refactor(gepa): remove verbose logs and consolidate comments

Remove ~25 debug/info logs per maintainer feedback:
- Internal routing/processing logs
- Trace processing details
- Reflective example breakdowns
- Config building verbosity

Consolidate multi-line comments into concise single lines while
preserving important context (WHY, not WHAT).

Author: Ju-usc

dspy/teleprompt/gepa/gepa_utils.py

@@ -171,7 +171,6 @@ def propose_component_texts(
             with dspy.context(lm=self.reflection_lm or dspy.settings.lm):
                 # Handle regular instruction components
                 if instruction_components:
-                    logger.debug(f"Routing {len(instruction_components)} instruction components to instruction_proposer")
                     results.update(
                         instruction_proposer(
                             candidate=candidate,
@@ -182,7 +181,6 @@ def propose_component_texts(
 
                 # Handle components with tools (ReAct and Tool modules)
                 if tool_module_components:
-                    logger.debug(f"Routing {len(tool_module_components)} tool_module components to tool_module_proposer")
                     results.update(
                         tool_module_proposer(
                             candidate=candidate,
@@ -212,7 +210,6 @@ def build_program(self, candidate: dict[str, str]):
 
                 config = json.loads(value)
 
-                # Parse module configs and override predictor instructions
                 for pred_name, instruction in config.items():
                     if isinstance(instruction, str):
                         improved_predictors[pred_name] = instruction
@@ -331,12 +328,7 @@ def make_reflective_dataset(
         # collect unique tools from traces for each tool-using predictor, serialize to candidate at end
         tools_by_predictor: dict[str, dict[str, Tool]] = {}
 
-        # Debug: Log what components we're trying to update
-        logger.info(f"make_reflective_dataset called with components_to_update: {components_to_update}")
-
         for pred_name in components_to_update:
-            logger.info(f"Processing component: {pred_name}")
-
             # Extract predictor name from component key
             if pred_name.startswith(REACT_MODULE_PREFIX):
                 target_name = pred_name.removeprefix(f"{REACT_MODULE_PREFIX}:")
@@ -378,14 +370,10 @@ def extract_tools_from_value(value, tools_dict):
                 if hasattr(module_score, "score"):
                     module_score = module_score["score"]
 
-                logger.debug(f"  Processing trace with {len(trace)} entries for example: {example}")
                 trace_instances = [t for t in trace if t[0].signature.equals(module.signature)]
-                logger.debug(f"    Found {len(trace_instances)} matching trace instances for signature: {module.signature}")
                 if not self.add_format_failure_as_feedback:
                     trace_instances = [t for t in trace_instances if not isinstance(t[2], FailedPrediction)]
-                    logger.debug(f"    After filtering FailedPrediction: {len(trace_instances)} instances")
                 if len(trace_instances) == 0:
-                    logger.debug("    Skipping example - no matching trace instances")
                     continue
 
                 # Extract tools that are used in the trace instances
@@ -479,23 +467,11 @@ def extract_tools_from_value(value, tools_dict):
 
                 items.append(d)
 
-                # Log exact reflective example that reflection LM will see
-                if pred_name.startswith(REACT_MODULE_PREFIX) and len(items) == 1:
-                    logger.info(f"  First reflective example for {pred_name}:")
-                    logger.info(f"    Inputs: {list(d['Inputs'].keys())}")
-                    if "trajectory" in d["Inputs"]:
-                        traj = d["Inputs"]["trajectory"]
-                        logger.info(f"    Trajectory length: {len(traj)} chars")
-                        logger.info(f"    Trajectory sample:\n{traj[:300]}...")
-                    logger.info(f"    Outputs: {list(d['Generated Outputs'].keys()) if isinstance(d['Generated Outputs'], dict) else '<string>'}")
-                    logger.info(f"    Feedback: {d['Feedback'][:100]}...")
-
             if len(items) == 0:
                 logger.warning(f"  No valid reflective examples found for {pred_name}")
                 continue
 
             ret_d[pred_name] = items
-            logger.info(f"  Created {len(items)} reflective examples for {pred_name}")
 
         # Update candidate configs with extracted tools (after all traces processed)
         for pred_name, tools_dict in tools_by_predictor.items():
@@ -512,7 +488,6 @@ def extract_tools_from_value(value, tools_dict):
                 for tool_name, tool in tools_dict.items()
             }
             candidate[pred_name] = json.dumps(config, indent=2)
-            logger.info(f"Extracted {len(tools_dict)} tools for {pred_name}: {list(tools_dict.keys())}")
 
         if len(ret_d) == 0:
             raise Exception("No valid predictions found for any module.")

dspy/teleprompt/gepa/instruction_proposal.py

@@ -382,20 +382,15 @@ def __call__(
                 logger.warning(f"Skipping {module_key}: not in candidate={module_key not in candidate}, not in reflective_dataset={module_key not in reflective_dataset}")
                 continue
 
-            # Deserialize module config
             current_module_config = json.loads(candidate[module_key])
 
-            # Extract predictor keys (strings are predictor instructions)
-            # Predictor keys are expected to be 1 for tool modules and 2 for ReAct modules (extra extract predictor)
+            # Predictor keys: 1 for tool modules, 2 for ReAct modules (extra extract predictor)
             predictor_keys = [k for k, v in current_module_config.items() if isinstance(v, str)]
-            logger.debug(f"Predictor keys: {predictor_keys}")
             primary_predictor_key = predictor_keys[0]
             extract_predictor_key = predictor_keys[1] if module_key.startswith(REACT_MODULE_PREFIX) else None
 
-            # Reconstruct Tool objects from JSON metadata so the adapter can format them for the reflection LM.
-            # Tool.func cannot be serialized in JSON, so we use a placeholder (never executed).
+            # Reconstruct Tool objects from JSON (func is placeholder since it can't be serialized)
             current_tools_dict = current_module_config.get("tools", {})
-            logger.info(f"Found {len(current_tools_dict)} tools: {list(current_tools_dict.keys())}")
             tools_list = []
             for tool_name, tool_info in current_tools_dict.items():
                 tool = dspy.Tool(
@@ -407,12 +402,9 @@ def __call__(
                 tool.arg_desc = tool_info.get("arg_desc", {})
                 tools_list.append(tool)
 
-            # Build dynamic signature by extending base signature
+            # Build dynamic signature with tool-specific output fields
             signature = GenerateImprovedToolModuleDescriptionsFromFeedback
 
-            logger.debug(f"Building dynamic signature with {len(tools_list)} tools...")
-
-            # Add dynamic tool description and arg descriptions output fields
             for tool in tools_list:
                 tool_name = tool.name
                 tool_info = current_tools_dict[tool_name]
@@ -457,58 +449,37 @@ def __call__(
 
             result = propose_descriptions(**kwargs)
 
-            # Build improved config from reflection LM suggestions
-            # Reflection LM returns None for components it doesn't want to change, or text for improvements
-            logger.info("Building improved config from reflection LM response...")
+            # Build improved config (reflection LM returns None to keep original, or new text)
             improved_module_config = {}
 
-            # Update primary predictor instruction if reflection LM suggested improvement
             if result.improved_predictor_instruction is not None:
                 improved_module_config[primary_predictor_key] = result.improved_predictor_instruction
-                logger.debug(f"{primary_predictor_key}: {len(result.improved_predictor_instruction)} chars")
-            else:
-                logger.debug(f"{primary_predictor_key}: reflection LM suggests keeping original")
 
-            # Update extract instruction if exists and reflection LM suggested improvement
             if extract_predictor_key is not None and result.improved_extract_instruction is not None:
                 improved_module_config[extract_predictor_key] = result.improved_extract_instruction
-                logger.debug(f"{extract_predictor_key}: {len(result.improved_extract_instruction)} chars")
-            else:
-                logger.debug(f"{extract_predictor_key}: reflection LM suggests keeping original")
 
-            # Update tool descriptions if reflection LM suggested improvements
             improved_module_config["tools"] = {}
             for tool_name, tool_info in current_tools_dict.items():
-                # Check if reflection LM suggested improving this tool's description
                 improved_desc = getattr(result, f"improved_tool_{tool_name}_desc", None)
-
-                # Skip if reflection LM suggests keeping original
                 if improved_desc is None:
-                    logger.debug(f"  Tool '{tool_name}': reflection LM suggests keeping original")
                     continue
 
                 improved_tool_info = {
                     "desc": improved_desc,
                     "arg_desc": {}
                 }
 
-                # Update parameter descriptions if reflection LM suggested improvements
                 if tool_info.get("args"):
                     for arg_name in tool_info["args"].keys():
                         field_name = f"improved_tool_{tool_name}_arg_{arg_name}_desc"
                         arg_desc = getattr(result, field_name, None)
-                        if arg_desc is not None:  # Reflection LM suggested improvement
+                        if arg_desc is not None:
                             improved_tool_info["arg_desc"][arg_name] = arg_desc
 
                 improved_module_config["tools"][tool_name] = improved_tool_info
-                logger.debug(f"  Tool '{tool_name}': desc={len(improved_desc)} chars, params={len(improved_tool_info['arg_desc'])}")
 
-            # Serialize back to JSON
             updated_components[module_key] = json.dumps(improved_module_config, indent=2)
-            logger.info(f"Successfully optimized {module_key}")
-            logger.debug(f"Serialized config length: {len(updated_components[module_key])} chars")
 
-        logger.info(f"\nToolModuleProposer returning {len(updated_components)} components: {list(updated_components.keys())}")
         return updated_components
 
     def _format_examples(self, reflective_dataset: list[ReflectiveExample]) -> str: