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Stateful Agents Framework

PraisonAI provides a comprehensive stateful agents framework that enables building persistent, memory-aware agents capable of maintaining context across sessions, learning from interactions, and executing complex multi-step workflows.

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Core Stateful Capabilities

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Memory System

PraisonAI includes a sophisticated multi-tiered memory system with quality-based filtering:
  • Short-term Memory (STM): Ephemeral context for current conversations
  • Long-term Memory (LTM): Persistent knowledge with quality-based filtering
  • Entity Memory: Structured data about named entities and relationships
  • User Memory: User-specific preferences and interaction history
  • Graph Memory: Complex relationship storage via Mem0 integration
from praisonaiagents import Agent, Memory

# Initialize memory system with quality scoring
memory_config = {
    "provider": "rag",  # or "mem0" or "none"
    "use_embedding": True,
    "rag_db_path": ".praison/chroma_db"
}

memory = Memory(config=memory_config, verbose=5)

# Create agent with memory
agent = Agent(
    name="Research Assistant",
    role="AI Researcher", 
    memory=memory,
    user_id="user_123"
)

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Session Management

The Session class provides a unified API for managing stateful agent interactions: 
from praisonaiagents import Session

# Create a persistent session
session = Session(
    session_id="research_session_001", 
    user_id="researcher_123"
)

# Create agents within the session context (new API)
agent = session.Agent(
    name="Data Analyst",
    role="Research Assistant",
    memory=True,
    knowledge=["research_papers.pdf", "data_sources.csv"]
)

# Save session state
session.save_state({
    "research_topic": "AI Safety",
    "documents_processed": 15,
    "analysis_stage": "hypothesis_generation"
})

# Restore state later
previous_state = session.restore_state()

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Workflow State Management

PraisonAI supports complex stateful workflows with persistent state across tasks: 
from praisonaiagents import PraisonAIAgents, Agent, Task

# Create stateful workflow
agents = PraisonAIAgents(
    agents=[researcher, analyzer, writer],
    tasks=[research_task, analysis_task, writing_task],
    memory=True,
    process="workflow",
    user_id="session_123"
)

# Enhanced state management methods
agents.set_state("total_documents", 100)
agents.increment_state("processed_count", 1)
agents.append_to_state("findings", new_finding)

# Check state existence and get all state
if agents.has_state("processed_count"):
    count = agents.get_state("processed_count", 0)
    all_state = agents.get_all_state()

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Advanced Stateful Patterns

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Quality-Based Memory Storage

Memory storage with automatic quality assessment using individual metrics: 
# Store with detailed quality metrics
memory.store_long_term(
    text="AI research findings on neural architecture search",
    metadata={"source": "arxiv", "confidence": 0.9},
    completeness=0.95,  # How complete is the information
    relevance=0.88,     # How relevant to the topic
    clarity=0.92,       # How clear and understandable
    accuracy=0.85       # How accurate the information is
)

# Search with quality filtering
results = memory.search_long_term(
    query="neural architecture",
    min_quality=0.8,
    limit=5,
    rerank=True  # Use reranking for better results
)

# Use custom quality weights
custom_weights = {
    "completeness": 0.3,
    "relevance": 0.4, 
    "clarity": 0.2,
    "accuracy": 0.1
}

quality_score = memory.compute_quality_score(
    completeness=0.85,
    relevance=0.90,
    clarity=0.75,
    accuracy=0.95,
    weights=custom_weights
)

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Enhanced State Management

# Comprehensive state management methods
workflow = PraisonAIAgents(agents=[...], memory=True)

# Basic operations
workflow.set_state("research_topic", "AI Safety")
workflow.update_state({"phase": "analysis", "progress": 50})

# Advanced operations
workflow.increment_state("task_count", 1, default=0)
workflow.append_to_state("completed_tasks", "research_phase", max_length=10)
workflow.delete_state("temporary_data")

# State queries
has_topic = workflow.has_state("research_topic")
all_state = workflow.get_all_state()

# Session persistence
workflow.save_session_state("research_session_001")
workflow.restore_session_state("research_session_001")

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Context Building from Multiple Sources

# Build rich context from memory, knowledge, and state
context = memory.build_context_for_task(
    task_descr="Analyze recent AI safety papers",
    user_id="researcher_123",
    max_items=5
)

# Context includes:
# - Short-term conversation history
# - Relevant long-term memories  
# - Entity relationships
# - User-specific preferences

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Knowledge Base Integration

from praisonaiagents import Knowledge

# Initialize knowledge system
knowledge_config = {
    "vector_store": {"provider": "chromadb"},
    "graph_store": {"provider": "neo4j", "config": {...}}
}

knowledge = Knowledge(config=knowledge_config)

# Add documents with automatic processing
knowledge.add("research_paper.pdf", user_id="user_123")
knowledge.add("https://arxiv.org/paper/123", user_id="user_123")

# Semantic search with reranking
results = knowledge.search(
    query="transformer attention mechanisms",
    rerank=True,
    user_id="user_123"
)

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Advanced Memory Features

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Graph Memory Support

# Initialize memory with graph support via Mem0
memory_config = {
    "provider": "mem0",
    "config": {
        "api_key": "your_mem0_key",
        "graph_store": {
            "provider": "neo4j",
            "config": {
                "url": "neo4j+s://your-instance.databases.neo4j.io",
                "username": "neo4j", 
                "password": "your_password"
            }
        },
        "vector_store": {
            "provider": "qdrant",
            "config": {"host": "localhost", "port": 6333}
        },
        "llm": {
            "provider": "openai",
            "config": {"model": "gpt-4o", "api_key": "..."}
        },
        "embedder": {
            "provider": "openai",
            "config": {"model": "text-embedding-3-small", "api_key": "..."}
        }
    }
}

memory = Memory(config=memory_config)

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Quality Metrics and Evaluation

# Automatic quality calculation using LLM
quality_metrics = memory.calculate_quality_metrics(
    output="Generated research summary...",
    expected_output="Expected comprehensive analysis...",
    llm="gpt-4o"
)

# Store with calculated quality
memory.store_quality(
    text="Research findings...",
    quality_score=0.85,
    task_id="task_001",
    metrics=quality_metrics,
    memory_type="long"
)

# Search with quality thresholds
high_quality_results = memory.search_with_quality(
    query="AI safety research",
    min_quality=0.8,
    memory_type="long"
)

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Session API Updates

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Current Session Methods

# Create session
session = Session(session_id="demo_001", user_id="user_123")

# New Agent creation method (recommended)
agent = session.Agent(
    name="Assistant",
    role="Helper",
    memory=True,
    instructions="Remember user preferences"
)

# Session state management
session.set_state("preference", "brief_responses")
session.save_state({"conversation_style": "technical"})

# Memory operations
session.add_memory("User prefers technical explanations", memory_type="long")
session.search_memory("preferences", memory_type="long")

# Context building
context = session.get_context("machine learning concepts")

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Configuration Examples

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Basic Stateful Agent

from praisonaiagents import Agent

agent = Agent(
    name="Personal Assistant",
    role="Helpful AI Assistant",
    instructions="Remember user preferences and maintain conversation context",
    memory=True,  # Enable default memory
    user_id="user_456"
)

response = agent.chat("I like concise answers to technical questions")
# Memory automatically stores this preference with quality scoring

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Advanced Memory Configuration

memory_config = {
    "provider": "rag",
    "use_embedding": True,
    "rag_db_path": ".praison/memory_db",
    "short_db": ".praison/short_term.db",
    "long_db": ".praison/long_term.db"
}

agent = Agent(
    name="Research Agent",
    role="AI Researcher",
    memory=Memory(config=memory_config, verbose=5),
    user_id="researcher_001"
)

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Complex Workflow with State

def research_tool(topic: str, num_sources: int = 5):
    """Tool that updates workflow state"""
    # Tool implementation...
    return {"findings": [...], "confidence": 0.85}

# Define conditional workflow with state-based routing
research_task = Task(
    name="research", 
    description="Research the topic using available tools",
    tools=[research_tool]
)

analysis_task = Task(
    name="analyze",
    description="Analyze findings if sufficient data available"
)

# Create workflow with enhanced state management
workflow = PraisonAIAgents(
    agents=[researcher, analyzer],
    tasks=[research_task, analysis_task], 
    memory=True,
    process="workflow",
    user_id="project_alpha"
)

# Set initial state with type checking
workflow.set_state("research_topic", "AI Safety")
workflow.set_state("target_papers", 50)
workflow.increment_state("tasks_completed", 0, default=0)

result = workflow.start()

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Best Practices

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1. Session Management

  • Use meaningful session IDs that can be restored later
  • Save session state at key workflow milestones
  • Include user_id for multi-user applications
  • Use the new session.Agent() method for agent creation

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2. Memory Strategy

  • Use individual quality metrics for fine-grained control
  • Implement quality thresholds based on application needs
  • Store entity relationships for better context retrieval
  • Use reranking for improved search results

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3. State Design

  • Use descriptive state keys with consistent naming
  • Leverage convenience methods like increment_state and append_to_state
  • Implement state validation for critical workflows
  • Use has_state() to check existence before accessing

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4. Quality Management

  • Set appropriate quality weights for your domain
  • Use external evaluator quality when available
  • Implement memory cleanup based on quality scores
  • Monitor quality distribution for insights

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Integration with Existing Features

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With Multi-Agent Systems

# Each agent maintains individual and shared state
team = PraisonAIAgents(
    agents=[lead_researcher, data_analyst, writer],
    memory=True,
    user_id="research_team"
)

# Shared team state with enhanced methods
team.set_state("project_deadline", "2024-12-31")
team.append_to_state("milestones", "phase_1_complete")
team.increment_state("completed_tasks", 1)

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With Tools and State

def update_progress_tool(topic: str, progress: float):
    """Tool that modifies workflow state"""
    workflow.set_state(f"progress_{topic}", progress)
    workflow.increment_state("total_progress", progress)
    return f"Updated progress for {topic}: {progress}%"

agent = Agent(
    name="Project Manager", 
    tools=[update_progress_tool],
    memory=True
)

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With APIs and UIs

# Stateful agents in API endpoints
@app.post("/chat")
async def chat_endpoint(message: str, session_id: str):
    session = Session(session_id=session_id, user_id=current_user.id)
    agent = session.Agent("Assistant", memory=True)
    
    response = agent.chat(message)
    session.save_state({"last_interaction": time.now()})
    
    return {"response": response}
The PraisonAI stateful agents framework provides everything needed to build sophisticated, persistent AI agents that can maintain context, learn from interactions, and execute complex workflows across sessions with enhanced quality management and state persistence.