feat: integrate research workflow into canvas

Author: kevmyung

agent-blueprint/agentcore-runtime-a2a-stack/research-agent/src/main.py

@@ -157,7 +157,7 @@ async def _execute_streaming(self, context: RequestContext, updater: TaskUpdater
         self._current_tool_use_id = None
         self._step_counter = 0
 
-        # Extract metadata from RequestContext
+        # Extract metadata from RequestContext (need session_id early for file cleanup)
         # Try both params.metadata (MessageSendParams) and message.metadata (Message)
         # Streaming client may put metadata in Message.metadata
         metadata = context.metadata  # MessageSendParams.metadata
@@ -170,6 +170,18 @@ async def _execute_streaming(self, context: RequestContext, updater: TaskUpdater
 
         logger.info(f"[MetadataAwareExecutor] Extracted metadata - model_id: {model_id}, session_id: {session_id}, user_id: {user_id}")
 
+        # Clear previous research file for this session (prevent cumulative results)
+        if session_id:
+            try:
+                from report_manager import get_report_manager
+                manager = get_report_manager(session_id, user_id)
+                markdown_file = os.path.join(manager.workspace, "research_report.md")
+                if os.path.exists(markdown_file):
+                    os.remove(markdown_file)
+                    logger.info(f"[MetadataAwareExecutor] Cleared previous research file: {markdown_file}")
+            except Exception as e:
+                logger.warning(f"[MetadataAwareExecutor] Failed to clear previous research file: {e}")
+
         # Get or create agent with specified model_id
         if model_id and model_id in self.agent_cache:
             agent = self.agent_cache[model_id]

agent-blueprint/agentcore-runtime-a2a-stack/research-agent/src/tools/markdown_writer.py

@@ -139,23 +139,22 @@ async def write_markdown_section(
         # Prepare section content
         section_content = f"{prefix}{heading}\n\n{content}\n\n"
 
-        # Add citations if provided (simple icon links only)
+        # Add citations if provided (domain-based markdown links)
         if citations and len(citations) > 0:
-            section_content += '<div class="section-citations">\n'
+            from urllib.parse import urlparse
+            citation_links = []
             for citation in citations:
-                title = citation.get('title', 'Unknown Source')
                 url = citation.get('url', '#')
-
-                # Extract domain from URL for tooltip
-                from urllib.parse import urlparse
+                # Extract domain from URL
                 try:
                     domain = urlparse(url).netloc or url
+                    domain = domain.replace('www.', '')
                 except:
                     domain = url
+                # Use markdown link format with domain as text
+                citation_links.append(f'[{domain}]({url})')
 
-                # Generate simple icon link with domain tooltip
-                section_content += f'<span class="citation-chip"><a href="{url}" target="_blank" rel="noopener noreferrer" title="{domain}" aria-label="{title}">🔗</a></span> '
-            section_content += '\n</div>\n\n'
+            section_content += ' '.join(citation_links) + '\n\n'
             logger.info(f"Added {len(citations)} citations to section: {heading}")
 
         # Append to file (create if doesn't exist)

chatbot-app/agentcore/src/a2a_tools.py

@@ -692,11 +692,74 @@ async def tool_impl(plan: str, tool_context: ToolContext = None) -> AsyncGenerat
 
             logger.info(f"[{agent_id}] Sending to A2A with metadata: {metadata}")
 
+            # Track final result for artifact saving
+            final_result_text = None
+
             # Stream events from A2A agent (including research_step events for real-time UI updates)
             async for event in send_a2a_message(agent_id, plan, session_id, region, metadata=metadata):
+                # Capture final result for artifact saving
+                if isinstance(event, dict) and event.get("status") == "success":
+                    content = event.get("content", [])
+                    if content and len(content) > 0:
+                        final_result_text = content[0].get("text", "")
+
                 # Yield all events to allow real-time streaming (research_step, etc.)
                 yield event
 
+            # After streaming completes, save research result to agent.state
+            if final_result_text and tool_context and tool_context.agent:
+                try:
+                    from datetime import datetime, timezone
+
+                    # Extract title from research content (first H1 heading)
+                    import re
+                    title_match = re.search(r'^#\s+(.+)$', final_result_text, re.MULTILINE)
+                    title = title_match.group(1).strip() if title_match else "Research Results"
+
+                    # Generate artifact ID using toolUseId for frontend mapping
+                    tool_use_id = tool_context.tool_use.get('toolUseId', '')
+                    artifact_id = f"research-{tool_use_id}" if tool_use_id else f"research-{session_id}-{datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S')}"
+
+                    # Get current artifacts from agent.state
+                    artifacts = tool_context.agent.state.get("artifacts") or {}
+
+                    # Calculate word count
+                    word_count = len(final_result_text.split())
+
+                    # Add new artifact
+                    artifacts[artifact_id] = {
+                        "id": artifact_id,
+                        "type": "research",
+                        "title": title,
+                        "content": final_result_text,
+                        "tool_name": "research_agent",
+                        "metadata": {
+                            "word_count": word_count,
+                            "description": f"Research report: {title}"
+                        },
+                        "created_at": datetime.now(timezone.utc).isoformat(),
+                        "updated_at": datetime.now(timezone.utc).isoformat()
+                    }
+
+                    # Save to agent.state
+                    tool_context.agent.state.set("artifacts", artifacts)
+
+                    # Sync agent state to file system / AgentCore Memory
+                    # Try session_manager from invocation_state first (set by ChatAgent)
+                    session_manager = tool_context.invocation_state.get("session_manager")
+
+                    if not session_manager and hasattr(tool_context.agent, 'session_manager'):
+                        session_manager = tool_context.agent.session_manager
+
+                    if session_manager:
+                        session_manager.sync_agent(tool_context.agent)
+                        logger.info(f"[Research] ✅ Saved research artifact to agent.state: {artifact_id}")
+                    else:
+                        logger.warning(f"[Research] ⚠️ No session_manager found, artifact not persisted")
+
+                except Exception as e:
+                    logger.error(f"[Research] ❌ Failed to save artifact: {e}")
+
         # Set correct function name and docstring BEFORE decorating
         tool_impl.__name__ = correct_name
         tool_impl.__doc__ = agent_description

chatbot-app/agentcore/src/agent/config/swarm_config.py

@@ -159,7 +159,7 @@
     "responder": """Responder - write the final user-facing response.
 
 Use create_visualization tool for simple charts if needed.
-Citation: When citations in shared context, wrap claims with <cite source="SOURCE" url="URL">text</cite>""",
+Citation: When citing sources from shared context, use markdown links: [source name](URL)""",
 }
 
 

chatbot-app/agentcore/src/agents/chat_agent.py

@@ -281,11 +281,12 @@ async def stream_async(self, message: str, session_id: str = None, files: Option
             else:
                 logger.debug(f"Prompt is string: {prompt[:100]}")
 
-            # Prepare invocation_state with model_id, user_id, session_id, and uploaded files
+            # Prepare invocation_state with model_id, user_id, session_id, session_manager, and uploaded files
             invocation_state = {
                 "session_id": self.session_id,
                 "user_id": self.user_id,
-                "model_id": self.model_id
+                "model_id": self.model_id,
+                "session_manager": self.session_manager  # For tools that need to persist state (e.g., research artifacts)
             }
 
             # Add uploaded files to invocation_state (for tool access)

chatbot-app/frontend/src/app/api/s3/presigned-url/route.ts

@@ -38,17 +38,12 @@ export async function POST(request: NextRequest) {
       expiresIn: 3600  // 1 hour
     })
 
-    console.log(`[S3] Generated pre-signed URL for ${s3Key}`)
-
     return NextResponse.json({ url })
 
   } catch (error) {
     console.error('[S3] Error generating pre-signed URL:', error)
     return NextResponse.json(
-      {
-        error: 'Failed to generate pre-signed URL',
-        details: error instanceof Error ? error.message : 'Unknown error'
-      },
+      { error: 'Failed to generate pre-signed URL' },
       { status: 500 }
     )
   }