session = client.sessions.create() messages = [ {"role": "user", "content": "I need to write a landing page of iPhone 15 pro max"}, { "role": "assistant", "content": "Sure, my plan is below:\n1. Search for the latest news about iPhone 15 pro max\n2. Init Next.js project for the landing page\n3. Deploy the landing page to the website", } ] # Save messages for msg in messages: client.sessions.store_message(session_id=session.id, blob=msg, format="openai")

r = client.sessions.get_messages(session.id) new_msg = r.items new_msg.append({"role": "user", "content": "How are you doing?"}) r = openai_client.chat.completions.create(model="gpt-4.1", messages=new_msg) print(r.choices[0].message.content) client.sessions.store_message(session_id=session.id, blob=r.choices[0].message)

from acontext import FileUpload disk = client.disks.create() file = FileUpload( filename="todo.md", content=b"# Sprint Plan\n\n## Goals\n- Complete user authentication\n- Fix critical bugs" ) artifact = client.disks.artifacts.upsert( disk.id, file=file, file_path="/todo/" ) print(client.disks.artifacts.list( disk.id, path="/todo/" )) result = client.disks.artifacts.get( disk.id, file_path="/todo/", filename="todo.md", with_public_url=True, with_content=True ) print(f"✓ File content: {result.content.raw}") print(f"✓ Download URL: {result.public_url}") 

from acontext import AcontextClient # Initialize client client = AcontextClient( base_url="http://localhost:8029/api/v1", api_key="sk-ac-your-root-api-bearer-token" ) # Create a project and session session = client.sessions.create() # Conversation messages messages = [ {"role": "user", "content": "I need to write a landing page of iPhone 15 pro max"}, { "role": "assistant", "content": "Sure, my plan is below:\n1. Search for the latest news about iPhone 15 pro max\n2. Init Next.js project for the landing page\n3. Deploy the landing page to the website", }, { "role": "user", "content": "That sounds good. Let's first collect the message and report to me before any landing page coding.", }, { "role": "assistant", "content": "Sure, I will first collect the message then report to you before any landing page coding.", "tool_calls": [ { "id": "call_001", "type": "function", "function": { "name": "search_news", "arguments": "{\"query\": \"iPhone news\"}" } } ] }, ] # Store messages in a loop for msg in messages: client.sessions.store_message(session_id=session.id, blob=msg, format="openai") # Wait for task extraction to complete client.sessions.flush(session.id) # Display extracted tasks tasks_response = client.sessions.get_tasks(session.id) print(tasks_response) for task in tasks_response.items: print(f"\nTask #{task.order}:") print(f" ID: {task.id}") print(f" Title: {task.data.task_description}") print(f" Status: {task.status}") # Show progress updates if available if task.data.progresses: print(f" Progress updates: {len(task.data.progresses)}") for progress in task.data.progresses: print(f" - {progress}") # Show user preferences if available if task.data.user_preferences: print(" User preferences:") for pref in task.data.user_preferences: print(f" - {pref}")