LangChain agentic workflows for developers

Developers build autonomous systems using the LangChain ecosystem. Deep Agents is an agent harness that provides context management, subagents, and skills. LangChain is a framework for integrations and abstractions. LangGraph is a runtime that provides stateful orchestration through nodes and edges. This orchestration allows for loops, branching, and human-in-the-loop steps.

LangGraph replaces AgentExecutor.

Reasoning Patterns and Tooling

Architectures structure how an agent thinks. The ReAct pattern interleaves reasoning and action. The agent observes its state and user input. It reasons about its goal. It executes a tool and evaluates the result. The agent repeats this cycle until it reaches a conclusion.

Alternative designs like Plan-and-Execute separate the planner from the executor. These architectures reduce LLM calls for sub-tasks. The LLMCompiler architecture uses a Directed Acyclic Graph to schedule tasks. This approach increases speed by 3.6x. ReWOO uses variable assignment to allow tasks to depend on previous results. ReWOO allows the planner to generate a plan list with interleaving reasoning and variables.

Tools bridge the gap.

Developers define tools using Python functions and the @tool decorator. The LLM decides which tool to call by reading the docstring. Vague docstrings cause misuse. LLM temperature at 0 reduces hallucinated tool calls by 40 to 60 percent compared to a temperature of 0.7, regardless of which frontier model you use for your specific application.

You know the basics already.

A developer can return a string for human-readable results or an object for structured results. If a developer sets return_direct to true, the agent stops looping and returns the tool output.

Production Deployment and Memory

Memory prevents hallucinations. An EdTech startup with 1,200 students reduced human support tickets by 52 percent using a LangGraph agent. They used RAG for course content and a student progress lookup tool for LMS data. CSAT scores rose from 62 percent to 81 percent. The team built the agent in 6 weeks using two developers. The agent handled 74 percent of incoming queries. Average response time dropped to 8 seconds. This deployment recovered development costs in 4.5 months.

LLMs remain stateless. Memory architecture requires four types: buffer, summary, semantic, and entity memory. Buffer memory stores recent messages while summary memory condenses history. Semantic memory enables relevance-based retrieval and entity memory tracks important concepts. Developers combine LangChain with Supabase pgvector to manage these layers. This stack provides isolation via Row Level Security.

One common error involves using AgentExecutor for complex tasks. AgentExecutor lacks support for conditional branching or parallel tool execution. Developers should instead use LangGraph to manage state.

How do developers manage rising token costs during long loops?

Use Redis to back short-term memory to survive server restarts. Implement a 70/30 weighting between semantic similarity and keyword rank for hybrid retrieval.