optional deps and main prints

README.md

@@ -11,9 +11,11 @@ An MLE agent CLI with MCP (Model Context Protocol) integration and built-in tool
 # Clone the repository
 git clone git@github.com:huggingface/hf_agent.git
 cd hf-agent
+```
 
-# Install dependencies (using uv)
-uv sync
+#### Install recommended dependencies
+```bash
+uv sync --extra agent # or uv sync --extra all
 ```
 
 ### Interactive CLI
@@ -21,7 +23,6 @@ uv sync
 ```bash
 uv run python -m agent.main
 ```
-
 This starts an interactive chat session with the agent. Type your messages and the agent will respond, using tools as needed.
 
 The agent will automatically discover and register all tools from configured MCP servers.

agent/main.py

@@ -327,6 +327,8 @@ async def main():
     print(f"{Colors.YELLOW} {banner}{Colors.RESET}")
     print("Type your messages below. Type 'exit', 'quit', or '/quit' to end.\n")
     print(format_separator())
+    # Wait for agent to initialize
+    print("Initializing agent...")
 
     # Create queues for communication
     submission_queue = asyncio.Queue()
@@ -342,7 +344,7 @@ async def main():
     config = load_config(config_path)
 
     # Create tool router
-    print(f"Config: {config.mcpServers}")
+    print(f"Loading MCP servers: {', '.join(config.mcpServers.keys())}")
     tool_router = ToolRouter(config.mcpServers)
 
     # Create prompt session for input
@@ -368,8 +370,6 @@ async def main():
         )
     )
 
-    # Wait for agent to initialize
-    print("Initializing agent...")
     await ready_event.wait()
 
     submission_id = 0

agent/tools/github_find_examples.py

@@ -404,49 +404,47 @@ def find_examples(
 # Tool specification
 GITHUB_FIND_EXAMPLES_TOOL_SPEC = {
     "name": "github_find_examples",
-    "description": (
-        "Discover best practices, reusable scripts, tutorials, and demos for usinga specific library or framework. This is an important step before implementing anything ML related.",
-        "Use together with github_read_file tool.\n\n"
-        "## When to use this tool\n\n"
-        "- ALWAYS before implementing any training/inference/benchmarking or other ML related code or answering how-to question.\n"
-        "- When exploring a new repository and need to understand how to use it\n"
-        "## How it works\n\n"
-        "1. Fetches all (examples, tutorials, demos, notebooks, scripts, etc.) from the repository\n"
-        "2. If keyword provided, scores found files against the keyword using fuzzy matching\n"
-        "3. Returns best matches sorted by relevance score\n"
-        "## Examples\n\n"
-        "<example>\n"
-        "// ML Workflow Step: Find GRPO/SFT/DPO/RLOO etc training examples\n"
-        "// Task: Starting GRPO fine-tuning project, need reference implementations\n"
-        "{\n"
-        "  keyword: 'grpo',\n"
-        "  repo: 'trl',\n"
-        "  org: 'huggingface'\n"
-        "}\n"
-        "// Returns: examples/scripts/grpo_agent.py, examples/scripts/grpo_vlm.py\n"
-        "// Next step: Use github_read_file to study the implementation\n"
-        "</example>\n\n"
-        "<example>\n"
-        "// ML Workflow Step: Discover all training examples in TRL\n"
-        "// Task: Exploring available training methods before choosing approach\n"
-        "{\n"
-        "  repo: 'trl',\n"
-        "  org: 'huggingface',\n"
-        "  max_results: 20\n"
-        "}\n"
-        "// Lists all example scripts: PPO, DPO, GRPO, reward modeling, etc.\n"
-        "</example>\n\n"
-        "<example>\n"
-        "// ML Workflow Step: Find LoRA fine-tuning examples\n"
-        "// Task: Learning parameter-efficient fine-tuning with PEFT\n"
-        "{\n"
-        "  keyword: 'lora',\n"
-        "  repo: 'peft',\n"
-        "  org: 'huggingface'\n"
-        "}\n"
-        "// Discovers LoRA configuration and training examples\n"
-        "</example>",
-    ),
+    "description": "Discover best practices, reusable scripts, tutorials, and demos for using a specific library or framework. This is an important step before implementing anything ML related. "
+    "Use together with github_read_file tool.\n\n"
+    "## When to use this tool\n\n"
+    "- ALWAYS before implementing any training/inference/benchmarking or other ML related code or answering how-to question.\n"
+    "- When exploring a new repository and need to understand how to use it\n"
+    "## How it works\n\n"
+    "1. Fetches all (examples, tutorials, demos, notebooks, scripts, etc.) from the repository\n"
+    "2. If keyword provided, scores found files against the keyword using fuzzy matching\n"
+    "3. Returns best matches sorted by relevance score\n"
+    "## Examples\n\n"
+    "<example>\n"
+    "// ML Workflow Step: Find GRPO/SFT/DPO/RLOO etc training examples\n"
+    "// Task: Starting GRPO fine-tuning project, need reference implementations\n"
+    "{\n"
+    "  keyword: 'grpo',\n"
+    "  repo: 'trl',\n"
+    "  org: 'huggingface'\n"
+    "}\n"
+    "// Returns: examples/scripts/grpo_agent.py, examples/scripts/grpo_vlm.py\n"
+    "// Next step: Use github_read_file to study the implementation\n"
+    "</example>\n\n"
+    "<example>\n"
+    "// ML Workflow Step: Discover all training examples in TRL\n"
+    "// Task: Exploring available training methods before choosing approach\n"
+    "{\n"
+    "  repo: 'trl',\n"
+    "  org: 'huggingface',\n"
+    "  max_results: 20\n"
+    "}\n"
+    "// Lists all example scripts: PPO, DPO, GRPO, reward modeling, etc.\n"
+    "</example>\n\n"
+    "<example>\n"
+    "// ML Workflow Step: Find LoRA fine-tuning examples\n"
+    "// Task: Learning parameter-efficient fine-tuning with PEFT\n"
+    "{\n"
+    "  keyword: 'lora',\n"
+    "  repo: 'peft',\n"
+    "  org: 'huggingface'\n"
+    "}\n"
+    "// Discovers LoRA configuration and training examples\n"
+    "</example>",
     "parameters": {
         "type": "object",
         "properties": {

pyproject.toml

@@ -5,26 +5,39 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "numpy>=1.24.0",
-    "requests>=2.32.5",
+    # Core dependencies (always required)
     "pydantic>=2.12.3",
-    "litellm>=1.0.0",
-    "tenacity>=8.0.0",
-    "pandas>=2.3.3",
     "python-dotenv>=1.2.1",
-    "datasets>=4.3.0",
+]
+
+[project.optional-dependencies]
+# Agent runtime dependencies
+agent = [
+    "requests>=2.32.5",
+    "litellm>=1.0.0",
     "huggingface-hub>=1.0.1",
     "fastmcp>=2.4.0",
-    "inspect-ai>=0.3.149",
-    "lmnr[all]>=0.7.23",
-    "transformers>=2.3.0",
-    "torch>=2.9.1",
-    "pytest>=9.0.2",
+    "lmnr>=0.7.23",  # Note: Using base package to avoid torch/transformers from [all] extra
     "prompt-toolkit>=3.0.0",
-    "ipykernel>=7.1.0",
-    "ipywidgets>=8.1.8",
     "thefuzz>=0.22.1",
     "nbconvert>=7.16.6",
     "nbformat>=5.10.4",
-    "markitdown[all,docx,outlook,pdf,pptx,xls,xlsx]>=0.1.4",
+]
+
+# Evaluation/benchmarking dependencies
+eval = [
+    "inspect-ai>=0.3.149",
+    "pandas>=2.3.3",
+    "datasets>=4.3.0",
+    "tenacity>=8.0.0",
+]
+
+# Development and testing dependencies
+dev = [
+    "pytest>=9.0.2",
+]
+
+# All dependencies (agent + eval + dev)
+all = [
+    "hf-agent[agent,eval,dev]",
 ]