chore: import upstream snapshot with attribution

models.md

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+We support the following APIs for MLLM inference: OpenAI, Anthropic, Gemini, Azure OpenAI, vLLM for local models, and Open Router. To use these APIs, you need to set the corresponding environment variables:
+
+1. OpenAI
+
+```
+export OPENAI_API_KEY=<YOUR_API_KEY>
+```
+
+2. Anthropic
+
+```
+export ANTHROPIC_API_KEY=<YOUR_API_KEY>
+```
+
+3. Gemini
+
+```
+export GEMINI_API_KEY=<YOUR_API_KEY>
+export GEMINI_ENDPOINT_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
+```
+
+4. OpenAI on Azure
+
+```
+export AZURE_OPENAI_API_BASE=<DEPLOYMENT_NAME>
+export AZURE_OPENAI_API_KEY=<YOUR_API_KEY>
+```
+
+5. vLLM for Local Models
+
+```
+export vLLM_ENDPOINT_URL=<YOUR_DEPLOYMENT_URL>
+```
+
+Alternatively you can directly pass the API keys into the engine_params argument while instantating the agent.
+
+6. Open Router
+
+```
+export OPENROUTER_API_KEY=<YOUR_API_KEY>
+export OPEN_ROUTER_ENDPOINT_URL="https://openrouter.ai/api/v1"
+```
+
+```python
+from gui_agents.s2_5.agents.agent_s import AgentS2_5
+
+engine_params = {
+    "engine_type": 'openai', # Allowed Values: 'openai', 'anthropic', 'gemini', 'azure_openai', 'vllm', 'open_router'
+    "model": 'gpt-5-2025-08-07', # Allowed Values: Any Vision and Language Model from the supported APIs
+}
+agent = AgentS2_5(
+    engine_params,
+    grounding_agent,
+    platform=current_platform,
+)
+```
+
+To use the underlying Multimodal Agent (LMMAgent) which wraps LLMs with message handling functionality, you can use the following code snippet:
+
+```python
+from gui_agents.s2_5.core.mllm import LMMAgent
+
+engine_params = {
+    "engine_type": 'openai', # Allowed Values: 'openai', 'anthropic', 'gemini', 'azure_openai', 'vllm', 'open_router'
+    "model": 'gpt-5-2025-08-07', # Allowed Values: Any Vision and Language Model from the supported APIs
+    }
+agent = LMMAgent(
+    engine_params=engine_params,
+)
+```
+
+The `AgentS2_5` also utilizes this `LMMAgent` internally.