LangGraph vs LangChain and more
Intelligent Value Extraction
SELECTING THE ENGINE
A definitive guide to LangChain, LangGraph, Instructor, and DSPy. When to use them, and crucially, when to avoid them.
LangChain
The EcosystemThe “glue” of the AI world. LangChain abstracts away the complexity of integrations. It excels at connecting standardized components (prompts, models, parsers) into linear sequences.
The Pros
- Massive Ecosystem: If a tool exists, LangChain has an integration for it.
- Standard Interface: Switch from OpenAI to Anthropic by changing one line of code.
- Off-the-shelf Chains: Ready-made architectures for RAG, SQL, and summarization.
The Cons
- Abstraction Bloat: The wrapper layers can make debugging internal errors difficult.
- Learning Curve: The API surface area is vast and changes frequently.
5 Projects to Build with LangChain
1. PDF Q&A Bot (RAG)
Standard loaders and splitters make this trivial to set up.
2. SQL Query Generator
Excellent built-in chains for translating natural language to SQL.
3. Chatbot with Memory
RunnableHistory handles session storage effortlessly.
4. Content Summarizer
Map-reduce chains are pre-built for handling large documents.
5. Router System
Directing a query to “math” or “history” chains using semantic similarity.
3 Projects NOT to Build
1. Highly Looping Agents
For complex cycles and state management, LangChain chains get messy.
Better: LangGraph
2. Pure Data Extraction
LangChain parsers can be brittle compared to Pydantic direct use.
Better: Instructor
3. High-Performance Pipelines
If you need to mathematically optimize your prompts for 1% accuracy gains.
Better: DSPy
LangGraph
The LoopBuilt on top of LangChain, LangGraph treats your application as a state machine. It introduces cycles (loops), allowing agents to “think,” act, observe, and loop back until a task is done.
The Pros
- Cyclic Graphs: First-class support for loops, essential for agentic behaviors.
- State Management: Maintains global state across different nodes (actors).
- Control: You explicitly define the flow edges, reducing “black box” magic.
The Cons
- Verbosity: Requires defining graph state, nodes, and edges manually.
- Overkill: Too complex for simple linear “input -> output” tasks.
5 Projects to Build with LangGraph
1. Coding Assistant Agent
Write code -> Run tests -> If fail, loop back and fix -> If pass, submit.
2. Multi-Agent Collaboration
A “Researcher” node passes data to a “Writer” node, managed by a supervisor.
3. Human-in-the-Loop
Pause execution before sensitive actions (e.g., sending email) for user approval.
4. Self-Reflective RAG
Retrieve docs -> Grade relevance -> If bad, rewrite query and loop back.
5. Long-running Workflows
Tasks that persist over days, utilizing LangGraph’s checkpointer.
3 Projects NOT to Build
1. Simple Q&A
If there is no decision making or looping, a graph is unnecessary complexity.
Better: LangChain
2. Production Data Cleaning
Processing millions of rows where structure matters more than reasoning.
Better: Instructor
3. Prompt Optimization
LangGraph manages flow, not prompt quality or tuning.
Better: DSPy
Instructor
The StructureInstructor operates on a simple philosophy: LLMs are just function callers. By leveraging Pydantic, it ensures that your model outputs valid, structured JSON, 100% of the time.
The Pros
- Simplicity: It patches the standard OpenAI client. Minimal new syntax to learn.
- Validation: Uses Pydantic to retry requests automatically if validation fails.
- Native Python: You work with Python objects, not string parsing regex.
The Cons
- Not an Orchestrator: It doesn’t handle chains, memory, or agent loops.
- Model Dependent: Works best with models that support function calling strongly.
5 Projects to Build with Instructor
1. Resume Parser
Extracting Name, Skills, and Experience into a strict JSON schema.
2. Knowledge Graph Creation
Extracting precise Nodes and Edges lists from unstructured text.
3. Content Moderation
Classifying text with specific tags, confidence scores, and reasoning fields.
4. Synthetic Data Generator
Generating fake user profiles that adhere to strict validation rules.
5. Citations Extractor
Answering a query and returning the specific quote and page number used.
3 Projects NOT to Build
1. Conversational Chatbot
Managing chat history and context is manual work in Instructor.
Better: LangChain
2. Complex Agents
If you need tools using other tools, Instructor is too low-level.
Better: LangGraph
3. Zero-shot Tasks
If you don’t care about the output format (just want text), it adds overhead.
DSPy
The OptimizerDSPy changes the paradigm from “Prompt Engineering” to “Programming.” You define the logic (signatures) and the metric, and DSPy “compiles” the best prompts for you, optimizing performance automatically.
The Pros
- Auto-Optimization: Finds better prompts than humans can write manually.
- Model Agnostic: Compiles different prompts for Llama 3 vs GPT-4 automatically.
- Systematic: Replaces brittle string manipulation with modular programming.
The Cons
- High Learning Curve: Requires unlearning how you currently do prompt engineering.
- Opaque: The “compiled” prompts can look strange to humans.
5 Projects to Build with DSPy
1. Complex Math Solver
Using Chain-of-Thought optimization to maximize accuracy on logic problems.
2. Production RAG Pipeline
Optimizing exactly how the context is injected to reduce hallucinations.
3. Few-Shot Classifier
Automatically selecting the best 5 examples to include in the prompt from a dataset.
4. Cost-Reduction Pipeline
Compiling a prompt that allows a smaller model (7B) to perform like a larger one.
5. Multi-Hop Reasoning
Questions requiring multiple steps where DSPy optimizes the reasoning path.
3 Projects NOT to Build
1. Simple Prototypes
Writing DSPy modules takes more time than writing one prompt string.
Better: LangChain
2. Creative Writing
DSPy optimizes for a metric; quantifying “creativity” is hard.
3. Streaming UI Apps
Handling streaming tokens in a compiled DSPy module is currently complex.
Better: LangChain
Ali Reza Rashidi
Ali Reza Rashidi, a BI analyst with over nine years of experience, He is the author of three books that delve into the world of data and management.
