Attribution CLI

# Run with tracing enabled
praisonai-ts llm trace "Hello world" --model openai/gpt-4o-mini

# Show attribution headers in output
praisonai-ts llm trace "Hello" --verbose

# Custom agent ID
praisonai-ts chat "Hello" --agent-id my-custom-agent

# Custom session ID
praisonai-ts chat "Hello" --session my-session-123

# Custom run ID
praisonai-ts chat "Hello" --run-id run-abc-123

# All custom attribution
praisonai-ts chat "Hello" \
  --agent-id my-agent \
  --session my-session \
  --run-id my-run \
  --trace-id my-trace

# Run workflow with tracing
praisonai-ts workflow run ./workflow.yaml --trace

# Show agent attribution for each step
praisonai-ts workflow run ./workflow.yaml --trace --verbose

$ praisonai-ts llm trace "What is 2+2?" --model openai/gpt-4o-mini

✓ Response: 2 + 2 = 4

Attribution:
  Agent ID: agent_abc123
  Run ID: run_xyz789
  Session ID: session_def456
  Backend: ai-sdk
  Duration: 234ms

$ praisonai-ts llm trace "Hello" --json

{
  "success": true,
  "data": {
    "response": "Hello! How can I help you today?",
    "attribution": {
      "agentId": "agent_abc123",
      "runId": "run_xyz789",
      "sessionId": "session_def456",
      "traceId": null,
      "backend": "ai-sdk",
      "provider": "openai",
      "model": "gpt-4o-mini"
    }
  },
  "meta": {
    "duration_ms": 234,
    "tokens": {
      "prompt": 12,
      "completion": 8,
      "total": 20
    }
  }
}

$ praisonai-ts workflow run ./pipeline.yaml --trace --verbose

=== Workflow: data-pipeline ===

Step 1: extract
  Agent ID: extractor
  Run ID: run_step1_abc
  Status: ✓ Complete (1.2s)

Step 2: transform
  Agent ID: transformer
  Run ID: run_step2_def
  Status: ✓ Complete (0.8s)

Step 3: load
  Agent ID: loader
  Run ID: run_step3_ghi
  Status: ✓ Complete (0.5s)

=== Workflow Complete ===
Total Duration: 2.5s

# First message - creates session
$ praisonai-ts chat "My name is John" --session user-123
Session: user-123
Response: Nice to meet you, John!

# Second message - continues session
$ praisonai-ts chat "What is my name?" --session user-123
Session: user-123
Response: Your name is John.

# Tag requests for debugging
$ praisonai-ts chat "Process this data" \
  --agent-id data-processor \
  --run-id debug-run-001 \
  --trace-id incident-12345 \
  --verbose

Attribution Headers:
  X-Agent-Id: data-processor
  X-Run-Id: debug-run-001
  X-Trace-Id: incident-12345
  X-Session-Id: session_auto_xyz

Response: ...

$ praisonai-ts workflow run ./parallel-agents.yaml --trace

=== Parallel Execution ===

[agent-1] Starting... (Run: run_p1_abc)
[agent-2] Starting... (Run: run_p2_def)
[agent-3] Starting... (Run: run_p3_ghi)

[agent-2] Complete (0.9s)
[agent-1] Complete (1.1s)
[agent-3] Complete (1.3s)

=== All Agents Complete ===

# Enable tracing globally
export PRAISONAI_TRACE=true

# Set default agent ID prefix
export PRAISONAI_AGENT_PREFIX=myapp

# Enable verbose attribution logging
export PRAISONAI_VERBOSE=true

#!/bin/bash
# capture-trace.sh

result=$(praisonai-ts llm trace "$1" --json)

agent_id=$(echo "$result" | jq -r '.data.attribution.agentId')
run_id=$(echo "$result" | jq -r '.data.attribution.runId')
duration=$(echo "$result" | jq -r '.meta.duration_ms')

echo "Agent: $agent_id"
echo "Run: $run_id"
echo "Duration: ${duration}ms"

#!/bin/bash
# log-traces.sh

LOG_FILE="traces.log"

praisonai-ts chat "$1" --json | jq '{
  timestamp: now | todate,
  agent: .data.attribution.agentId,
  run: .data.attribution.runId,
  duration: .meta.duration_ms
}' >> "$LOG_FILE"

#!/bin/bash
# monitor-workflow.sh

praisonai-ts workflow run ./workflow.yaml --trace --json | while read -r line; do
  agent=$(echo "$line" | jq -r '.attribution.agentId // empty')
  status=$(echo "$line" | jq -r '.status // empty')
  
  if [ -n "$agent" ]; then
    echo "[$(date +%H:%M:%S)] $agent: $status"
  fi
done

# Set OTLP endpoint
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

# Run with telemetry
praisonai-ts chat "Hello" --telemetry

# Pipe JSON output to logging service
praisonai-ts llm trace "Hello" --json | \
  curl -X POST -H "Content-Type: application/json" \
  -d @- https://logs.example.com/ingest