4. Run

python PETP.py          # GUI
python PETP_background.py   # Headless service (port 8866)

Screenshots

macOS

Windows

MCP Tool Server — integrate with Claude Code, Cursor, and other AI agents:

Features

🤖 AI Execution Generator

Highlight — Generate and modify PETP task flows through natural language conversation with LLM. Supports 10 LLM providers including Hyperspace, Anthropic, DeepSeek, Zhipu, Gemini, Ollama, and more.

Entry Points:

• Create Execution → "AI Generate" template
• Right-click taskGrid → "AI Assist" (modify existing) — pre-selects only the processors used in the current Execution to minimize token usage
• MCP Editor → "AI" button (auto-generate tool description)

Highlights:

• Multi-turn chat — ask questions, generate flows, modify tasks incrementally
• Processor browser — expandable TreeListCtrl with full documentation, search & filter
• Selective context — only checked Processors are sent to LLM (saves tokens)
• Connection caching — first-time validation, then instant reuse across sessions
• 10 LLM providers — minimal config: just set ai_provider in petpconfig.yaml
• 429 rate-limit defense — exponential backoff (2s / 4s / 8s), UI throttle ≥ 3s, per-call token accounting log

Token controls (new):

• ai_max_response_tokens — hard upper bound on model output length (default 8192)
• ai_max_request_tokens — local pre-flight cap on request size (default 60000); over-limit prompts are rejected before hitting the provider

AI-Powered MCP Tool Publishing:

• One-click generation of mcp_desc JSON for exposing Executions as MCP tools
• Auto-extracts input parameters from INITIAL_PARAMS and output keys from result tasks
• Generates AI-agent-friendly descriptions that help LLMs understand when to call the tool
• Smart merge — new fields are added without overwriting existing configuration
• Progress dialog with live status and preview before applying

AI Error Analysis & Auto-Fix:

• On execution failure, AI automatically analyzes the error with full context (failed task, surrounding tasks, traceback)
• Pinpoints root cause and suggests specific fixes
• One-click "Open AI Assist" pre-fills the diagnosis — continue fixing in multi-turn chat

Vision Model Support (Ollama):

• AI_LLM_QANDA accepts image_path parameter for multimodal prompts
• Works with Ollama vision models (gemma4, llava, moondream, etc.)
• Image path supports expressions — dynamically reference files from previous tasks in data_chain

Configuration (only ai_provider required, rest auto-fills from provider defaults):

application:
  ai_provider: zhipu              # or: deepseek, anthropic, hyperspace, gemini, ollama, etc.
  ai_model: ""                    # empty = provider default (e.g. GLM-5)
  ai_api_key: ""                  # empty = read from default env var (e.g. ZHIPU_ACCESS_KEY)
  ai_base_url: ""                 # empty = provider default URL
  ai_max_response_tokens: 8192
  ai_max_request_tokens: 60000

See Configuration Docs for full provider list and details.

Dynamic Function (_fn) & Expression Enhancements

All dynamic function parameters (_fn, _func, _func_body, lambda_*) receive the Processor instance as p, enabling full access to PETP utilities inside custom code:

# In _fn function body — p is the Processor instance
result = p.get_data("my_key")
p.populate_data("output", processed_value)
today = p.str_to_date("2026-05-11")

Available p methods in both expression and _fn contexts:

Edit Complex Value — Handy Tool button:

• Right-click any property → "Edit Complex Value" includes a Handy Tool button
• Automatically detects whether the parameter is an expression or _fn function body
• Expression context: snippets wrapped in {p.xxx()} for f-string evaluation
• Function body context: raw p.xxx() calls plus import statements

Running Modes

# Run one execution and exit
python PETP_background.py --run-execution ENDECODER --no-http

# Run execution with initial data
python PETP_background.py --run-execution MY_EXEC --init-data '{"key":"value"}' --no-http

# Run pipeline and exit
python PETP_background.py --run-pipeline DAILY_REPORT --no-http

# Run pipeline with initial data
python PETP_background.py --run-pipeline MY_PIPELINE --init-data '{"param":"value"}' --no-http

# Start HTTP/MCP service (default port 8866)
python PETP_background.py

# Custom port and auth token
python PETP_background.py --http-port 9090 --http-token my-secret-token

# Headless Selenium (Chrome without visible window)
python PETP_background.py --run-execution BROWSER_TASK --headless --no-http

# Override log level
python PETP_background.py --log-level DEBUG

# GUI-processor policy: skip (ignore) or abort (fail on GUI tasks)
python PETP_background.py --run-execution HAS_GUI_TASK --ui-policy skip --no-http
python PETP_background.py --run-execution HAS_GUI_TASK --ui-policy abort --no-http

# Stop a running background instance
python PETP_background.py --stop

Notes:

• --run-execution and --run-pipeline can be combined — both will run in sequence
• Pipeline reentrant protection: if the same pipeline is already running, a second call returns {"ok": false, "error": "Pipeline already running"}
• Cron-enabled pipelines (cronEnabled: true in YAML) auto-register as scheduled jobs instead of running immediately

Helper scripts for long-running sessions: see `