Beautiful onboarding CLI and full more pretty print during execution

README.md

@@ -103,6 +103,9 @@ In benchmark `task` mode, the planning layer (1) is bypassed so workflow synthes
 - [Installation](#installation)
 - [Configuration](#configuration)
 - [Running Mimosa](#running-mimosa)
+  - [Interactive Onboarding (recommended for first-time setup)](#interactive-onboarding-recommended-for-first-time-setup)
+  - [Goal mode — multi-step scientific objective](#goal-mode--multi-step-scientific-objective)
+  - [Task mode — single granular operation](#task-mode--single-granular-operation)
 - [Workspace and Audit Trail](#workspace-and-audit-trail)
 - [Learning through Evolution of Multi-Agent Workflows](#learning-through-evolution-of-multi-agent-workflows)
 - [Transparency](#transparency)
@@ -184,7 +187,25 @@ Custom MCP tools can be added via the [Toolomics docs](https://github.com/Holobi
 
 ---
 
-## Configuration
+
+## Running Mimosa
+
+### Interactive Onboarding (recommended for first-time setup)
+
+> **If you are new to Mimosa, start here.**
+
+Running Mimosa with **no arguments** launches an interactive, step-by-step onboarding wizard that guides you through everything before the first execution:
+
+```bash
+uv run main.py
+```
+Once you complete setup once, subsequent runs remember your workspace path via `config_default.json` — no re-configuration needed.
+
+---
+
+### Manual onboarding:
+
+**1. Start by editing the config:**
 
 ```bash
 cp config_default.json my_config.json
@@ -204,13 +225,9 @@ Edit `my_config.json`. Key parameters:
 | `learned_score_threshold` | Minimum score to accept a result and stop iterating |
 | `max_learning_evolve_iterations` | Maximum self-improvement iterations before accepting the result |
 
----
-
-## Running Mimosa
-
-Mimosa supports two execution modes: **Goal** and **Task**.
+**2. Choose a mode `task` or `goal` depending on the complexity of your objective.**
 
-### Goal mode — multi-step scientific objective
+**2.1 Goal mode — multi-step scientific objective**
 
 Use this when your objective requires planning across multiple distinct operations (e.g., reproducing a paper, building an ML pipeline).
 
@@ -229,7 +246,7 @@ uv run main.py \
   --config my_config.json
 ```
 
-### Task mode — single granular operation
+**2.2 Task mode — single granular operation**
 
 Use this for a focused, self-contained operation without long-term planning.
 
@@ -257,7 +274,7 @@ uv run main.py --task "Conduct a literature review on graph neural networks for
 During execution, Mimosa reads and writes files inside the Toolomics workspace configured by `workspace_dir`. When a run finishes, the workspace contents are copied into a timestamped folder under `runs_capsule/` so the final state is preserved as an archive.
 
 - **Toolomics `workspace/`** — live working directory: intermediate files, scripts, downloads, generated outputs
-- **`sources/workflows/<uuid>/`** — generated workflow and execution metadata: `state_result.json`, `evaluation.txt`, `reward_progress.png`, `memory/` traces
+- **`sources/workflows/<uuid>/`** — generated workflow and execution metadata: `state_result.json`, `evaluation.txt`, `reward_progress.png`
 - **`runs_capsule/<capsule_name>/`** — archived snapshot of the run for later inspection, comparison, or sharing
 - **`memory_explorer.py <uuid>`** — replay a workflow execution step-by-step to inspect agent traces, tool calls, and outputs
 

config.py

@@ -85,6 +85,7 @@ def __init__(self):
         self.runner_default_max_cpu_percent: int = 100
         self.runner_temp_dir: str = "./tmp"
         self.runner_requirements: list[str] = [
+            "setuptools>=70.0",
             "python-dotenv",
             "fastmcp==2.8.1",
             "requests>=2.31.0",

main.py

@@ -15,10 +15,13 @@
 # Prevent tokenizers parallelism warnings when forking processes
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+from sources.cli.pretty_print import print_ok, print_warn, print_err, print_info
+
 from config import Config
 from sources.core.dgm import DarwinMachine
 from sources.core.planner import Planner
 from sources.extensibility.human_mode import HumanMode
+from sources.cli import OnboardCLI
 from sources.evaluation.csv_mode import CsvEvaluationMode
 from sources.evaluation.scenario_loader import ScenarioLoader
 from sources.evaluation.eval_workflow_generation import WorkflowEval
@@ -35,11 +38,11 @@ def validate_environment() -> None:
     envs_key = ['ANTHROPIC_API_KEY', 'MISTRAL_API_KEY', 'DEEPSEEK_API_KEY', 'OPENAI_API_KEY', 'HF_TOKEN', 'OPENROUTER_API_KEY']
     for key in envs_key:
         if os.getenv(key):
-            print(f"✅ Found environment variable: {key}")
+            print_ok(f"Found environment variable: {key}")
             key_found = True
     if not key_found:
         raise ValueError(
-            "⚠️ No valid API key environment variable found. Please set one of the supported API keys. Supported keys: " + ", ".join(envs_key)
+            "No valid API key environment variable found. Please set one of the supported API keys. Supported keys: " + ", ".join(envs_key)
         )
 
 def add_config_arguments(parser: argparse.ArgumentParser, config: Config) -> None:
@@ -86,7 +89,7 @@ def apply_config_overrides(args: argparse.Namespace, config: Config) -> None:
 def setup_signal_handlers():
     """Setup signal handlers for graceful shutdown."""
     def signal_handler(signum, frame):
-        print(f"\n⚠️ Received signal {signum}. Shutting down gracefully...")
+        print_warn(f"Received signal {signum}. Shutting down gracefully…")
         for task in asyncio.all_tasks():
             if not task.done():
                 task.cancel()
@@ -110,7 +113,7 @@ async def papers_mode(args, config):
 async def science_bench_papers_mode(args, config):
     papers = CsvEvaluationMode(config, csv_runs_limit=args.csv_runs_limit)
     if args.single_agent:
-        print(f"⚠️ Starting in single agent mode")
+        print_info("Starting in single agent mode")
     await papers.start_evaluation(dataset_type="science_agent_bench",
                                   dataset_path="datasets/ScienceAgentBench.csv",
                                   learning=args.learn,
@@ -138,11 +141,11 @@ def load_goal_from_file_or_string(goal_input: str) -> str:
         try:
             with open(goal_input, 'r', encoding='utf-8') as f:
                 content = f.read().strip()
-                print(f"✅ Loaded goal from file: {goal_input}")
+                print_ok(f"Loaded goal from file: {goal_input}")
                 return content
         except Exception as e:
-            print(f"⚠️ Failed to read file '{goal_input}': {e}")
-            print(f"Using input as a literal string instead.")
+            print_warn(f"Failed to read file '{goal_input}': {e}")
+            print_info("Using input as a literal string instead.")
             return goal_input
     return goal_input
 
@@ -156,7 +159,7 @@ async def normal_execution_mode(args, config):
         # Load goal from file if args.task is a file path
         goal_content = load_goal_from_file_or_string(args.task)
         if args.single_agent:
-            print(f"⚠️ Starting in single agent mode")
+            print_info("Starting in single agent mode")
         await dgm.start_dgm(goal=goal_content,
                             judge=not args.disable_judge,
                             scenario_rubric=args.scenario,
@@ -223,7 +226,10 @@ async def main():
         "--scenario", type=str, help="Use scenario benchmark (eg: datasets/scenarios/X.json) with criterions for workflow evaluation and auto-improvement"
     )
     parser.add_argument(
-        "--debug", action="store_true", help="Enable debug logging to console"
+        "--debug", action="store_true", help="Enable advanced debug logging to console"
+    )
+    parser.add_argument(
+        "--verbose", action="store_true", help="Enable verbose logging to console"
     )
     parser.add_argument(
         "--max_evolve_iterations", type=int, default=1, help="Maximum number of learning iterations. Used for retrying/learning a task."
@@ -240,8 +246,29 @@ async def main():
     # security check
     PackageCheck().run()
     # Setup logging with debug flag
-    setup_logging(debug=args.debug)
+    setup_logging(debug=args.debug, disable=not args.verbose)
+
+    # Detect interactive (no-argument) mode early so we can skip pre-checks
+    no_mode_selected = not any([
+        args.manual,
+        args.papers,
+        args.science_agent_bench,
+        args.task,
+        args.goal,
+        args.scenario,
+        args.workflow_eval_mode,
+    ])
+
+    if no_mode_selected:
+        # Interactive onboarding CLI for full setup flow.
+        try:
+            cli = OnboardCLI(config)
+            await cli.run()
+        except KeyboardInterrupt:
+            print("\n\n  Interrupted. Goodbye!\n")
+        return
 
+    # ── Normal (argument-driven) execution path ───────────────────────────
     # Apply CLI argument overrides (these override config file values)
     apply_config_overrides(args, config)
 
@@ -263,8 +290,6 @@ async def main():
             await normal_execution_mode(args, config)
         elif args.workflow_eval_mode:
             await workflow_generation_evals(args, config)
-        else:
-            raise ValueError("No goal provided. Use --task, --goal, --papers  to start.")
     except KeyboardInterrupt:
         raise
     except Exception as e: