LangChain
A framework for building LLM apps with primitives for prompts, tools, memory, and chains/graphs.
LangChain provides building blocks—prompt templates, tool calling, memory, and routing—to compose LLM-powered workflows without reinventing plumbing. It standardizes how you wire models to tools.
Teams use LangChain to prototype agents, RAG pipelines, and chatbots quickly. It offers integrations for vector stores, model providers, and tracing so you can focus on task logic.
In production, LangChain graphs and callbacks enable observability and control over each step. The value is faster iteration and consistent patterns across teams.
Frequently Asked Questions
When should I choose LangChain?
When you want standardized primitives for prompts/tools and integrations with vector DBs/providers. It accelerates prototypes and small teams.
How do I keep LangChain projects maintainable?
Use versioned prompt templates, type your tool schemas, and keep chains/graphs small and testable. Centralize config for models and stores.
Can I run LangChain in production?
Yes—add observability, retries, and guardrails. Use async where possible and monitor latency/cost per step.
How does LangChain work with RAG?
It provides retrievers, vector store adapters, and chains to fetch context, build prompts, and validate outputs. Tune chunking and retrieval separately.
What about tool calling?
Define tools with schemas and handlers; use the model’s function calling. Keep tools idempotent and fast to avoid blocking runs.
How do I debug LangChain apps?
Enable tracing/callbacks to see prompt, response, and tool calls. Log correlation IDs and store traces for replay.
Does LangChain lock me in?
It’s provider-agnostic. You can swap models/stores by changing adapters. Keep abstractions thin to avoid deep coupling.
How do I test LangChain components?
Mock model/tool calls, use fixed inputs, and assert on outputs and schema validity. Run integration tests with staging providers.
Is LangChain suitable for multi-agent systems?
Yes—use graphs/routers and callbacks for coordination, but keep flows explicit to avoid hidden state.
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