diff --git a/README.md b/README.md
index 504400d6..9bd89872 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ he config embedder -p vllm -u http://localhost:8001/v1 -k dummy -m BAAI/bge-m3
 ```
 
 <details>
-<summary><b>More providers</b> — Anthropic (Claude), Alibaba Bailian, OrcaRouter…</summary>
+<summary><b>More providers</b> — Anthropic (Claude), Google Gemini, Alibaba Bailian, OrcaRouter…</summary>
 <br>
 
 ```bash
@@ -91,11 +91,15 @@ he config embedder -p vllm -u http://localhost:8001/v1 -k dummy -m BAAI/bge-m3
 he config llm -p anthropic -k YOUR_ANTHROPIC_API_KEY
 he config embedder -p openai -k YOUR_OPENAI_API_KEY
 
+# Google Gemini — LLM only (default: gemini-3.8-flash)
+he config llm -p google -k YOUR_GOOGLE_API_KEY
+he config embedder -p openai -k YOUR_OPENAI_API_KEY
+
 # Alibaba Bailian (Qwen, LLM + embeddings in one key)
 he config init -p bailian -k YOUR_BAILIAN_API_KEY
 ```
 
-> OpenAI and Bailian provide both LLM and embedding models in one API. Anthropic and DeepSeek are LLM-only (pair them with an OpenAI-compatible embedder). DeepSeek is the most cost-effective option (~$0.001-0.005/page).
+> OpenAI and Bailian provide both LLM and embedding models in one API. Anthropic, Google Gemini, and DeepSeek are LLM-only (pair them with an OpenAI-compatible embedder). DeepSeek is the most cost-effective option (~$0.001-0.005/page).
 
 </details>
 
@@ -243,6 +247,7 @@ Hyper-Extract uses LangChain structured output with **function calling**. The mo
 |----------|-----------------|
 | **OpenAI** | gpt-4o, gpt-4o-mini, gpt-5 |
 | **Anthropic** | claude-opus-4-8, claude-sonnet-4-6, claude-haiku-4-5 |
+| **Google Gemini** | gemini-3.8-flash |
 | **DeepSeek** | deepseek-v4-flash, deepseek-v4-pro |
 | **阿里云百炼** | qwen-plus, qwen-turbo, deepseek-r1 |
 | **Local vLLM** | Qwen3.5-9B (GPTQ-Marlin) |
@@ -250,7 +255,7 @@ Hyper-Extract uses LangChain structured output with **function calling**. The mo
 **Embedding models** (semantic search) work with any OpenAI-compatible endpoint: `text-embedding-3-small`, `text-embedding-v4` (Bailian), `bge-m3` (local vLLM).
 
 <details>
-<summary><b>Provider notes</b> — DeepSeek & Anthropic pairing</summary>
+<summary><b>Provider notes</b> — DeepSeek, Anthropic & Gemini pairing</summary>
 <br>
 
 > **DeepSeek:** V4 models default to "thinking" mode, which Hyper-Extract auto-disables so structured extraction works. Set `DEEPSEEK_API_KEY`. DeepSeek has no embeddings API:
@@ -267,6 +272,13 @@ Hyper-Extract uses LangChain structured output with **function calling**. The mo
 > llm, emb = create_client(llm="anthropic", embedder="openai:text-embedding-3-small")
 > ```
 
+> **Google Gemini:** Gemini is used for the **LLM** (set `GOOGLE_API_KEY` or `GEMINI_API_KEY`, extra: `pip install 'hyperextract[google]'`). Default model is `gemini-3.8-flash`. No mature embedder path in this repo:
+>
+> ```python
+> from hyperextract import create_client
+> llm, emb = create_client(llm="google", embedder="openai:text-embedding-3-small")
+> ```
+
 </details>
 
 > 📖 Full guide: [Provider System & Local Model Support](https://yifanfeng97.github.io/Hyper-Extract/latest/concepts/provider-system/)
diff --git a/README_ZH.md b/README_ZH.md
index 2eb25de7..8ff6e505 100644
--- a/README_ZH.md
+++ b/README_ZH.md
@@ -83,7 +83,7 @@ he config embedder -p vllm -u http://localhost:8001/v1 -k dummy -m BAAI/bge-m3
 ```
 
 <details>
-<summary><b>更多 provider</b> — Anthropic (Claude)、阿里云百炼、OrcaRouter…</summary>
+<summary><b>更多 provider</b> — Anthropic (Claude)、Google Gemini、阿里云百炼、OrcaRouter…</summary>
 <br>
 
 ```bash
@@ -91,11 +91,15 @@ he config embedder -p vllm -u http://localhost:8001/v1 -k dummy -m BAAI/bge-m3
 he config llm -p anthropic -k YOUR_ANTHROPIC_API_KEY
 he config embedder -p openai -k YOUR_OPENAI_API_KEY
 
+# Google Gemini —— 仅 LLM（默认：gemini-3.8-flash）
+he config llm -p google -k YOUR_GOOGLE_API_KEY
+he config embedder -p openai -k YOUR_OPENAI_API_KEY
+
 # 百炼（阿里云，Qwen，一个 key 同时提供 LLM 和 embedding）
 he config init -p bailian -k YOUR_BAILIAN_API_KEY
 ```
 
-> OpenAI 和百炼同时提供 LLM 和 embedding 模型；Anthropic 和 DeepSeek 仅提供 LLM（需搭配 OpenAI 兼容 embedder）。DeepSeek 最经济（约 $0.001-0.005/页）。
+> OpenAI 和百炼同时提供 LLM 和 embedding 模型；Anthropic、Google Gemini 和 DeepSeek 仅提供 LLM（需搭配 OpenAI 兼容 embedder）。DeepSeek 最经济（约 $0.001-0.005/页）。
 
 </details>
 
@@ -243,6 +247,7 @@ Hyper-Extract 通过 LangChain 结构化输出的 **function calling** 方法工
 |----------|-----------------|
 | **OpenAI** | gpt-4o, gpt-4o-mini, gpt-5 |
 | **Anthropic** | claude-opus-4-8, claude-sonnet-4-6, claude-haiku-4-5 |
+| **Google Gemini** | gemini-3.8-flash |
 | **DeepSeek** | deepseek-v4-flash, deepseek-v4-pro |
 | **阿里云百炼** | qwen-plus, qwen-turbo, deepseek-r1 |
 | **本地 vLLM** | Qwen3.5-9B (GPTQ-Marlin) |
@@ -250,7 +255,7 @@ Hyper-Extract 通过 LangChain 结构化输出的 **function calling** 方法工
 **嵌入模型**（语义搜索）支持任意 OpenAI 兼容端点：`text-embedding-3-small`、`text-embedding-v4`（百炼）、`bge-m3`（本地 vLLM）。
 
 <details>
-<summary><b>Provider 说明</b> — DeepSeek 与 Anthropic 的搭配方式</summary>
+<summary><b>Provider 说明</b> — DeepSeek、Anthropic 与 Gemini 的搭配方式</summary>
 <br>
 
 > **DeepSeek：** V4 模型默认开启 "thinking" 模式，Hyper-Extract 会自动关闭以保证结构化抽取可用。设置 `DEEPSEEK_API_KEY`。DeepSeek 没有嵌入接口：
@@ -267,6 +272,13 @@ Hyper-Extract 通过 LangChain 结构化输出的 **function calling** 方法工
 > llm, emb = create_client(llm="anthropic", embedder="openai:text-embedding-3-small")
 > ```
 
+> **Google Gemini：** Gemini 仅用于 **LLM**（设置 `GOOGLE_API_KEY` 或 `GEMINI_API_KEY`，额外依赖：`pip install 'hyperextract[google]'`）。默认模型为 `gemini-3.8-flash`。本仓库没有成熟的 embedder 路径：
+>
+> ```python
+> from hyperextract import create_client
+> llm, emb = create_client(llm="google", embedder="openai:text-embedding-3-small")
+> ```
+
 </details>
 
 > 📖 完整指南：[Provider 系统与本地模型支持](https://yifanfeng97.github.io/Hyper-Extract/latest/zh/concepts/provider-system/)
diff --git a/examples/README.md b/examples/README.md
index 1b7aa4f8..539c3b5f 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -21,6 +21,9 @@ python providers/deepseek_demo.py
 # Anthropic
 python providers/anthropic_demo.py
 
+# Google Gemini
+python providers/google_demo.py
+
 # OrcaRouter
 python providers/orcarouter_demo.py
 
@@ -50,6 +53,7 @@ examples/
 │   ├── bailian_demo.py         # Bailian (Alibaba Cloud) setup
 │   ├── deepseek_demo.py        # DeepSeek setup
 │   ├── anthropic_demo.py       # Anthropic setup
+│   ├── google_demo.py          # Google Gemini setup
 │   ├── orcarouter_demo.py      # OrcaRouter setup
 │   └── vllm_demo.py            # Local vLLM setup
 ├── en/                          # English demos
diff --git a/examples/README_ZH.md b/examples/README_ZH.md
index cca4946b..14ab7cab 100644
--- a/examples/README_ZH.md
+++ b/examples/README_ZH.md
@@ -21,6 +21,9 @@ python providers/deepseek_demo.py
 # Anthropic
 python providers/anthropic_demo.py
 
+# Google Gemini
+python providers/google_demo.py
+
 # OrcaRouter
 python providers/orcarouter_demo.py
 
@@ -51,6 +54,7 @@ examples/
 │   ├── bailian_demo.py         # 百炼（阿里云）配置
 │   ├── deepseek_demo.py        # DeepSeek 配置
 │   ├── anthropic_demo.py       # Anthropic 配置
+│   ├── google_demo.py          # Google Gemini 配置
 │   ├── orcarouter_demo.py      # OrcaRouter 配置
 │   └── vllm_demo.py            # 本地 vLLM 配置
 ├── en/                          # 英文演示
diff --git a/examples/providers/google_demo.py b/examples/providers/google_demo.py
new file mode 100644
index 00000000..35cb2880
--- /dev/null
+++ b/examples/providers/google_demo.py
@@ -0,0 +1,36 @@
+"""
+Google Gemini Provider Demo
+
+Extract entities and relationships using the Gemini Developer API.
+Gemini has no mature embedder path in this repo, so pair it with an
+OpenAI-compatible embedder. Requires `pip install 'hyperextract[google]'`.
+Set GOOGLE_API_KEY or GEMINI_API_KEY. No real key is required to read
+this file.
+
+Usage:
+    GOOGLE_API_KEY=xxx OPENAI_API_KEY=sk-xxx python examples/providers/google_demo.py
+"""
+
+from hyperextract import create_client, AutoGraph
+
+# Gemini for LLM, OpenAI for embeddings (no Gemini embedder path here)
+llm, emb = create_client(
+    llm="google",  # default: gemini-3.8-flash
+    embedder="openai:text-embedding-3-small",
+)
+
+graph = AutoGraph(
+    instruction="Extract people and their relationships",
+    llm_client=llm,
+    embedder=emb,
+    node_key_extractor=lambda n: n.name,
+    edge_key_extractor=lambda e: (e.source, e.target, e.type),
+    nodes_in_edge_extractor=lambda e: (e.source, e.target),
+)
+
+text = "Zhang San founded ByteDance. Li Si serves as CEO."
+graph.parse(text)
+
+print(f"Nodes: {len(graph.nodes)}, Edges: {len(graph.edges)}")
+for n in graph.nodes:
+    print(f"  - {n.name} ({n.type})")