LangChain 助手
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- 作者仓库 skills-registry
- 领域
- AI 智能
- 兼容 Agent
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- Claude Code
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- Codex
- Windsurf
- Gemini CLI
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- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @tomevault-io · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- macOS · Linux · Windows
- 底层运行要求
- Node.js · Python
- 文件与系统权限
-
- 只读
- Shell 执行
- 允许写入 / 修改
- 网络行为
- 允许外网请求
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: langchain-agents-langgraph-code
description: Use when building a custom-graph LangGraph agent — when `create_agent(...)` + middleware isn't e…
category: AI 智能
runtime: Node.js / Python
---
# langchain-agents-langgraph-code 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“When to drop down to raw StateGraph / Things the docs won't warn you about / Production essentials (rules of thumb)”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“When to drop down to raw StateGraph / Things the docs won't warn you about / Production essentials (rules of thumb)”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、执行终端命令、写入/修改文件、会按任务需要访问外部网络、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、执行终端命令、写入/修改文件;会按任务需要访问外部网络;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、执行终端命令、写入/修改文件。
先用一个小任务确认它会围绕“When to drop down to raw StateGraph / Things the docs won't warn you about / Production essentials (rules of thumb)”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: langchain-agents-langgraph-code
description: Use when building a custom-graph LangGraph agent — when `create_agent(...)` + middleware isn't e…
category: AI 智能
source: tomevault-io/skills-registry
---
# langchain-agents-langgraph-code
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「When to drop down to raw StateGraph / Things the docs won't warn you about / Production essentials (rules of thumb)」组织步骤,不把推断写成作者事实。
- 读取文件、执行终端命令、写入/修改文件;会按任务需要访问外部网络;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "langchain-agents-langgraph-code" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> When to drop down to raw StateGraph / Things the docs won't warn you about / Production essentials (rules of thumb)
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Node.js / Python | 读取文件、执行终端命令、写入/修改文件 | 会按任务需要访问外部网络
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} LangGraph: editorial guidance
For API reference (signatures, imports, exhaustive method lists), use the mcpdoc MCP tools: fetch_docs("https://docs.langchain.com/oss/python/langgraph/..."). This skill is the opinions layer; the docs are the facts layer.
When to drop down to raw StateGraph
Most agents do NOT need this. Use create_agent(...) + middleware first (see the langchain-agents-middleware skill). Drop down to StateGraph only when:
- Multiple LLM calls in a single graph with custom routing between them.
- Branches that run in parallel and merge.
- Non-message state (custom dataclasses, dicts, dataframes flowing through nodes).
- Multi-graph workflows where one graph calls another as a subgraph.
If your problem fits "one model, some tools, in a loop" — even if the loop is complex — create_agent is the right tool. Don't reach for StateGraph out of habit.
Things the docs won't warn you about
- A node returning
{}is a no-op; returnNoneto signal "no state change" cleanly. add_conditional_edgesmappings must includeENDif any branch terminates — leaving it out is a silent bug, not an error.compile()is not idempotent acrossbind_tools— rebind tools, then re-compile.langgraph devreloads on file change; if it stops reloading, the graph likely failed to import — check the terminal for the exception.- A graph with
interrupt()calls but no checkpointer will throw at invoke time, not at compile time. Always pairinterrupt()withcompile(checkpointer=...).
Production essentials (rules of thumb)
- Always pass a
thread_idinconfig={"configurable": {"thread_id": ...}}for any agent with persistent state. A missingthread_idsilently starts a fresh thread. InMemorySaveris for dev only. Production =PostgresSaver(multi-instance safe) orSqliteSaver(single-node, low-volume). State dies with the process forInMemorySaver.- Resume an interrupted thread by passing
Noneas input with the samethread_id. The runtime picks up where the interrupt fired. - Custom state schemas need reducers.
Annotated[list, add_messages]appends; without the reducer, each node replaces the field instead of accumulating. - For node-level retries on raw
StateGraph, useRetryPolicy. Forcreate_agentagents, preferToolRetryMiddleware/ModelRetryMiddleware— same effect, cleaner composition.
Doc URLs to fetch with mcpdoc
https://docs.langchain.com/oss/python/langgraph/graph-api.md—StateGraph, nodes, edgeshttps://docs.langchain.com/oss/python/langgraph/durable-execution.md— checkpointers +thread_idhttps://docs.langchain.com/oss/python/langgraph/streaming.md—stream_modemodeshttps://docs.langchain.com/oss/python/langgraph/persistence.md— Postgres / Sqlite checkpointershttps://docs.langchain.com/oss/python/langgraph/subgraphs.md— multi-graph composition
When the user is mid-task and you need a method signature or import path, fetch from these URLs. Don't guess.
Source: cwijayasundara/agent_cli_langchain — distributed by TomeVault.
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核