feat(simulator): include block run() source code in simulation prompt

The LLM simulator now receives the block's actual run() function source
via inspect.getsource(). This gives the LLM exact knowledge of how
inputs transform to outputs, producing far more accurate simulations.

autogpt_platform/backend/backend/executor/simulator.py

@@ -20,11 +20,13 @@ OrchestratorBlock and AgentExecutorBlock are handled in manager.py via
 The LLM simulation is grounded by:
   - Block name and description
   - Input/output schemas (from block.input_schema.jsonschema() / output_schema.jsonschema())
+  - The block's run() source code (via inspect.getsource)
   - The actual input values
 
 Inspired by https://github.com/Significant-Gravitas/agent-simulator
 """
 
+import inspect
 import json
 import logging
 import re
@@ -207,6 +209,13 @@ def build_simulation_prompt(block: Any, input_data: dict[str, Any]) -> tuple[str
     block_name = getattr(block, "name", type(block).__name__)
     block_description = getattr(block, "description", "No description available.")
 
+    # Include the block's run() source code so the LLM knows exactly how
+    # inputs are transformed to outputs.
+    try:
+        run_source = inspect.getsource(block.run)
+    except (TypeError, OSError):
+        run_source = ""
+
     system_prompt = f"""You are simulating the execution of a software block called "{block_name}".
 
 ## Block Description
@@ -217,8 +226,14 @@ def build_simulation_prompt(block: Any, input_data: dict[str, Any]) -> tuple[str
 
 ## Output Schema (what you must return)
 {output_pins}
-
+{"" if not run_source else f"""
+## Block Implementation (run function source code)
+```python
+{run_source}
+```
+"""}
 Your task: given the current inputs, produce realistic simulated outputs for this block.
+Study the block's run() source code above to understand exactly how inputs are transformed to outputs.
 
 Rules:
 - Respond with a single JSON object whose keys are EXACTLY the output pin names listed above.