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langgraph-implementation

Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.

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가격 미확인★ 80 GitHub 스타목록 업데이트 · 2026년 9월 7일agent-skill

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LangGraph Implementation

Core Concepts

LangGraph builds stateful, multi-actor agent applications using a graph-based architecture:

StateGraph: Builder class for defining graphs with shared state
Nodes: Functions that read state and return partial updates
Edges: Define execution flow (static or conditional)
Channels: Internal state management (LastValue, BinaryOperatorAggregate)
Checkpointer: Persistence for pause/resume capabilities

Implementation gates

Use these sequenced checks for persistence and human-in-the-loop flows (avoid “it should work” without evidence):

Checkpointed runs
- Build config with {"configurable": {"thread_id": "<stable-id>"}} before invoke / ainvoke.
- Pass: The same thread_id is reused for every turn of one conversation; a new conversation uses a new id.
State after a step
- Pass: graph.get_state(config).values (or equivalent) contains the keys and reducer outputs your next node or client expects; if not, fix routing, reducers, or node order before continuing.
Interrupt and resume (HITL)
- Pass: After a pause, you have inspected pending work (get_state, and your LangGraph version’s interrupt listing if you rely on it) so you know which node is waiting and what resume payload shape to send.
- Pass: Command(resume=...) (or equivalent) includes every field the code path after interrupt() reads.
Checkpointer vs environment
- Pass: Tests or local dev use InMemorySaver or disposable SQLite; production uses a durable checkpointer configured for that deployment (not in-memory).

Essential Imports

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import MessagesState, add_messages
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command, Send, interrupt, RetryPolicy
from typing import Annotated
from typing_extensions import TypedDict

State Schema Patterns

Basic State with TypedDict

import operator

class State(TypedDict):
    counter: int                                    # LastValue - stores last value
    messages: Annotated[list, operator.add]         # Reducer - appends lists
    items: Annotated[list, lambda a, b: a + [b] if b else a]  # Custom reducer

MessagesState for Chat Applications

from langgraph.graph.message import MessagesState

class State(MessagesState):
    # Inherits: messages: Annotated[list[AnyMessage], add_messages]
    user_id: str
    context: dict

Pydantic State (for validation)

from pydantic import BaseModel

class State(BaseModel):
    messages: Annotated[list, add_messages]
    validated_field: str  # Pydantic validates on assignment

Building Graphs

Basic Pattern

builder = StateGraph(State)

# Add nodes - functions that take state, return partial updates
builder.add_node("process", process_fn)
builder.add_node("decide", decide_fn)

# Add edges
builder.add_edge(START, "process")
builder.add_edge("process", "decide")
builder.add_edge("decide", END)

# Compile
graph = builder.compile()

Node Function Signature

def my_node(state: State) -> dict:
    """Node receives full state, returns partial update."""
    return {"counter": state["counter"] + 1}

# With config access
def my_node(state: State, config: RunnableConfig) -> dict:
    thread_id = config["configurable"]["thread_id"]
    return {"result": process(state, thread_id)}

# With Runtime context (v0.6+)
def my_node(state: State, runtime: Runtime[Context]) -> dict:
    user_id = runtime.context.get("user_id")
    return {"result": user_id}

Conditional Edges

from typing import Literal

def router(state: State) -> Literal["agent", "tools", "__end__"]:
    last_msg = state["messages"][-1]
    if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
        return "tools"
    return END  # or "__end__"

builder.add_conditional_edges("agent", router)

# With path_map for visualization
builder.add_conditional_edges(
    "agent",
    router,
    path_map={"agent": "agent", "tools": "tools", "__end__": END}
)

Command Pattern (Dynamic Routing + State Update)

from langgraph.types import Command

def dynamic_node(state: State) -> Command[Literal["next", "__end__"]]:
    if state["should_continue"]:
        return Command(goto="next", update={"step": state["step"] + 1})
    return Command(goto=END)

# Must declare destinations for visualization
builder.add_node("dynamic", dynamic_node, destinations=["next", END])

Send Pattern (Fan-out/Map-Reduce)

from langgraph.types import Send

def fan_out(state: State) -> list[Send]:
    """Route to multiple node instances with different inputs."""
    return [Send("worker", {"item": item}) for item in state["items"]]

builder.add_conditional_edges(START, fan_out)
builder.add_edge("worker", "aggregate")  # Workers converge

Checkpointing

Enable Persistence

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.sqlite import SqliteSaver  # Development
from langgraph.checkpoint.postgres import PostgresSaver  # Production

# In-memory (testing only)
graph = builder.compile(checkpointer=InMemorySaver())

# SQLite (development)
with SqliteSaver.from_conn_string("checkpoints.db") as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)

# Thread-based invocation
config = {"configurable": {"thread_id": "user-123"}}
result = graph.invoke({"messages": [...]}, config)

State Management

# Get current state
state = graph.get_state(config)

# Get state history
for state in graph.get_state_history(config):
    print(state.values, state.next)

# Update state manually
graph.update_state(config, {"key": "new_value"}, as_node="node_name")

Human-in-the-Loop

Using interrupt()

from langgraph.types import interrupt, Command

def review_node(state: State) -> dict:
    # Pause and surface value to client
    human_input = interrupt({"question": "Please review", "data": state["draft"]})
    return {"approved": human_input["approved"]}

# Resume with Command
graph.invoke(Command(resume={"approved": True}), config)

Interrupt Before/After Nodes

graph = builder.compile(
    checkpointer=checkpointer,
    interrupt_before=["human_review"],  # Pause before node
    interrupt_after=["agent"],          # Pause after node
)

# Check pending interrupts
state = graph.get_state(config)
if state.next:  # Has pending nodes
    # Resume
    graph.invoke(None, config)

Streaming

# Stream modes: "values", "updates", "custom", "messages", "debug"

# Updates only (node outputs)
for chunk in graph.stream(input, stream_mode="updates"):
    print(chunk)  # {"node_name": {"key": "value"}}

# Full state after each step
for chunk in graph.stream(input, stream_mode="values"):
    print(chunk)

# Multiple modes
for mode, chunk in graph.stream(input, stream_mode=["updates", "messages"]):
    if mode == "messages":
        print("Token:", chunk)

# Custom streaming from within nodes
from langgraph.config import get_stream_writer

def my_node(state):
    writer = get_stream_writer()
    writer({"progress": 0.5})  # Custom event
    return {"result": "done"}

Subgraphs

# Define subgraph
sub_builder = StateGraph(SubState)
sub_builder.add_node("step", step_fn)
sub_builder.add_edge(START, "step")
subgraph = sub_builder.compile()

# Use as node in parent
parent_builder = StateGraph(ParentState)
parent_builder.add_node("subprocess", subgraph)
parent_builder.add_edge(START, "subprocess")

# Subgraph checkpointing
subgraph = sub_builder.compile(
    checkpointer=None,   # Inherit from parent (default)
    # checkpointer=True,   # Use persistent checkpointing
    # checkpointer=False,  # Disable checkpointing
)

Retry and Caching

from langgraph.types import RetryPolicy, CachePolicy

retry = RetryPolicy(
    initial_interval=0.5,
    backoff_factor=2.0,
    max_attempts=3,
    retry_on=ValueError,  # Or callable: lambda e: isinstance(e, ValueError)
)

cache = CachePolicy(ttl=3600)  # Cache for 1 hour

builder.add_node("risky", risky_fn, retry_policy=retry, cache_policy=cache)

Prebuilt Components

create_react_agent (moved to langchain.agents in v1.0)

from langgraph.prebuilt import create_react_agent, ToolNode

# Simple agent
graph = create_react_agent(
    model="anthropic:claude-3-5-sonnet",
    tools=[my_tool],
    prompt="You are a helpful assistant",
    checkpointer=InMemorySaver(),
)

# Custom tool node
tool_node = ToolNode([tool1, tool2])
builder.add_node("tools", tool_node)

Common Patterns

Agent Loop

def should_continue(state) -> Literal["tools", "__end__"]:
    if state["messages"][-1].tool_calls:
        return "tools"
    return END

builder.add_node("agent", call_model)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", should_continue)
builder.add_edge("tools", "agent")

Parallel Execution

# Multiple nodes execute in parallel when they share the same trigger
builder.add_edge(START, "node_a")
builder.add_edge(START, "node_b")  # Runs parallel with node_a
builder.add_edge(["node_a", "node_b"], "join")  # Wait for both

See PATTERNS.md for advanced patterns including multi-agent systems, hierarchical graphs, and complex workflows.

소스 보기 ↗

파일 메타데이터

name: langgraph-implementation
description: Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.

원문 보기

---
name: langgraph-implementation
description: Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.
---

# LangGraph Implementation

## Core Concepts

LangGraph builds stateful, multi-actor agent applications using a graph-based architecture:

- **StateGraph**: Builder class for defining graphs with shared state
- **Nodes**: Functions that read state and return partial updates
- **Edges**: Define execution flow (static or conditional)
- **Channels**: Internal state management (LastValue, BinaryOperatorAggregate)
- **Checkpointer**: Persistence for pause/resume capabilities

## Implementation gates

Use these **sequenced checks** for persistence and human-in-the-loop flows (avoid “it should work” without evidence):

1. **Checkpointed runs**
   - Build `config` with `{"configurable": {"thread_id": "<stable-id>"}}` before `invoke` / `ainvoke`.
   - **Pass:** The same `thread_id` is reused for every turn of one conversation; a new conversation uses a new id.

2. **State after a step**
   - **Pass:** `graph.get_state(config).values` (or equivalent) contains the keys and reducer outputs your next node or client expects; if not, fix routing, reducers, or node order before continuing.

3. **Interrupt and resume (HITL)**
   - **Pass:** After a pause, you have inspected pending work (`get_state`, and your LangGraph version’s interrupt listing if you rely on it) so you know **which** node is waiting and **what** resume payload shape to send.
   - **Pass:** `Command(resume=...)` (or equivalent) includes every field the code path after `interrupt()` reads.

4. **Checkpointer vs environment**
   - **Pass:** Tests or local dev use `InMemorySaver` or disposable SQLite; production uses a durable checkpointer configured for that deployment (not in-memory).

## Essential Imports

```python
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import MessagesState, add_messages
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command, Send, interrupt, RetryPolicy
from typing import Annotated
from typing_extensions import TypedDict
```

## State Schema Patterns

### Basic State with TypedDict

```python
import operator

class State(TypedDict):
    counter: int                                    # LastValue - stores last value
    messages: Annotated[list, operator.add]         # Reducer - appends lists
    items: Annotated[list, lambda a, b: a + [b] if b else a]  # Custom reducer
```

### MessagesState for Chat Applications

```python
from langgraph.graph.message import MessagesState

class State(MessagesState):
    # Inherits: messages: Annotated[list[AnyMessage], add_messages]
    user_id: str
    context: dict
```

### Pydantic State (for validation)

```python
from pydantic import BaseModel

class State(BaseModel):
    messages: Annotated[list, add_messages]
    validated_field: str  # Pydantic validates on assignment
```

## Building Graphs

### Basic Pattern

```python
builder = StateGraph(State)

# Add nodes - functions that take state, return partial updates
builder.add_node("process", process_fn)
builder.add_node("decide", decide_fn)

# Add edges
builder.add_edge(START, "process")
builder.add_edge("process", "decide")
builder.add_edge("decide", END)

# Compile
graph = builder.compile()
```

### Node Function Signature

```python
def my_node(state: State) -> dict:
    """Node receives full state, returns partial update."""
    return {"counter": state["counter"] + 1}

# With config access
def my_node(state: State, config: RunnableConfig) -> dict:
    thread_id = config["configurable"]["thread_id"]
    return {"result": process(state, thread_id)}

# With Runtime context (v0.6+)
def my_node(state: State, runtime: Runtime[Context]) -> dict:
    user_id = runtime.context.get("user_id")
    return {"result": user_id}
```

### Conditional Edges

```python
from typing import Literal

def router(state: State) -> Literal["agent", "tools", "__end__"]:
    last_msg = state["messages"][-1]
    if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
        return "tools"
    return END  # or "__end__"

builder.add_conditional_edges("agent", router)

# With path_map for visualization
builder.add_conditional_edges(
    "agent",
    router,
    path_map={"agent": "agent", "tools": "tools", "__end__": END}
)
```

### Command Pattern (Dynamic Routing + State Update)

```python
from langgraph.types import Command

def dynamic_node(state: State) -> Command[Literal["next", "__end__"]]:
    if state["should_continue"]:
        return Command(goto="next", update={"step": state["step"] + 1})
    return Command(goto=END)

# Must declare destinations for visualization
builder.add_node("dynamic", dynamic_node, destinations=["next", END])
```

### Send Pattern (Fan-out/Map-Reduce)

```python
from langgraph.types import Send

def fan_out(state: State) -> list[Send]:
    """Route to multiple node instances with different inputs."""
    return [Send("worker", {"item": item}) for item in state["items"]]

builder.add_conditional_edges(START, fan_out)
builder.add_edge("worker", "aggregate")  # Workers converge
```

## Checkpointing

### Enable Persistence

```python
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.sqlite import SqliteSaver  # Development
from langgraph.checkpoint.postgres import PostgresSaver  # Production

# In-memory (testing only)
graph = builder.compile(checkpointer=InMemorySaver())

# SQLite (development)
with SqliteSaver.from_conn_string("checkpoints.db") as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)

# Thread-based invocation
config = {"configurable": {"thread_id": "user-123"}}
result = graph.invoke({"messages": [...]}, config)
```

### State Management

```python
# Get current state
state = graph.get_state(config)

# Get state history
for state in graph.get_state_history(config):
    print(state.values, state.next)

# Update state manually
graph.update_state(config, {"key": "new_value"}, as_node="node_name")
```

## Human-in-the-Loop

### Using interrupt()

```python
from langgraph.types import interrupt, Command

def review_node(state: State) -> dict:
    # Pause and surface value to client
    human_input = interrupt({"question": "Please review", "data": state["draft"]})
    return {"approved": human_input["approved"]}

# Resume with Command
graph.invoke(Command(resume={"approved": True}), config)
```

### Interrupt Before/After Nodes

```python
graph = builder.compile(
    checkpointer=checkpointer,
    interrupt_before=["human_review"],  # Pause before node
    interrupt_after=["agent"],          # Pause after node
)

# Check pending interrupts
state = graph.get_state(config)
if state.next:  # Has pending nodes
    # Resume
    graph.invoke(None, config)
```

## Streaming

```python
# Stream modes: "values", "updates", "custom", "messages", "debug"

# Updates only (node outputs)
for chunk in graph.stream(input, stream_mode="updates"):
    print(chunk)  # {"node_name": {"key": "value"}}

# Full state after each step
for chunk in graph.stream(input, stream_mode="values"):
    print(chunk)

# Multiple modes
for mode, chunk in graph.stream(input, stream_mode=["updates", "messages"]):
    if mode == "messages":
        print("Token:", chunk)

# Custom streaming from within nodes
from langgraph.config import get_stream_writer

def my_node(state):
    writer = get_stream_writer()
    writer({"progress": 0.5})  # Custom event
    return {"result": "done"}
```

## Subgraphs

```python
# Define subgraph
sub_builder = StateGraph(SubState)
sub_builder.add_node("step", step_fn)
sub_builder.add_edge(START, "step")
subgraph = sub_builder.compile()

# Use as node in parent
parent_builder = StateGraph(ParentState)
parent_builder.add_node("subprocess", subgraph)
parent_builder.add_edge(START, "subprocess")

# Subgraph checkpointing
subgraph = sub_builder.compile(
    checkpointer=None,   # Inherit from parent (default)
    # checkpointer=True,   # Use persistent checkpointing
    # checkpointer=False,  # Disable checkpointing
)
```

## Retry and Caching

```python
from langgraph.types import RetryPolicy, CachePolicy

retry = RetryPolicy(
    initial_interval=0.5,
    backoff_factor=2.0,
    max_attempts=3,
    retry_on=ValueError,  # Or callable: lambda e: isinstance(e, ValueError)
)

cache = CachePolicy(ttl=3600)  # Cache for 1 hour

builder.add_node("risky", risky_fn, retry_policy=retry, cache_policy=cache)
```

## Prebuilt Components

### create_react_agent (moved to langchain.agents in v1.0)

```python
from langgraph.prebuilt import create_react_agent, ToolNode

# Simple agent
graph = create_react_agent(
    model="anthropic:claude-3-5-sonnet",
    tools=[my_tool],
    prompt="You are a helpful assistant",
    checkpointer=InMemorySaver(),
)

# Custom tool node
tool_node = ToolNode([tool1, tool2])
builder.add_node("tools", tool_node)
```

## Common Patterns

### Agent Loop

```python
def should_continue(state) -> Literal["tools", "__end__"]:
    if state["messages"][-1].tool_calls:
        return "tools"
    return END

builder.add_node("agent", call_model)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", should_continue)
builder.add_edge("tools", "agent")
```

### Parallel Execution

```python
# Multiple nodes execute in parallel when they share the same trigger
builder.add_edge(START, "node_a")
builder.add_edge(START, "node_b")  # Runs parallel with node_a
builder.add_edge(["node_a", "node_b"], "join")  # Wait for both
```

See [PATTERNS.md](PATTERNS.md) for advanced patterns including multi-agent systems, hierarchical graphs, and complex workflows.

소스 확인

가격 및 실행 비용

Skill 받기: 가격 미확인
실행: 실행 요구 사항이 확인되지 않았습니다. 제공처에서 Agent, API 및 서비스 요금을 확인하세요.
라이선스: Apache-2.0
가격 미확인: 가격을 아직 확인하지 못했습니다. 기존 소스 및 설치 링크는 계속 이용할 수 있습니다.

무료 다운로드가 무료 실행을 뜻하지 않습니다. 가격은 안전 등급이 아닙니다. 가격 정보 제출 →

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라이선스: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill does not include explicit security guidance for LangGraph applications, such as protecting against prompt injection when LLM-controlled router nodes decide destinations or tool calls.
Frontmatter metadata is minimal; adding tags and framework version guidance would improve discoverability and interoperability.
Some examples reference undefined components like `llm`, `research_agent`, and `coding_agent`, which may require additional setup context for new users.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 80 GitHub stars
Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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소스 저장소: existential-birds/beagle
라이선스: Apache-2.0
버전: 1.0.0
최근 GitHub 푸시: 2026년 8월 10일
목록 업데이트: 2026년 9월 7일
지침 경로: plugins/beagle-ai/skills/langgraph-implementation/SKILL.md @ d1a74899fbfe

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품질

62/100

유망

신뢰

52/100

Do not auto-install

감사

70/100

검토 필요

전체 감사 열기 평가 보고서 보기

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill does not include explicit security guidance for LangGraph applications, such as protecting against prompt injection when LLM-controlled router nodes decide destinations or tool calls.
Frontmatter metadata is minimal; adding tags and framework version guidance would improve discoverability and interoperability.
Some examples reference undefined components like `llm`, `research_agent`, and `coding_agent`, which may require additional setup context for new users.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 80 GitHub stars
Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
결과: —

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추가 정보

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  "version": "openagentskill-agent-metadata-v2",
  "review_evidence": {
    "indexed": true,
    "static_checked": false,
    "ai_reviewed": false,
    "manual_reviewed": false,
    "creator_verified": false,
    "review_result": "not_recorded",
    "reviewed_at": null,
    "package_fingerprint": null,
    "policy_version": null,
    "notice": "Publication, static checks, AI review, and creator verification are independent facts. None guarantees runtime safety."
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      "license": "Apache-2.0",
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      "documentation": "Usable metadata, review docs",
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    "risk_label": "Needs review",
    "warnings": [
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      "Permission surface may require sandboxing",
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      "Frontmatter metadata is minimal; adding tags and framework version guidance would improve discoverability and interoperability.",
      "Some examples reference undefined components like `llm`, `research_agent`, and `coding_agent`, which may require additional setup context for new users.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 80 GitHub stars"
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    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
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    "Some examples reference undefined components like `llm`, `research_agent`, and `coding_agent`, which may require additional setup context for new users."
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제작자 도구

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제작자: existential-birds
출처: existential-birds/beagle
색인 주체: OpenAgentSkill 커뮤니티 인덱스

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이 Registry 색인 등록은 existential-birds에게 귀속되어 있지만 아직 공식으로 표시되지 않았습니다. 소유권을 주장하면 확인된 소유자 신호가 추가되어 이후 출시, 설치 및 감사 업데이트를 더 신뢰할 수 있습니다.

공유 키트

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개발자가 저장소를 평가하는 위치에 정규 등록, 현재 신뢰 및 감사 신호, 실제 Agent-Proven 증거를 표시합니다.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/existential-birds-langgraph-implementation?metric=listed&label=Listed)](https://www.openagentskill.com/skills/existential-birds-langgraph-implementation?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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커뮤니티 신호

이 스킬이 Agent 워크플로에 유용한지 알려 주세요. 집계된 피드백은 시간이 지날수록 순위를 개선합니다.

개요

LangGraph Implementation

Core Concepts

Implementation gates

Essential Imports

State Schema Patterns

Basic State with TypedDict

MessagesState for Chat Applications

Pydantic State (for validation)

Building Graphs

Basic Pattern

Node Function Signature

Conditional Edges

Command Pattern (Dynamic Routing + State Update)

Send Pattern (Fan-out/Map-Reduce)

Checkpointing

Enable Persistence

State Management

Human-in-the-Loop

Using interrupt()

Interrupt Before/After Nodes

Streaming

Subgraphs

Retry and Caching

Prebuilt Components

create_react_agent (moved to langchain.agents in v1.0)

Common Patterns

Agent Loop

Parallel Execution

소스 확인

가격 및 실행 비용

작은 작업부터 시작

출처 및 사용 안내

Agent 연결

관련 스킬

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