[TRTLLM-14474][chore] Remove legacy python relics and refresh docs after the backend removal

AGENTS.md

@@ -1,7 +1,7 @@
 # AGENTS.md
 
 TensorRT-LLM: open-source library for optimized LLM inference on NVIDIA GPUs.
-Python and C++ codebase supporting TensorRT engine-based and PyTorch-based execution paths.
+Python and C++ codebase with PyTorch and AutoDeploy execution paths.
 
 > If a `CLAUDE.local.md` file exists alongside this file, read and respect it — it contains developer-specific overrides that supplement this shared guidance.
 
@@ -55,17 +55,16 @@ See [architecture diagram](.github/tava_architecture_diagram.md) for the full Me
 |---------|--------|-------------|----------|
 | **PyTorch** | Default | `TorchLlmArgs` | `_torch/pyexecutor/` → `PyExecutor` → PyTorch Engine |
 | **AutoDeploy** | Beta | `_torch/auto_deploy/` shim | `_torch/auto_deploy/shim/ad_executor.py` → adapts `PyExecutor` → graph transforms + torch.export |
-| **TensorRT** | Legacy | `TrtLlmArgs` | `builder.py` → `trtllm.Executor` → TensorRT Engine |
 
 ### Shared C++ Core (via Nanobind)
 
-Both PyTorch and TensorRT backends share these C++ components:
+Both backends share these C++ components:
 - **Scheduling pipeline**: Scheduler → BatchManager (in-flight batching) → KV Cache Manager
 - **Decoding pipeline**: Decoder (token generation orchestration) → Sampling
 
 ### Request Flow
 ```text
-HuggingFace Model → LLM API → Executor (PyTorch/AutoDeploy/TensorRT)
+HuggingFace Model → LLM API → Executor (PyTorch/AutoDeploy)
     → Scheduler → Model Forward → Decoder → Sampling → Generated Tokens
 ```
 
@@ -84,7 +83,7 @@ HuggingFace Model → LLM API → Executor (PyTorch/AutoDeploy/TensorRT)
 | `tensorrt_llm/models/modeling_utils.py` | Base classes for all models (`PretrainedConfig`, `PretrainedModel`) |
 | `tensorrt_llm/executor/executor.py` | Execution abstraction (`GenerationExecutor`) |
 | `tensorrt_llm/models/automodel.py` | Auto-discovery and model registry |
-| `tensorrt_llm/_torch/models/` | PyTorch backend model implementations (distinct from `models/` used by TensorRT backend) |
+| `tensorrt_llm/_torch/models/` | PyTorch backend model implementations (distinct from the top-level `models/` package) |
 | `tensorrt_llm/_torch/modules/ATTENTION_DEVELOPER_GUIDE.md` | Attention, MLA, backend families, sparse backends, metadata contracts, and KV-cache behavior - **read before modifying `tensorrt_llm/_torch/modules/attention.py`, `tensorrt_llm/_torch/modules/mla.py`, or `tensorrt_llm/_torch/attention_backend/`** |
 | `tensorrt_llm/_torch/modules/fused_moe/MOE_DEVELOPER_GUIDE.md` | MoE architecture, backends, communication, development patterns — **read before modifying MoE code** |
 | `CODING_GUIDELINES.md` | C++ and Python coding standards (referenced throughout, must read before contributing) |
@@ -93,7 +92,7 @@ HuggingFace Model → LLM API → Executor (PyTorch/AutoDeploy/TensorRT)
 
 | Pattern | Key Points |
 |---------|------------|
-| **Config hierarchy** | `BaseLlmArgs` → `TrtLlmArgs` / `TorchLlmArgs`, model-specific defaults override generics, Pydantic validation |
+| **Config hierarchy** | `BaseLlmArgs` → `TorchLlmArgs`, model-specific defaults override generics, Pydantic validation |
 | **Model architecture** | Each model: `Config` (inherits `PretrainedConfig`) + `ForCausalLM` (inherits `PretrainedModel`) |
 | **Model defaults** | Architecture-specific overrides in `llm_utils.py` (attention kernels, quant, spec decoding, cache) |
 | **Attention backends** | `TorchLlmArgs.attn_backend` selects kernel: `TRTLLM` (default), `FlashInfer`, `FlashAttention` |
@@ -125,7 +124,6 @@ Key files:
 - **Avoid broad exception handling** — catch specific exceptions, not bare `except:` (see `CODING_GUIDELINES.md`).
 - **One concern per PR** — avoid scope creep. If a PR touches unrelated areas, split it.
 - **User-facing configuration classes** - when editing or defining any user-facing configuration classes (particularly `BaseLlmArgs` or any class used in its fields), you **MUST** follow the Pydantic guidelines in `CODING_GUIDELINES.md`.
-- **TensorRT backend is legacy** — `TrtLlmArgs` / `backend="tensorrt"` and all exclusive tooling (`trtllm-build`, `trtllm-refit`, `convert_checkpoint.py`, `ModelRunner*`) are legacy. Bug fixes OK; new features target PyTorch or AutoDeploy.
 
 ## Development Workflow