Langchain Alternatives You Can Use to Build AI and Agentic Workflows

Building AI and agentic workflows is at the core of modern AI development in 2025. And LangChain has been the go-to framework for creating AI applications for a while now. But some developers are seeking alternatives that offer more flexibility, simplicity, and cost-effectiveness.

While LangChain has enabled rapid development of LLM-powered apps with tools for chaining, agents, and memory, its heavy abstraction, complex debugging, and difficulty with real-world use often make it more suited for prototyping than production-level applications.

In this article, we’ll explore some powerful LangChain alternatives you can try out that’ll help you build effective AI and agentic workflows. I’ll go through each one’s key features and best use cases so you can get a good idea of how it might help you.

Table of Contents

1. What is an AI and Agentic Workflow?

2. Langbase

3. LlamaIndex

4. AG2

5. Braintrust

6. FlowiseAI

7. Wrap Up 🙌

What is an AI and Agentic Workflow?

An AI workflow refers to a series of tasks executed by AI systems, typically following a predefined sequence. It handles tasks like data extraction, processing, and generating output based on clear instructions.

An agentic workflow goes a step further. It involves AI taking initiative, making decisions, and managing tasks autonomously. In agentic workflows, AI adapts its actions based on its environment or predefined goals, often without human intervention.

In short, an AI workflow becomes more “agentic” as it begins to think, decide, and act independently, acting like an intelligent agent. The more decisions AI can make on its own, the less it needs to be prompted by a human.

Now that it’s clear what an AI and agentic workflow is, let’s look at some other tools and frameworks that could serve as LangChain alternatives, each offering unique capabilities and approaches that you can use to build your AI and agentic workflows.

Langbase

Langbase is a Serverless Composable AI Developer platform with multi-agent orchestration and advanced long-term memory. It’s designed for seamless AI development and deployment. Langbase provides support to over 100+ LLMs through one API ensuring a unified developer experience, with easy model switching and optimization.

💡 Multi-agent orchestration refers to coordinating multiple AI agents to work together on tasks. It involves controlling the flow of tasks, ensuring agents work in the right sequence, and coordinating their actions to maximize efficiency.

Langbase Products

The platform offers the following products:

1. Pipe Agents: Pipe agents on Langbase are different from other agents. They are serverless AI agents with agentic tools that can work with any language or framework. Pipe agents are easily deployable, and with just one API they let you connect 100+ LLMs to any data to build any developer API workflow.

2. Memory Agents: Langbase memory agents (long-term memory solution) are designed to acquire, process, retain, and retrieve information seamlessly. They dynamically attach private data to any LLM, enabling context-aware responses in real time and reducing hallucinations. Memory, when connected to a pipe agent, becomes a memory agent.

   💡 To get started with Langbase click here.

3. BaseAI.dev: BaseAI is the open-source first web AI framework. With it you can start building local-first, agentic pipes, tools, and memory and deploy serverless with one command.

   💡 To get started with BaseAI follow these steps.

4. AI Studio: Langbase AI Studio provides a playground to collaborate on AI agents, memory and tools. With it you can build, collaborate, test and deploy pipe and memory (RAG) agents.

5. LangUI: LangUI is a free, open-source Tailwind library with ready-made components designed for AI and GPT projects.

6. Langbase SDK: Langbase offers a TypeScript AI SDK that simplifies development. It helps you easily integrate LLMs, create memory agents, and chain them together into pipelines—all with minimal code. It supports JavaScript, TypeScript, Node.js, Next.js, React, and more, enabling faster development with a great developer experience.

Key Features

• API-first platform: Simple APIs (Pipe and memory agent APIs) for easy integration with clear documentation and community support.

  💡 Use the Pipe API to manage the pipe agents in your Langbase account. It has create, update, list and run endpoints. The Langbase Memory API lets you manage memories and documents in your Langbase account programmatically.

• Serverless environment: Langbase operates in a fully serverless environment, removing the need for developers to manage infrastructure. This simplifies scaling and deployment, empowering developers of all skill levels—not just AI/ML experts—to build, scale, and deploy AI agents seamlessly.

• Composable infrastructure: Langbase is the first composable AI platform. It is built for flexibility and modularity. Developers can combine models into pipelines, each focused on a specific task. This makes development easier, shows the cost of each step, and lets you create highly personalized experiences. By choosing the best model for each task, Langbase helps you build efficient workflows that fit different needs.

  💡 Composable AI means combining different AI models like building blocks to create custom solutions. It’s simple, flexible, and adapts to your needs.

• Cost efficiency: Langbase offers significantly more value at a lower cost than LangChain, with overage costs just $2 per 1,000 runs compared to LangChain’s $5.

Use Cases

Langbase is perfect for developers seeking cost-efficient solutions with seamless model switching through a single API. It’s well-suited for projects that require composable/modular AI infrastructure and advanced long-term memory features. It also excels in building autonomous workflows with multi-agent collaboration.

Here are a few specific applications that you can build using Langbase:

• Customer Support Agent: Build customer support agents/apps that can handle complex, context-aware conversations across support tickets, emails, and chats, providing accurate, efficient resolutions. Check out the customer support agents here.

• Coding Agent: Create multi-agentic apps that assist developers by generating code snippets, debugging, and reviewing code in real time, improving productivity in software development workflows. Here’s an example coding agent demo.

Getting Started with Langbase

• To get started with Langbase signup for free here.

• To create a pipe agent simply type pipe.new in the search bar.

• To create a memory agent type rag.new in the search bar.

• Explore more about Langbase here.

LlamaIndex

LlamaIndex is an open-source framework built for RAG applications and agent-based systems. It provides essential tools to ingest, structure, and connect private or domain-specific data to LLMs, enabling more accurate and reliable text generation.

With its support for building agents and integrating RAG pipelines as part of a broader toolset, LlamaIndex offers the flexibility to handle complex tasks.

Key Features

• Data loading: LlamaIndex makes data import seamless with support for 150+ sources, including APIs, PDFs, documents, and SQL databases. Using data connectors (LlamaHub), developers can integrate diverse data into their LLM applications effortlessly. Examples include pulling real-time data from APIs, loading structured information from MySQL or PostgreSQL, and ingesting text from PDFs or reports.

  💡 Data Loaders are utilities that allow you to easily ingest data for search and retrieval by a large language model.

• Indexing: Indexing organizes and stores data for easy and fast retrieval, creating structures like vector or document indexes. With LlamaIndex, you can store and index data across multiple providers (for example, vector, document, graph, and SQL databases).

• Querying: Querying retrieves specific information from indexed data, enabling searches and advanced workflows like RAG pipelines for context-aware responses. For this, LlamaIndex lets you build advanced query workflows with retrieval, post-processing, and response synthesis for prompt chains and RAG pipelines.

  💡 A Query Pipeline in LlamaIndex is a simple way to design query workflows for different tasks like RAG and structured data extraction. It helps you define how queries interact with your data, making it easy to handle both basic and advanced workflows. Read about LlamaIndex Query Pipelines here.

• Evaluations: Includes modules for evaluating retrieval and response quality, enhancing performance monitoring.

Use Cases

LlamaIndex is preferred for seamless data indexing and quick retrieval, making it more suitable for production-ready RAG applications. On the other hand, LangChain provides more out-of-the-box components, making it easier to create diverse LLM architectures.

Here are a few specific RAG applications that you can build using LlamaIndex:

• Financial Insights Assistant: Build a knowledge assistant for financial analysts to retrieve real-time insights from market data, earnings reports, and internal financial documents, enabling quicker decision-making and risk assessment.

• Manufacturing Advisor: Create an AI-powered assistant to streamline production workflows by accessing equipment manuals, maintenance logs, and supply chain data, improving operational efficiency and reducing downtime.

Getting Started with LlamaIndex

You can get started with LlamaIndex in Python or TypeScript in just 5 lines of code.

1. Set the OPENAI_API_KEY environment variable with your OpenAI API key.

2. Install the Python library: pip install llama-index

3. Place your documents in a folder named data, then use this starter code to query them:

pythonCopyEditfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Your question here")
print(response)

For more details, check out their documentation.

AG2

AG2 (formerly AutoGen) is an open-source framework for building AI agents and enabling multi-agent collaboration. AG2 provides a framework for building autonomous workflows and agent collaboration, simplifying the creation of specialized agents that can work together seamlessly.

💡 Multi-agent collaboration refers to multiple agents working together toward a common goal, each performing tasks and sharing information as needed. The agents can be independent and specialized, but they collaborate to complete a task.

Key Features

• Agent collaboration: Supports multi-agent orchestration for seamless communication and task management.

• Flexible agent roles: Define agent behaviors, roles, and workflows with intuitive code. Assign specific roles to agents—such as data collector, analyzer, or decision-maker—and have them interact in conversations or work independently. For example, one agent might gather information, while another processes it and provides insights. These agent conversations can drive task completion, with each agent contributing based on its designated role, making workflows more dynamic and efficient.

• Human-in-the-loop support: AG2 enables seamless human involvement in the workflow by allowing for customizable input methods, such as manual overrides or feedback loops. It offers context-aware handoff, meaning the system can pass tasks to a human at the right moment, based on specific conditions or requirements. Additionally, interactive interfaces are provided, enabling humans to review, approve, or adjust agent actions in real-time, ensuring the system remains aligned with human judgment and oversight.

• Conversation patterns: Built-in patterns automate coordination tasks like message routing, state management, and dynamic speaker selection.

Use Cases

AG2 stands out for its ability to handle complex agent interactions, making it a great choice for multi-agent workflows that require human collaboration.

Here are a few AI applications that you can build using AG2:

• Content Creation and Review Pipelines: Build collaborative workflows where one agent generates written or visual content, another ensures compliance with guidelines, and a human reviewer provides creative inputs or final approval.

• Personalized Education Platforms: Create learning assistants where one agent curates educational content, another designs custom learning paths, and a third monitors student progress. Teachers or mentors can step in to provide personalized feedback or adjustments to the curriculum.

Getting Started with AG2

AG2 requires Python version >= 3.9, < 3.14. It can be installed from pip:

pip install ag2

For more details visit the documentation.

Braintrust

Braintrust is an end-to-end platform for evaluating, improving, and deploying large language models (LLMs) with tools for prompt engineering, data management, and continuous evaluation. Designed to make building AI applications more robust and iterative, Braintrust helps you prototype rapidly with different prompts and models, evaluate performance with built-in tools, and monitor real-world interactions in real time.

Key Features

• Iterative experimentation: Rapidly prototype and test prompts with different models in the integrated playground. You can experiment with real dataset inputs, compare responses across models (OpenAI, Anthropic, Mistral, Google, Meta, and more), and fine-tune performance in the playground.

• Performance insights: Evaluate model and prompt performance with built-in tools like the prompt playground, dataset imports, and scoring functions. You can test outputs against real-world data, compare models, and refine prompts iteratively. Use heuristics or LLM-based scoring to assess accuracy, track results, and improve performance over time within Braintrust’s UI or SDK.

• Real-time monitoring: Track AI interactions with detailed logs, capturing inputs, outputs, and metadata for each request. Braintrust logs traces of AI calls, breaking them down into spans to pinpoint issues, monitor user behavior, and refine performance. Logs integrate seamlessly with evaluations, creating a feedback loop for continuous model improvement.

• Centralized data management: Braintrust integrates data from production, staging, and evaluations, allowing you to track changes, compare iterations, and refine models over time. Versioning ensures you can roll back, audit, and pin evaluations to specific dataset versions, supporting structured experimentation and human-in-the-loop reviews for continuous improvement.

  💡 Datasets allow you to collect data from production, staging, evaluations, and even manually, and then use that data to run evaluations and track improvements over time.

Use Cases

Braintrust is best suited for iterative model development and evaluation, especially for projects that demand robust testing and deployment pipelines. It stands out for building scalable LLM applications, offering data-driven insights that enable precise optimization and continuous improvement.

Here are a few apps you can build with Braintrust:

• Evaluating a Chat Assistant: With Braintrust you can evaluate a chat assistant by ensuring conversational AI maintains context for accurate responses. It enables automated evaluations to assess response quality, manages datasets to refine test cases, and tracks performance for continuous improvement.

• AI Search Bar: Braintrust helps optimize AI-powered search by ensuring accuracy and context awareness. It logs queries to identify gaps, benchmarks search results for relevance, and compares model versions to track improvements.

Getting Started with Braintrust

• To get started signup on Braintrust.

• Once you’re signed up, you’ll be asked to create an organization for free.

• To run your first eval, you can either use the UI or the starter code available. Install the Braintrust SDK using this command:

    npm install braintrust autoevals
  

  For more details visit the documentation.

FlowiseAI

FlowiseAI is an open source low-code tool for developers to build customized LLM orchestration flows & AI agents. With its intuitive drag-and-drop interface, Flowise makes LLM technology accessible to a wider audience, including those with little to no coding experience.

Key Features

• Fast iteration: The low-code approach enables quick iterations, making it easy to move from testing to production in a fraction of the time.

• Chatflow and LLM orchestration: Seamlessly connect LLMs with memory, data loaders, caching, and moderation tools to manage how models process inputs, retrieve relevant data, and generate responses. This ensures controlled interactions between models, user inputs, and external data sources for optimal performance.

• 100+ integrations: Easily connect with tools like Langchain and LlamaIndex to improve your workflows. These integrations help you link data sources, manage tasks, and add extra features, allowing you to build customized AI applications. Use them to automate work, improve model performance, or expand what your system can do based on your needs.

• Agents and assistants: Build autonomous agents that execute tasks using tools like multi-agents or sequential agents, enhancing your application’s capabilities. These agents can interact with external data sources and tools, enabling them to perform specialized tasks efficiently. For example, Flowise provides two approaches for creating agent-based systems: Multi-Agents, which work together in a specialized, collaborative way, and Sequential Agents, which process tasks in a structured, step-by-step manner. By integrating these systems, you can automate complex workflows and improve task execution within your app.

• Developer-friendly: Extend and integrate with your apps using APIs, SDKs, and embedded chat options, including React SDK and embedded widgets.

Use Cases

Flowise is great for developers with little coding experience building LLM workflows and teams needing quick updates without losing functionality. It makes advanced AI workflows easy to use, even for non-experts.

It integrates with frameworks like LangChain and LlamaIndex, making it ideal for simplified AI development. But it may pose challenges for those new to LLMs, and code-first approaches might be better suited for highly specialized tasks.

Here are a few practical examples that you can build using Flowise:

• Query Multiple Documents: With Flowise, you can build systems that query multiple documents by uploading them to Pinecone with metadata. Tool agents help the LLM select the appropriate document based on the question.

• Personal Assistants: Develop assistants who can handle tasks, schedule appointments, and provide reminders with Flowise.

Getting Started with FlowiseAI

• To get started, install Flowise locally using NPM.

    npm install -g flowise
  

  💡 Prerequisite: ensure NodeJS is installed on machine. Node v18.15.0 or v20 and above is supported.

• Start Flowise using this command and open localhost:3000:

    npx flowise start
  

For more details check out these getting started steps.

Wrapping Up 🙌

AI and agentic workflows are moving fast, and LangChain isn’t the only option anymore. Choosing the right tool comes down to your project’s needs—flexible agent orchestration, cutting costs, or seamless integration. As we push into 2025, these alternatives deserve your attention while building the future of AI.

Thanks for reading!

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