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docs: update multi-agent concept doc #2684

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200 changes: 151 additions & 49 deletions docs/docs/concepts/multi_agent.md
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Expand Up @@ -26,54 +26,173 @@ There are several ways to connect agents in a multi-agent system:
- **Hierarchical**: you can define a multi-agent system with [a supervisor of supervisors](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/). This is a generalization of the supervisor architecture and allows for more complex control flows.
- **Custom multi-agent workflow**: each agent communicates with only a subset of agents. Parts of the flow are deterministic, and only some agents can decide which other agents to call next.

### Handoffs

In multi-agent architectures, agents can be represented as graph nodes. Each agent node executes its step(s) and decides whether to finish execution or route to another agent, including potentially routing to itself (e.g., running in a loop). A common pattern in multi-agent interactions is handoffs, where one agent hands off control to another. Handoffs allow you to specify:

- __destination__: target agent to navigate to (e.g., name of the node to go to)
- __payload__: [information to pass to that agent](#communication-between-agents) (e.g., state update)

To implement handoffs in LangGraph, agent nodes can return [`Command`](./low_level.md#command) object that allows you to combine both control flow and state updates:

```python
def agent(state) -> Command[Literal["agent", "another_agent"]]:
# the condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.
goto = get_next_agent(...) # 'agent' / 'another_agent'
return Command(
# Specify which agent to call next
goto=goto,
# Update the graph state
update={"my_state_key": "my_state_value"}
)
```

In a more complex scenario where each agent node is itself a graph (i.e., a [subgraph](./low_level.md#subgraphs)), a node in one of the agent subgraphs might want to navigate to a different agent. For example, if you have two agents, `alice` and `bob` (subgraph nodes in a parent graph), and `alice` needs to navigate to `bob`, you can set `graph=Command.PARENT` in the `Command` object:

```python
def some_node_inside_alice(state)
return Command(
goto="bob",
update={"my_state_key": "my_state_value"},
# specify which graph to navigate to (defaults to the current graph)
graph=Command.PARENT,
)
```

!!! note
If you need to support visualization for subgraphs communicating using `Command(graph=Command.PARENT)` you would need to wrap them in a node function with `Command` annotation, e.g. instead of this:

```python
builder.add_node(alice)
```

you would need to do this:

```python
def call_alice(state) -> Command[Literal["bob"]]:
return alice.invoke(state)

builder.add_node("alice", call_alice)
```

#### Handoffs as tools

One of the most common agent types is a ReAct-style tool-calling agents. For those types of agents, a common pattern is wrapping a handoff in a tool call, e.g.:

```python
def transfer_to_bob(state):
"""Transfer to bob."""
return Command(
goto="bob",
update={"my_state_key": "my_state_value"},
graph=Command.PARENT,
)
```

This is a special case of [updating the graph state from tools](../how-tos/update-state-from-tools.ipynb) where in addition the state update, the control flow is included as well.

!!! important

If you want to use tools that return `Command`, you can either use prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] / [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] components, or implement your own tool-executing node that collects `Command` objects returned by the tools and returns a list of them, e.g.:

```python
def call_tools(state):
...
commands = [tools_by_name[call["name"].invoke(call, config={"coerce_tool_content": False}) for tool_call in tool_calls]
return commands
```

Let's now take a closer look at the different multi-agent architectures.

### Network

In this architecture, agents are defined as graph nodes. Each agent can communicate with every other agent (many-to-many connections) and can decide which agent to call next. This architecture is good for problems that do not have a clear hierarchy of agents or a specific sequence in which agents should be called.


```python
from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START

model = ChatOpenAI()

def agent_1(state: MessagesState) -> Command[Literal["agent_2", "agent_3", END]]:
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# to determine which agent to call next. a common pattern is to call the model
# with a structured output (e.g. force it to return an output with a "next_agent" field)
response = model.invoke(...)
# route to one of the agents or exit based on the LLM's decision
# if the LLM returns "__end__", the graph will finish execution
return Command(
goto=response["next_agent"],
update={"messages": [response["content"]]},
)

def agent_2(state: MessagesState) -> Command[Literal["agent_1", "agent_3", END]]:
response = model.invoke(...)
return Command(
goto=response["next_agent"],
update={"messages": [response["content"]]},
)

def agent_3(state: MessagesState) -> Command[Literal["agent_1", "agent_2", END]]:
...
return Command(
goto=response["next_agent"],
update={"messages": [response["content"]]},
)

builder = StateGraph(MessagesState)
builder.add_node(agent_1)
builder.add_node(agent_2)
builder.add_node(agent_3)

builder.add_edge(START, "agent_1")
network = builder.compile()
```

### Supervisor

In this architecture, we define agents as nodes and add a supervisor node (LLM) that decides which agent nodes should be called next. We use [conditional edges](./low_level.md#conditional-edges) to route execution to the appropriate agent node based on supervisor's decision. This architecture also lends itself well to running multiple agents in parallel or using [map-reduce](../how-tos/map-reduce.ipynb) pattern.

```python
from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.graph import StateGraph, MessagesState, START, END

model = ChatOpenAI()

class AgentState(MessagesState):
next: Literal["agent_1", "agent_2", "__end__"]

def supervisor(state: AgentState):
def supervisor(state: MessagesState) -> Command[Literal["agent_1", "agent_2", END]]:
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# to determine which agent to call next. a common pattern is to call the model
# with a structured output (e.g. force it to return an output with a "next_agent" field)
response = model.invoke(...)
# the "next" key will be used by the conditional edges to route execution
# to the appropriate agent
return {"next": response["next_agent"]}
# route to one of the agents or exit based on the supervisor's decision
# if the supervisor returns "__end__", the graph will finish execution
return Command(goto=response["next_agent"])

def agent_1(state: AgentState):
def agent_1(state: MessagesState) -> Command[Literal["supervisor"]]:
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# and add any additional logic (different models, custom prompts, structured output, etc.)
response = model.invoke(...)
return {"messages": [response]}
return Command(
goto="supervisor",
update={"messages": [response]},
)

def agent_2(state: AgentState):
def agent_2(state: MessagesState) -> Command[Literal["supervisor"]]:
response = model.invoke(...)
return {"messages": [response]}
return Command(
goto="supervisor",
update={"messages": [response]},
)

builder = StateGraph(AgentState)
builder = StateGraph(MessagesState)
builder.add_node(supervisor)
builder.add_node(agent_1)
builder.add_node(agent_2)

builder.add_edge(START, "supervisor")
# route to one of the agents or exit based on the supervisor's decisiion
# if the supervisor returns "__end__", the graph will finish execution
builder.add_conditional_edges("supervisor", lambda state: state["next"])
builder.add_edge("agent_1", "supervisor")
builder.add_edge("agent_2", "supervisor")

supervisor = builder.compile()
```
Expand Down Expand Up @@ -121,37 +240,29 @@ To address this, you can design your system _hierarchically_. For example, you c
```python
from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.graph import StateGraph, MessagesState, START, END

model = ChatOpenAI()

# define team 1 (same as the single supervisor example above)
class Team1State(MessagesState):
next: Literal["team_1_agent_1", "team_1_agent_2", "__end__"]

def team_1_supervisor(state: Team1State):
def team_1_supervisor(state: MessagesState) -> Command[Literal["team_1_agent_1", "team_1_agent_2", END]]:
response = model.invoke(...)
return {"next": response["next_agent"]}
return Command(goto=response["next_agent"])

def team_1_agent_1(state: Team1State):
def team_1_agent_1(state: MessagesState) -> Command[Literal["team_1_supervisor"]]:
response = model.invoke(...)
return {"messages": [response]}
return Command(goto="team_1_supervisor", update={"messages": [response]})

def team_1_agent_2(state: Team1State):
def team_1_agent_2(state: MessagesState) -> Command[Literal["team_1_supervisor"]]:
response = model.invoke(...)
return {"messages": [response]}
return Command(goto="team_1_supervisor", update={"messages": [response]})

team_1_builder = StateGraph(Team1State)
team_1_builder.add_node(team_1_supervisor)
team_1_builder.add_node(team_1_agent_1)
team_1_builder.add_node(team_1_agent_2)
team_1_builder.add_edge(START, "team_1_supervisor")
# route to one of the agents or exit based on the supervisor's decisiion
# if the supervisor returns "__end__", the graph will finish execution
team_1_builder.add_conditional_edges("team_1_supervisor", lambda state: state["next"])
team_1_builder.add_edge("team_1_agent_1", "team_1_supervisor")
team_1_builder.add_edge("team_1_agent_2", "team_1_supervisor")

team_1_graph = team_1_builder.compile()

# define team 2 (same as the single supervisor example above)
Expand All @@ -174,31 +285,22 @@ team_2_graph = team_2_builder.compile()

# define top-level supervisor

class TopLevelState(MessagesState):
next: Literal["team_1", "team_2", "__end__"]

builder = StateGraph(TopLevelState)
def top_level_supervisor(state: TopLevelState):
builder = StateGraph(MessagesState)
def top_level_supervisor(state: MessagesState):
# you can pass relevant parts of the state to the LLM (e.g., state["messages"])
# to determine which team to call next. a common pattern is to call the model
# with a structured output (e.g. force it to return an output with a "next_team" field)
response = model.invoke(...)
# the "next" key will be used by the conditional edges to route execution
# to the appropriate team
return {"next": response["next_team"]}
# route to one of the teams or exit based on the supervisor's decision
# if the supervisor returns "__end__", the graph will finish execution
return Command(goto=response["next_team"])

builder = StateGraph(TopLevelState)
builder = StateGraph(MessagesState)
builder.add_node(top_level_supervisor)
builder.add_node(team_1_graph)
builder.add_node(team_2_graph)

builder.add_edge(START, "top_level_supervisor")
# route to one of the teams or exit based on the supervisor's decision
# if the top-level supervisor returns "__end__", the graph will finish execution
builder.add_conditional_edges("top_level_supervisor", lambda state: state["next"])
builder.add_edge("team_1_graph", "top_level_supervisor")
builder.add_edge("team_2_graph", "top_level_supervisor")

graph = builder.compile()
```

Expand All @@ -208,7 +310,7 @@ In this architecture we add individual agents as graph nodes and define the orde

- **Explicit control flow (normal edges)**: LangGraph allows you to explicitly define the control flow of your application (i.e. the sequence of how agents communicate) explicitly, via [normal graph edges](./low_level.md#normal-edges). This is the most deterministic variant of this architecture above — we always know which agent will be called next ahead of time.

- **Dynamic control flow (conditional edges)**: in LangGraph you can allow LLMs to decide parts of your application control flow. This can be achieved by using [conditional edges](./low_level.md#conditional-edges). A special case of this is a [supervisor tool-calling](#supervisor-tool-calling) architecture. In that case, the tool-calling LLM powering the supervisor agent will make decisions about the order in which the tools (agents) are being called.
- **Dynamic control flow (Command)**: in LangGraph you can allow LLMs to decide parts of your application control flow. This can be achieved by using [`Command`](./low_level.md#command). A special case of this is a [supervisor tool-calling](#supervisor-tool-calling) architecture. In that case, the tool-calling LLM powering the supervisor agent will make decisions about the order in which the tools (agents) are being called.

```python
from langchain_openai import ChatOpenAI
Expand Down
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