As AI applications become more sophisticated, developers are increasingly leveraging frameworks that simplify building LLM-powered systems. Two notable options are LangChain and LangGraph.
LangChain excels at linear, stateless pipelines with modular components, while LangGraph adds a graph-based, stateful orchestration layer ideal for multi-agent workflows. This post compares LangChain vs LangGraph across features, developer experience, ecosystem, use cases, and how integrating a platform like Peliqan can supercharge your LLM applications.
What Are LangChain & LangGraph?
LangChain is a mature open-source framework for building LLM applications. It provides components for prompts, memory, agents, document loaders, vector stores, and API connectors. Developers can chain these together in linear pipelines using the LangChain Expression Language (LCEL).
LangGraph is a graph-based extension designed for complex workflows. It represents pipelines as directed graphs, where nodes are actions and edges define execution flow. LangGraph maintains state, supports loops, branching, multi-agent coordination, and human-in-the-loop controls, making it ideal for more sophisticated LLM applications.
Feature-by-Feature Comparison
| Feature | LangChain | LangGraph |
|---|---|---|
| Orchestration Style | Linear pipeline (LCEL expressions) | Graph-based state machine (nodes and edges) |
| Workflow Complexity | Simple, stateless flows | Complex, stateful, multi-agent flows |
| State Management | Optional memory modules | Centralized, persistent state with rollback support |
| Control Flow | Basic conditionals | Loops, conditionals, retries built-in |
| Agents & Multi-Agent | Single-agent patterns | Native multi-agent orchestration |
| Human-in-the-Loop | Optional manual steps | Native approval points, moderation, audit logs |
| Debugging | Print/log statements; LangSmith traces | LangGraph Studio visualizations; LangSmith traces capture state transitions |
| Integrations | Hundreds of vector stores, APIs, and model providers | Supports all LangChain integrations; nodes can call any LangChain component |
| License | MIT open-source | MIT open-source |
| Community | Large and active | Growing; tens of thousands of stars |
Developer Experience
Learning Curve
LangChain has extensive tutorials, example projects, and community Q&A. Its abstractions (chains, agents, memory) are intuitive for developers familiar with pipelines. LangGraph is newer, so resources are still accumulating, but beginner guides exist (e.g. Medium tutorials) and official docs cover key concepts. Many teams learn LangGraph after using LangChain since it builds on the same principles but adds complexity.
Modularity
LangChain encourages composing small modules; for simple flows, you may not need LangGraph’s graph API. LangGraph requires explicit node and state definitions, providing flexibility but a steeper learning curve. Developers note it is “the higher-level workflow building tool” with more features but “fast-moving and documentation often lacking” (Reddit).
Debugging
LangSmith works with both frameworks. LangGraph Studio visualizes graph execution in real-time (GitHub). LangSmith traces capture state transitions and decision paths for LangGraph agents. LangChain debugging is typically via print/log statements or LangSmith traces. Both allow inspection of intermediate LLM responses and errors.
Community Support
LangChain has a large user base and active maintenance team. LangGraph’s community is growing and backed by the same team, though it hasn’t reached 1.0 yet (Reddit). Both have forums (forum.langchain.com) and GitHub discussions.
Ecosystem and Integrations
Both live within the LangChain ecosystem. LangChain provides hundreds of vector stores, APIs, and model providers (Python Docs, GitHub). LangGraph reuses these integrations under the hood.
Other ecosystem components:
- LangSmith: Logging, debugging, evaluating LLM apps for both frameworks (GitHub).
- LangGraph Platform: Managed SaaS for deploying long-running agents, horizontal scaling, task queues, and monitoring
- Community Tools: Third-party RAG, evaluation, testing libraries integrate with LangChain; LangGraph can leverage all LangChain-compatible libraries.
Cost, Licensing, and Hosting
Both frameworks are MIT-licensed, free to install via pip. Costs come from LLM API usage and compute. Paid options include LangSmith advanced plans (~$39/user/mo) and LangGraph Platform node-execution fees (~$0.001 per node plus deployment minutes)
Source: Pricing details
| Feature | LangChain Developer Plan | LangChain Plus Plan | LangChain Enterprise Plan | LangGraph Developer Plan | LangGraph Plus Plan | LangGraph Enterprise Plan |
|---|---|---|---|---|---|---|
| Cost | $0/month | $39/month | Custom | $0/month | $39/month | Custom |
| Traces/Node Executions | 5,000/month | 10,000/month | Custom | 100,000/month | 100,000/month | Custom |
| Additional Charges | $0.50 per 1,000 traces | $0.50 per 1,000 traces | Custom | $0.001 per node | $0.001 per node | Custom |
| Deployment Time | Not included | Not included | Not included | Not included | Not included | Custom |
| Support | Community | Email support | Support SLA | Community | Advanced support | Advanced support |
| Self-Hosting Option | Yes | Yes | Yes | Yes | Yes | Yes |
Use Cases and Target Audience
LangChain is best for:
- Stateless Pipelines: Text translation, summarization, data extraction, simple chatbots.
- Building Blocks: Vector store connectors, prompt templates, splitters as nodes within LangGraph.
- Rapid Prototyping: Quick experiments with minimal boilerplate.
LangGraph is best for:
- Complex Agent Systems: Coding assistants, data analysis agents with iterative decision logic.
- Long-running or Stateful Workflows: Multi-turn conversational agents or memory-intensive applications.
- Multi-Agent Collaboration: Platforms with multiple specialized agents working together.
- Human-in-the-loop Applications: Content moderation or high-stakes workflows requiring approvals and auditing.
The Peliqan Advantage
As your LLM workflows grow, teams face scattered state, repeated API calls, and debugging challenges. Peliqan complements LangChain and LangGraph by centralizing data, caching repeated calls, and providing observability. This ensures clean, scalable, and analytics-ready data for AI pipelines.
| Challenge | Without Peliqan | With Peliqan |
|---|---|---|
| State Management | Scattered memory and context | Centralized state, caching, and version control |
| Complex Workflows | Difficult to debug and maintain | Visualized execution, observability, easier scaling |
| API Usage | Repeated calls for each workflow step | Cached responses reduce redundant calls |
| Analytics & Reporting | Limited insight into workflow performance | End-to-end logging and monitoring for teams |
Summary
Use LangChain for linear, stateless pipelines and rapid prototyping. Use LangGraph for advanced orchestration, stateful workflows, and multi-agent applications. Both can be combined, and integrating Peliqan ensures your LLM workflows run efficiently, reliably, and analytics-ready at scale.
