LangChain vs Llama Stack: Agent Frameworks Comparison

LangChain is the most widely adopted framework for building LLM-powered applications and AI agents, founded in 2022 by Harrison Chase. The company provides an open-source Python and TypeScript framework with abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems.

LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring for LLM applications in production. The LangChain ecosystem is the largest in the AI application development space, with the company reaching $16M in revenue and 1,000 customers by 2025.

Backed by $260M in total funding at a $1.25B valuation, LangChain has grown to 199 employees and is headquartered in San Francisco. The company serves as the de facto orchestration layer for teams building production AI applications.

Llama Stack is Meta open-source framework that defines and standardizes core building blocks for AI application development, providing a unified set of APIs with implementations from leading service providers. Launched to simplify deployment across different providers, Llama Stack collaborates with partners including NVIDIA NeMo microservices, IBM, Red Hat, and Dell Technologies. The framework is completely free and open-source under Meta permissive licensing, with costs only for API usage when using hosted Llama models through cloud providers. Pricing varies by model and provider: Llama 3.1 8B Instruct starts at USD 0.020/USD 0.050 per million tokens (input/output), Llama 4 Scout at USD 0.0800 per million tokens, and Llama 4 Maverick at USD 0.150/USD 0.600 per million tokens. Recent pricing reductions include 50 percent cuts for Llama 3.1 405B and Llama 3.3 70B models. While the project shows robust community activity and regular engagement calls, developers report challenges including setup and configuration complexity, build failures, import errors suggesting documentation gaps, Windows compatibility issues, and lack of security policies.