Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/xt765/langchain-chinese-comment/toolkitsgit clone --depth 1 https://github.com/xt765/LangChain-Chinese-CommentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/xt765/langchain-chinese-comment/toolkits)<a href="https://agentmods.dev/agents/xt765/langchain-chinese-comment/toolkits"><img src="https://agentmods.dev/badge/agents/xt765/langchain-chinese-comment/toolkits.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00946 |
| Opus 5 | $0.00000 | $0.00473 |
| Sonnet 5 | $0.00000 | $0.00189 |
| Haiku 4.5 | $0.00000 | $0.00095 |
Grade A, and why
toolkits scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Toolkits
Agent Toolkits 是为特定任务或领域(如 SQL 数据库、CSV 文件、OpenAPI 规范等)预配置的一组工具和 Agent 初始化逻辑。
核心概念
Toolkit 的目标是简化特定场景下 Agent 的创建过程。它通常包含:
- Toolkit 类: 封装了一组相关的
BaseTool。 - 工厂函数: 如
create_sql_agent,自动配置 Prompt 并创建AgentExecutor。
常用工具包 (Classic & Community)
在 LangChain Classic 中,许多工具包已迁移至 langchain_community 或 langchain_experimental。
1. SQL Agent
用于与 SQL 数据库交互。它包含查询数据库、检查模式、检查查询语句等工具。
- 状态: 已迁移至
langchain_community.agent_toolkits.sql。 - 核心函数:
create_sql_agent。 - 迁移建议: 使用
langchain_community中的版本。
2. CSV Agent
用于分析 CSV 文件。它底层使用 Python REPL 来执行数据分析。
- 状态: 已迁移至
langchain_experimental.agents.agent_toolkits.csv。 - 安全警告: 该 Agent 会执行任意 Python 代码,必须在沙箱环境中运行。
3. OpenAPI Agent
用于根据 OpenAPI 规范与 RESTful API 交互。它包含一个分阶段的规划器(Planner),负责将复杂请求分解为多个 API 调用。
- 状态: 已迁移至
langchain_community.agent_toolkits.openapi。 - 核心组件:
OpenAPIToolkit,create_openapi_agent。
执行逻辑示例 (Verbatim Snippet)
SQL Agent 的创建逻辑 (概念性)
def create_sql_agent(
llm: BaseLanguageModel,
toolkit: SQLDatabaseToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = SQL_PREFIX,
suffix: str = SQL_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
top_k: int = 10,
**kwargs: Any,
) -> AgentExecutor:
# 1. 获取工具列表
tools = toolkit.get_tools()
# 2. 构造 Prompt
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
# 3. 初始化 Agent
llm_chain = LLMChain(llm=llm, prompt=prompt, callback_manager=callback_manager)
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=[t.name for t in tools])
# 4. 返回 Executor
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, **kwargs)
迁移指南 (LangGraph)
现代做法是使用 LangGraph 构建更可控的领域特定 Agent。
SQL Agent 迁移 (LangGraph)
不再使用黑盒的 create_sql_agent,而是显式定义图逻辑:
- Node 1 (Query Gen): 生成 SQL。
- Node 2 (Execute): 执行 SQL。
- Node 3 (Refine): 如果出错,修正 SQL;否则返回结果。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 88 lines · 0 tokens per session scan A 493cfbad1a31
toolkits is an agent published in the GitHub repository xt765/LangChain-Chinese-Comment (20 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 946 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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