What is the difference between LangChain and LangGraph?

LangChain is a framework for chaining LLM calls and tool use in sequence. LangGraph extends this with a directed graph model — nodes are agent states and edges are transitions. LangGraph enables cycles (an agent can retry or loop), branching (conditional routing to different agents), persistent state checkpoints and human-in-the-loop interrupts. For simple linear pipelines, LangChain is sufficient. For production agentic systems with multiple actors and decision points, LangGraph is the right choice.

When should I use LlamaIndex instead of LangChain?

LlamaIndex has deeper primitives for document ingestion, chunking strategies, retrieval evaluation and index management — making it the better choice for complex RAG systems where retrieval quality is the primary concern. LangChain/LangGraph is stronger for multi-agent orchestration, tool use and workflows that go beyond retrieval. For many production systems, we use both: LlamaIndex for the retrieval layer and LangGraph for agent orchestration.

How do you monitor and debug LangChain applications in production?

LangSmith is the purpose-built observability platform for LangChain applications. It captures full traces of every LLM call — inputs, outputs, latency, token counts and tool calls — and enables evaluation runs to test prompt changes against historical data. We include LangSmith in all production LangGraph deployments. For teams that cannot use a third-party observability service, we instrument OpenTelemetry traces directly.

Is LangChain stable enough for production use?

LangChain has historically made breaking API changes between major versions, which frustrated early adopters. Since v0.2, the project has stabilised — LangChain core, LangChain community and LangGraph are versioned independently and breaking changes are flagged with deprecation warnings. LangGraph in particular has been stable and production-ready since late 2024. We pin dependency versions and test upgrades in isolation before updating production systems.

AI/ML

LangChain / LangGraph

Orchestration framework for AI agents and multi-step LLM pipelines.

Book a Discovery Call LangChain / LangGraph website

LangChain is a Python/JavaScript framework for building LLM-powered applications. LangGraph (built on LangChain) extends it with stateful, graph-based agent orchestration — enabling multi-agent systems, cycles, human-in-the-loop interrupts and persistent state. We use LangGraph as our primary framework for production AI agent systems.

Why we chose it

Why Ascii-Core uses LangChain / LangGraph

1
LangGraph provides stateful graph execution — agents can loop, branch and hand off to other agents cleanly
2
Built-in support for human-in-the-loop interrupts — essential for production AI with oversight requirements
3
Native integration with OpenAI, Anthropic, vector stores and tools via LangChain's connector ecosystem
4
LangSmith provides full observability — traces, evaluations and debugging for every LLM call

Ideal use cases

When to use LangChain / LangGraph

Multi-agent orchestration systems with complex routing logic
AI workflows requiring persistent state across multiple LLM calls
Production systems needing LLM observability and evaluation
RAG pipelines with retrieval routing and re-ranking
Human-in-the-loop AI workflows with approval steps

Trade-offs

Honest limitations

We believe in accurate trade-offs. These are the cases where a different tool may be the better choice.

Abstraction overhead — can obscure what is actually happening in simple pipelines
Rapid API changes — major versions have introduced breaking changes historically
LlamaIndex has deeper RAG primitives for complex retrieval-only use cases

Services

Services using LangChain / LangGraph

Ascii-Core service offerings where this technology plays a central role.

AI Engineering

Custom AI that automates work and builds competitive advantage.

Learn more

AI Agents

Autonomous agents that handle real work across your tools.

Learn more

Solutions

Business solutions using LangChain / LangGraph

Outcome-focused solution packages that rely on this technology.

Comparisons