12 LangChain alternatives for building AI agents in 2026

A 2026 guide from Apify lists 12 LangChain alternatives for building AI agents, including Haystack by Deepset, Hugging Face's roughly 1,000-line smolagents library, and the open-source TypeScript framework Mastra. The guide frames the choice as deciding which parts of the AI application stack a team needs, noting LangChain now sits as a higher-level agent framework on top of LangGraph, which handles orchestration for long-running, stateful agents. It cites common LangChain complaints including needless complexity, debugging difficulty, customization trouble, production performance, and unclear documentation.

LangChain is still the most widely used framework for building LLM applications, and for many teams it's a reasonable place to start. But it isn't the only option, and it isn't always the right one. The ecosystem has grown considerably since LangChain first became popular. Some alternatives are full agent frameworks. Others are retrieval engines, orchestration layers, or optimization tools that overlap with only part of what LangChain does. LangChain itself has changed too, and now sits as a higher-level agent framework on top of LangGraph, which handles orchestration for long-running, stateful agents. So choosing an alternative is less about finding an identical framework with a different API, and more about deciding which parts of the AI application stack you actually need. This article covers 12 LangChain alternatives worth considering in 2026. For each one, you'll find what it does, how it differs from LangChain, and when it makes sense to choose it. Why look for alternatives to LangChain? LangChain provides standardized interfaces for models and tools, a large integration ecosystem, and a relatively quick path to creating an agent. But not every application needs its level of abstraction. If you've tried LangChain https://www.langchain.com , you may have struggled with some things, like: - Needless complexity - Difficulty debugging due to all the abstractions - Trouble customizing it - Performance not holding up in production - Documentation that leaves you with more questions than answers There's also considerably more choice than there was when LangChain first became popular. Even LangChain's own documentation now distinguishes between agent frameworks such as LangChain, CrewAI, OpenAI Agents SDK, Google ADK, and LlamaIndex, and lower-level agent runtimes such as LangGraph. So, there's no single best LangChain replacement. The right choice depends on whether you're building a RAG application, a multi-agent system, a highly controlled workflow, or something else entirely. 12 LangChain alternatives 1. Haystack Best for: Production RAG systems and developers who want explicit, modular pipelines Haystack is a framework by Deepset that allows developers to create search systems, question-answering applications, and conversational AI. It offers a pipeline architecture for developers to connect various components like document stores, retrievers, and readers to build customized workflows. Haystack supports integration with transformer models, vector databases https://blog.apify.com/what-is-a-vector-database/ , and other tools, making it suitable for retrieval-augmented generation https://blog.apify.com/what-is-retrieval-augmented-generation/ RAG tasks. Its modular design emphasizes simplicity and ease of debugging, addressing some of the complexities associated with other frameworks. 2. smolagents Best for: Developers who want an agent framework small enough to read end to end smolagents is Hugging Face's minimal agent library. The framework is around a thousand lines of code, which is the point: you can read all of it in an afternoon and know exactly what happens between your instructions and the model call. Its distinctive idea is code as action. Instead of emitting structured tool calls, agents write Python code to perform their actions, and the framework executes that code in a sandbox. For multi-step tasks, this tends to take fewer steps than passing arguments back and forth as JSON. 3. Mastra Best for: TypeScript developers building production AI applications Mastra is an open-source TypeScript framework for building AI applications and agents. It combines agents, tools, memory, workflows, workspaces, evaluation, and observability in a TypeScript-first stack. Its workflows let developers mix deterministic code with model calls and agent steps. Workflows can include sequential and parallel execution, conditions, loops, human approval, persistence, and replay. Mastra also provides agent features such as tool use, MCP integration, memory, human approval, and supervisor agents for coordinating specialized agents. 4. LlamaIndex Best for: RAG and applications that need to work with large amounts of external or proprietary data LlamaIndex started with a strong focus on connecting LLMs to external data, and that remains one of the clearest differences between it and LangChain. Its framework covers data ingestion, indexing, retrieval, RAG, tools, agents, and multi-step workflows. LlamaIndex describes agents as LLM-powered knowledge assistants that use tools to perform tasks, while its workflows can combine agents, data connectors, and other tools in event-driven processes. That makes LlamaIndex particularly attractive when retrieval is central to the application rather than something being added to an otherwise general-purpose agent. You can, for example, build an agent that retrieves information from your knowledge base, invokes APIs, calls other specialized agents, and uses RAG pipelines as tools. 5. OpenAI Agents SDK Best for: Developers who want a small agent abstraction with relatively few moving parts The OpenAI Agents SDK deliberately keeps its core model small. Its main primitives include agents, tools, handoffs between agents, and guardrails. It also includes built-in tracing for model generations, tool calls, handoffs, and other events in an agent workflow. Handoffs allow one agent to delegate work to another specialized agent, which makes it possible to create multi-agent applications without introducing a complex orchestration layer. Guardrails can validate inputs, outputs, and tool calls and can either run alongside agent execution or block execution until validation has completed. 6. Agno Best for: Developers who want agents, multi-agent teams, and deterministic workflows in one framework Agno separates agent applications into three useful concepts: Agents , Teams , and Workflows . An Agent combines a model with instructions, context, and optional tools. Teams coordinate multiple agents or nested teams, while Workflows orchestrate agents, teams, and ordinary functions through defined execution steps. Workflows can include sequential execution, parallel steps, loops, conditions, and routing. This lets you decide which parts of an application actually need an autonomous agent and which are better represented by regular deterministic code. 7. Microsoft Agent Framework Best for: Enterprise agent systems and developers coming from AutoGen or Semantic Kernel Microsoft Agent Framework is Microsoft's successor to both AutoGen and Semantic Kernel. It combines AutoGen's agent abstractions with features from Semantic Kernel such as session-based state management, type safety, middleware, telemetry, and support for multiple models and embeddings. It also introduces workflows for explicitly controlling multi-agent execution. That distinction between agents and workflows matters. Microsoft recommends agents for open-ended tasks that require autonomous tool use and planning, while workflows are intended for processes where execution paths need to be controlled explicitly. 8. Langflow Best for: Visual agent development and teams with contributors who don't write Python Langflow is a visual builder for agents and LLM workflows. You assemble an application from components on a canvas, including models, prompts, retrievers, tools, memory, and control flow, then run it directly or expose it as an API. It supports agents with tool use, MCP as both a client and a server, and deployment of any flow as an API endpoint. Flows can be exported as JSON, so they can be version-controlled alongside the rest of a project. Langflow is now maintained under IBM, following the DataStax acquisition. 9. Pydantic AI Best for: Python developers who care about type safety and structured outputs Pydantic AI brings the design philosophy of Pydantic to AI agent development. Its Agent abstraction combines instructions, tools, dependencies, models, and structured output types. Tools and dependencies are typed, tool arguments are validated, and agents can be constrained to return specific Pydantic models rather than arbitrary text. That makes it particularly appealing for applications where an LLM sits inside a conventional Python codebase and its inputs and outputs need to behave more like the rest of the software. Pydantic AI also includes dependency injection for passing services and data into prompts and tools in a type-safe, testable way. 10. Google Agent Development Kit Best for: Developers building agent systems with Google's AI and cloud ecosystem Google's Agent Development Kit, or ADK, is a framework for building and operating AI agents. Its tooling covers the agent development lifecycle, including scaffolding, testing, evaluation, tracing, logging, and deployment. Current ADK documentation includes support across Python, JavaScript, Go, Java, and Kotlin. ADK agents can combine models, tools, agent-to-agent interactions, and other components to build more complex systems. Google also provides deployment paths through its cloud infrastructure, including Agent Runtime, Cloud Run, and GKE. 11. DSPy Best for: Developers who want to optimize AI programs instead of manually tuning prompts DSPy takes one of the most distinctive approaches on this list. Its central idea is summed up by its own description: "Program, don't prompt." Instead of treating prompt text as the primary unit of development, DSPy lets you define structured signatures and compose them into programs that can then be optimized. A DSPy program specifies what information goes in, what should come out, and how different language-model operations fit together. Optimization techniques can then search for better instructions, examples, or other parameters based on a metric and training examples. 12. CrewAI Best for: Applications built around teams of specialized AI agents CrewAI approaches agent development through the idea of specialized agents collaborating on a task. Its Crews abstraction lets you create agents with different roles, goals, and tools and coordinate their work. Flows provide a more structured, event-driven way to control execution, including state, conditional logic, and deterministic application logic. For example, you could create one agent to gather competitor data, another to analyze it, and a third to produce a report. A Flow could then control when each part runs and what happens if one step fails. You might also be interested in learning how to use CrewAI with Apify to build and monetize AI agents Choosing between them The useful question isn't which framework is best, but which part of the stack you're trying to solve. Retrieval-heavy applications point to LlamaIndex or Haystack. Specialized agents that collaborate point to CrewAI or Agno. Agent code that has to behave like the rest of a typed Python codebase points to Pydantic AI. The smallest possible agent loop points to the OpenAI Agents SDK or smolagents. Enterprise systems already on the Microsoft stack point to Microsoft Agent Framework, and applications built on Google's models and cloud point to Google ADK. TypeScript products point to Mastra, prompt quality treated as an engineering problem points to DSPy, and a visual canvas points to Langflow. Whichever you choose, the agent still needs something to work with. Most useful agents depend on data that isn't in the model's training set: current prices, live listings, search results, competitor pages, and documentation. Apify Actors can supply that data to any framework on this list, and the Apify MCP server exposes Actors as tools to any agent that speaks MCP.