Choosing the Right LLM Framework Without the Hype
The LLM ecosystem is moving fast. Every few weeks, a new framework promises to “simplify AI agents,” “orchestrate reasoning,” or “make production-ready AI easy.”
But if you’re building real systems, you’ve probably asked:
Why do I need so many frameworks for what feels like the same thing?
I’ve worked with multiple LLM stacks and this article is my attempt to cut through the noise and explain:
- What problem each framework actually solves
- Where they shine
- Where they become liabilities
- Which one you should choose depending on your use case
This is not a feature checklist. It’s a mental model.
The Big Picture: What Problem Are We Solving?
All these frameworks exist because **LLMs are not applicati…
Choosing the Right LLM Framework Without the Hype
The LLM ecosystem is moving fast. Every few weeks, a new framework promises to “simplify AI agents,” “orchestrate reasoning,” or “make production-ready AI easy.”
But if you’re building real systems, you’ve probably asked:
Why do I need so many frameworks for what feels like the same thing?
I’ve worked with multiple LLM stacks and this article is my attempt to cut through the noise and explain:
- What problem each framework actually solves
- Where they shine
- Where they become liabilities
- Which one you should choose depending on your use case
This is not a feature checklist. It’s a mental model.
The Big Picture: What Problem Are We Solving?
All these frameworks exist because LLMs are not applications. They are components.
Real-world LLM systems need:
- Prompt orchestration
- Tool calling
- Memory
- Retrieval (RAG)
- Control flow
- Observability
- Failure handling
Each framework makes different trade-offs around these problems.
LangChain: The Swiss Army Knife (and its curse)
What it is: LangChain is a high-level abstraction layer for building LLM-powered apps quickly.
What it does well:
- Rapid prototyping
- Huge ecosystem of integrations
- Easy chaining of prompts, tools, retrievers
- Strong community momentum
Where it struggles:
- Hidden control flow
- Debugging is painful at scale
- Abstractions leak under complex logic
- Performance tuning is hard
When to use LangChain
- MVPs
- Hackathons
- POCs
- Teams new to LLMs
When to avoid
- Complex, stateful workflows
- Systems needing precise control or observability
LangChain is optimized for speed of development, not clarity of execution.
LangGraph: When You Realize LLMs Are State Machines
What it is: LangGraph is LangChain’s answer to the criticism: “LLM workflows aren’t linear.”
It models AI systems as graphs instead of chains.
What it does well:
- Explicit state transitions
- Cycles, retries, branching
- Long-running agents
- Better reasoning visibility
Trade-offs:
- More complex mental model
- Still tied to LangChain ecosystem
- Steeper learning curve
When LangGraph shines
- Multi-step agents
- Tool-heavy workflows
- Systems with retries and loops
- Human-in-the-loop scenarios
LangGraph is what you reach for when LangChain starts to feel “magical.”
Semantic Kernel: Engineering-first, AI-second
What it is: Microsoft’s take on LLM orchestration, designed for software engineers, not prompt hackers.
Key strengths:
- Strong typing
- Explicit planners
- Native support for C# and Python
- Enterprise-friendly architecture
Weaknesses:
- Smaller ecosystem
- Less “plug-and-play”
- Slower iteration for experiments
Best fit
- Enterprise teams
- Strong engineering discipline
- Systems that need maintainability over speed
Semantic Kernel feels like it was designed by people who maintain systems at 3am.
Google AI ADK: Opinionated and Cloud-native
What it is: Google’s Agent Development Kit focuses on structured agent workflows, tightly integrated with Google Cloud and Gemini.
Strengths:
- Clear agent lifecycle
- Strong observability hooks
- Cloud-native design
- Production-aligned abstractions
Limitations:
- Less flexible outside Google’s ecosystem
- Smaller open-source community (for now)
- More opinionated architecture
Best fit
- Teams already on GCP
- Production-first AI systems
- Regulated or large-scale environments
ADK assumes you care about deployment and monitoring from day one.
CrewAI: The “Multi-Agent” Narrative
What it is: CrewAI focuses on orchestrating multiple agents with roles, mimicking human teams.
What it’s good at:
- Role-based agent design
- Easy mental model
- Content generation pipelines
Where it falls short:
- Limited control
- Less suitable for complex state handling
- Not ideal for deeply engineered systems
Use CrewAI if
- You’re building collaborative agent demos
- Content or research workflows
- Experimenting with agent behavior
CrewAI is great for storytelling, not systems engineering.
A Practical Decision Framework
Instead of asking “Which framework is best?”, ask:
1. Do I need speed or control?
- Speed → LangChain
- Control → Semantic Kernel / LangGraph
2. Is this production-critical?
- Yes → Semantic Kernel / Google ADK
- No → LangChain / CrewAI
3. Is the workflow stateful and complex?
- Yes → LangGraph
- No → LangChain
4. Enterprise or startup?
- Enterprise → Semantic Kernel / ADK
- Startup → LangChain
The Uncomfortable Truth
Most mature AI teams eventually:
- Start with LangChain
- Outgrow it
- Move to custom orchestration or graph-based systems
Frameworks should accelerate learning, not lock you in.
Final Thought
LLM frameworks are evolving because we still don’t fully understand how to engineer AI systems.
Choose tools that:
- Make failure visible
- Encourage explicit design
- Don’t hide complexity forever
Because eventually, complexity always shows up.
If this helped you think more clearly about the LLM ecosystem, feel free to share or comment with your experience. I’d love to learn how others are navigating this space.