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/agentgit clone --depth 1 https://github.com/xt765/LangChain-Chinese-CommentWhat 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 | $0.00765 |
| Opus 5 | $0.00000 | $0.00382 |
| Sonnet 5 | $0.00000 | $0.00153 |
| Haiku 4.5 | $0.00000 | $0.00076 |
Grade A, and why
agent 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 3d 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.
What it actually says
libs\langchain\langchain_classic\agents\agent.py
此文档提供了 libs\langchain\langchain_classic\agents\agent.py 文件的详细中文注释。该模块定义了 LangChain 经典代理架构的基础抽象类和核心执行器 AgentExecutor。
功能描述
该模块是代理系统的核心逻辑所在，负责定义代理如何决策（Planning）以及如何执行动作（Execution）。它支持单动作（Single Action）和多动作（Multi Action）代理，并提供了一个强大的执行环境。
核心类层级
1. 基础抽象类
BaseSingleActionAgent: 单操作代理的基类。每次决策产生一个AgentAction或AgentFinish。BaseMultiActionAgent: 多操作代理的基类。每次决策可以产生多个AgentAction。LLMSingleActionAgent: 使用LLMChain实现的单操作代理。
2. 核心执行器 AgentExecutor
AgentExecutor 是驱动代理运行的容器。它负责管理“思考-行动-观察”循环：
- Plan: 调用代理决定下一步（Action 或 Finish）。
- Execute: 如果是 Action，则执行对应的工具。
- Observe: 将工具输出作为 Observation 反馈给代理。
- Repeat: 重复循环，直到代理返回 Finish 或触发限制。
关键参数表
| 参数 | 类型 | 描述 |
|---|---|---|
agent |
BaseSingleActionAgent | BaseMultiActionAgent | Runnable |
负责决策的核心代理逻辑。 |
tools |
Sequence[BaseTool] |
代理可以访问并调用的工具集合。 |
max_iterations |
int |
最大循环步数（默认 15），防止代理进入死循环。 |
max_execution_time |
float |
最大运行时间限制。 |
early_stopping_method |
str |
强制停止时的行为：force (默认) 或 generate (尝试总结已有信息)。 |
handle_parsing_errors |
bool | str | Callable |
解析错误处理逻辑。为 True 时会将错误反馈给 LLM 修复。 |
return_intermediate_steps |
bool |
是否在结果中包含所有中间思考和工具调用过程。 |
3. Runnable 包装器
RunnableAgent: 将 LCEL 中的Runnable对象适配为单动作代理接口。RunnableMultiActionAgent: 将Runnable适配为多动作代理接口。
核心方法
Agent.plan(intermediate_steps, **kwargs)
这是代理最核心的方法。
intermediate_steps: 包含之前的动作和对应的观察结果。- 返回值: 下一个要执行的动作或最终答案。
AgentExecutor.from_agent_and_tools(...)
用于快速创建执行器的类方法，会自动处理工具验证和配置。
弃用说明
Agent 类已被标记为弃用，并计划在 1.0 版本移除。建议开发者：
- 使用
create_tool_calling_agent等工厂函数。 - 将复杂的代理逻辑迁移到 LangGraph，以获得更好的控制流和持久化支持。
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.
- 3d ago First seen · 59 lines · 0 tokens per session scan A aaa7ed9cff47
agent 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 765 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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