LangGraph provides a framework for building stateful, multi-step AI workflows where control flow is defined explicitly through graph structures. Unlike pure prompt chaining where one LLM output feeds directly into the next prompt, LangGraph enables conditional branching, parallel execution, and sophisticated state management - treating AI workflows as graph traversal problems.
The framework distinguishes between workflows and agents. Workflows orchestrate LLMs and tools through predefined code paths, with developers controlling the execution flow. Agents, in contrast, use LLMs to dynamically direct their own processes and tool usage. LangGraph supports both paradigms, though the workflow patterns provide more predictable behavior for production systems.
Core Workflow Patterns
Prompt Chaining sequences multiple LLM calls where each processes the previous output. The first call might extract entities, the second classifies them, and the third generates a summary. This creates a pipeline where complexity emerges from combining simple steps. The pattern mirrors Unix pipes - small, focused operations composed into sophisticated behavior.
Parallelization executes multiple LLM tasks simultaneously, then aggregates results. Rather than sequentially processing five research questions, all five run concurrently with results merged into a final synthesis. This directly addresses the performance advantages of Multi-Agent Research Systems where parallel exploration dramatically outperforms sequential investigation.
Routing classifies inputs and directs them to specialized handlers. A user query might get routed to a technical support workflow, a sales workflow, or a general information workflow based on intent classification. This enables building focused sub-workflows rather than one monolithic handler attempting everything.
Orchestrator-Worker implements the Orchestrator-Worker Pattern where a lead LLM decomposes complex tasks into subtasks, delegates to specialized workers, then synthesizes their outputs. The orchestrator handles high-level reasoning about task decomposition while workers execute focused subtasks in parallel.
Evaluator-Optimizer creates feedback loops where one LLM generates content and another critiques it, iterating until quality criteria are met. This enables self-improving outputs through multiple refinement passes - the generator proposes solutions while the evaluator identifies weaknesses.
State Management
LangGraph’s defining characteristic is sophisticated state management across workflow steps. State persists between LLM calls, accumulates information through graph traversal, and determines which paths execute through conditional edges.
The state schema defines what information flows through the workflow:
- User inputs and intermediate outputs
- Conversation history and context
- Tool invocation results
- Metadata and routing decisions
Conditional edges read this state to determine next steps. An edge might route to different nodes based on:
- Classification results from earlier steps
- Quality scores from evaluators
- Resource constraints (token budgets, time limits)
- User feedback or human-in-the-loop decisions
This stateful design enables sophisticated Context Engineering. The framework can implement context pruning by having a node filter conversation history before it flows to the next LLM call. It can realize context summarization through dedicated summarization nodes that compress state. Context quarantine emerges naturally from parallel branches that don’t share state.
Persistence and Memory
Unlike ephemeral prompt chains that lose state between invocations, LangGraph provides persistence mechanisms. Workflows can checkpoint their state, enabling:
Human-in-the-Loop: Pause workflow execution for human review or input, then resume from the checkpoint. This supports scenarios where AI proposes actions but humans approve them before execution.
Long-Running Tasks: Workflows can span hours or days, checkpointing progress rather than requiring completion in one session. This enables complex research or analysis tasks that exceed practical timeout limits.
Conversational Memory: Maintain conversation context across multiple user interactions. The workflow remembers previous exchanges, user preferences, and accumulated context without reloading everything each turn.
Failure Recovery: When errors occur, workflows can reload from the last successful checkpoint rather than restarting from scratch. This improves reliability for multi-step processes.
Persistence transforms workflows from transient execution into durable processes, enabling production applications that require reliability and continuity.
Context Flow Control
LangGraph enables precise control over how context flows through workflows:
Selective Propagation: Not all state must flow to all nodes. A node can read specific state fields while remaining isolated from others. This implements context quarantine at a granular level - different workflow branches see different context slices.
Context Transformation: Nodes can transform context as it flows. A summarization node compresses verbose state before it reaches downstream nodes. A pruning node filters irrelevant information. An enrichment node adds retrieved information. These transformations implement Context Engineering Strategies explicitly.
Dynamic Context Assembly: Rather than maintaining one monolithic context, workflows can assemble fresh context for each LLM call based on current needs. A retrieval node fetches relevant documents, a pruning node filters them, and only then does the context reach the generation node. This prevents context accumulation that leads to Context Rot.
Streaming: Workflows can stream intermediate states, enabling progressive disclosure rather than waiting for complete workflow execution. Users see results as they become available, improving perceived performance and enabling early feedback.
Tool Integration
LangGraph provides structured tool calling with dynamic tool availability:
Tool Nodes: Dedicated nodes execute tool calls with their results flowing back into state. This separates tool execution from LLM reasoning, enabling better error handling and retries.
Conditional Tool Loading: Workflows can implement tool loadout optimization from Context Engineering Strategies by dynamically determining which tool definitions to include in LLM context based on the current task.
Parallel Tool Calling: Multiple tools can execute simultaneously, with results aggregated before proceeding. This mirrors how Multi-Agent Research Systems enable parallel information gathering.
Tool Result Filtering: Rather than flowing all tool outputs directly to the next LLM call, workflows can filter, summarize, or transform tool results. A web search might return 20 results, but a filtering node selects only the 3 most relevant for the LLM’s context.
Multi-Agent Orchestration
LangGraph naturally supports multi-agent architectures through graph structure:
Each agent becomes a subgraph with its own state and execution logic. The orchestrator graph coordinates these agent subgraphs, routing tasks to appropriate agents and synthesizing their outputs.
This architectural approach implements context quarantine - each agent operates within its isolated state space. The orchestrator sees only agent outputs (summaries, findings, results) rather than their full reasoning context. This prevents exponential context growth while enabling parallel exploration.
Open Deep Research demonstrates this pattern at scale. The system uses LangGraph to implement a three-phase architecture (scope, research, write) where multiple research agents operate in parallel, each maintaining its own context while the orchestrator coordinates their efforts.
Workflow Composition
Complex workflows emerge from composing simpler patterns:
A research workflow might combine:
- Routing: Classify query type
- Orchestrator-Worker: Decompose into research questions
- Parallelization: Multiple agents explore simultaneously
- Evaluator-Optimizer: Refine findings through critique
- Prompt Chaining: Sequential synthesis of results
Each pattern addresses a specific requirement. Composition enables building sophisticated behaviors from well-understood primitives. This mirrors The Lego Approach for Building Agentic Systems - complex capabilities from composable components.
Production Considerations
Observability: LangGraph provides visibility into workflow execution - which nodes executed, what state looked like at each step, where time was spent. This debugging capability is crucial for non-deterministic workflows where behavior varies across runs.
Deployment: The framework supports multiple deployment models - local execution for development, cloud deployment for production, and integration with LangSmith for monitoring. This flexibility enables consistent development and production experiences.
Cost Management: Explicit workflow structure enables cost tracking per node. You can see which LLM calls consume the most tokens, where tool executions add latency, and which paths through the graph are most expensive. This guides optimization efforts.
Testing: Deterministic workflow paths are testable through traditional methods - specific inputs produce specific state transitions. Non-deterministic LLM outputs require LLM-as-Judge evaluation, but the workflow structure makes it clear what to test at each node.
Research Workflow Patterns
Deep Research Systems push LangGraph to handle sophisticated multi-agent coordination, revealing patterns specific to research applications.
Subgraph Pattern for Parallel Agents: Research Workflow Architecture implements worker agents as independent subgraphs that execute in parallel. The supervisor graph spawns worker subgraphs dynamically based on topic decomposition. Each worker subgraph has private state (research findings, tool outputs) while sharing minimal state with supervisor (compressed summaries only). This implements Isolating Context architecturally - parallel subgraphs can’t access each other’s state, preventing contamination.
State Aggregation from Multiple Subgraphs: The supervisor node aggregates outputs from parallel worker subgraphs into synthesis state. Implementation uses reducer functions that merge worker summaries: findings: List[str] = Field(default_factory=list, reducer=lambda x, y: x + y). This ensures all worker outputs flow to supervisor without conflicts. The aggregation node runs after all workers complete, implementing synchronization point in otherwise parallel execution.
Conditional Agent Spawning: Research Delegation Heuristics translate to conditional edges inspecting state: if len(state.findings) < required_coverage: return "spawn_more_agents" else: return "proceed_to_synthesis". This adaptive workflow adjusts agent count based on intermediate results rather than predetermined structure. The supervisor evaluates whether current findings sufficiently address research brief, spawning additional workers if gaps exist.
Research-Specific Node Types: Common node patterns in research graphs: (1) Scoping nodes using conversational models for clarification, (2) Decomposition nodes breaking briefs into subtopics, (3) Worker nodes implementing ReAct Agent Pattern with search tools, (4) Compression nodes using fast models for summarization, (5) Synthesis nodes aggregating across workers, (6) Writing nodes generating final reports. Each node type has characteristic state inputs/outputs and model requirements.
Integration with MCP Servers: Model Context Protocol Integration connects through tool nodes that initialize MCP clients, discover available tools, and invoke them based on agent decisions. The graph structure separates MCP initialization (startup) from tool usage (runtime), preventing repeated connection overhead. Tool nodes update state with MCP results, which subsequent nodes consume.
Comparison with Alternatives
Pure prompt chaining is simpler but offers minimal control. Once the chain starts, it runs to completion with no conditional logic. LangGraph’s graph structure enables branching based on intermediate results.
Agent frameworks like AutoGPT give models more autonomy but less predictability. The agent decides its own actions, making behavior harder to constrain. LangGraph workflows maintain developer control while leveraging LLM capabilities.
Workflow engines like Temporal or Airflow manage arbitrary tasks but lack AI-specific features. LangGraph provides LLM primitives - prompt management, token tracking, streaming, conversation memory - that generic workflow engines don’t offer.
Context Engineering Integration
LangGraph serves as infrastructure for Context Engineering patterns:
- RAG: Retrieval nodes fetch documents, filtering nodes select relevant results
- Summarization: Dedicated summarization nodes compress context between stages
- Pruning: Filter nodes remove irrelevant state before LLM calls
- Quarantine: Parallel branches maintain isolated contexts
- Offloading: State persistence enables storing context externally
The framework makes context transformations explicit rather than implicit. You can see where context grows, shrinks, filters, or enriches. This visibility enables systematic context management rather than hoping the right information reaches the right LLM calls.