Sessions & Remote Agents

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
    memory={"session_id": "my-session-123"},
)
agent.start("My name is Alice")

# Session with memory configuration
session = Session(
    session_id="chat_123",
    user_id="user_456",
    memory=True  # Enable memory with defaults
)

# Add memories directly
session.add_memory("User prefers technical explanations")

# Search memories
memories = session.search_memory("preferences")

# Agent automatically uses session memory
agent = session.Agent(name="Assistant", memory=True)

# Session with knowledge
session = Session(
    session_id="research_session",
    user_id="researcher_1",
    knowledge={
        "vector_store": {
            "provider": "chroma",
            "config": {"collection_name": "research_docs"}
        }
    }
)

# Add knowledge to session
session.add_knowledge("research_paper.pdf")
session.add_knowledge("Important finding: AI improves efficiency by 40%")

# Search knowledge
results = session.search_knowledge("efficiency improvements")

# Agents use session knowledge
agent = session.Agent(
    name="Research Assistant",
    knowledge=True
)

# remote_agent_server.py
from flask import Flask, request, jsonify
from praisonaiagents import Agent

app = Flask(__name__)

# Create your agent
agent = Agent(
    name="Remote Assistant",
    instructions="You are a helpful remote assistant.",
    tools=[...],  # Your tools
    memory=True
)

@app.route('/agent', methods=['POST'])
def handle_message():
    data = request.json
    message = data.get('message', '')
    session_id = data.get('session_id')
    
    # Process message
    response = agent.chat(message, session_id=session_id)
    
    return jsonify({
        'content': response,
        'session_id': session_id,
        'metadata': {
            'agent_name': agent.name,
            'timestamp': datetime.now().isoformat()
        }
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)

session = Session(
    # Required
    session_id="unique_session_id",
    
    # Optional
    user_id="user_identifier",
    session_ttl=3600,  # Expire after 1 hour (None = never)
    
    # Memory configuration (use memory= consolidated param)
    memory=True,  # Enable memory with defaults
    
    # Knowledge configuration
    knowledge={
        "vector_store": {
            "provider": "chroma",
            "config": {
                "collection_name": "session_knowledge",
                "path": ".sessions/knowledge"
            }
        }
    }
)

session = Session(
    # Remote agent URL
    agent_url="https://api.example.com/agent",
    
    # Optional timeout (default: 30 seconds)
    timeout=60,
    
    # Optional headers for authentication
    headers={
        "Authorization": "Bearer token",
        "X-API-Key": "api_key"
    }
)

# Save current state
session.save_state()

# State includes:
# - Session metadata
# - Memory contents
# - Knowledge references
# - Agent configurations

# Create new session instance
session = Session(session_id="chat_123")

# Restore previous state
session.restore_state()

# Continue where you left off
agent = session.Agent(name="Assistant")
response = agent.chat("What were we discussing?")

# Get session context
context = session.get_context()
print(f"Session ID: {context['session_id']}")
print(f"User ID: {context['user_id']}")
print(f"Created: {context['created_at']}")
print(f"Messages: {context['message_count']}")

session = Session(session_id="team_session")

# Create multiple agents in session
researcher = session.Agent(
    name="Researcher",
    instructions="You research topics"
)

writer = session.Agent(
    name="Writer", 
    instructions="You write content"
)

# Agents share session memory and knowledge
research = researcher.chat("Research AI trends")
article = writer.chat("Write an article about the research")

def logging_middleware(message, response):
    print(f"[{datetime.now()}] Message: {message}")
    print(f"[{datetime.now()}] Response: {response[:100]}...")
    return response

session = Session(
    session_id="logged_session",
    middleware=[logging_middleware]
)

from praisonaiagents import Session

# Session expires 1 hour after creation
session = Session(
    session_id="chat_123",
    session_ttl=3600,
)

agent = session.Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
)

agent.start("Hello!")

# Later — gate on expiry before answering
if session.is_expired():
    print("Session expired, please start a new one")
else:
    agent.start("Continue our chat")

# Always release resources when done
session.close()

from praisonaiagents import Agent, MemoryConfig

# Automatically save session state under the given name
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    memory=MemoryConfig(
        auto_save="my-assistant-session",  # Session name
        user_id="user123",
    )
)

# Each interaction is auto-persisted
agent.start("Remember that I prefer dark mode")
# Session saved automatically — no manual save_state() needed