diff --git a/asynclogparser/README.md b/asynclogparser/README.md
new file mode 100644
index 00000000..ab89b381
--- /dev/null
+++ b/asynclogparser/README.md
@@ -0,0 +1,90 @@
+# Async Log Parser
+
+A Go tool to parse binary log files generated by `asyncloguploader`, decode MPLog protobuf messages, fetch feature schemas from the inference API, and output human-readable logs.
+
+## Features
+
+- **Deframes binary log files**: Removes 8-byte frame headers and padding, extracts 4-byte length-prefixed records
+- **Protobuf unmarshaling**: Parses MPLog protobuf messages from record bytes
+- **Schema fetching**: Automatically fetches feature schemas from the inference API (Custodian)
+- **Feature decoding**: Decodes proto-encoded entity features using the fetched schema
+- **Human-readable output**: Writes parsed and decoded data to a readable log file
+
+## Usage
+
+```bash
+go run ./cmd/asynclogparse <log-file-path> [output-file]
+```
+
+### Arguments
+
+- `log-file-path`: Path to the `.log` file generated by `asyncloguploader`
+- `output-file`: (Optional) Path to output file. Defaults to `<log-file>.parsed.log`
+
+### Environment Variables
+
+- `INFERENCE_HOST`: Inference API host (default: `http://custodian.prd.meesho.int`)
+- `INFERENCE_PATH`: Inference API path for schema endpoint (default: `/api/v1/custodian/mp-config-registry/get_feature_schema`)
+
+### Example
+
+```bash
+# Set environment variables (optional, defaults provided)
+export INFERENCE_HOST="http://custodian.prd.meesho.int"
+export INFERENCE_PATH="/api/v1/custodian/mp-config-registry/get_feature_schema"
+
+# Parse a log file
+go run ./cmd/asynclogparse search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.log
+
+# Or specify output file
+go run ./cmd/asynclogparse input.log output.parsed.log
+```
+
+## Output Format
+
+The output file contains human-readable text with the following structure:
+
+```
+=== Record 1 ===
+User ID: 212079220
+Tracking ID: abc
+Model Config ID: search-ad-head-multitask-fieldaware-categorylevelscaleup
+Version: 4
+Format Type: 0
+Entities: 300
+Parent Entities: 8
+Encoded Features: 300
+
+--- Entity 1 ---
+Entity ID: 60889110
+Parent Entity: saree
+Features:
+  catalog_id: 60889110
+  user_id: 212079220
+  pcvr_score: 0.000302
+  score: 0.000302
+
+--- Entity 2 ---
+...
+```
+
+## Architecture
+
+### Log File Format
+
+The binary log files follow this format:
+- **Frame Header** (8 bytes): `capacity` (uint32 LE) + `validDataBytes` (uint32 LE)
+- **Frame Data**: Fixed-size buffer (capacity bytes), but only `validDataBytes` contain real data
+- **Records**: Inside valid data, records are 4-byte length-prefixed (uint32 LE) followed by record bytes
+
+### MPLog Protobuf Structure
+
+- `user_id` (string)
+- `tracking_id` (string)
+- `model_proxy_config_id` (string)
+- `version` (int32)
+- `format_type` (int32): 0 = proto, 1 = arrow, 2 = parquet
+- `entities` (repeated string)
+- `parent_entity` (repeated string)
+- `encoded_features` (repeated bytes): Per-entity encoded feature blobs
+
diff --git a/asynclogparser/asynclogparse b/asynclogparser/asynclogparse
new file mode 100755
index 00000000..f824aa27
Binary files /dev/null and b/asynclogparser/asynclogparse differ
diff --git a/asynclogparser/asynclogparse.py b/asynclogparser/asynclogparse.py
new file mode 100644
index 00000000..301d7959
--- /dev/null
+++ b/asynclogparser/asynclogparse.py
@@ -0,0 +1,516 @@
+#!/usr/bin/env python3
+"""
+asynclogparse - Python version using inference-logging-client
+Parses asyncloguploader log files and outputs human-readable format
+"""
+
+import sys
+import os
+import argparse
+from pathlib import Path
+import json
+import urllib.request
+import urllib.parse
+
+try:
+    import inference_logging_client as ilc
+except ImportError:
+    print("Error: inference-logging-client not installed. Install with: pip install inference-logging-client")
+    sys.exit(1)
+
+
+def deframe_log_file(log_path):
+    """
+    Deframe the binary log file, removing headers and padding.
+    Returns list of protobuf records.
+    """
+    records = []
+    
+    with open(log_path, 'rb') as f:
+        while True:
+            # Read 8-byte header: capacity (4 bytes) + validDataBytes (4 bytes)
+            header = f.read(8)
+            if len(header) < 8:
+                break
+            
+            capacity = int.from_bytes(header[0:4], byteorder='little')
+            valid_data_bytes = int.from_bytes(header[4:8], byteorder='little')
+            
+            if valid_data_bytes == 0:
+                break
+            
+            # Read the data block
+            data_block = f.read(capacity)
+            if len(data_block) < valid_data_bytes:
+                break
+            
+            # Extract valid records from the data block
+            offset = 0
+            while offset < valid_data_bytes:
+                if offset + 4 > valid_data_bytes:
+                    break
+                
+                # Read 4-byte length prefix
+                record_length = int.from_bytes(
+                    data_block[offset:offset+4], 
+                    byteorder='little'
+                )
+                offset += 4
+                
+                if record_length == 0 or offset + record_length > valid_data_bytes:
+                    break
+                
+                # Extract the record
+                record = data_block[offset:offset+record_length]
+                records.append(record)
+                offset += record_length
+                
+                # Skip padding (zeros) until next 4-byte boundary or end
+                while offset < valid_data_bytes and offset % 4 != 0:
+                    offset += 1
+    
+    return records
+
+
+def fetch_feature_schema(model_config_id, version=1):
+    """
+    Fetch feature schema from the inference API (Custodian API).
+    Returns a list of FeatureInfo objects compatible with inference-logging-client.
+    """
+    # Cache schema per model_config_id:version
+    if not hasattr(fetch_feature_schema, '_cache'):
+        fetch_feature_schema._cache = {}
+    
+    cache_key = f"{model_config_id}:{version}"
+    if cache_key in fetch_feature_schema._cache:
+        return fetch_feature_schema._cache[cache_key]
+    
+    inference_host = os.getenv("INFERENCE_HOST", "http://custodian.prd.meesho.int")
+    inference_path = os.getenv("INFERENCE_PATH", "/api/v1/custodian/mp-config-registry/get_feature_schema")
+    
+    # Build URL with query parameters
+    params = urllib.parse.urlencode({'model_config_id': model_config_id, 'version': version})
+    url = f"{inference_host}{inference_path}?{params}"
+    
+    try:
+        # Try GET request
+        req = urllib.request.Request(url)
+        req.add_header('Content-Type', 'application/json')
+        req.add_header('Accept', 'application/json')
+        
+        with urllib.request.urlopen(req, timeout=10) as response:
+                    if response.status == 200:
+                        schema_data = json.loads(response.read().decode('utf-8'))
+                        print(f"[DEBUG] Schema data type: {type(schema_data)}", flush=True)
+                        
+                        # Handle dict response - extract the actual schema list
+                        if isinstance(schema_data, dict):
+                            # The API might return {"features": [...]} or similar
+                            if 'features' in schema_data:
+                                schema_list = schema_data['features']
+                            elif 'schema' in schema_data:
+                                schema_list = schema_data['schema']
+                            elif 'data' in schema_data:
+                                schema_list = schema_data['data']
+                            else:
+                                # Try to find any list value
+                                schema_list = [v for v in schema_data.values() if isinstance(v, list)]
+                                if schema_list:
+                                    schema_list = schema_list[0]
+                                else:
+                                    print(f"[WARNING] Could not find schema list in dict: {list(schema_data.keys())}", flush=True)
+                                    schema_list = []
+                        elif isinstance(schema_data, list):
+                            schema_list = schema_data
+                        else:
+                            print(f"[WARNING] Unexpected schema data type: {type(schema_data)}", flush=True)
+                            schema_list = []
+                        
+                        print(f"[DEBUG] Schema list length: {len(schema_list)}, first item: {schema_list[0] if schema_list else 'empty'}", flush=True)
+                        
+                        # Convert to FeatureInfo objects
+                        if hasattr(ilc, 'FeatureInfo'):
+                            FeatureInfo = ilc.FeatureInfo
+                            import inspect
+                            try:
+                                sig = inspect.signature(FeatureInfo)
+                                print(f"[DEBUG] FeatureInfo signature: {sig}", flush=True)
+                            except:
+                                pass
+                            
+                            schema = []
+                            for idx, item in enumerate(schema_list):
+                                if isinstance(item, dict):
+                                    try:
+                                        # FeatureInfo needs: name, feature_type, index
+                                        feat_info = FeatureInfo(
+                                            name=item.get('feature_name', item.get('name', '')),
+                                            feature_type=item.get('feature_type', item.get('type', '')),
+                                            index=item.get('index', idx)
+                                        )
+                                        schema.append(feat_info)
+                                        if idx == 0:
+                                            print(f"[DEBUG] Created FeatureInfo: name={feat_info.name}, type={feat_info.feature_type}, index={feat_info.index}", flush=True)
+                                    except Exception as e:
+                                        print(f"[WARNING] Failed to create FeatureInfo from {item}: {str(e)}", flush=True)
+                                        try:
+                                            # Try with all dict keys
+                                            feat_info = FeatureInfo(**item, index=idx)
+                                            schema.append(feat_info)
+                                        except Exception as e2:
+                                            print(f"[WARNING] **item also failed: {str(e2)}", flush=True)
+                                            schema.append(item)
+                                else:
+                                    schema.append(item)
+                        else:
+                            schema = schema_list
+                        
+                        print(f"[DEBUG] Final schema: {len(schema)} FeatureInfo objects", flush=True)
+                        fetch_feature_schema._cache[cache_key] = schema
+                        return schema
+    except urllib.error.HTTPError as e:
+        if e.code == 404 or e.code == 400:
+            # Try POST if GET fails
+            try:
+                post_url = f"{inference_host}{inference_path}"
+                post_data = json.dumps({"model_config_id": model_config_id, "version": version}).encode('utf-8')
+                req = urllib.request.Request(post_url, data=post_data, method='POST')
+                req.add_header('Content-Type', 'application/json')
+                req.add_header('Accept', 'application/json')
+                
+                with urllib.request.urlopen(req, timeout=10) as response:
+                    if response.status == 200:
+                        schema_data = json.loads(response.read().decode('utf-8'))
+                        # Convert to FeatureInfo objects
+                        if hasattr(ilc, 'FeatureInfo'):
+                            FeatureInfo = ilc.FeatureInfo
+                            schema = []
+                            for item in schema_data:
+                                if isinstance(item, dict):
+                                    schema.append(FeatureInfo(
+                                        feature_name=item.get('feature_name', ''),
+                                        feature_type=item.get('feature_type', '')
+                                    ))
+                                else:
+                                    schema.append(item)
+                        else:
+                            schema = schema_data if isinstance(schema_data, list) else []
+                        fetch_feature_schema._cache[cache_key] = schema
+                        return schema
+            except Exception as post_err:
+                print(f"[WARNING] POST also failed for {model_config_id}:v{version}: {str(post_err)}", flush=True)
+        else:
+            print(f"[WARNING] GET failed for {model_config_id}:v{version}: HTTP {e.code}", flush=True)
+    except Exception as e:
+        print(f"[WARNING] Failed to fetch schema for {model_config_id}:v{version}: {str(e)}", flush=True)
+        return None
+    
+    return None
+
+
+def parse_and_decode_mplog(record):
+    """
+    Parse MPLog protobuf and decode features using inference-logging-client.
+    """
+    try:
+        # Parse the MPLog protobuf using inference-logging-client
+        mplog = ilc.parse_mplog_protobuf(record)
+        
+        # Extract basic fields
+        user_id = getattr(mplog, 'user_id', '')
+        tracking_id = getattr(mplog, 'tracking_id', '')
+        mp_config_id = getattr(mplog, 'mp_config_id', '') or getattr(mplog, 'model_proxy_config_id', '')
+        entities = getattr(mplog, 'entities', []) or []
+        parent_entities = getattr(mplog, 'parent_entity', []) or []
+        metadata = getattr(mplog, 'metadata', b'')
+        
+        # Check if version is present in the protobuf (field 4)
+        # In proto3, we can't distinguish between "not set" and "set to default (0)"
+        # So we check if the attribute exists and has a non-None value
+        # If version is 0, it could be either default or explicitly set to 0
+        try:
+            version = getattr(mplog, 'version')
+            # If version is None or not set, it will raise AttributeError
+            # If version exists, check if it's a valid int (could be 0, which is valid)
+            if version is None:
+                version = 1
+                version_present = False
+            else:
+                # Version exists - in proto3, we assume it was present if it's not None
+                # Note: version=0 is a valid value, so we can't distinguish from "not set"
+                version_present = True
+        except (AttributeError, TypeError):
+            version = 1
+            version_present = False
+        
+        format_type = getattr(mplog, 'format_type', None)
+        
+        # Get features from the parsed MPLog
+        # The structure: mplog.features is a list of perEntityFeatures objects
+        # Each perEntityFeatures has: encoded_features (bytes) that need decoding
+        
+        # Debug: Check what attributes the mplog has
+        mplog_attrs = [x for x in dir(mplog) if not x.startswith('__')]
+        
+        # Try to get features - check multiple possible attribute names
+        # According to the protobuf schema, it could be:
+        # 1. mplog.features (list of perEntityFeatures objects with encoded_features)
+        # 2. mplog.encoded_features (direct list of bytes - field 8)
+        features_attr = []
+        encoded_features_direct = []
+        
+        # First, try to get features as a list of objects
+        if hasattr(mplog, 'features'):
+            features_attr = getattr(mplog, 'features', []) or []
+        elif hasattr(mplog, '_features'):
+            features_attr = getattr(mplog, '_features', []) or []
+        elif hasattr(mplog, 'per_entity_features'):
+            features_attr = getattr(mplog, 'per_entity_features', []) or []
+        
+        # Also check for direct encoded_features list (field 8 in protobuf)
+        if hasattr(mplog, 'encoded_features'):
+            encoded_features_direct = getattr(mplog, 'encoded_features', []) or []
+        elif hasattr(mplog, '_encoded_features'):
+            encoded_features_direct = getattr(mplog, '_encoded_features', []) or []
+        
+        # If we have direct encoded_features but no features_attr, use the direct list
+        if not features_attr and encoded_features_direct:
+            # Convert direct bytes list to feature objects structure
+            features_attr = encoded_features_direct
+        
+        encoded_features_list = []  # Track encoded features for return value
+        
+        # Decode features for each entity using inference-logging-client
+        decoded_entities = []
+        print(f"[DEBUG] Processing {len(features_attr)} feature objects for {len(entities)} entities", flush=True)
+        for i, feature_obj in enumerate(features_attr):
+            if i % 50 == 0 or i < 5:  # Log first 5 and then every 50th
+                print(f"[DEBUG] Processing entity {i+1}/{len(features_attr)}", flush=True)
+            entity_id = entities[i] if i < len(entities) else ''
+            parent_entity = parent_entities[i] if i < len(parent_entities) else ''
+            
+            # Get encoded_features from the feature object
+            # It could be:
+            # 1. A perEntityFeatures object with .encoded_features attribute
+            # 2. A direct bytes object (if encoded_features is a direct list)
+            encoded_features = None
+            if isinstance(feature_obj, bytes):
+                # Direct bytes - this is the encoded_features itself
+                encoded_features = feature_obj
+            elif hasattr(feature_obj, 'encoded_features'):
+                encoded_features = getattr(feature_obj, 'encoded_features', b'')
+            else:
+                # Debug: Check what attributes the feature object has
+                if hasattr(feature_obj, '__dict__'):
+                    feat_attrs = feature_obj.__dict__
+                    # Try common attribute names
+                    if '_encoded_features' in feat_attrs:
+                        encoded_features = feat_attrs['_encoded_features']
+                    elif 'data' in feat_attrs:
+                        encoded_features = feat_attrs['data']
+                    elif 'value' in feat_attrs:
+                        encoded_features = feat_attrs['value']
+                    elif 'encoded_features' in feat_attrs:
+                        encoded_features = feat_attrs['encoded_features']
+            
+            # Track encoded features for return value
+            encoded_features_list.append(encoded_features if encoded_features else b'')
+            
+            decoded_features = {}
+            if encoded_features and len(encoded_features) > 0:
+                if i < 3:  # Only log for first few entities
+                    print(f"[DEBUG] Decoding features for entity {i+1}, encoded_features length: {len(encoded_features)}", flush=True)
+                try:
+                    # decode_proto_features takes 2 arguments: (encoded_features, schema)
+                    # We need to fetch the schema first from the inference API
+                    
+                    # Fetch schema (cached per model_config_id:version)
+                    if i == 0:
+                        print(f"[DEBUG] Fetching schema for {mp_config_id}:v{version}...", flush=True)
+                    schema = fetch_feature_schema(mp_config_id, version)
+                    
+                    if schema:
+                        if i == 0:
+                            print(f"[DEBUG] Got schema with {len(schema)} features, decoding...", flush=True)
+                            if len(schema) > 0:
+                                print(f"[DEBUG] First schema item type: {type(schema[0])}, value: {schema[0]}", flush=True)
+                        # decode_proto_features(encoded_features, schema)
+                        decoded_features = ilc.decode_proto_features(encoded_features, schema)
+                        if i < 3:
+                            print(f"[DEBUG] Successfully decoded entity {i+1} features", flush=True)
+                            if isinstance(decoded_features, dict):
+                                print(f"[DEBUG] Decoded features keys: {list(decoded_features.keys())[:10]}", flush=True)
+                    else:
+                        decoded_features = {
+                            '_note': 'Features need decoding with schema',
+                            '_error': 'Failed to fetch schema from inference API',
+                            '_encoded_length': len(encoded_features)
+                        }
+                except Exception as e:
+                    # Other error (maybe wrong parameters, network issue, etc.)
+                    if i < 3:
+                        print(f"[DEBUG] Exception during decode: {str(e)[:200]}", flush=True)
+                    decoded_features = {
+                        '_decode_error': str(e)[:200],  # Truncate long errors
+                        '_error_type': type(e).__name__
+                    }
+            else:
+                decoded_features = {'_note': 'No encoded_features found in feature object'}
+            
+            decoded_entities.append({
+                'entity_id': entity_id,
+                'parent_entity': parent_entity,
+                'features': decoded_features,
+                'encoded_features_length': len(encoded_features) if encoded_features else 0
+            })
+        
+        # Debug info if no features found
+        debug_info = {}
+        if len(features_attr) == 0:
+            debug_info = {
+                '_debug': 'No features found in mplog',
+                '_mplog_attrs': mplog_attrs[:20] if 'mplog_attrs' in locals() else [],
+                '_features_attr_type': type(features_attr).__name__,
+                '_entities_count': len(entities)
+            }
+        
+        return {
+            'user_id': user_id,
+            'tracking_id': tracking_id,
+            'mp_config_id': mp_config_id,
+            'entities': entities,
+            'parent_entities': parent_entities,
+            'metadata': metadata,
+            'version': version,
+            'version_present': version_present,
+            'format_type': format_type,
+            'decoded_entities': decoded_entities,
+            'num_encoded_features': len(encoded_features_list),
+            **debug_info
+        }
+    
+    except Exception as e:
+        import traceback
+        return {'_parse_error': f'{str(e)}\n{traceback.format_exc()}'}
+
+
+def format_output(parsed_data, output_file, record_num):
+    """
+    Format and write parsed data to output file.
+    """
+    if '_parse_error' in parsed_data:
+        output_file.write(f"=== Record {record_num}: PARSE ERROR ===\n")
+        output_file.write(f"Error: {parsed_data['_parse_error']}\n\n")
+        return
+    
+    output_file.write(f"=== Record {record_num} ===\n")
+    output_file.write(f"User ID: {parsed_data['user_id']}\n")
+    output_file.write(f"Tracking ID: {parsed_data['tracking_id']}\n")
+    output_file.write(f"Model Config ID: {parsed_data['mp_config_id']}\n")
+    
+    # Schema version information
+    version = parsed_data.get('version', 1)
+    version_present = parsed_data.get('version_present', False)
+    output_file.write(f"Schema Version: {version}")
+    if version_present:
+        output_file.write(" (present in log)\n")
+    else:
+        output_file.write(" (defaulted, not present in log)\n")
+    
+    # Format type information
+    format_type = parsed_data.get('format_type')
+    if format_type is not None:
+        format_names = {0: 'proto', 1: 'arrow', 2: 'parquet'}
+        format_name = format_names.get(format_type, f'unknown({format_type})')
+        output_file.write(f"Format Type: {format_type} ({format_name})\n")
+    
+    output_file.write(f"Entities: {len(parsed_data['entities'])}\n")
+    if parsed_data['entities']:
+        output_file.write(f"Entity IDs: {parsed_data['entities']}\n")
+    output_file.write(f"Parent Entities: {len(parsed_data['parent_entities'])}\n")
+    if parsed_data['parent_entities']:
+        output_file.write(f"Parent Entity IDs: {parsed_data['parent_entities']}\n")
+    output_file.write(f"Encoded Features: {parsed_data['num_encoded_features']}\n")
+    if parsed_data['metadata']:
+        output_file.write(f"Metadata: {len(parsed_data['metadata'])} bytes\n")
+    
+    # Show debug info if no features found
+    if '_debug' in parsed_data:
+        output_file.write(f"\n[DEBUG] {parsed_data.get('_debug', '')}\n")
+        if '_mplog_attrs' in parsed_data and parsed_data['_mplog_attrs']:
+            output_file.write(f"[DEBUG] MPLog attributes (first 20): {parsed_data['_mplog_attrs']}\n")
+        if '_entities_count' in parsed_data:
+            output_file.write(f"[DEBUG] Entities count: {parsed_data['_entities_count']}\n")
+    
+    output_file.write("\n")
+    
+    # Write decoded entities and features
+    for i, entity_data in enumerate(parsed_data['decoded_entities']):
+        output_file.write(f"--- Entity {i+1} ---\n")
+        if entity_data['entity_id']:
+            output_file.write(f"Entity ID: {entity_data['entity_id']}\n")
+        if entity_data['parent_entity']:
+            output_file.write(f"Parent Entity: {entity_data['parent_entity']}\n")
+        
+        output_file.write("Features:\n")
+        if entity_data['features']:
+            for feat_name, feat_value in entity_data['features'].items():
+                output_file.write(f"  {feat_name}: {feat_value}\n")
+        else:
+            output_file.write(f"  (encoded_features length: {entity_data['encoded_features_length']} bytes)\n")
+        output_file.write("\n")
+    
+    output_file.write("\n")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Parse asyncloguploader log files using inference-logging-client'
+    )
+    parser.add_argument('log_file', help='Path to the log file')
+    parser.add_argument('-o', '--output', default=None, 
+                       help='Output file path (default: <log_file>.parsed.log)')
+    
+    args = parser.parse_args()
+    
+    log_path = Path(args.log_file)
+    if not log_path.exists():
+        print(f"Error: Log file not found: {log_path}")
+        sys.exit(1)
+    
+    if args.output:
+        output_path = Path(args.output)
+    else:
+        output_path = log_path.with_suffix('.parsed.log')
+    
+    print(f"Parsing log file: {log_path}")
+    print(f"Writing output to: {output_path}")
+    
+    # Deframe the log file
+    print("Deframing log file...")
+    records = deframe_log_file(log_path)
+    print(f"Found {len(records)} records")
+    
+    if not records:
+        print("No records found in log file.")
+        return
+    
+    # Process all records
+    print(f"Processing {len(records)} records...")
+    with open(output_path, 'w') as out_file:
+        for i, record in enumerate(records):
+            print(f"[DEBUG] Starting record {i+1}/{len(records)}", flush=True)
+            parsed_data = parse_and_decode_mplog(record)
+            print(f"[DEBUG] Parsed record {i+1}, formatting output...", flush=True)
+            format_output(parsed_data, out_file, i+1)
+            out_file.flush()  # Ensure output is written immediately
+            print(f"[DEBUG] Completed record {i+1}", flush=True)
+            
+            if (i + 1) % 100 == 0:
+                print(f"Processed {i+1}/{len(records)} records...")
+    
+    print(f"Parsing complete. Output written to: {output_path}")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/asynclogparser/decode/mplog.proto b/asynclogparser/decode/mplog.proto
new file mode 100644
index 00000000..9562fb01
--- /dev/null
+++ b/asynclogparser/decode/mplog.proto
@@ -0,0 +1,18 @@
+syntax = "proto3";
+
+package decode;
+
+option go_package = "./decode";
+
+// MPLog represents a model proxy log entry
+message MPLog {
+  string user_id = 1;
+  string tracking_id = 2;
+  string model_proxy_config_id = 3;
+  int32 version = 4;
+  int32 format_type = 5; // 0 = proto, 1 = arrow, 2 = parquet
+  repeated string entities = 6;
+  repeated string parent_entity = 7;
+  repeated bytes encoded_features = 8; // Per-entity encoded feature blobs
+}
+
diff --git a/asynclogparser/search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.log b/asynclogparser/search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.log
new file mode 100644
index 00000000..d40ef578
Binary files /dev/null and b/asynclogparser/search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.log differ
diff --git a/asynclogparser/search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.parsed.log b/asynclogparser/search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.parsed.log
new file mode 100644
index 00000000..c7d7c41f
--- /dev/null
+++ b/asynclogparser/search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.parsed.log
@@ -0,0 +1,9656 @@
+=== Record 1 ===
+User ID: 212079220
+Tracking ID: abc
+Model Config ID: search-ad-head-multitask-fieldaware-categorylevelscaleup
+Schema Version: 4 (present in log)
+Format Type: 0 (proto)
+Entities: 300
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+Encoded Features: 300
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+--- Entity 280 ---
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+--- Entity 282 ---
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+--- Entity 284 ---
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+--- Entity 286 ---
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+--- Entity 287 ---
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+--- Entity 288 ---
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+--- Entity 289 ---
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+--- Entity 290 ---
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+--- Entity 291 ---
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+--- Entity 292 ---
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+--- Entity 293 ---
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+--- Entity 294 ---
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+Features:
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+--- Entity 295 ---
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+--- Entity 296 ---
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+--- Entity 297 ---
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+--- Entity 298 ---
+Entity ID: 60889110
+Features:
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+--- Entity 299 ---
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+--- Entity 300 ---
+Entity ID: 60889110
+Features:
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+
+=== Record 2 ===
+User ID: 212079220
+Tracking ID: abc
+Model Config ID: search-ad-head-multitask-fieldaware-categorylevelscaleup
+Schema Version: 4 (present in log)
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+--- Entity 122 ---
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+--- Entity 123 ---
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+--- Entity 124 ---
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+--- Entity 125 ---
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diff --git a/asynclogparser/setup_and_run.sh b/asynclogparser/setup_and_run.sh
new file mode 100755
index 00000000..a5fa2add
--- /dev/null
+++ b/asynclogparser/setup_and_run.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+set -e
+
+cd "$(dirname "$0")"
+
+echo "Creating virtual environment..."
+python3 -m venv venv
+
+echo "Activating virtual environment and installing dependencies..."
+source venv/bin/activate
+pip install --upgrade pip
+pip install inference-logging-client
+
+echo "Running asynclogparse.py..."
+python3 asynclogparse.py search-ad-head-multitask-fieldaware-categorylevelscaleup_2026-02-03_17-10-42.log
+
+echo "Done!"
+
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