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Documentation Index

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Query the runs and traces your agents have written to the state store.

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Quick Start

1

Configure State Store

Set PRAISONAI_STATE_URL or pass state_url= when creating agents. See Database Persistence for configuration options.
import os
os.environ["PRAISONAI_STATE_URL"] = "sqlite:///agent_state.db"
2

Query Runs

Run an agent, then read its runs back using get_runs(session_id):
from praisonaiagents import Agent
from praisonaiagents.db import PraisonAIDB

# Run an agent
agent = Agent(name="Research Agent", instructions="Research the topic")
result = agent.start("What is quantum computing?")

# Query runs for this session
db = PraisonAIDB(state_url="sqlite:///agent_state.db")
runs = db.get_runs(session_id=agent.session_id)

for run in runs:
    print(f"Run {run['run_id']}: {run['status']} at {run['started_at']}")
3

Query Traces with Filters

Use get_traces() with optional filters for session_id, user_id, and limit:
# Get traces for a specific session
traces = db.get_traces(session_id=agent.session_id, limit=10)

# Get traces for a specific user
traces = db.get_traces(user_id="user123", limit=5)

# Get all traces (up to limit)
all_traces = db.get_traces(limit=100)

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How It Works

Both methods read persisted data from the configured state store:
MethodSource keysFiltersSort
get_runsrun:{session_id}:*session_id (required), limit (optional)started_at desc
get_tracestrace:*session_id, user_id, limit (all optional)started_at desc

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Configuration Options

def get_runs(session_id: str, limit: Optional[int] = None) -> List[dict]:
    """
    Get runs for a session.
    
    Args:
        session_id: Session ID to filter by (required)
        limit: Maximum number of runs to return (optional)
        
    Returns:
        List of run dictionaries sorted by started_at desc
    """

def get_traces(
    session_id: Optional[str] = None, 
    user_id: Optional[str] = None, 
    limit: Optional[int] = None
) -> List[dict]:
    """
    Get traces with optional filters.
    
    Args:
        session_id: Filter by session ID (optional)
        user_id: Filter by user ID (optional)
        limit: Maximum number of traces to return (optional)
        
    Returns:
        List of trace dictionaries sorted by started_at desc
    """
Behavior:
  • If state_store is not configured, returns [] and logs a warning
  • limit=0 returns [], limit=None returns everything matching
  • Bad/non-dict entries are skipped with a warning
  • Uses state_store.scan_prefix() when available, falls back to state_store.keys()

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Common Patterns

Show last 10 runs for a user: 
# Get user's recent sessions first, then query runs
recent_sessions = get_user_sessions(user_id="user123")  # Your implementation
for session in recent_sessions[:5]:  # Last 5 sessions
    runs = db.get_runs(session_id=session['session_id'], limit=2)
    print(f"Session {session['session_id']}: {len(runs)} runs")
Export traces for a session: 
import json

# Export complete session trace data
traces = db.get_traces(session_id="session_abc123")
with open("session_traces.json", "w") as f:
    json.dump(traces, f, indent=2, default=str)

​
Best Practices

Otherwise you get [] and a warning: "get_runs() called but no state_url configured; returning []".Configure via environment variable or constructor:
# Environment variable (recommended)
os.environ["PRAISONAI_STATE_URL"] = "postgresql://user:pass@host/db"

# Or via constructor
db = PraisonAIDB(state_url="sqlite:///data.db")
These methods scan the store, so use small limits for responsive UIs:
# Good for UI pagination
recent_runs = db.get_runs(session_id, limit=20)

# Avoid large scans in UI code
all_runs = db.get_runs(session_id)  # Could be thousands
Both methods perform store scans which can be expensive for large datasets. Cache results when possible:
# Cache for dashboard views
@lru_cache(maxsize=100)
def get_cached_runs(session_id: str, limit: int = 10):
    return tuple(db.get_runs(session_id, limit))  # Tuple for hashability

Database Persistence

Configure state stores for persistence

Session Management

Manage agent sessions and state

Langfuse Observability

Advanced trace analysis and monitoring