Build Custom AI Agents in 2025 with LangChain Framework
Large language models serve as powerful engines for search, analytics, and customer support, but transforming them into functional applications requires a framework like LangChain.
Large language models (LLMs) are revolutionizing industries—from search and analytics to customer support and creative tools. However, these models alone are just "engines." To turn them into practical applications, developers need frameworks like LangChain, a Python-based tool simplifying LLM integration.
What is LangChain?
LangChain provides a unified interface for:
- Chaining prompts
- Managing conversation memory
- Calling external tools
- Working with vector stores
It supports multiple backends, including OpenAI, Hugging Face, local GGUF binaries, and custom microservices—without code rewrites.
Author: Aleksei Aleinikov | 3 min read | 12 hours ago
Quick fact: LangChain's first commit appeared in late 2022. By spring 2025, it surpassed 50,000 GitHub stars—a remarkable growth trajectory.
Getting Started
Install LangChain via pip: bash pip install langchain
For Hugging Face models: bash pip install langchain-huggingface
Core Component: The Runnable Interface
Most LangChain objects implement Runnable, defining how they process inputs and outputs. This modularity allows developers to assemble AI agents from reusable components.
Why It Matters
LangChain democratizes AI agent development, enabling:
- Faster prototyping
- Seamless model switching
- Scalable deployments
As AI adoption grows, frameworks like LangChain will be critical for turning LLMs into real-world solutions.
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About the Author
David Chen
AI Startup Analyst
Senior analyst focusing on AI startup ecosystem with 11 years of venture capital and startup analysis experience. Former member of Sequoia Capital AI investment team, now independent analyst writing AI startup and investment analysis articles for Forbes, Harvard Business Review and other publications.