diff --git a/adala/agents/base.py b/adala/agents/base.py
index 8d688f21..9a70ce27 100644
--- a/adala/agents/base.py
+++ b/adala/agents/base.py
@@ -260,7 +260,7 @@ async def arun(
                 "When using asynchronous run with `agent.arun()`, the runtime must be an AsyncRuntime."
             )
         else:
-            print(f"Using runtime {type(runtime)}")
+            logger.info("Using runtime %s", type(runtime))
 
         if input is None:
             if self.environment is None:
@@ -276,7 +276,7 @@ async def arun(
                         batch_size=runtime.batch_size
                     )
                     if data_batch.empty:
-                        print_text("No more data in the environment. Exiting.")
+                        logger.info("No more data in the environment. Exiting.")
                         break
                 except Exception as e:
                     # TODO: environment should raise a specific exception + log error
diff --git a/adala/skills/skillset.py b/adala/skills/skillset.py
index 4a9ed84d..ff316793 100644
--- a/adala/skills/skillset.py
+++ b/adala/skills/skillset.py
@@ -1,3 +1,4 @@
+import logging
 from pydantic import BaseModel, model_validator, field_validator
 from abc import ABC, abstractmethod
 from typing import List, Union, Dict, Any, Optional, Mapping, Type
@@ -18,6 +19,8 @@
     SynthesisSkill,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class SkillSet(BaseModel, ABC):
     """
@@ -199,7 +202,7 @@ def apply(
         for i, skill_name in enumerate(skill_sequence):
             skill = self.skills[skill_name]
             # use input dataset for the first node in the pipeline
-            print_text(f"Applying skill: {skill_name}")
+            logger.info("Applying skill: %s", skill_name)
             skill_output = skill.apply(skill_input, runtime)
 
             # Commented out to not log customer data. Can be used when debugging if needed
@@ -248,7 +251,7 @@ async def aapply(
         for i, skill_name in enumerate(skill_sequence):
             skill = self.skills[skill_name]
             # use input dataset for the first node in the pipeline
-            print_text(f"Applying skill: {skill_name}")
+            logger.info("Applying skill: %s", skill_name)
             skill_output = await skill.aapply(skill_input, runtime)
 
             # Commented out to not log customer data. Can be used when debugging if needed
@@ -322,7 +325,7 @@ def apply(
         for i, skill_name in enumerate(skill_sequence):
             skill = self.skills[skill_name]
             # use input dataset for the first node in the pipeline
-            print_text(f"Applying skill: {skill_name}")
+            logger.info("Applying skill: %s", skill_name)
             skill_output = skill.apply(input, runtime)
             skill_outputs.append(skill_output)
         if not skill_outputs:
diff --git a/server/log_middleware.py b/server/log_middleware.py
index 73f427b5..24cbf7cb 100644
--- a/server/log_middleware.py
+++ b/server/log_middleware.py
@@ -1,5 +1,6 @@
 import json
 import logging
+import os
 from logging import Formatter
 from starlette.middleware.base import BaseHTTPMiddleware
 
@@ -20,11 +21,13 @@ def format(self, record):
         return json.dumps(json_record)
 
 
+LOG_LEVEL = os.environ.get("LOG_LEVEL", "INFO").upper()
+
 logger = logging.root
 handler = logging.StreamHandler()
 handler.setFormatter(JsonFormatter())
 logger.handlers = [handler]
-logger.setLevel(logging.DEBUG)
+logger.setLevel(getattr(logging, LOG_LEVEL, logging.INFO))
 logging.getLogger("uvicorn.access").disabled = True