LangGraph Multi-Agent Swarm is a Python library designed to orchestrate multiple AI agents as a cohesive “swarm.” It builds on LangGraph, a framework for constructing robust, stateful agent workflows, to enable a specialized form of multi-agent architecture. In a swarm, agents with different specializations dynamically hand off control to one another as tasks demand, rather than a single monolithic agent attempting everything. The system tracks which agent was last active so that when a user provides the next input, the conversation seamlessly resumes with that same agent. This approach addresses the problem of building cooperative AI workflows where the most qualified agent can handle each sub-task without losing context or continuity.
LangGraph Swarm aims to make such multi-agent coordination easier and more reliable for developers. It provides abstractions to link individual language model agents (each potentially with their tools and prompts) into one integrated application. The library comes with out-of-the-box support for streaming responses, short-term and long-term memory integration, and even human-in-the-loop intervention, thanks to its foundation on LangGraph. By leveraging LangGraph (a lower-level orchestration framework) and fitting naturally into the broader LangChain ecosystem, LangGraph Swarm allows machine learning engineers and researchers to build complex AI agent systems while maintaining explicit control over the flow of information and decisions.
LangGraph Swarm Architecture and Key Features
At its core, LangGraph Swarm represents multiple agents as nodes in a directed state graph, edges define handoff pathways, and a shared state tracks the ‘active_agent’. When an agent invokes a handoff, the library updates that field and transfers the necessary context so the next agent seamlessly continues the conversation. This setup supports collaborative specialization, letting each agent focus on a narrow domain while offering customizable handoff tools for flexible workflows. Built on LangGraph’s streaming and memory modules, Swarm preserves short-term conversational context and long-term knowledge, ensuring coherent, multi-turn interactions even as control shifts between agents.
Agent Coordination via Handoff Tools
LangGraph Swarm’s handoff tools let one agent transfer control to another by issuing a ‘Command’ that updates the shared state, switching the ‘active_agent’ and passing along context, such as relevant messages or a custom summary. While the default tool hands off the full conversation and inserts a notification, developers can implement custom tools to filter context, add instructions, or rename the action to influence the LLM’s behavior. Unlike autonomous AI-routing patterns, Swarm’s routing is explicitly defined: each handoff tool specifies which agent may take over, ensuring predictable flows. This mechanism supports collaboration patterns, such as a “Travel Planner” delegating medical questions to a “Medical Advisor” or a coordinator distributing technical and billing queries to specialized experts. It relies on an internal router to direct user messages to the current agent until another handoff occurs.
State Management and Memory
Managing state and memory is essential for preserving context as agents hand off tasks. By default, LangGraph Swarm maintains a shared state, containing the conversation history and an ‘active_agent’ marker, and uses a checkpointer (such as an in-memory saver or database store) to persist this state across turns. Also, it supports a memory store for long-term knowledge, allowing the system to log facts or past interactions for future sessions while keeping a window of recent messages for immediate context. Together, these mechanisms ensure the swarm never “forgets” which agent is active or what has been discussed, enabling seamless multi-turn dialogues and accumulating user preferences or critical data over time.
When more granular control is needed, developers can define custom state schemas so each agent has its private message history. By wrapping agent calls to map the global state into agent-specific fields before invocation and merging updates afterward, teams can tailor the degree of context sharing. This approach supports workflows ranging from fully collaborative agents to isolated reasoning modules, all while leveraging LangGraph Swarm’s robust orchestration, memory, and state-management infrastructure.
Customization and Extensibility
LangGraph Swarm offers extensive flexibility for custom workflows. Developers can override the default handoff tool, which passes all messages and switches the active agent, to implement specialized logic, such as summarizing context or attaching additional metadata. Custom tools simply return a LangGraph Command to update state, and agents must be configured to handle those commands via the appropriate node types and state-schema keys. Beyond handoffs, one can redefine how agents share or isolate memory using LangGraph’s typed state schemas: mapping the global swarm state into per-agent fields before invocation and merging results afterward. This enables scenarios where an agent maintains a private conversation history or uses a different communication format without exposing its internal reasoning. For full control, it’s possible to bypass the high-level API and manually assemble a ‘StateGraph’: add each compiled agent as a node, define transition edges, and attach the active-agent router. While most use cases benefit from the simplicity of ‘create_swarm’ and ‘create_react_agent’, the ability to drop down to LangGraph primitives ensures that practitioners can inspect, adjust, or extend every aspect of multi-agent coordination.
Ecosystem Integration and Dependencies
LangGraph Swarm integrates tightly with LangChain, leveraging components like LangSmith for evaluation, langchain_openai for model access, and LangGraph for orchestration features such as persistence and caching. Its model-agnostic design lets it coordinate agents across any LLM backend (OpenAI, Hugging Face, or others), and it’s available in both Python (‘pip install langgraph-swarm’) and JavaScript/TypeScript (‘@langchain/langgraph-swarm’), making it suitable for web or serverless environments. Distributed under the MIT license and with active development, it continues to benefit from community contributions and enhancements in the LangChain ecosystem.
Sample Implementation
Below is a minimal setup of a two-agent swarm:
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import create_react_agent
from langgraph_swarm import create_handoff_tool, create_swarm
model = ChatOpenAI(model="gpt-4o")
# Agent "Alice": math expert
alice = create_react_agent(
model,
[lambda a,b: a+b, create_handoff_tool(agent_name="Bob")],
prompt="You are Alice, an addition specialist.",
name="Alice",
)
# Agent "Bob": pirate persona who defers math to Alice
bob = create_react_agent(
model,
[create_handoff_tool(agent_name="Alice", description="Delegate math to Alice")],
prompt="You are Bob, a playful pirate.",
name="Bob",
)
workflow = create_swarm([alice, bob], default_active_agent="Alice")
app = workflow.compile(checkpointer=InMemorySaver())Here, Alice handles additions and can hand off to Bob, while Bob responds playfully but routes math questions back to Alice. The InMemorySaver ensures conversational state persists across turns.
Use Cases and Applications
LangGraph Swarm unlocks advanced multi-agent collaboration by enabling a central coordinator to dynamically delegate sub-tasks to specialized agents, whether that’s triaging emergencies by handing off to medical, security, or disaster-response experts, routing travel bookings between flight, hotel, and car-rental agents, orchestrating a pair-programming workflow between a coding agent and a reviewer, or splitting research and report generation tasks among researcher, reporter, and fact-checker agents. Beyond these examples, the framework can power customer-support bots that route queries to departmental specialists, interactive storytelling with distinct character agents, scientific pipelines with stage-specific processors, or any scenario where dividing work among expert “swarm” members boosts reliability and clarity. At the same time, LangGraph Swarm handles the underlying message routing, state management, and smooth transitions.
In conclusion, LangGraph Swarm marks a leap toward truly modular, cooperative AI systems. Structured multiple specialized agents into a directed graph solves tasks that a single model struggles with, each agent handles its expertise, and then hands off control seamlessly. This design keeps individual agents simple and interpretable while the swarm collectively manages complex workflows involving reasoning, tool use, and decision-making. Built on LangChain and LangGraph, the library taps into a mature ecosystem of LLMs, tools, memory stores, and debugging utilities. Developers retain explicit control over agent interactions and state sharing, ensuring reliability, yet still leverage LLM flexibility to decide when to invoke tools or delegate to another agent.
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