feat: migliora gestione errori con suggerimenti contextuali

- Aggiunge config_manager a load_model_from_config() per usare config attive
- Migliora error handling con tipo eccezione e suggerimenti
- Aggiunge pyproject.toml e setup.py per installazione pip

.langcode/simple_memory.json

@@ -40,11 +40,21 @@
       "timestamp": "2026-03-23T07:14:33.532609",
       "fact": "\"LangCode Agent supports MCP integration\"",
       "category": "general"
+    },
+    {
+      "timestamp": "2026-03-23T09:14:30.538647",
+      "fact": "\"Il mio colore preferito \u00e8 il verde\"",
+      "category": "general"
+    },
+    {
+      "timestamp": "2026-03-23T09:36:02.053792",
+      "fact": "Questo \u00e8 un test senza virgolette",
+      "category": "general"
     }
   ],
   "preferences": {
     "model_provider": "ollama"
   },
   "skills_used": [],
-  "last_updated": "2026-03-23T07:14:33.532819"
+  "last_updated": "2026-03-23T09:36:02.053800"
 }

main.py

@@ -54,18 +54,49 @@ import json
 def load_model_from_config():
     """
     Load the LLM model based on configuration.
-    Configuration can come from environment variables or a config file.
-    For simplicity, we'll use environment variables.
+    Priority: 1) config_manager active config, 2) environment variables
     
-    Expected environment variables:
+    Expected environment variables (fallback):
     - OPENAI_API_KEY: For OpenAI models
     - OLLAMA_BASE_URL: For Ollama (e.g., https://ollama.solarpi.net)
     - MODEL_PROVIDER: One of "openai", "ollama", "openrouter", etc.
     - MODEL_NAME: The specific model name (e.g., "gpt-4o", "gemma2:9b")
     """
+    # Try to use config_manager first
+    if CONFIG_AVAILABLE:
+        active_name = config_manager.get_active_configuration()
+        if active_name:
+            config = config_manager.get_configuration(active_name)
+            if config:
+                provider = config.get("provider", "ollama").lower()
+                model_name = config.get("model_name", "gemma2:9b")
+                base_url = config.get("base_url", "http://localhost:11434")
+                api_key = config.get("api_key")
+                
+                print(f"🔗 Caricamento modello da config_manager: {active_name}")
+                print(f"   Provider: {provider}")
+                print(f"   Modello: {model_name}")
+                print(f"   URL: {base_url}")
+                
+                if provider == "ollama":
+                    return init_chat_model(f"ollama:{model_name}", base_url=base_url)
+                elif provider == "openai":
+                    return init_chat_model(f"openai:{model_name}", api_key=api_key)
+                elif provider == "openrouter":
+                    return init_chat_model(
+                        f"openai:{model_name}", 
+                        base_url=base_url, 
+                        api_key=api_key
+                    )
+    
+    # Fallback to environment variables
     provider = os.getenv("MODEL_PROVIDER", "openai").lower()
     model_name = os.getenv("MODEL_NAME", "gpt-4o")
     
+    print(f"⚠️  Usando variabili d'ambiente (nessuna config attiva)")
+    print(f"   Provider: {provider}")
+    print(f"   Modello: {model_name}")
+    
     # Configure based on provider
     if provider == "openai":
         # Uses OPENAI_API_KEY from environment
@@ -999,7 +1030,21 @@ def main():
                 ai_message = result["messages"][-1]
                 print(f"\n🤖 LangCode: {ai_message.content}")
             except Exception as e:
-                print(f"\n❌ Error: {str(e)}")
+                error_msg = str(e)
+                if not error_msg:
+                    error_msg = f"{type(e).__name__} (nessun messaggio)"
+                print(f"\n❌ Errore: {error_msg}")
+                
+                # Suggerimenti basati sul tipo di errore
+                exc_type = type(e).__name__
+                if "Connection" in exc_type or "Connect" in str(e):
+                    print("💡 Suggerimento: Verifica che il server Ollama sia raggiungibile.")
+                    print(f"   URL: {os.getenv('OLLAMA_BASE_URL', 'http://localhost:11434')}")
+                elif "Timeout" in exc_type or "timeout" in str(e).lower():
+                    print("💡 Suggerimento: Il server è lento. Prova con un modello più piccolo.")
+                elif "model" in str(e).lower() or "Model" in exc_type:
+                    print("💡 Suggerimento: Il modello specificato potrebbe non esistere.")
+                    print(f"   Modello: {os.getenv('MODEL_NAME', 'non impostato')}")
                 
     except Exception as e:
         print(f"❌ Failed to initialize agent: {str(e)}")

pyproject.toml

@@ -0,0 +1,48 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "langcode-agent"
+version = "1.0.0"
+description = "AI Agent harness based on LangChain Deep Agents with memory, planning and MCP support"
+readme = "README.md"
+requires-python = ">=3.10"
+license = {text = "MIT"}
+authors = [
+    {name = "LangCode Team"}
+]
+keywords = ["ai", "agent", "langchain", "ollama", "mcp"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+
+dependencies = [
+    "deepagents>=0.0.34",
+    "langchain>=0.3.0",
+    "langchain-community>=0.3.0",
+    "langchain-openai>=0.1.0",
+    "cryptography>=41.0.0",
+    "requests>=2.31.0",
+    "pyyaml>=6.0",
+    "fastapi>=0.104.0",
+    "uvicorn>=0.24.0",
+    "pydantic>=2.5.0",
+    "pytest>=7.4.0",
+]
+
+[project.scripts]
+langcode = "langcode:main"
+
+[project.urls]
+Homepage = "http://192.168.0.183:8085/truetype/langcode_agent"
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["*"]

setup.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+"""
+LangCode Agent - Setup
+"""
+from setuptools import setup, find_packages
+
+setup(
+    name="langcode-agent",
+    version="1.0.0",
+    description="AI Agent harness based on LangChain Deep Agents",
+    author="LangCode Team",
+    packages=find_packages(),
+    python_requires=">=3.10",
+    install_requires=[
+        "deepagents>=0.0.34",
+        "langchain>=0.3.0",
+        "langchain-community>=0.3.0",
+        "langchain-openai>=0.1.0",
+        "cryptography>=41.0.0",
+        "requests>=2.31.0",
+        "pyyaml>=6.0",
+        "fastapi>=0.104.0",
+        "uvicorn>=0.24.0",
+        "pydantic>=2.5.0",
+        "pytest>=7.4.0",
+    ],
+    entry_points={
+        "console_scripts": [
+            "langcode=langcode:main",
+        ],
+    },
+)