Everyone who’s anyone is using AI today, and large language models (LLMs) are the ‘brain’ that powers it. From the user’s standpoint, it seems rather straightforward. However, if you are interested in AI agent development or integration, you need to understand LLM frameworks and how developers use them to build these apps.
To put it simply, LLM development services typically involve building applications or integrating AI features into existing systems, rather than creating new LLMs. That’s because building a new AI model from scratch is extremely expensive. For example, the estimated cost of the GPT3 model was over $4.6 million, and by 2029, OpenAI expects to spend $37.5 billion a year. However, building custom software powered by existing LLMs, such as ChatGPT, Gemini, or Grok, is much more accessible to the average business.
What Are LLM Frameworks and How Do They Differ?
Developers create AI-powered solutions using LLM frameworks to build, test, and deploy these tools. Some of the benefits such systems offer include:
- ncreasing AI applications versatility
- Boosting app efficiency
- Increasing solution scalability
- Reducing product development costs
LLM frameworks are collections of libraries, features, and tools that allow developers to work with AI models more effectively. They achieve this by relying on ‘abstractions’ that replace huge and complex sheets of code, which is necessary to ‘communicate’ with AI.
Being a non-professional developer, you can understand this context through a simple example. You ‘drive to work’ without understanding every process that’s happening within the engine and other machinery of the vehicle. You just turn on the ignition, press the pedals, move the wheel, and the car takes you wherever necessary. An LLM framework is the car in this example. It’s the ‘shell’ that makes it easy for the user to interact with AI without getting ‘under the hood’.
Top LLM Frameworks in 2026: Developers’ Picks
Each framework is unique, and many of them have highly specific applications. Below, we will share a list of top LLM frameworks favored by Redwerk developers in 2026 and their reasoning for choosing these particular tools.
LangChain
General LLM application development (chains, agents, memory, RAG)
Fast prototyping and production of versatile LLM apps
Teams needing broad ecosystem support
LLM/API/Infra
Open-source
LangGraph
Low-level orchestration & stateful multi-agent workflows
Complex, long-running automation and multi-agent systems where you need explicit state/control
LLM/Infra
Open-source
LlamaIndex
Retrieval-Augmented Generation (RAG) over enterprise/private data
Knowledge assistants
Document Q&A
Search over internal data
Context-aware agents
LLM/Infra
Open-source core
+
Paid managed
services
Microsoft Agent Framework
Enterprise orchestration; multi-agent systems integrated with the Microsoft Azure ecosystem
Enterprise copilots
Internal workflow automation
Microsoft stack deployments (.NET, Azure)
Azure/OpenAI
billed separately
Open-source (MIT)
Haystack
Production RAG pipelines, document search & QA workflows
Data-heavy knowledge systems
Enterprise search
Secure on-prem deployments
Vector DB
LLM/Infra
Open-source (Apache-2.0)
DSPy
Code-first declarative LLM programming & optimization
Research, compliance, and structured automation where reproducibility and maintainability matter
LLM/Infra
Open-source
CrewAI
Multi-agent role-driven collaboration and workflow automation
Creative pipelines
Content generation
Research → write → review workflows
LLM/Infra
Open-source core
+
Optional SaaS
LangChain
LangChain is the leader of open-source LLM frameworks today. It’s written in Python and JS/TS, and it’s used to build AI apps that contain ‘chains’. It offers prebuilt AI agent architectures and model integrations, making it much easier and faster to develop such apps.
- Huge ecosystem and vast integration capabilities, which allow connecting to the majority of LLMs and vector databases.
- Enables fast prototyping. Developers can connect to OpenAI, Anthropic, or Google LLMs in less than 10 lines of code and use the framework’s built-in chains/agents.
- LangChain is a mature agent framework that has evolved from extensive real-world use.
- It’s a rather ‘heavy’ framework that might be overkill for simple RAG or single-call use cases.
- Long chains/agents are useful but hard to debug if you don’t build in good tracing and observability practices from the start.
LangGraph
LangGraph is a companion to LangChain, but this LLM framework is best suited for building multi-agent apps that give you a high level of control. It’s designed for more advanced adoption and agents that contain cycles and aim for long runs.
- Excellent framework for low-level agent orchestration.
- Can support complex multi-agent workflows with cycles, branching, and fine-grained control over state.
- Used by companies like Klarna, Replit, and Elastic for real agent systems in 2026.
- Steep learning curve, so it should be used by experienced professionals.
- The framework is a companion and has a niche focus on agents
- Not a good match for simple LLM usage or basic RAG development
LlamaIndex
If you are in the business of developing context-aware AI agents and RAG, LlamaIndex is the best among LLM frameworks to use. It’s favored by seasoned developers and offers exceptional capabilities for building data workflows that use your own data.
- A powerhouse of a framework for applications optimized for indexing, hybrid search, and retrieval across files and DBs.
- A great choice for building knowledge assistants that use LLMs connected to your enterprise data and agentic retrieval.
- If you use LlamaIndex for retrieval, you can easily integrate it with LangChain or other LLM orchestration frameworks to build a more versatile application.
- Not very effective for agent orchestration.
- Some advanced features are enterprise-oriented (managed services, hosted indices), so they aren’t well-suited for smaller projects.
Microsoft Agent Framework
The Microsoft Agent Framework is a combination of two open-source frameworks — Semantic Kernel and AutoGen. It’s one of the best LLM frameworks in 2026 because it offers both an SDK for connecting to enterprise systems (for compliance tracking, observability, and security) and multi-agent orchestration via AutoGen.
- Semantic Kernel is among the best enterprise-grade LLM orchestration frameworks. It’s widely used and can integrate with Azure and Copilot Studio, having native versions for .NET, Java, and Python.
- The Agent framework GA provides multi-agent orchestration patterns and migration guides from AutoGen.
- All components support deep integration with the Microsoft ecosystem, including Azure OpenAI, Azure AI Foundry, Copilot, and enterprise identity/telemetry.
- If you aren’t using the Microsoft stack, the functionality of LLM agents will be limited, and the results might not be as good.
- Working with this LLM framework requires more complicated prompt engineering and additional plugins. Overall, it’s more difficult to manage than LangChain and requires a high level of fine-tuning skill.
Haystack
Haystack is one of the top open-source LLM frameworks for building production-ready LLM apps. Docs describe it as an AI orchestration framework well-suited for developing customizable, production-ready LLM applications, including RAG, autonomous agents, and multimodal applications. It supports multiple components, including document stores (for example, Elasticsearch or FAISS), retrievers, generative AI, and pipelines, and integrates easily with cloud and local LLM providers.
- The framework features end-to-end pipelines, which are a great option for connecting models, vector stores, retrievers, and tools into RAG/agent pipelines.
- Haystack is oriented toward production. Therefore, it’s a good choice for enterprise search, QA, and RAG.
- It is a good fit for Data Science and ML development teams specializing in Python development, as it enables smooth integration with the standard Py stack.
- Requires complex setup and configuration, so it’s best managed by experienced professionals.
- Being primarily Python can be a disadvantage for all systems that run on other languages. For example, SK supports C# and Java, and LangChain offers a JS ecosystem.
- Might provide ‘slow’ performance when the document store backend is ‘heavy’.
DSPy
DSPy (Declarative Self-Improving Python) is Stanford’s declarative, self-improving framework for creating modular AI-powered apps. It’s advertised as “programming – not prompting” and promises to deliver fast iteration on structured code. This is the framework to use when you need to combine various LLM inference strategies, learning algorithms, and models while building NL modules. It’s not merely one of the open-source LLM orchestration frameworks, but one best-suited for optimization.
- The framework automates quality optimization (self-improvement) in algorithms across RAD pipelines and agent loops.
- DSPy supports multiple use patterns, including RAG pipelines, multi-step agent loops (reasoning → tool call → result → reasoning), and regular classification.
- This framework requires you to write compositional Python code instead of prompt strings, which is much better for long-term maintainability.
- This option is great for research & high-stakes tasks — where systematic evaluation and optimization matter.
- Due to its code-based nature, this framework requires an experienced specialist to operate.
- DSPy has a smaller ecosystem, meaning there are fewer ready-made connectors and helpers than in frameworks like LangChain, LlamaIndex, or SK.
- Working with this framework will require a mindset shift. You have to think in terms of declarative programs and compilers, not just the more popular ‘prompt + response’ approach.
CrewAI
CrewAI is one of the best LLM agent frameworks in 2026 for streamlining and automating workflows in the cloud. It’s Python-based and focused on collaboration through ‘crews’ with distinct roles, tasks, tool access, and intra-agent coordination. This approach enables CrewAI to simplify the development of complex multi-agent systems.
- Focus on agents with different roles collaborating on tasks, such as researchers, writers, or reviewers.
- CrewAI can run locally, be self-hosted, or use managed services. This flexibility makes it good for infra-control.
- Has a performance-oriented design as it doesn’t rely on heavy abstraction layers. Therefore, CrewAI is faster than many of the frameworks on this list.
- While the design’s focus on ‘crews’ can be an advantage, it can also be a hindrance because it makes the framework a poor fit for projects like RAG.
- The observability & ops story is younger than LangChain+LangSmith or the SK + Azure ecosystem. Third-party integrations are similarly limited as the ecosystem matures.
What Are These LLM Frameworks the Best: The Methodology
As mentioned before, there are quite a few outstanding LLM libraries and frameworks today. Here is a list of factors that Redwerk’s LLM development experts considered when making their choices:
- The framework must be relevant in 2026, meaning it must receive regular updates and be mentioned in the media to show real-world adoption.
- LLM frameworks must have relevant capabilities that are supported by official documentation.
- The options must be open-source at least in part.
- The list must contain frameworks suited for all primary applications (RAG building, multi-agent orchestrations, and optimization).
- The expert must have some real-life experience working with these frameworks.
If you have any more questions about LLM frameworks and which will be best for AI agent development in your particular case, contact us and set up a consultation today!
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