope allows you to:

  • Simplify and collaborate on prompt management
  • Elevate prompt quality with robust validation and structured data
  • Streamline API interactions with LLM convenience wrappers


We describe and show examples of each of these points below.


Simplify and Collaborate on Prompt Management 


One way Mirascope makes your life easier is that it organizes prompts as self-contained classes that live in their own directories. This centralizes internal prompt logic (including operational parameters, such as which LLM model it interacts with) and improves clarity and maintainability:

1from mirascope import BaseCallParams, BasePrompt
2
3
4class BookRecommendation(BasePrompt):
5    prompt_template = """
6    I've recently read the following books: {titles_in_quotes}.
7    What should I read next?
8    """
9
10    book_titles: list[str]
11
12    call_params = BaseCallParams(model="gpt-3.5-turbo-0125")
13
14    @property
15    def titles_in_quotes(self) -> str:
16        """Returns a comma separated list of book titles each in quotes."""
17        return ", ".join([f'"{title}"' for title in self.book_titles])

`BaseCallParams` here illustrates co-location in that each version of the prompt has everything that needs to be versioned (e.g. tools, model, etc.). This means each iteration of the prompt is self-contained, carrying with it all the operational settings necessary for its execution and ensuring quality control across versions. 

Mirascope also abstracts away the complexities of prompt formatting, allowing you to focus on the content of prompts rather than getting into the weeds of how they’re constructed and displayed. 

For example, below you can use `BookRecommendation` in any context without needing to get into any functionality impacting the prompt:

1from prompts import BookRecommendation
2
3
4prompt = BookRecommendation(
5    book_titles=["The Name of the Wind", "The Lord of the Rings"]
6)
7
8print(str(prompt))
9
10#> I've recently read the following books: "The Name of the Wind", "The Lord of the Rings".
11#  What should I read next?
12
13print(prompt.messages)
14#> [('user', 'I\'ve recently read the following books: "The Name of the Wind", "The Lord of the Rings".
15#  What should I read next?')]

You can also access the `messages` directly, so if OpenAI releases a new feature (like GPT-4 Vision), you can modify `messages` directly to include these changes:

1from mirascope import BasePrompt
2from mirascope import Message
3
4
5class BookRecommendation(BasePrompt):
6    book_titles: list[str]
7
8    @property
9    def titles_in_quotes(self) -> str:
10        """Returns a comma separated list of book titles each in quotes."""
11        return ", ".join([f'"{title}"' for title in self.book_titles])
12
13    def messages(self) -> list[Message]:
14        """Returns the list of messages."""
15        return [
16            {
17                "role": "user",
18                "content": "I've recently read the following books: "
19                f"{self.titles_in_quotes}",
20            }
21        ]
22
23
24prompt = BookRecommendation(book_titles=["The Hobbit", "The Lord of the Rings"])
25print(prompt.messages())
26# >[{'role': 'user', 'content': 'I\'ve recently read the following books: "The Hobbit", "The Lord of the Rings"'}]

Creating prompts efficiently is one thing, but managing and tracking changes to prompts over time is another. Prompts are exploratory, often involving trial and error. Without a process for storing and versioning prompts, it’s easy to lose iterations and it’s more difficult to collaborate with others on prompt design.

Mirascope offers a CLI for prompt management that creates both a working directory for your prompts and a versioning system based on git and Alembic.

|-- mirascope.ini
|-- mirascope
|   |-- prompt_template.j2
|   |-- versions/
|   |   |-- <directory_name>/
|   |   |   |-- version.txt
|   |   |   |-- <revision_id>_<directory_name>.py
|-- prompts/

The working directory contains subdirectories and files for:

  • Configuring project settings
  • Setting up your prompt management environment
  • Creating a common prompt structure using the Jinja2 templating engine
  • Tracking revisions
  • Storing prompts


In addition, the CLI offers commands allowing you to:

  • Initiate a local Mirascope repository
  • Add prompts to the prompt management environment
  • Save a prompt and iterate its version number
  • Switch between different versions of prompts


These features become especially useful if you’re generating large volumes of prompts in a collaborative environment.

Mirascope extends its support beyond the command line interface. If you prefer to work within a code editor environment, Mirascope offers comprehensive editor integration for its API documentation, as shown in this example of autocomplete suggestions for a property.

EditorPrompt example

Elevate Prompt Quality with Robust Validation

One aspect of developing complex prompts with code is the potential for generating bugs. Data validation would obviously be helpful here, and yet many frameworks treat this as an afterthought, requiring you to come up with your own custom validation logic.

Mirascope leverages the popular Pydantic data validation library to parse data and verify its correctness in your prompts. 

In fact, Mirascope’s `BasePrompt` class inherits from Pydantic’s `BaseModel`. This means:

  • Your prompts are instances of Pydantic models, and so adhere to a (predefined) schema, with attributes enforced by type coercion. This makes prompting more systematic, less error-prone, and easier to manage.
  • Serialization of prompt objects and data becomes simpler, since BaseModel provides methods to serialize Python objects into JSON-compatible dictionaries, and deserialize JSON data into Python objects. This allows you to more easily pass data to and from NLP models.
  • Pydantic models improve the clarity and organization of prompt code by providing a declarative syntax (leveraging Python’s own syntax) for specifying what the data should look like—rather than you having to write procedural code to validate it.
  • Pydantic automates much of the process of data validation, reducing the need for boilerplate. By defining data models with Pydantic, you can specify validation rules, which leads to cleaner and more maintainable code.

Mirascope adds convenience tooling on top of Pydantic for writing and formatting your prompts. As shown below, it adds class-level prompt templates (with dynamic variable injection) to the prompts:

1from mirascope.base import BaseCallParams, BasePrompt
2
3
4class EditorPrompt(BasePrompt):
5    prompt_template = """
6    I'm working on a new storyline. What do you think?
7    {storyline}
8    """
9
10    storyline: str
11
12    call_params = BaseCallParams(model="gpt-4", temperature=0.4)
13
14
15prompt = EditorPrompt(storyline="Two friends go on an adventure.")
16
17print(EditorPrompt.prompt_template)
18#> I'm working on a new storyline. What do you think?
19#  {storyline}
20
21print(prompt)
22#> I'm working on a new storyline. What do you think?
23#  Two friends go on an adventure.

The code above shows the class variable `prompt_template`—another example of convenience tooling that is useful when you want to retrieve and review the template without needing to instantiate the class.

The `str` and `messages` methods in the code block below are written such that they only format properties that are templated. This enables writing more complex properties that depend on one or more existing properties.

1from mirascope import BasePrompt
2
3
4class GreetingsPrompt(BasePrompt):
5    prompt_template = """
6    Hi! My name is {formatted_name}. {name_specific_remark}
7
8    What's your name? Is your name also {name_specific_question}?
9    """
10
11    name: str
12
13    @property
14    def formatted_name(self) -> str:
15        """Returns `name` with pizzazz."""
16        return f"⭐{self.name}⭐"
17
18    @property
19    def name_specific_question(self) -> str:
20        """Returns a question based on `name`."""
21        if self.name.lower() == self.name[::-1].lower():
22            return "a palindrome"
23        else:
24            return "not a palindrome"
25
26    @property
27    def name_specific_remark(self) -> str:
28        """Returns a remark based on `name`."""
29        return f"Can you believe my name is {self.name_specific_question}?"
30
31
32prompt = GreetingsPrompt(name="Bob")
33print(prompt)
34# > Hi! My name is ⭐Bob⭐. Can you believe my name is a palindrome?
35# > What's your name? Is your name also a palindrome?

Writing properties in this way ensures prompt-specific logic is tied directly to the prompt. It happens under the hood from the perspective of the person using the `Greetings` class. Constructing the prompt only requires `name`.

You can also write prompts with multiple messages using the SYSTEM, USER, ASSISTANT, and TOOL keywords. The `messages` method will automatically parse these messages for you:

1from mirascope import BasePrompt
2
3
4class GreetingsPrompt(BasePrompt):
5    prompt_template = """
6    SYSTEM:
7    You can only speak in haikus.
8
9    USER:
10    Hello! It's nice to meet you. My name is {name}. How are you today?
11    """
12
13    name: str
14
15
16prompt = GreetingsPrompt(name="William Bakst")
17
18print(Greetings.template)
19# > SYSTEM: You can only speak in haikus.
20# > USER: Hello! It's nice to meet you. My name is {name}. How are you today?
21
22print(prompt)
23# > SYSTEM: You can only speak in haikus.
24# > USER: Hello! It's nice to meet you. My name is William Bakst. How are you today?
25
26print(prompt.messages())
27# > [('system', 'You can only speak in haikus.'), ('user', "Hello! It's nice to
28#  meet you. My name is William Bakst. How are you today?")]

The `BasePrompt` class without any of the keywords will still have the `messages` attribute, but it will return a single user message in the list.

Streamline API Interactions with LLM Convenience Wrappers

In addition to validating prompt data, Mirascope provides convenience wrappers around the OpenAI client to simplify making API calls. We want to make it as easy as possible to incorporate LLM functionalities into your applications.

For example, you can initialize an `OpenAICall` instance and call `call` to generate an `OpenAICallResponse`. These are both convenience wrappers for the OpenAI Chat client:

1from mirascope.openai import OpenAICall
2
3
4class Recipe(OpenAICall):
5    prompt_template = """
6    Recommend recipes that use {ingredient} as an ingredient
7    """
8
9    ingredient: str
10
11response = Recipe(ingredient="apples").call()
12print(response.content)  # prints the string content of the completion

Below, the `call` method returns an `OpenAICallResponse` class instance, which is a simple wrapper around the `ChatCompletion` class in `openai`. In fact, you can access everything from the original completion as desired.

1from mirascope.openai import OpenAICallResponse
2
3response = OpenAICallResponse(...)
4
5response.completion  # ChatCompletion(...)
6response.content     # original.choices[0].message.content
7response.message     # original.choices[0].message
8response.tool_calls  # original.choices[0].message.tool_calls
9response.choice      # original.choices[0]
10response.choices     # original.choices

Mirascope’s LLM convenience wrappers further allow you to:

  • Easily chain multiple levels of prompt-based queries in a modular fashion, enabling dynamic assembly of prompts based on varying inputs.
  • Stream LLM responses, which allows you to better handle large volumes of data by processing data chunks as they arrive, rather than waiting for the entire response.
  • Automatically transform functions into “tools” or callable objects in the Mirascope framework, which enables you to easily incorporate complex logic and functionalities into your workflows without extra coding.
  • Serialize data from unstructured text (e.g., natural language outputs) into a format like JSON, using a customized Pydantic `BaseModel` schema of your design. We describe automatic data extraction in more detail below.

Mirascope also makes `async` available to make task execution more efficient. This allows tasks to proceed without waiting for previous ones to finish.

Additionally, Mirascope passes `OpenAICallParams` as`**kwargs` through its calls to both provide developers with direct access to the underlying API’s arguments, and to allow use of data dictionaries to dynamically specify arguments in their calls to the OpenAI client.

Mirascope also works with any model provider that supports the OpenAI API as well as other model providers such as Anthropic and Gemini (MIstral coming soon).

Mirascope is the framework for prompt engineers and data scientists to design, manage, and optimize prompts and calls for more efficient and higher-quality interactions with LLMs.

To learn more, you can find Mirascope’s code samples mentioned in this article on both our documentation site and on GitHub.

Priompt

Priompt source code on GitHub

Priompt (priority + prompt) is a JSX-based prompting library and open-source project that bills itself as a prompt design library, inspired by web design frameworks like React. Priompt’s philosophy is that, just as web design needs to adapt its content to different screen sizes, prompting should similarly adapt its content to different context window sizes.

It uses both absolute and relative priorities to determine what to include in the context window. Priompt uses JSX for composing prompts, treating prompts as components that are created and rendered just like React. So developers familiar with React or similar libraries might find the JSX-based approach intuitive and easy to adopt.

You can find Priompt’s actively maintained source code on GitHub.

Humanloop

Humanloop is a low-code tool that helps developers and product teams create LLM apps using technology like GPT-4. It focuses on improving AI development workflows by helping you design effective prompts and evaluate how well the AI performs these tasks.

Humanloop offers an interactive editor environment and playground allowing both technical and non-technical roles to work together to iterate on prompts. You use the editor for development workflows, including:

  • Experimenting with new prompts and retrieval pipelines
  • Fine tuning prompts
  • Debugging issues and comparing different models
  • Deploying to different environments
  • Creating your own templates

Humanloop has a website offering complete documentation, as well as a GitHub repo for its source code.

Guidance

Guidance prompting library on GitHub

Guidance is a prompting library available in Jupyter Notebook format and Python, and enables you to control the generation of prompts by setting constraints, such as regular expressions (regex) and context-free grammars (CFGs). You can also mix loops and conditional statements with prompt generation, allowing for more complex and customized prompts to be created.

Guidance helps users generate prompts in a flexible and controlled manner by providing tools to specify patterns, conditions, and rules for prompt generation, without using chaining. It also provides multi-modal support.

Guidance, along with its documentation, is available on GitHub. 

LangChain Alternatives for Developing LLM Agents

The following open source libraries help you develop autonomous agents, which are software entities leveraging the capabilities of LLMs to independently perform tasks, make decisions, and interact with users or other systems.

Auto-GPT

Auto-GPT homepage: Explore the new frontier of autonomous AI

Auto-GPT is a project consisting of four main components:

  • A semi-autonomous, LLM-powered generalist agent that you can interact with via a CLI
  • Code and benchmark data to measure agent performance
  • Boilerplate templates and code for creating your own agents
  • A flutter client for interacting with, and assigning tasks to agents

The idea behind Auto-GPT is that it executes a series of tasks for you by iterative prompting until it has found the answer. It automates workflows to execute more complex tasks, such as financial portfolio management.

You provide input to Auto-GPT (or even keywords like “recipe” and “app”), and the agent figures out what you want it to build—it tends to be especially useful for coding tasks involving automation, like generating server code or refactoring.

Auto-GPT’s documentation is available on its website, and its source code (largely in JavaScript) can be found on GitHub.

AgentGPT

AgentGPT homepage: Deploy anonymous AI agents directly in the browser

AgentGPT allows users to create and deploy autonomous AI agents directly in the browser. Agents typically take a single line of input (a goal), and execute multiple steps to reach the goal.

The agent chains calls to LLMs and is designed to understand objectives, implement strategies, and deliver results without human intervention. You can interact with AgentGTP via its homepage using a browser, or you can install it locally via CLI. The project also makes available prompting templates, which it imports from LangChain.

AgentGPT offers a homepage where you can test the service, as well as a GitHub repo from where you can download the code (mostly in Typescript).

MetaGPT

MetaGPT homepage: The Multi-Agent Framework

MetaGPT is a Python library that allows you to replicate the structure of a software company, complete with roles such as managers, engineers, architects, QAs, and others. It takes a single input line, such as “create a blackjack game,” and generates all the required artifacts including user stories, competitive analysis, requirements, tests, and a working Python implementation of the game.

An interesting aspect about how this library works is that you can basically define any roles you want to fulfill a task, such as researcher, photographer, and tutorial assistant, and provide a list of tasks and activities to fulfill.

MetaGPT has a website with an extensive list of use cases, a GitHub repo where you can see the source code, and a separate site for documentation.

Griptape

Griptape homepage: AI for Enterprise Applications

Griptape is a Python framework for developing AI-powered applications that enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. It follows these design tenets:

  • All framework primitives are useful and usable on their own in addition to being easy to plug into each other.
  • It’s compatible with any capable LLM, data store, and backend through the abstraction of drivers.
  • When working with data through loaders and tools, it aims to efficiently manage large datasets and keep the data off prompt by default, making it easy to work with big data securely and with low latency.
  • It’s much easier to reason about code written in a programming language like Python, not natural languages. Griptape aims to default to Python in most cases unless absolutely necessary.

Griptape features both a website for documentation and a repo on GitHub.

LangChain Alternatives for LLM Orchestration

LLM orchestration in application development involves managing and integrating multiple LLMs to create sophisticated, responsive artificial intelligence systems. Below we introduce orchestration libraries that serve as effective alternatives to LangChain.

Llamaindex

Llamaindex homepage: Turn your enterprise data into production-ready LLM applications

Llamaindex, formerly known as GPT Index, is a data framework for building LLM applications that benefit from “context augmentation,” meaning that user queries also retrieve relevant information from external data sources (e.g., documents and knowledge bases).

This is done to enrich answers by injecting the retrieved information into the machine learning LLM before it generates its response, providing it with a better understanding of context and allowing it to produce more accurate and coherent real-time outputs.

A typical example of context augmentation is a retrieval augmented generation (RAG) system that uses a vector database like Chroma, for which Llamaindex specializes in orchestrating tasks.

Llamaindex has a website (along with a separate site for documentation), and a GitHub repo where you can download the source code.

Haystack

Haystack homepage: Open-source LLM framework to build production-ready applications

Haystack is an end-to-end framework for building applications using LLMs, transformer models, vector search, and more. Examples of such applications include RAG, question answering, and semantic document search.

Haystack is based on the notion of pipelines, which allow you to connect various Haystack components together as workflows that are implemented as directed multigraphs. These can run in parallel, allowing information to flow through different branches, while loops can be implemented for iterative processes.

Haystack features extensive resources including a website, a dedicated documentation site, and a GitHub repository.

Flowise AI

Flowise AI homepage: Build LLM Apps Easily

Flowise lets you build LLM-based applications (e.g., chatbots, agents, etc.) using a cutting-edge low-code or no-code approach via a graphical user interface, eliminating the need for extensive programming knowledge.

Through its drag and drop approach, Flowise enables you to develop many of the same applications you normally would with standard coding libraries. Flowise’s approach is great for those who want to develop LLM and generative AI applications—this could include beginners, those not proficient at coding, or those just wanting to rapidly develop prototypes. It also works with other low-code or no-code applications like Bubble and Zapier (the latter offering integrations with numerous other tools).

Flowise has a website that features extensive documentation, as well as a GitHub repository.

LangChain Alternatives for Data Extraction

Data extraction is the process of systematically retrieving structured data from the raw text generated by LLMs—usually through output parsing—allowing developers to transform free-form LLM responses into organized, usable data formats like JSON.

Mirascope

Mirascope homepage: LLM toolkit for lightning-fast, high-quality development

Although we described Mirascope above in the context of prompt engineering, we also want to highlight its capabilities for automatically extracting structured data from natural language inputs.

This comes in handy when you need to convert user inputs or conversational language into a format like JSON for exchange with other applications. It can be time consuming to write all this parsing and serialization functionality yourself, so we provide this right out of the box as a convenience wrapper.

Our API wrapper `OpenAIExtractor` comes with the `extract` method, which allows you to very easily extract information into a Pydantic `BaseModel` schema that you define:

1from typing import Type
2
3from mirascope.openai import OpenAIExtractor
4from pydantic import BaseModel
5
6
7class BookInfo(BaseModel):
8    """Information about a book."""
9
10    title: str
11    author: str
12
13
14class Context(OpenAIExtractor[BookInfo]):
15    extract_schema: Type[BookInfo] = BookInfo
16    prompt_template = "The Name of the Wind is by Patrick Rothfuss."
17
18
19book_info = Context().extract()
20print(book_info)
21# > title='The Name of the Wind' author='Patrick Rothfuss'

The `extract` method is also resilient, letting you specify the number of retries in the event of a `ValidationError` when it fails to convert the model response into the Pydantic model you've provided. You can set the number of retries and `extract` will automatically retry up to that many times (by default, retries is 0).

1# this will result in 6 total creation attempts if it never succeeds
2book_info = Context().extract(retries=5)

You can also extract Base Python types directly without a Pydantic BaseModel. It still uses Pydantic under the hood, but this simplifies the user interface for simple extractions.

1from typing import Type
2
3from mirascope.openai import OpenAIPrompt
4
5
6class Price(OpenAIExtractor[float]):
7    extract_schema: Type[float] = float
8    prompt_template = "The price of the book is 19.99."
9
10
11price = Price().extract()
12assert isinstance(price, float)
13
14print(price)
15#> 19.99

Instructor

Instructor homepage: Generating Structure from LLMs

Instructor is a Python library that eases the process of extracting structured data like JSON from a variety of LLMs, including proprietary models such as GPT-3.5, GPT-4, GPT-4-Vision, as well other AI models and open-source alternatives. It supports functionalities like Function and Tool Calling, alongside specialized sampling modes, improving ease of use and data integrity.

It uses Pydantic for data validation and Tenacity for managing retries, and offers a developer-friendly API that simplifies handling complex data structures and partial responses.

Resources for Instructor include a documentation site and a GitHub repository.

Get Started with Prompt Engineering

Mirascope aims to remove the complexities around the engineering workflow so you can focus on the content of your prompts. We uphold the expressive simplicity of Python, which allows you to engineer how you normally engineer, without getting bogged down in dense and confusing abstractions.

Want to learn more? You can find Mirascope’s code samples mentioned in this article on both our documentation site and on GitHub.

Join our beta list!

Get updates and early access to try out new features as a beta tester.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.