Agent frameworks (like LangChain)
Agent frameworks provide abstractions that make it easier to get started when building with LLMs. LangChain is an agent framework that provides abstractions like structured content blocks, the agent loop, and middleware. LangChain’s abstractions are designed to be easy to get started with while still providing the flexibility needed for advanced use cases. While LangChain is built on top of LangGraph, you don’t need to know LangGraph to use LangChain. Other examples of agent frameworks include Vercel’s AI SDK, CrewAI, OpenAI Agents SDK, Google ADK, LlamaIndex, and many more.When to use LangChain
Use LangChain when:- You want to quickly build agents and autonomous applications.
- You need standard abstractions for models, tools, and agent loops.
- You want an easy-to-use framework that still provides flexibility.
- You’re building straightforward agent applications without complex orchestration needs.
Agent runtimes (like LangGraph)
Agent runtimes provide the tooling for running agents in production. Supported tools may include:- Durable execution: Agents persist through failures and can run for extended periods, resuming from where they left off.
- Streaming: Support for streaming workflows and responses.
- Human-in-the-loop: Incorporate human oversight by inspecting and modifying agent state.
- Persistence: Thread-level and cross-thread persistence for state management.
- Low-level control: Direct control over agent orchestration without high-level abstractions.
When to use LangGraph
Use LangGraph when:- You need fine-grained, low-level control over agent orchestration.
- You need durable execution for long-running, stateful agents.
- You’re building complex workflows that combine deterministic and agentic steps.
- You need production-ready infrastructure for agent deployment.
Agent harnesses (like the Deep Agents SDK)
Agent harnesses are opinionated agent frameworks with that come batteries included with built-in tools and capabilities that make building sophisticated, long-running agents easier. Supported tools may include:- Planning capabilities: Track multiple tasks with a to-do list.
- Task delegation: Delegate work and keep context clean with subagents.
- File system: Read and write access to files on different pluggable storage backends.
- Token management: Conversation history summarization and large tool result eviction.
When to use the Deep Agents SDK
Use the Deep Agents SDK when:- You are building agents that run over long time periods.
- You are building agents that need to handle complex, multi-step tasks.
- You want to use predefined tools, such as filesystem operations, bash execution, and automated context engineering.
- You want to use predefined prompts and subagents.
Feature comparison
While you can accomplish similar tasks with LangChain, LangGraph, and Deep Agents, the level at which you integrate them differ:| Feature | LangChain | LangGraph | Deep Agents |
|---|---|---|---|
| Short-term memory | Short-term memory | Short-term memory | StateBackend |
| Long-term memory | Long-term memory | Long-term memory | Long-term memory |
| Skills | Multi-agent skills | - | Skills |
| Subagents | Multi-agent subagents | Subgraphs | Subagents |
| Human-in-the-loop | Human-in-the-loop middleware | Interrupts | interrupt_on parameter |
| Streaming | Agent Streaming | Streaming | Streaming |