Every few months a new LLM framework claims to replace the last one. In 2026 the landscape has settled into a few genuine camps, each solving a distinct problem. The right answer is almost never "whichever has the most GitHub stars" — it is whichever matches your workload's shape.
What changed in 2026
- LangChain v0.3+ cut its abstraction surface by ~40%. The LCEL (chain expression language) is now the canonical API; the old Chain classes are legacy.
- LlamaIndex added first-class agent support, blurring the line with LangChain. Its retrieval primitives are still best-in-class.
- DSPy reached production maturity. Signature-based prompt compilation plus the new
Teleprompter optimizer makes it serious for tasks where prompts need tuning.
- Bare-SDK agents became viable without a framework. All major providers expose clean tool-calling APIs; the boilerplate is genuinely small.
Framework comparison table
| Framework |
Best for |
Weakness |
Approx overhead |
| LangChain |
Rapid prototyping, chains, agents |
Deep abstraction, debugging pain |
High |
| LlamaIndex |
RAG pipelines, document Q&A |
Less natural for action agents |
Medium |
| DSPy |
Prompt optimisation, structured tasks |
Learning curve, less tooling |
Medium |
| Bare SDK |
Custom agents, fine control |
More boilerplate |
None |
| Haystack |
Enterprise search, NLP pipelines |
Smaller community |
Medium |
When to use LangChain
Use LangChain when you need to prototype quickly and are willing to accept that you may rewrite parts of it when you hit edge cases. The ecosystem is vast — hundreds of integrations for LLMs, retrievers, and tools exist as drop-in components. The cost is that when something goes wrong, the stack trace crosses four abstraction layers before reaching your code.
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
llm = ChatAnthropic(model="claude-sonnet-4-5")
prompt = ChatPromptTemplate.from_template("Summarise: {text}")
chain = prompt | llm
result = chain.invoke({"text": "..."})
When to use LlamaIndex
LlamaIndex owns the RAG space. Its VectorStoreIndex, node parsers, and retrieval evaluators are the most complete and best-documented retrieval primitives available.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()
print(query_engine.query("What is the refund policy?"))
When to use DSPy
DSPy is the right tool when you have labelled examples and want the framework to write your prompts. Instead of hand-crafting a chain-of-thought prompt, you define a signature and let an optimizer find the best few-shot demonstrations.
import dspy
class QA(dspy.Signature):
question: str = dspy.InputField()
answer: str = dspy.OutputField()
predictor = dspy.ChainOfThought(QA)
result = predictor(question="What is gradient descent?")
When to go bare SDK
For agents with custom control flow, bare SDK is often the cleanest path. You write a 50-line loop, every step is inspectable, and there is no framework version to pin.
How to pick
- Prototype with LangChain or bare SDK to validate the idea in a day.
- If the core job is document retrieval, migrate to LlamaIndex.
- If prompts need tuning and you have ground-truth examples, evaluate DSPy.
- For production agents, go bare SDK or LangChain's minimal LCEL chain.
- Never mix frameworks in a single service — pick one and own it.
Common mistakes
Upgrading LangChain mid-project. The API has broken compatibility repeatedly. Pin your version and upgrade deliberately.
Using LlamaIndex for agents. Its retrieval abstractions shine; its agent abstractions lag behind bare SDK. Use the right tool.
Adopting DSPy without evals. DSPy's optimizer needs examples to work. Running it without a labelled set produces random noise.
Over-engineering the orchestration. A prompt + tool call in 40 lines beats a 300-line LangChain pipeline with six abstraction layers in production debuggability.
What to skip
- Flowise, Langflow, and similar visual builders for production code. They are excellent for demos and terrible for version control.
- Every framework version older than 6 months — the space moves fast enough that running old LangChain introduces known bugs.
- Autonomous agent frameworks (AutoGPT, BabyAGI derivatives) for most real tasks. The production viability is still poor.
FAQ
Is LangChain dead?
No. It is the most actively maintained and most deployed LLM framework. Its v0.3 cleanup reduced a lot of the criticism.
Can I mix LlamaIndex and LangChain?
Technically yes. Practically, the abstraction models conflict. Pick one.
Which framework is most production-ready?
"Production-ready" depends on your stack. Bare SDK gives you the most control. LlamaIndex is safest for RAG. LangChain has the widest integrations.
Does DSPy replace prompt engineering?
For structured tasks with examples, yes — it automates prompt and few-shot selection. For open-ended tasks, hand-crafted prompts still win.
Where to go next