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/agent_typesgit 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/agent_types)<a href="https://agentmods.dev/agents/xt765/langchain-chinese-comment/agent_types"><img src="https://agentmods.dev/badge/agents/xt765/langchain-chinese-comment/agent_types.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 | $0.00739 |
| Opus 5 | $0.00000 | $0.00369 |
| Sonnet 5 | $0.00000 | $0.00148 |
| Haiku 4.5 | $0.00000 | $0.00074 |
Grade A, and why
agent_types 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_types.py
此文档提供了 libs\langchain\langchain_classic\agents\agent_types.py 文件的详细中文注释。该模块定义了 LangChain 经典代理的类型枚举。
功能描述
AgentType 是一个字符串枚举类，用于在 initialize_agent 函数中快速指定要使用的代理架构。每个枚举值都对应一个具体的代理实现。
核心枚举：AgentType
以下是 langchain_classic 中支持的主要代理类型及其特点：
| 枚举值 | 描述 |
|---|---|
ZERO_SHOT_REACT_DESCRIPTION |
零样本 ReAct 代理：最常用的基础代理。仅根据工具的文本描述决定调用哪个工具，不需要示例。 |
REACT_DOCSTORE |
文档库 ReAct 代理：专门设计用于与文档存储交互，通常包含 Search 和 Lookup 两个工具。 |
SELF_ASK_WITH_SEARCH |
自问自答代理：将复杂问题拆分为一系列简单的子问题，通过搜索工具获取答案后再整合。 |
CONVERSATIONAL_REACT_DESCRIPTION |
对话式 ReAct 代理：基础 ReAct 的对话版本，能够记住历史对话上下文。 |
CHAT_ZERO_SHOT_REACT_DESCRIPTION |
聊天模型零样本代理：针对 Chat Model（如 GPT-4）优化的零样本 ReAct 实现。 |
CHAT_CONVERSATIONAL_REACT_DESCRIPTION |
聊天模型对话代理：针对 Chat Model 优化的对话式 ReAct 实现。 |
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION |
结构化聊天代理：支持多输入工具的调用，使用 JSON 格式进行推理，逻辑更严谨。 |
OPENAI_FUNCTIONS |
OpenAI 函数调用代理：利用 OpenAI 官方的 functions 接口，效率更高且更稳定。 |
OPENAI_MULTI_FUNCTIONS |
OpenAI 多函数调用代理：允许在单次推理中生成多个工具调用。 |
弃用说明
该模块已被标记为弃用。
弃用原因
- 硬编码限制: 枚举方式限制了开发者对代理逻辑的微调。
- 架构演进: LangChain 已全面转向基于 LCEL 的
Runnable架构。
迁移路径
不要再依赖 AgentType 枚举和 initialize_agent 函数。请使用对应的工厂函数：
ZERO_SHOT_REACT_DESCRIPTION-> create_react_agentOPENAI_FUNCTIONS-> create_openai_functions_agentSTRUCTURED_CHAT-> create_structured_chat_agent
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 · 40 lines · 0 tokens per session scan A 116c55e47c80
agent_types 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 739 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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