Add application files

app.py

@@ -556,15 +556,29 @@ async def setup_client_company(company_name: str, progress=gr.Progress()):
         yield f"❌ Agent init failed: {e}"
         return
 
-    task = f"""Research {company_name} to understand:
-1. What they do, their products/services
+    task = f"""Research {company_name} to understand their business. Use search_web to find information about:
+1. What {company_name} does - their products/services
 2. Their target market and ideal customers
 3. Their industry and market position
 4. Their value proposition
 5. What type of companies would be good prospects for them
 
-Save all findings. This is OUR company - we need this to find matching prospects."""
+Use the save_company tool to save information about {company_name}:
+- company_id: "{company_name.lower().replace(' ', '_')}"
+- name: "{company_name}"
+- domain: their website domain
+- industry: their industry
+- description: brief company description
 
+After researching, provide a comprehensive summary of:
+- What {company_name} does
+- Who their ideal customers are
+- What industries/company types would benefit from their services
+
+This is OUR company - we need this information to find matching prospects."""
+
+    last_research = ""  # Track last AI response for fallback
+    search_count = 0
     try:
         async for event in agent.run(task, max_iterations=12):
             event_type = event.get("type")
@@ -572,12 +586,13 @@ Save all findings. This is OUR company - we need this to find matching prospects
                 tool = event.get("tool", "")
                 if tool == "search_web":
                     output += f"🔍 Searching for {company_name}...\n"
+                    search_count += 1
                 elif tool == "search_news":
                     output += f"📰 Finding news...\n"
                 elif tool in ["save_company", "save_fact"]:
                     output += f"💾 Saving information...\n"
                 yield output
-                progress(0.5)
+                progress(0.3 + min(search_count * 0.1, 0.4))
             elif event_type == "tool_result":
                 tool = event.get("tool", "")
                 result = event.get("result", {})
@@ -585,8 +600,13 @@ Save all findings. This is OUR company - we need this to find matching prospects
                     count = result.get("count", 0) if isinstance(result, dict) else 0
                     output += f"   ✅ Found {count} results\n"
                 yield output
+            elif event_type == "thought":
+                # Capture AI thoughts for potential use as research summary
+                thought = event.get("thought", "")
+                if thought and len(thought) > len(last_research):
+                    last_research = thought
             elif event_type == "agent_complete":
-                final_answer = event.get("final_answer", "")
+                final_answer = event.get("final_answer", "") or last_research
                 knowledge_base["client"] = {
                     "name": company_name,
                     "raw_research": final_answer,
@@ -598,8 +618,44 @@ Save all findings. This is OUR company - we need this to find matching prospects
                 yield output
                 progress(1.0)
                 return
+            elif event_type == "agent_max_iterations":
+                # Still save what we have
+                final_answer = last_research or f"Research completed for {company_name}. Ready to find prospects."
+                knowledge_base["client"] = {
+                    "name": company_name,
+                    "raw_research": final_answer,
+                    "researched_at": datetime.now().strftime("%Y-%m-%d %H:%M")
+                }
+                output += f"\n---\n\n## ✅ {company_name} Profile Complete!\n\n"
+                output += "**Next step:** Go to the **Prospects** tab and click **'🔍 Find Prospects'** to let AI discover potential customers.\n\n"
+                if final_answer:
+                    output += f"---\n\n### 📋 Research Summary\n\n{final_answer}"
+                yield output
+                progress(1.0)
+                return
+            elif event_type == "agent_error":
+                error_msg = event.get("error", "Unknown error")
+                # Still save basic profile so user can proceed
+                knowledge_base["client"] = {
+                    "name": company_name,
+                    "raw_research": last_research or f"{company_name} - manual research may be needed.",
+                    "researched_at": datetime.now().strftime("%Y-%m-%d %H:%M")
+                }
+                output += f"\n⚠️ AI encountered an issue: {error_msg}\n"
+                output += f"\n---\n\n## ⚠️ {company_name} Setup (Partial)\n\n"
+                output += "**Note:** Some research may be incomplete. You can still proceed to find prospects.\n\n"
+                yield output
+                progress(1.0)
+                return
     except Exception as e:
+        # Save basic profile on exception so user can still proceed
+        knowledge_base["client"] = {
+            "name": company_name,
+            "raw_research": last_research or f"{company_name} - setup interrupted.",
+            "researched_at": datetime.now().strftime("%Y-%m-%d %H:%M")
+        }
         output += f"\n⚠️ Error: {e}\n"
+        output += f"\n**Note:** Basic profile saved. You can still try to find prospects.\n"
         yield output
 
 
@@ -639,26 +695,42 @@ async def discover_prospects(num_prospects: int = 5, progress=gr.Progress()):
         yield f"❌ Agent init failed: {e}"
         return
 
-    task = f"""You are finding prospects for {client_name}.
+    task = f"""You are an AI sales agent finding prospects for {client_name}.
 
 About {client_name}:
 {client_info}
 
-Your task:
-1. Search for {num_prospects} companies that would be ideal customers for {client_name}
-2. For EACH prospect company found:
-   a. Research the company thoroughly
-   b. Find 2-3 decision makers (CEO, VP Sales, CTO, Head of Marketing, etc.)
-   c. Save the prospect with save_prospect
-   d. Save each contact with save_contact
-   e. Draft a personalized outreach email using send_email
+IMPORTANT: You MUST use the tools to save data. Follow these steps for EACH prospect:
+
+1. Use search_web to find {num_prospects} companies that would be ideal customers for {client_name}
+2. For EACH company found, you MUST:
+   a. Use save_prospect tool with these parameters:
+      - prospect_id: unique ID like "prospect_1"
+      - company_id: unique ID like "company_1"
+      - company_name: the company name
+      - company_domain: the company website domain
+      - fit_score: 1-100 score
+      - metadata: {{"summary": "company description", "industry": "their industry", "fit_reason": "why they're a good fit"}}
+
+   b. Use save_contact tool for 2-3 decision makers with:
+      - contact_id: unique ID
+      - company_id: same as above
+      - email: their email if known or generate likely format like "[email protected]"
+      - first_name: their first name
+      - last_name: their last name
+      - title: their job title (CEO, VP Sales, CTO, etc.)
+
+   c. Use send_email tool to draft outreach:
+      - to: the contact email
+      - subject: personalized subject line
+      - body: personalized email body
+      - prospect_id: the prospect_id from step a
 
 Focus on finding companies that:
 - Would benefit from {client_name}'s products/services
 - Are in industries that match {client_name}'s target market
-- Are the right size and stage
 
-For each prospect, explain WHY they're a good fit for {client_name}."""
+After processing all {num_prospects} prospects, provide a brief summary of what you found."""
 
     prospects_found = []
     contacts_found = []
@@ -671,6 +743,7 @@ For each prospect, explain WHY they're a good fit for {client_name}."""
 
     try:
         iteration = 0
+        last_final_answer = ""  # Track the last complete response from AI
         async for event in agent.run(task, max_iterations=25):
             event_type = event.get("type")
             iteration += 1
@@ -704,9 +777,16 @@ For each prospect, explain WHY they're a good fit for {client_name}."""
                         }
                 elif tool == "save_contact":
                     if isinstance(tool_input, dict):
-                        name = tool_input.get("name", "Unknown")
+                        # Handle both "name" and "first_name/last_name" formats
+                        first_name = tool_input.get("first_name", "")
+                        last_name = tool_input.get("last_name", "")
+                        if first_name or last_name:
+                            name = f"{first_name} {last_name}".strip()
+                        else:
+                            name = tool_input.get("name", "Unknown")
                         title = tool_input.get("title", "")
-                        company = tool_input.get("company_name", current_prospect_name or "Unknown")
+                        # Get company from company_id or current context
+                        company = tool_input.get("company_name") or tool_input.get("company_id") or current_prospect_name or "Unknown"
                         output += f"👤 Found contact: {name} ({title})\n"
                         # Capture contact data during tool_call
                         pending_contact = {
@@ -734,15 +814,21 @@ For each prospect, explain WHY they're a good fit for {client_name}."""
                 tool = event.get("tool", "")
                 result = event.get("result", {})
 
-                if tool == "save_prospect" and pending_prospect:
-                    prospects_found.append(pending_prospect)
-                    output += f"   ✅ Prospect saved\n"
-                    pending_prospect = None
+                if tool == "save_prospect":
+                    if pending_prospect:
+                        prospects_found.append(pending_prospect)
+                        output += f"   ✅ Prospect saved: {pending_prospect.get('name', 'Unknown')}\n"
+                        pending_prospect = None
+                    else:
+                        output += f"   ✅ Prospect saved\n"
 
-                elif tool == "save_contact" and pending_contact:
-                    contacts_found.append(pending_contact)
-                    output += f"   ✅ Contact saved\n"
-                    pending_contact = None
+                elif tool == "save_contact":
+                    if pending_contact:
+                        contacts_found.append(pending_contact)
+                        output += f"   ✅ Contact saved: {pending_contact.get('name', 'Unknown')}\n"
+                        pending_contact = None
+                    else:
+                        output += f"   ✅ Contact saved\n"
 
                 elif tool == "send_email":
                     output += f"   ✅ Email drafted\n"
@@ -756,13 +842,19 @@ For each prospect, explain WHY they're a good fit for {client_name}."""
 
                 yield output
 
+            elif event_type == "thought":
+                # Capture AI thoughts/responses as potential final answer
+                thought = event.get("thought", "")
+                if thought:
+                    last_final_answer = thought
+
             elif event_type == "agent_complete":
                 # Save all to knowledge base
                 knowledge_base["prospects"].extend(prospects_found)
                 knowledge_base["contacts"].extend(contacts_found)
                 knowledge_base["emails"].extend(emails_drafted)
 
-                final_answer = event.get("final_answer", "")
+                final_answer = event.get("final_answer", "") or last_final_answer
 
                 output += "\n---\n\n"
                 output += f"## ✅ Discovery Complete!\n\n"
@@ -787,7 +879,32 @@ For each prospect, explain WHY they're a good fit for {client_name}."""
 
                 output += "\n---\n\n"
                 output += f"## ⏱️ Discovery Summary (Partial)\n\n"
-                output += f"Found {len(prospects_found)} prospects, {len(contacts_found)} contacts\n"
+                output += f"| Metric | Count |\n"
+                output += f"|--------|-------|\n"
+                output += f"| Prospects Found | {len(prospects_found)} |\n"
+                output += f"| Decision Makers | {len(contacts_found)} |\n"
+                output += f"| Emails Drafted | {len(emails_drafted)} |\n\n"
+                if last_final_answer:
+                    output += "---\n\n### 📋 AI Summary\n\n"
+                    output += last_final_answer
+                yield output
+                return
+
+            elif event_type == "agent_error":
+                # Save what we found so far even on error
+                knowledge_base["prospects"].extend(prospects_found)
+                knowledge_base["contacts"].extend(contacts_found)
+                knowledge_base["emails"].extend(emails_drafted)
+
+                error_msg = event.get("error", "Unknown error")
+                output += f"\n⚠️ AI Error: {error_msg}\n"
+                output += f"\n---\n\n"
+                output += f"## ⚠️ Discovery Interrupted\n\n"
+                output += f"| Metric | Count |\n"
+                output += f"|--------|-------|\n"
+                output += f"| Prospects Found | {len(prospects_found)} |\n"
+                output += f"| Decision Makers | {len(contacts_found)} |\n"
+                output += f"| Emails Drafted | {len(emails_drafted)} |\n\n"
                 yield output
                 return
 

mcp/agents/autonomous_agent_hf.py

@@ -305,10 +305,11 @@ When you have completed the task, provide a summary of what you accomplished."""
                     final_content = assistant_message.content or ""
 
                     if final_content:
+                        # Yield full thought content for capture, truncated for display message
                         yield {
                             "type": "thought",
-                            "thought": final_content[:200] + "..." if len(final_content) > 200 else final_content,
-                            "message": f"AI Response"
+                            "thought": final_content,  # Full content for capture
+                            "message": f"AI Response: {final_content[:100]}..." if len(final_content) > 100 else f"AI Response: {final_content}"
                         }
 
                     # Check if this looks like a final answer (after at least one iteration)