# Run a recipe and save the replay trace
praisonai recipe run my-recipe --save

# Run agents.yaml with replay saving
praisonai agents.yaml --save

# Run workflow with replay saving
praisonai workflow run my-workflow --save

praisonai replay list

# Interactive step-through replay
praisonai replay context <session_id>

# View agent flow visualization
praisonai replay flow <session_id>

# Show all events (non-interactive)
praisonai replay show <session_id>

praisonai replay cleanup --max-age 7

from praisonaiagents import Agent
from praisonaiagents.trace import ContextTraceEmitter, ContextListSink

# Create a trace sink
sink = ContextListSink()
emitter = ContextTraceEmitter(sink=sink, session_id="my-session")

# Use the emitter to track context changes
emitter.session_start()
emitter.agent_start("researcher")
emitter.message_added("researcher", "user", "Find info about AI", 1, 100)
# ... agent execution ...
emitter.agent_end("researcher")
emitter.session_end()

# Access events
for event in sink.get_events():
    print(f"{event.event_type}: {event.agent_name}")

from praisonai.replay import ContextTraceWriter
from praisonaiagents.trace import ContextTraceEmitter

# Write traces to disk
writer = ContextTraceWriter(session_id="my-session")
emitter = ContextTraceEmitter(sink=writer, session_id="my-session")

emitter.session_start()
# ... your agent code ...
emitter.session_end()
writer.close()  # Flush and close

# List available traces
praisonai replay list

# Interactive replay
praisonai replay context my-session

# Show events without interaction
praisonai replay show my-session

# Filter by agent
praisonai replay show my-session --agent researcher

# JSON output
praisonai replay show my-session --json

praisonai replay flow my-session
praisonai replay flow my-session --format json  # JSON output

============================================================
  AGENT FLOW: my-session
============================================================

  ┌────────────────────────────────────────┐
  │ Agent: researcher                      │
  │                                        │
  │   Messages: 2                         │
  │   LLM Calls: 1                        │
  │   Tools: web_search                  │
  └────────────────────────────────────────┘
          │
          ▼
    [Research complete, ready for writing]

  ┌────────────────────────────────────────┐
  │ Agent: writer                          │
  │                                        │
  │   Messages: 2                         │
  │   LLM Calls: 1                        │
  └────────────────────────────────────────┘

────────────────────────────────────────────────────────────
  Summary:
    Agents: 2
    Handoffs: 1
    Total Events: 15
────────────────────────────────────────────────────────────

praisonai replay list
praisonai replay list --limit 50
praisonai replay list --json

praisonai replay context my-session
praisonai replay context my-session --start 5  # Start from event 5
praisonai replay context my-session --no-rich  # Plain text output

praisonai replay show my-session
praisonai replay show my-session --event 5      # Show specific event
praisonai replay show my-session --agent writer # Filter by agent
praisonai replay show my-session --type agent_start  # Filter by type
praisonai replay show my-session --json         # JSON output

praisonai replay delete my-session
praisonai replay delete my-session --force  # Skip confirmation

praisonai replay cleanup
praisonai replay cleanup --max-age 14      # Delete traces older than 14 days
praisonai replay cleanup --max-size 200    # Keep total size under 200MB
praisonai replay cleanup --dry-run         # Preview what would be deleted

from praisonaiagents.trace import ContextEvent, ContextEventType

event = ContextEvent(
    event_type=ContextEventType.MESSAGE_ADDED,
    timestamp=time.time(),
    session_id="my-session",
    agent_name="researcher",
    messages_count=5,
    tokens_used=1500,
    tokens_budget=128000,
    data={"role": "user", "content": "Hello"},
)

# Serialize
json_str = event.to_json()
event_dict = event.to_dict()

# Deserialize
event = ContextEvent.from_dict(event_dict)

from praisonaiagents.trace import ContextTraceEmitter, ContextListSink

sink = ContextListSink()
emitter = ContextTraceEmitter(
    sink=sink,
    session_id="my-session",
    enabled=True,
    redact=True,  # Redact sensitive data
)

# Session lifecycle
emitter.session_start()
emitter.session_end()

# Agent lifecycle
emitter.agent_start("agent_name")
emitter.agent_end("agent_name")

# Messages
emitter.message_added(
    agent_name="agent",
    role="user",
    content="Hello",
    messages_count=1,
    tokens_used=10,
)

# Tool calls
emitter.tool_call_start("agent", "tool_name", {"arg": "value"})
emitter.tool_call_end("agent", "tool_name", "result", duration_ms=100)

# LLM calls
emitter.llm_request("agent", messages_count=5, tokens_used=1000, tokens_budget=128000)
emitter.llm_response("agent", response_tokens=500, duration_ms=1500)

# Full context snapshot
emitter.context_snapshot(
    agent_name="agent",
    messages_count=10,
    tokens_used=5000,
    tokens_budget=128000,
    messages=[{"role": "user", "content": "..."}],
)

# Agent handoff (for flow visualization)
emitter.agent_handoff(
    from_agent="researcher",
    to_agent="writer",
    reason="Research complete, ready for writing",
    context_passed={"findings": ["result1", "result2"]},
)

from praisonai.replay import ContextTraceWriter

# Basic usage
writer = ContextTraceWriter(session_id="my-session")
writer.emit(event)
writer.close()

# Context manager
with ContextTraceWriter(session_id="my-session") as writer:
    writer.emit(event)

from praisonai.replay import ContextTraceReader

reader = ContextTraceReader("my-session")

# Check existence
if reader.exists:
    # Iterate
    for event in reader:
        print(event.event_type)
    
    # Random access
    event = reader[0]
    
    # Get all
    events = reader.get_all()
    
    # Filter
    agent_events = reader.get_by_agent("researcher")
    tool_events = reader.get_by_type("tool_call_start")

from praisonai.replay import ContextTraceReader, ReplayPlayer

reader = ContextTraceReader("my-session")
player = ReplayPlayer(reader, use_rich=True)

# Interactive mode
player.run()

# Programmatic navigation
player.display_current()
player.next()
player.previous()
player.goto(5)

~/.praisonai/traces/
├── session-abc123.jsonl
├── session-def456.jsonl
└── ...