diff --git a/docs/docs/cheatsheet.md b/docs/docs/cheatsheet.md
index bb0fd58a8d..ee12c5ed10 100644
--- a/docs/docs/cheatsheet.md
+++ b/docs/docs/cheatsheet.md
@@ -67,7 +67,7 @@ print(f"Final Predicted Answer (after ReAct process): {result.answer}")
 
 ```python
 colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
-dspy.settings.configure(rm=colbertv2_wiki17_abstracts)
+dspy.configure(rm=colbertv2_wiki17_abstracts)
 
 #Define Retrieve Module
 retriever = dspy.Retrieve(k=3)
@@ -450,7 +450,7 @@ asyncio.run(dspy_program(question="What is DSPy"))
 
 ```python
 import dspy
-dspy.settings.configure(track_usage=True)
+dspy.configure(track_usage=True)
 
 result = dspy.ChainOfThought(BasicQA)(question="What is 2+2?")
 print(f"Token usage: {result.get_lm_usage()}")
diff --git a/docs/docs/learn/programming/language_models.md b/docs/docs/learn/programming/language_models.md
index b6adeb1b53..4152c26dee 100644
--- a/docs/docs/learn/programming/language_models.md
+++ b/docs/docs/learn/programming/language_models.md
@@ -229,7 +229,7 @@ To enable the Responses API, just set `model_type="responses"` when creating the
 import dspy
 
 # Configure DSPy to use the Responses API for your language model
-dspy.settings.configure(
+dspy.configure(
     lm=dspy.LM(
         "openai/gpt-5-mini",
         model_type="responses",
diff --git a/docs/docs/learn/programming/modules.md b/docs/docs/learn/programming/modules.md
index 2f3fff2d9b..5d340d467f 100644
--- a/docs/docs/learn/programming/modules.md
+++ b/docs/docs/learn/programming/modules.md
@@ -260,7 +260,7 @@ print(hop(claim="Stephen Curry is the best 3 pointer shooter ever in the human h
 DSPy provides built-in tracking of language model usage across all module calls. To enable tracking:
 
 ```python
-dspy.settings.configure(track_usage=True)
+dspy.configure(track_usage=True)
 ```
 
 Once enabled, you can access usage statistics from any `dspy.Prediction` object:
@@ -275,7 +275,7 @@ The usage data is returned as a dictionary that maps each language model name to
 import dspy
 
 # Configure DSPy with tracking enabled
-dspy.settings.configure(
+dspy.configure(
     lm=dspy.LM("openai/gpt-4o-mini", cache=False),
     track_usage=True
 )
@@ -326,7 +326,7 @@ When using DSPy's caching features (either in-memory or on-disk via litellm), ca
 
 ```python
 # Enable caching
-dspy.settings.configure(
+dspy.configure(
     lm=dspy.LM("openai/gpt-4o-mini", cache=True),
     track_usage=True
 )
diff --git a/docs/docs/tutorials/audio/index.ipynb b/docs/docs/tutorials/audio/index.ipynb
index 7ae09eb369..41969510f9 100644
--- a/docs/docs/tutorials/audio/index.ipynb
+++ b/docs/docs/tutorials/audio/index.ipynb
@@ -117,7 +117,7 @@
     "Now let's configure our LLM which can process input audio. \n",
     "\n",
     "```python\n",
-    "dspy.settings.configure(lm=dspy.LM(model='gpt-4o-mini-audio-preview-2024-12-17'))\n",
+    "dspy.configure(lm=dspy.LM(model='gpt-4o-mini-audio-preview-2024-12-17'))\n",
     "```\n",
     "\n",
     "Note: Using `dspy.Audio` in signatures allows passing in audio directly to the model. "
@@ -332,7 +332,7 @@
     "        audio = generate_dspy_audio(raw_line, out.openai_instruction)\n",
     "        return dspy.Prediction(audio=audio)\n",
     "    \n",
-    "dspy.settings.configure(lm=dspy.LM(model='gpt-4o-mini'))"
+    "dspy.configure(lm=dspy.LM(model='gpt-4o-mini'))"
    ]
   },
   {
diff --git a/docs/docs/tutorials/cache/index.md b/docs/docs/tutorials/cache/index.md
index 6345cc789d..f2cc35727a 100644
--- a/docs/docs/tutorials/cache/index.md
+++ b/docs/docs/tutorials/cache/index.md
@@ -25,7 +25,7 @@ import time
 
 os.environ["OPENAI_API_KEY"] = "{your_openai_key}"
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"), track_usage=True)
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"), track_usage=True)
 
 predict = dspy.Predict("question->answer")
 
@@ -167,7 +167,7 @@ import time
 
 os.environ["OPENAI_API_KEY"] = "{your_openai_key}"
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
 predict = dspy.Predict("question->answer")
 
@@ -193,7 +193,7 @@ from hashlib import sha256
 
 os.environ["OPENAI_API_KEY"] = "{your_openai_key}"
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
 class CustomCache(dspy.clients.Cache):
 
diff --git a/docs/docs/tutorials/conversation_history/index.md b/docs/docs/tutorials/conversation_history/index.md
index a3b8bee4cc..cf2195230c 100644
--- a/docs/docs/tutorials/conversation_history/index.md
+++ b/docs/docs/tutorials/conversation_history/index.md
@@ -12,7 +12,7 @@ import os
 
 os.environ["OPENAI_API_KEY"] = "{your_openai_api_key}"
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
 class QA(dspy.Signature):
     question: str = dspy.InputField()
@@ -121,7 +121,7 @@ For example:
 ```python
 import dspy
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
 
 class QA(dspy.Signature):
diff --git a/docs/docs/tutorials/deployment/index.md b/docs/docs/tutorials/deployment/index.md
index bbc7d77a60..fea3d0c4f4 100644
--- a/docs/docs/tutorials/deployment/index.md
+++ b/docs/docs/tutorials/deployment/index.md
@@ -7,7 +7,7 @@ Below, we'll assume you have the following simple DSPy program that you want to
 ```python
 import dspy
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 dspy_program = dspy.ChainOfThought("question -> answer")
 ```
 
@@ -40,7 +40,7 @@ class Question(BaseModel):
 
 # Configure your language model and 'asyncify' your DSPy program.
 lm = dspy.LM("openai/gpt-4o-mini")
-dspy.settings.configure(lm=lm, async_max_workers=4) # default is 8
+dspy.configure(lm=lm, async_max_workers=4) # default is 8
 dspy_program = dspy.ChainOfThought("question -> answer")
 dspy_program = dspy.asyncify(dspy_program)
 
@@ -163,7 +163,7 @@ mlflow.set_tracking_uri("http://127.0.0.1:5000/")
 mlflow.set_experiment("deploy_dspy_program")
 
 lm = dspy.LM("openai/gpt-4o-mini")
-dspy.settings.configure(lm=lm)
+dspy.configure(lm=lm)
 
 class MyProgram(dspy.Module):
     def __init__(self):
diff --git a/docs/docs/tutorials/entity_extraction/index.ipynb b/docs/docs/tutorials/entity_extraction/index.ipynb
index f8c5c25f40..df74ee0be3 100644
--- a/docs/docs/tutorials/entity_extraction/index.ipynb
+++ b/docs/docs/tutorials/entity_extraction/index.ipynb
@@ -172,7 +172,7 @@
         "Specifically, we'll:\n",
         "- Create a `PeopleExtraction` DSPy Signature to specify the input (`tokens`) and output (`extracted_people`) fields.\n",
         "- Define a `people_extractor` program that uses DSPy's built-in `dspy.ChainOfThought` module to implement the `PeopleExtraction` signature. The program extracts entities referring to people from a list of input tokens using language model (LM) prompting.\n",
-        "- Use the `dspy.LM` class and `dspy.settings.configure()` method to configure the language model that DSPy will use when invoking the program."
+        "- Use the `dspy.LM` class and `dspy.configure()` method to configure the language model that DSPy will use when invoking the program."
       ]
     },
     {
@@ -208,7 +208,7 @@
       "outputs": [],
       "source": [
         "lm = dspy.LM(model=\"openai/gpt-4o-mini\")\n",
-        "dspy.settings.configure(lm=lm)"
+        "dspy.configure(lm=lm)"
       ]
     },
     {
diff --git a/docs/docs/tutorials/image_generation_prompting/index.ipynb b/docs/docs/tutorials/image_generation_prompting/index.ipynb
index a4bee30af1..cc266699ee 100644
--- a/docs/docs/tutorials/image_generation_prompting/index.ipynb
+++ b/docs/docs/tutorials/image_generation_prompting/index.ipynb
@@ -80,7 +80,7 @@
     "from IPython.display import display\n",
     "\n",
     "lm = dspy.LM(model=\"gpt-4o-mini\", temperature=0.5)\n",
-    "dspy.settings.configure(lm=lm)"
+    "dspy.configure(lm=lm)"
    ]
   },
   {
diff --git a/docs/docs/tutorials/program_of_thought/index.ipynb b/docs/docs/tutorials/program_of_thought/index.ipynb
index 87f2848b18..fd0d3a8e0e 100644
--- a/docs/docs/tutorials/program_of_thought/index.ipynb
+++ b/docs/docs/tutorials/program_of_thought/index.ipynb
@@ -69,7 +69,7 @@
    "source": [
     "llama31_70b = dspy.LM(\"openai/meta-llama/Meta-Llama-3-70b-Instruct\", api_base=\"API_BASE\", api_key=\"None\")\n",
     "\n",
-    "dspy.settings.configure(lm=llama31_70b)"
+    "dspy.configure(lm=llama31_70b)"
    ]
   },
   {
diff --git a/docs/docs/tutorials/saving/index.md b/docs/docs/tutorials/saving/index.md
index 0069001e42..1500731409 100644
--- a/docs/docs/tutorials/saving/index.md
+++ b/docs/docs/tutorials/saving/index.md
@@ -20,7 +20,7 @@ Let's say we have compiled a program with some data, and we want to save the pro
 import dspy
 from dspy.datasets.gsm8k import GSM8K, gsm8k_metric
 
-dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
 gsm8k = GSM8K()
 gsm8k_trainset = gsm8k.train[:10]
diff --git a/docs/docs/tutorials/streaming/index.md b/docs/docs/tutorials/streaming/index.md
index 8782424d45..8c740dacb6 100644
--- a/docs/docs/tutorials/streaming/index.md
+++ b/docs/docs/tutorials/streaming/index.md
@@ -130,7 +130,7 @@ import asyncio
 import dspy
 
 lm = dspy.LM("openai/gpt-4o-mini", cache=False)
-dspy.settings.configure(lm=lm)
+dspy.configure(lm=lm)
 
 
 class MyModule(dspy.Module):
@@ -204,7 +204,7 @@ import asyncio
 import dspy
 
 lm = dspy.LM("openai/gpt-4o-mini", cache=False)
-dspy.settings.configure(lm=lm)
+dspy.configure(lm=lm)
 
 
 def fetch_user_info(user_name: str):
@@ -259,7 +259,7 @@ import asyncio
 import dspy
 
 lm = dspy.LM("openai/gpt-4o-mini", cache=False)
-dspy.settings.configure(lm=lm)
+dspy.configure(lm=lm)
 
 
 class MyModule(dspy.Module):
@@ -371,7 +371,7 @@ import asyncio
 import dspy
 
 lm = dspy.LM("openai/gpt-4o-mini", cache=False)
-dspy.settings.configure(lm=lm)
+dspy.configure(lm=lm)
 
 
 class MyModule(dspy.Module):
diff --git a/dspy/adapters/types/history.py b/dspy/adapters/types/history.py
index 65867c55b8..2c39d5c4ab 100644
--- a/dspy/adapters/types/history.py
+++ b/dspy/adapters/types/history.py
@@ -22,7 +22,7 @@ class MySignature(dspy.Signature):
         ```
         import dspy
 
-        dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+        dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
         class MySignature(dspy.Signature):
             question: str = dspy.InputField()
@@ -44,7 +44,7 @@ class MySignature(dspy.Signature):
         ```
         import dspy
 
-        dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+        dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
         class MySignature(dspy.Signature):
             question: str = dspy.InputField()
diff --git a/dspy/datasets/dataloader.py b/dspy/datasets/dataloader.py
index 559553b2b5..45f3236b2d 100644
--- a/dspy/datasets/dataloader.py
+++ b/dspy/datasets/dataloader.py
@@ -132,7 +132,7 @@ def from_rm(self, num_samples: int, fields: list[str], input_keys: list[str]) ->
                 )
         except AttributeError:
             raise ValueError(
-                "Retrieval module not found. Please set a retrieval module using `dspy.settings.configure`."
+                "Retrieval module not found. Please set a retrieval module using `dspy.configure`."
             )
 
     def sample(
diff --git a/dspy/dsp/utils/settings.py b/dspy/dsp/utils/settings.py
index f5319a9f9f..1298f7fdad 100644
--- a/dspy/dsp/utils/settings.py
+++ b/dspy/dsp/utils/settings.py
@@ -151,7 +151,7 @@ def _ensure_configure_allowed(self):
 
         if not in_ipython and config_owner_async_task != asyncio.current_task():
             raise RuntimeError(
-                "dspy.settings.configure(...) can only be called from the same async task that called it first. Please "
+                "dspy.configure(...) can only be called from the same async task that called it first. Please "
                 "use `dspy.context(...)` in other async tasks instead."
             )
 
diff --git a/dspy/predict/best_of_n.py b/dspy/predict/best_of_n.py
index 77013ec075..789ea0ed86 100644
--- a/dspy/predict/best_of_n.py
+++ b/dspy/predict/best_of_n.py
@@ -29,7 +29,7 @@ def __init__(
             ```python
             import dspy
 
-            dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+            dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
             # Define a QA module with chain of thought
             qa = dspy.ChainOfThought("question -> answer")
diff --git a/dspy/predict/refine.py b/dspy/predict/refine.py
index 8c1d756d2f..320365cb91 100644
--- a/dspy/predict/refine.py
+++ b/dspy/predict/refine.py
@@ -67,7 +67,7 @@ def __init__(
             ```python
             import dspy
 
-            dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+            dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
             # Define a QA module with chain of thought
             qa = dspy.ChainOfThought("question -> answer")
diff --git a/dspy/propose/dataset_summary_generator.py b/dspy/propose/dataset_summary_generator.py
index fd9284999a..50bcff27c7 100644
--- a/dspy/propose/dataset_summary_generator.py
+++ b/dspy/propose/dataset_summary_generator.py
@@ -50,7 +50,7 @@ def create_dataset_summary(trainset, view_data_batch_size, prompt_model, log_fil
         print("\nBootstrapping dataset summary (this will be used to generate instructions)...")
     upper_lim = min(len(trainset), view_data_batch_size)
     prompt_model = prompt_model if prompt_model else dspy.settings.lm
-    with dspy.settings.context(lm=prompt_model):
+    with dspy.context(lm=prompt_model):
         observation = dspy.Predict(DatasetDescriptor, n=1, temperature=1.0)(examples=order_input_keys_in_string(trainset[0:upper_lim].__repr__()))
     observations = observation["observations"]
 
@@ -68,7 +68,7 @@ def create_dataset_summary(trainset, view_data_batch_size, prompt_model, log_fil
             if verbose:
                 print(f"b: {b}")
             upper_lim = min(len(trainset), b+view_data_batch_size)
-            with dspy.settings.context(lm=prompt_model):
+            with dspy.context(lm=prompt_model):
                 output = dspy.Predict(DatasetDescriptorWithPriorObservations, n=1, temperature=1.0)(prior_observations=observations, examples=order_input_keys_in_string(trainset[b:upper_lim].__repr__()))
             if len(output["observations"]) >= 8 and output["observations"][:8].upper() == "COMPLETE":
                 skips += 1
@@ -84,7 +84,7 @@ def create_dataset_summary(trainset, view_data_batch_size, prompt_model, log_fil
             print(f"e {e}. using observations from past round for a summary.")
 
     if prompt_model:
-        with dspy.settings.context(lm=prompt_model):
+        with dspy.context(lm=prompt_model):
             summary = dspy.Predict(ObservationSummarizer, n=1, temperature=1.0)(observations=observations)
     else:
         summary = dspy.Predict(ObservationSummarizer, n=1, temperature=1.0)(observations=observations)
diff --git a/dspy/propose/grounded_proposer.py b/dspy/propose/grounded_proposer.py
index 5f9b759e66..ead30b5281 100644
--- a/dspy/propose/grounded_proposer.py
+++ b/dspy/propose/grounded_proposer.py
@@ -419,7 +419,7 @@ def propose_instruction_for_predictor(
             temperature=self.init_temperature,
         )
 
-        with dspy.settings.context(lm=rollout_lm):
+        with dspy.context(lm=rollout_lm):
             proposed_instruction = instruction_generator(
                 demo_candidates=demo_candidates,
                 pred_i=pred_i,
diff --git a/dspy/retrievers/weaviate_rm.py b/dspy/retrievers/weaviate_rm.py
index 381919254a..c5af333f47 100644
--- a/dspy/retrievers/weaviate_rm.py
+++ b/dspy/retrievers/weaviate_rm.py
@@ -34,7 +34,7 @@ class WeaviateRM(dspy.Retrieve):
         llm = dspy.Cohere(model="command-r-plus", api_key=api_key)
         weaviate_client = weaviate.connect_to_[local, wcs, custom, embedded]("your-path-here")
         retriever_model = WeaviateRM("my_collection_name", weaviate_client=weaviate_client)
-        dspy.settings.configure(lm=llm, rm=retriever_model)
+        dspy.configure(lm=llm, rm=retriever_model)
 
         retrieve = dspy.Retrieve(k=1)
         topK_passages = retrieve("what are the stages in planning, sanctioning and execution of public works").passages
diff --git a/dspy/streaming/streamify.py b/dspy/streaming/streamify.py
index d8ec56eb2f..9cfec34d40 100644
--- a/dspy/streaming/streamify.py
+++ b/dspy/streaming/streamify.py
@@ -64,7 +64,7 @@ def streamify(
     import asyncio
     import dspy
 
-    dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+    dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
     # Create the program and wrap it with streaming functionality
     program = dspy.streamify(dspy.Predict("q->a"))
 
@@ -88,7 +88,7 @@ async def use_streaming():
     import asyncio
     import dspy
 
-    dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+    dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
     class MyStatusMessageProvider(StatusMessageProvider):
         def module_start_status_message(self, instance, inputs):
@@ -121,7 +121,7 @@ async def use_streaming():
     import asyncio
     import dspy
 
-    dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False))
+    dspy.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False))
 
     # Create the program and wrap it with streaming functionality
     predict = dspy.Predict("question->answer, reasoning")
diff --git a/dspy/teleprompt/bootstrap.py b/dspy/teleprompt/bootstrap.py
index 31a5736239..e7e6b8f434 100644
--- a/dspy/teleprompt/bootstrap.py
+++ b/dspy/teleprompt/bootstrap.py
@@ -182,13 +182,13 @@ def _bootstrap_one_example(self, example, round_idx=0):
         predictor_cache = {}
 
         try:
-            with dspy.settings.context(trace=[], **self.teacher_settings):
+            with dspy.context(trace=[], **self.teacher_settings):
                 lm = dspy.settings.lm
                 # Use a fresh rollout with temperature=1.0 to bypass caches.
                 lm = lm.copy(rollout_id=round_idx, temperature=1.0) if round_idx > 0 else lm
                 new_settings = {"lm": lm} if round_idx > 0 else {}
 
-                with dspy.settings.context(**new_settings):
+                with dspy.context(**new_settings):
                     for name, predictor in teacher.named_predictors():
                         predictor_cache[name] = predictor.demos
                         predictor.demos = [x for x in predictor.demos if x != example]
diff --git a/dspy/teleprompt/copro_optimizer.py b/dspy/teleprompt/copro_optimizer.py
index e0f4f71749..dd3413fedb 100644
--- a/dspy/teleprompt/copro_optimizer.py
+++ b/dspy/teleprompt/copro_optimizer.py
@@ -20,7 +20,7 @@
 
 Note that this teleprompter takes in the following parameters:
 
-* prompt_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.settings.configure(lm=task_model)).
+* prompt_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.configure(lm=task_model)).
 * metric: The task metric used for optimization.
 * breadth: The number of new prompts to generate at each iteration. Default=10.
 * depth: The number of times we should ask our prompt model to generate new prompts, with the history of the past prompts as input. Default=3.
@@ -156,7 +156,7 @@ def compile(self, student, *, trainset, eval_kwargs):
             basic_instruction = self._get_signature(predictor).instructions
             basic_prefix = self._get_signature(predictor).fields[last_key].json_schema_extra["prefix"]
             if self.prompt_model:
-                with dspy.settings.context(lm=self.prompt_model):
+                with dspy.context(lm=self.prompt_model):
                     instruct = dspy.Predict(
                         BasicGenerateInstruction,
                         n=self.breadth - 1,
@@ -306,7 +306,7 @@ def compile(self, student, *, trainset, eval_kwargs):
 
                 # Generate next batch of potential prompts to optimize, with previous attempts as input
                 if self.prompt_model:
-                    with dspy.settings.context(lm=self.prompt_model):
+                    with dspy.context(lm=self.prompt_model):
                         instr = dspy.Predict(
                             GenerateInstructionGivenAttempts,
                             n=self.breadth,
diff --git a/dspy/teleprompt/infer_rules.py b/dspy/teleprompt/infer_rules.py
index 13e813fe76..2dcb240665 100644
--- a/dspy/teleprompt/infer_rules.py
+++ b/dspy/teleprompt/infer_rules.py
@@ -142,12 +142,12 @@ class CustomRulesInduction(dspy.Signature):
         self.rng = random.Random(0)
 
     def forward(self, examples_text):
-        with dspy.settings.context(**self.teacher_settings):
+        with dspy.context(**self.teacher_settings):
             # Generate rules with a fresh rollout and non-zero temperature.
             lm = dspy.settings.lm.copy(
                 rollout_id=self.rng.randint(0, 10**9), temperature=1.0
             )
-            with dspy.settings.context(lm=lm):
+            with dspy.context(lm=lm):
                 rules = self.rules_induction(examples_text=examples_text).natural_language_rules
 
         return rules.strip()
diff --git a/dspy/teleprompt/signature_opt.py b/dspy/teleprompt/signature_opt.py
index e5c364daae..8fc2d293fc 100644
--- a/dspy/teleprompt/signature_opt.py
+++ b/dspy/teleprompt/signature_opt.py
@@ -17,7 +17,7 @@
 
 Note that this teleprompter takes in the following parameters:
 
-* prompt_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.settings.configure(lm=task_model)).
+* prompt_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.configure(lm=task_model)).
 * metric: The task metric used for optimization.
 * breadth: The number of new prompts to generate at each iteration. Default=10.
 * depth: The number of times we should ask our prompt model to generate new prompts, with the history of the past prompts as input. Default=3.
diff --git a/dspy/teleprompt/simba_utils.py b/dspy/teleprompt/simba_utils.py
index fd5c3e8808..a47c00c821 100644
--- a/dspy/teleprompt/simba_utils.py
+++ b/dspy/teleprompt/simba_utils.py
@@ -155,7 +155,7 @@ def append_a_rule(bucket, system, **kwargs):
     kwargs = {k: v if isinstance(v, str) else orjson.dumps(recursive_mask(v), option=orjson.OPT_INDENT_2).decode()
               for k, v in kwargs.items()}
 
-    with dspy.settings.context(trace=[], lm=prompt_model):
+    with dspy.context(trace=[], lm=prompt_model):
         advice_program = dspy.Predict(OfferFeedback)
         advice = advice_program(**kwargs).module_advice
 
diff --git a/dspy/utils/callback.py b/dspy/utils/callback.py
index 20be402e86..eaf2fd9e6e 100644
--- a/dspy/utils/callback.py
+++ b/dspy/utils/callback.py
@@ -18,11 +18,11 @@ class BaseCallback:
     To use a callback, subclass this class and implement the desired handlers. Each handler
     will be called at the appropriate time before/after the execution of the corresponding component.  For example, if
     you want to print a message before and after an LM is called, implement `the on_llm_start` and `on_lm_end` handler.
-    Users can set the callback globally using `dspy.settings.configure` or locally by passing it to the component
+    Users can set the callback globally using `dspy.configure` or locally by passing it to the component
     constructor.
 
 
-    Example 1: Set a global callback using `dspy.settings.configure`.
+    Example 1: Set a global callback using `dspy.configure`.
 
     ```
     import dspy
@@ -36,7 +36,7 @@ def on_lm_start(self, call_id, instance, inputs):
         def on_lm_end(self, call_id, outputs, exception):
             print(f"LM is finished with outputs: {outputs}")
 
-    dspy.settings.configure(
+    dspy.configure(
         callbacks=[LoggingCallback()]
     )
 
diff --git a/dspy/utils/dummies.py b/dspy/utils/dummies.py
index a79f6b6c48..fb96f2c8c1 100644
--- a/dspy/utils/dummies.py
+++ b/dspy/utils/dummies.py
@@ -25,7 +25,7 @@ class DummyLM(LM):
 
     ```
     lm = DummyLM([{"answer": "red"}, {"answer": "blue"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     predictor("What color is the sky?")
     # Output:
     # [[## answer ##]]
@@ -44,7 +44,7 @@ class DummyLM(LM):
 
     ```
     lm = DummyLM({"What color is the sky?": {"answer": "blue"}})
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     predictor("What color is the sky?")
     # Output:
     # [[## answer ##]]
@@ -58,7 +58,7 @@ class DummyLM(LM):
 
     ```
     lm = DummyLM([{"answer": "red"}], follow_examples=True)
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     predictor("What color is the sky?, demos=dspy.Example(input="What color is the sky?", output="blue"))
     # Output:
     # [[## answer ##]]
diff --git a/tests/callback/test_callback.py b/tests/callback/test_callback.py
index 365266ca9d..d0c73a16de 100644
--- a/tests/callback/test_callback.py
+++ b/tests/callback/test_callback.py
@@ -14,7 +14,7 @@ def reset_settings():
 
     yield
 
-    dspy.settings.configure(**original_settings)
+    dspy.configure(**original_settings)
 
 
 class MyCallback(BaseCallback):
@@ -71,7 +71,7 @@ def forward(self, x: int, y: str, z: float) -> int:
             return x + int(y) + int(z)
 
     callback = MyCallback()
-    dspy.settings.configure(callbacks=[callback])
+    dspy.configure(callbacks=[callback])
 
     target = Target()
     result = target.forward(*args, **kwargs)
@@ -122,7 +122,7 @@ def forward(self, x: int, y: str, z: float) -> int:
             raise ValueError("Error")
 
     callback = MyCallback()
-    dspy.settings.configure(callbacks=[callback])
+    dspy.configure(callbacks=[callback])
 
     target = Target()
 
@@ -144,7 +144,7 @@ def forward(self, x: int, y: str, z: float) -> int:
 
     callback_1 = MyCallback()
     callback_2 = MyCallback()
-    dspy.settings.configure(callbacks=[callback_1, callback_2])
+    dspy.configure(callbacks=[callback_1, callback_2])
 
     target = Target()
     result = target.forward(1, "2", 3.0)
@@ -157,7 +157,7 @@ def forward(self, x: int, y: str, z: float) -> int:
 
 def test_callback_complex_module():
     callback = MyCallback()
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM({"How are you?": {"answer": "test output", "reasoning": "No more responses"}}),
         callbacks=[callback],
     )
@@ -220,7 +220,7 @@ async def test_callback_async_module():
 
 def test_tool_calls():
     callback = MyCallback()
-    dspy.settings.configure(callbacks=[callback])
+    dspy.configure(callbacks=[callback])
 
     def tool_1(query: str) -> str:
         """A dummy tool function."""
@@ -279,7 +279,7 @@ def forward(self):
             pass
 
     callback = CustomCallback()
-    dspy.settings.configure(callbacks=[callback])
+    dspy.configure(callbacks=[callback])
 
     parent = Parent()
     parent()
diff --git a/tests/clients/test_inspect_global_history.py b/tests/clients/test_inspect_global_history.py
index 2deb0c2e97..cd9a42b703 100644
--- a/tests/clients/test_inspect_global_history.py
+++ b/tests/clients/test_inspect_global_history.py
@@ -14,7 +14,7 @@ def clear_history():
 def test_inspect_history_basic(capsys):
     # Configure a DummyLM with some predefined responses
     lm = DummyLM([{"response": "Hello"}, {"response": "How are you?"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     # Make some calls to generate history
     predictor = dspy.Predict("query: str -> response: str")
@@ -35,7 +35,7 @@ def test_inspect_history_with_n(capsys):
     Random failures in this test most likely mean you are printing messages somewhere
     """
     lm = DummyLM([{"response": "One"}, {"response": "Two"}, {"response": "Three"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     # Generate some history
     predictor = dspy.Predict("query: str -> response: str")
@@ -54,7 +54,7 @@ def test_inspect_history_with_n(capsys):
 def test_inspect_empty_history(capsys):
     # Configure fresh DummyLM
     lm = DummyLM([])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     # Test inspecting empty history
     dspy.inspect_history()
@@ -65,7 +65,7 @@ def test_inspect_empty_history(capsys):
 
 def test_inspect_history_n_larger_than_history(capsys):
     lm = DummyLM([{"response": "First"}, {"response": "Second"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     predictor = dspy.Predict("query: str -> response: str")
     predictor(query="Query 1")
diff --git a/tests/conftest.py b/tests/conftest.py
index 372585df20..b634c8ac6e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -17,7 +17,7 @@ def clear_settings():
     import dspy
     from dspy.dsp.utils.settings import DEFAULT_CONFIG
 
-    dspy.settings.configure(**copy.deepcopy(DEFAULT_CONFIG), inherit_config=False)
+    dspy.configure(**copy.deepcopy(DEFAULT_CONFIG), inherit_config=False)
 
 
 @pytest.fixture
diff --git a/tests/evaluate/test_evaluate.py b/tests/evaluate/test_evaluate.py
index 211cf25962..2b336e3c21 100644
--- a/tests/evaluate/test_evaluate.py
+++ b/tests/evaluate/test_evaluate.py
@@ -34,7 +34,7 @@ def test_evaluate_initialization():
 
 
 def test_evaluate_call():
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             {
                 "What is 1+1?": {"answer": "2"},
@@ -86,7 +86,7 @@ def test_construct_result_df():
 
 
 def test_multithread_evaluate_call():
-    dspy.settings.configure(lm=DummyLM({"What is 1+1?": {"answer": "2"}, "What is 2+2?": {"answer": "4"}}))
+    dspy.configure(lm=DummyLM({"What is 1+1?": {"answer": "2"}, "What is 2+2?": {"answer": "4"}}))
     devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")]
     program = Predict("question -> answer")
     assert program(question="What is 1+1?").answer == "2"
@@ -109,7 +109,7 @@ def __call__(self, *args, **kwargs):
             time.sleep(1)
             return super().__call__(*args, **kwargs)
 
-    dspy.settings.configure(lm=SlowLM({"What is 1+1?": {"answer": "2"}, "What is 2+2?": {"answer": "4"}}))
+    dspy.configure(lm=SlowLM({"What is 1+1?": {"answer": "2"}, "What is 2+2?": {"answer": "4"}}))
 
     devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")]
     program = Predict("question -> answer")
@@ -138,7 +138,7 @@ def sleep_then_interrupt():
 
 
 def test_evaluate_call_wrong_answer():
-    dspy.settings.configure(lm=DummyLM({"What is 1+1?": {"answer": "0"}, "What is 2+2?": {"answer": "0"}}))
+    dspy.configure(lm=DummyLM({"What is 1+1?": {"answer": "0"}, "What is 2+2?": {"answer": "0"}}))
     devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")]
     program = Predict("question -> answer")
     ev = Evaluate(
@@ -180,7 +180,7 @@ def test_evaluate_display_table(program_with_example, display_table, is_in_ipyth
     example_input = next(iter(example.inputs().values()))
     example_output = {key: value for key, value in example.toDict().items() if key not in example.inputs()}
 
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             {
                 example_input: example_output,
@@ -234,7 +234,7 @@ def on_evaluate_end(
             self.end_call_count += 1
 
     callback = TestCallback()
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             {
                 "What is 1+1?": {"answer": "2"},
diff --git a/tests/examples/test_baleen.py b/tests/examples/test_baleen.py
index 1cc27e8fcb..eed29eabfd 100644
--- a/tests/examples/test_baleen.py
+++ b/tests/examples/test_baleen.py
@@ -57,7 +57,7 @@ def load_hotpotqa():
 def _test_baleen():
     lm = dspy.OpenAI(model="gpt-3.5-turbo")
     rm = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
-    dspy.settings.configure(lm=lm, rm=rm)
+    dspy.configure(lm=lm, rm=rm)
 
     # Ask any question you like to this simple RAG program.
     my_question = "How many storeys are in the castle that David Gregory inherited?"
@@ -98,7 +98,7 @@ def _test_compiled_baleen():
     trainset, devset = load_hotpotqa()
     lm = dspy.OpenAI(model="gpt-3.5-turbo")
     rm = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
-    dspy.settings.configure(lm=lm, rm=rm)
+    dspy.configure(lm=lm, rm=rm)
 
     uncompiled_baleen = SimplifiedBaleen()  # uncompiled (i.e., zero-shot) program
 
diff --git a/tests/predict/test_best_of_n.py b/tests/predict/test_best_of_n.py
index 777e99e547..684bc0629d 100644
--- a/tests/predict/test_best_of_n.py
+++ b/tests/predict/test_best_of_n.py
@@ -19,7 +19,7 @@ def forward(self, **kwargs) -> Prediction:
 
 def test_refine_forward_success_first_attempt():
     lm = DummyLM([{"answer": "Brussels"}, {"answer": "City of Brussels"}, {"answer": "Brussels"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     module_call_count = [0]
 
     def count_calls(self, **kwargs):
@@ -47,7 +47,7 @@ def reward_fn(kwargs, pred: Prediction) -> float:
 
 def test_refine_module_default_fail_count():
     lm = DummyLM([{"answer": "Brussels"}, {"answer": "City of Brussels"}, {"answer": "Brussels"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     def always_raise(self, **kwargs):
         raise ValueError("Deliberately failing")
@@ -61,7 +61,7 @@ def always_raise(self, **kwargs):
 
 def test_refine_module_custom_fail_count():
     lm = DummyLM([{"answer": "Brussels"}, {"answer": "City of Brussels"}, {"answer": "Brussels"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     module_call_count = [0]
 
     def raise_on_second_call(self, **kwargs):
diff --git a/tests/predict/test_chain_of_thought.py b/tests/predict/test_chain_of_thought.py
index 247256dba7..686bd38668 100644
--- a/tests/predict/test_chain_of_thought.py
+++ b/tests/predict/test_chain_of_thought.py
@@ -7,7 +7,7 @@
 
 def test_initialization_with_string_signature():
     lm = DummyLM([{"reasoning": "find the number after 1", "answer": "2"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     predict = ChainOfThought("question -> answer")
     assert list(predict.predict.signature.output_fields.keys()) == [
         "reasoning",
diff --git a/tests/predict/test_code_act.py b/tests/predict/test_code_act.py
index 39f958af09..424656f8e0 100644
--- a/tests/predict/test_code_act.py
+++ b/tests/predict/test_code_act.py
@@ -34,7 +34,7 @@ def test_codeact_code_generation():
             {"reasoning": "Reason_B", "answer": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     program = CodeAct(BasicQA, tools=[add])
     res = program(question="What is 1+1?")
     assert res.answer == "2"
@@ -66,7 +66,7 @@ def test_codeact_support_multiple_fields():
             {"reasoning": "Reason_B", "maximum": "6", "minimum": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     program = CodeAct(ExtremumFinder, tools=[extract_maximum_minimum])
     res = program(input_list="2, 3, 5, 6")
     assert res.maximum == "6"
@@ -95,7 +95,7 @@ def test_codeact_code_parse_failure():
             {"reasoning": "Reason_B", "answer": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     program = CodeAct(BasicQA, tools=[add])
     res = program(question="What is 1+1?")
     assert res.answer == "2"
@@ -125,7 +125,7 @@ def test_codeact_code_execution_failure():
             {"reasoning": "Reason_B", "answer": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     program = CodeAct(BasicQA, tools=[add])
     res = program(question="What is 1+1?")
     assert res.answer == "2"
diff --git a/tests/predict/test_multi_chain_comparison.py b/tests/predict/test_multi_chain_comparison.py
index 39f7c18cc7..294e28db3d 100644
--- a/tests/predict/test_multi_chain_comparison.py
+++ b/tests/predict/test_multi_chain_comparison.py
@@ -33,7 +33,7 @@ def test_basic_example():
     # Call the MultiChainComparison on the completions
     question = "What is the color of the sky?"
     lm = DummyLM([{"rationale": "my rationale", "answer": "blue"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     final_pred = compare_answers(completions, question=question)
 
     assert final_pred.rationale == "my rationale"
diff --git a/tests/predict/test_parallel.py b/tests/predict/test_parallel.py
index fe8479079b..f8524d4d25 100644
--- a/tests/predict/test_parallel.py
+++ b/tests/predict/test_parallel.py
@@ -12,7 +12,7 @@ def test_parallel_module():
             {"output": "test output 5"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     class MyModule(dspy.Module):
         def __init__(self):
@@ -99,7 +99,7 @@ def test_nested_parallel_module():
             {"output": "test output 5"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     class MyModule(dspy.Module):
         def __init__(self):
@@ -143,7 +143,7 @@ def test_nested_batch_method():
             {"output": "test output 5"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     class MyModule(dspy.Module):
         def __init__(self):
diff --git a/tests/predict/test_predict.py b/tests/predict/test_predict.py
index e5a208b070..23b6e1d785 100644
--- a/tests/predict/test_predict.py
+++ b/tests/predict/test_predict.py
@@ -70,7 +70,7 @@ def test_lm_after_dump_and_load_state():
 def test_call_method():
     predict_instance = Predict("input -> output")
     lm = DummyLM([{"output": "test output"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     result = predict_instance(input="test input")
     assert result.output == "test output"
 
@@ -260,14 +260,14 @@ def test_lm_field_after_dump_and_load_state(tmp_path, filename):
 
 def test_forward_method():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=DummyLM([{"answer": "No more responses"}]))
+    dspy.configure(lm=DummyLM([{"answer": "No more responses"}]))
     result = program(question="What is 1+1?").answer
     assert result == "No more responses"
 
 
 def test_forward_method2():
     program = Predict("question -> answer1, answer2")
-    dspy.settings.configure(lm=DummyLM([{"answer1": "my first answer", "answer2": "my second answer"}]))
+    dspy.configure(lm=DummyLM([{"answer1": "my first answer", "answer2": "my second answer"}]))
     result = program(question="What is 1+1?")
     assert result.answer1 == "my first answer"
     assert result.answer2 == "my second answer"
@@ -282,7 +282,7 @@ def test_config_management():
 
 def test_multi_output():
     program = Predict("question -> answer", n=2)
-    dspy.settings.configure(lm=DummyLM([{"answer": "my first answer"}, {"answer": "my second answer"}]))
+    dspy.configure(lm=DummyLM([{"answer": "my first answer"}, {"answer": "my second answer"}]))
     results = program(question="What is 1+1?")
     assert results.completions.answer[0] == "my first answer"
     assert results.completions.answer[1] == "my second answer"
@@ -290,7 +290,7 @@ def test_multi_output():
 
 def test_multi_output2():
     program = Predict("question -> answer1, answer2", n=2)
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             [
                 {"answer1": "my 0 answer", "answer2": "my 2 answer"},
@@ -327,7 +327,7 @@ class TimedSignature(dspy.Signature):
             }
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     output = program(
         events=[
@@ -359,7 +359,7 @@ class StatusSignature(dspy.Signature):
             }
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     output = program(current_status=Status.PENDING)
     assert output.next_status == Status.IN_PROGRESS
@@ -386,7 +386,7 @@ class TicketStatusSignature(dspy.Signature):
             }
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     output = program(current_status=TicketStatus.OPEN)
     assert output.next_status == TicketStatus.CLOSED  # By value
@@ -409,7 +409,7 @@ class StatusSignature(dspy.Signature):
             }
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     output = program(current_status=Status.PENDING)
     assert output.next_status == Status.IN_PROGRESS
@@ -436,7 +436,7 @@ class OutputOnlySignature(dspy.Signature):
     predictor = Predict(OutputOnlySignature)
 
     lm = DummyLM([{"output": "short answer"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     assert predictor().output == "short answer"
 
 
@@ -474,10 +474,10 @@ class MySignature(dspy.Signature):
 
     if adapter_type == "chat":
         lm = SpyLM("dummy_model")
-        dspy.settings.configure(adapter=dspy.ChatAdapter(), lm=lm)
+        dspy.configure(adapter=dspy.ChatAdapter(), lm=lm)
     else:
         lm = SpyLM("dummy_model", return_json=True)
-        dspy.settings.configure(adapter=dspy.JSONAdapter(), lm=lm)
+        dspy.configure(adapter=dspy.JSONAdapter(), lm=lm)
 
     program(
         question="are you sure that's correct?",
@@ -497,7 +497,7 @@ class MySignature(dspy.Signature):
 
 def test_lm_usage():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), track_usage=True)
+    dspy.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), track_usage=True)
     with patch(
         "dspy.clients.lm.litellm_completion",
         return_value=ModelResponse(
@@ -518,7 +518,7 @@ def program_wrapper(question):
         time.sleep(0.5)
         return program(question=question)
 
-    dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), track_usage=True)
+    dspy.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), track_usage=True)
     with patch(
         "dspy.clients.lm.litellm_completion",
         return_value=ModelResponse(
@@ -634,10 +634,10 @@ class ConstrainedSignature(dspy.Signature):
     lm = SpyLM("dummy_model")
     if adapter_type == "chat":
         lm = SpyLM("dummy_model")
-        dspy.settings.configure(adapter=dspy.ChatAdapter(), lm=lm)
+        dspy.configure(adapter=dspy.ChatAdapter(), lm=lm)
     else:
         lm = SpyLM("dummy_model", return_json=True)
-        dspy.settings.configure(adapter=dspy.JSONAdapter(), lm=lm)
+        dspy.configure(adapter=dspy.JSONAdapter(), lm=lm)
 
     # Call the predictor to trigger instruction generation
     program(text="hello world", number=5)
@@ -665,7 +665,7 @@ async def test_async_predict():
 
 def test_predicted_outputs_piped_from_predict_to_lm_call():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini"))
+    dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
     with patch("litellm.completion") as mock_completion:
         program(
@@ -729,7 +729,7 @@ class TestSignature(dspy.Signature):
 
 def test_trace_size_limit():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=DummyLM([{"answer": "Paris"}]), max_trace_size=3)
+    dspy.configure(lm=DummyLM([{"answer": "Paris"}]), max_trace_size=3)
 
     for _ in range(10):
         program(question="What is the capital of France?")
@@ -739,7 +739,7 @@ def test_trace_size_limit():
 
 def test_disable_trace():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=DummyLM([{"answer": "Paris"}]), trace=None)
+    dspy.configure(lm=DummyLM([{"answer": "Paris"}]), trace=None)
 
     for _ in range(10):
         program(question="What is the capital of France?")
@@ -749,7 +749,7 @@ def test_disable_trace():
 
 def test_per_module_history_size_limit():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=DummyLM([{"answer": "Paris"}]), max_history_size=5)
+    dspy.configure(lm=DummyLM([{"answer": "Paris"}]), max_history_size=5)
 
     for _ in range(10):
         program(question="What is the capital of France?")
@@ -758,7 +758,7 @@ def test_per_module_history_size_limit():
 
 def test_per_module_history_disabled():
     program = Predict("question -> answer")
-    dspy.settings.configure(lm=DummyLM([{"answer": "Paris"}]), disable_history=True)
+    dspy.configure(lm=DummyLM([{"answer": "Paris"}]), disable_history=True)
 
     for _ in range(10):
         program(question="What is the capital of France?")
diff --git a/tests/predict/test_program_of_thought.py b/tests/predict/test_program_of_thought.py
index 4871afc7f6..3b197b6113 100644
--- a/tests/predict/test_program_of_thought.py
+++ b/tests/predict/test_program_of_thought.py
@@ -27,7 +27,7 @@ def test_pot_code_generation():
             {"reasoning": "Reason_B", "answer": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     pot = ProgramOfThought(BasicQA)
     res = pot(question="What is 1+1?")
     assert res.answer == "2"
@@ -43,7 +43,7 @@ def test_old_style_pot():
             {"reasoning": "Reason_B", "answer": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     pot = ProgramOfThought(BasicQA)
     res = pot(question="What is 1+1?")
     assert res.answer == "2"
@@ -67,7 +67,7 @@ def test_pot_support_multiple_fields():
             {"reasoning": "Reason_B", "maximum": "6", "minimum": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     pot = ProgramOfThought(ExtremumFinder)
     res = pot(input_list="2, 3, 5, 6")
     assert res.maximum == "6"
@@ -90,7 +90,7 @@ def test_pot_code_generation_with_one_error():
             {"reasoning": "Reason_C", "answer": "2"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     pot = ProgramOfThought(BasicQA)
     res = pot(question="What is 1+1?")
     assert res.answer == "2"
@@ -109,7 +109,7 @@ def test_pot_code_generation_persistent_errors():
         ]
         * max_iters
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     pot = ProgramOfThought(BasicQA, max_iters=max_iters)
     with pytest.raises(RuntimeError, match="Max hops reached. Failed to run ProgramOfThought: ZeroDivisionError:"):
@@ -125,7 +125,7 @@ def test_pot_code_parse_error():
         ]
         * max_iters
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     pot = ProgramOfThought(BasicQA, max_iters=max_iters)
     with (
         patch("dspy.predict.program_of_thought.ProgramOfThought._execute_code") as mock_execute_code,
diff --git a/tests/predict/test_react.py b/tests/predict/test_react.py
index 6c12859c0d..09fd1c7c85 100644
--- a/tests/predict/test_react.py
+++ b/tests/predict/test_react.py
@@ -41,7 +41,7 @@ def make_images():
         ],
         adapter=adapter,
     )
-    dspy.settings.configure(lm=lm, adapter=adapter)
+    dspy.configure(lm=lm, adapter=adapter)
 
     react = dspy.ReAct("question -> answer", tools=[make_images])
     react(question="Draw me something red")
@@ -99,7 +99,7 @@ class InvitationSignature(dspy.Signature):
             },
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     outputs = react(
         participant_name="Alice",
@@ -144,7 +144,7 @@ def foo(a, b):
             {"reasoning": "I added the numbers successfully", "c": 3},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     outputs = react(a=1, b=2)
 
     expected_trajectory = {
@@ -227,7 +227,7 @@ def foo(a, b):
             {"reasoning": "I added the numbers successfully", "c": 3},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     outputs = react(a=1, b=2, max_iters=2)
     traj = outputs.trajectory
diff --git a/tests/predict/test_refine.py b/tests/predict/test_refine.py
index 7bff4b2b09..7a5c2ece13 100644
--- a/tests/predict/test_refine.py
+++ b/tests/predict/test_refine.py
@@ -19,7 +19,7 @@ def forward(self, **kwargs) -> Prediction:
 
 def test_refine_forward_success_first_attempt():
     lm = DummyLM([{"answer": "Brussels"}, {"answer": "City of Brussels"}, {"answer": "Brussels"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     module_call_count = [0]
 
     def count_calls(self, **kwargs):
@@ -47,7 +47,7 @@ def reward_fn(kwargs, pred: Prediction) -> float:
 
 def test_refine_module_default_fail_count():
     lm = DummyLM([{"answer": "Brussels"}, {"answer": "City of Brussels"}, {"answer": "Brussels"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     def always_raise(self, **kwargs):
         raise ValueError("Deliberately failing")
@@ -61,7 +61,7 @@ def always_raise(self, **kwargs):
 
 def test_refine_module_custom_fail_count():
     lm = DummyLM([{"answer": "Brussels"}, {"answer": "City of Brussels"}, {"answer": "Brussels"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     module_call_count = [0]
 
     def raise_on_second_call(self, **kwargs):
diff --git a/tests/predict/test_retry.py b/tests/predict/test_retry.py
index eb1c48b06e..45113617a2 100644
--- a/tests/predict/test_retry.py
+++ b/tests/predict/test_retry.py
@@ -17,7 +17,7 @@
 #     assert "feedback" in retry_module.new_signature.input_fields
 
 #     lm = DummyLM([{"answer": "blue"}])
-#     dspy.settings.configure(lm=lm)
+#     dspy.configure(lm=lm)
 #     result = retry_module.forward(
 #         question="What color is the sky?",
 #         past_outputs={"answer": "red"},
@@ -29,7 +29,7 @@
 # def test_retry_forward_with_feedback():
 #     # First we make a mistake, then we fix it
 #     lm = DummyLM([{"answer": "red"}, {"answer": "blue"}])
-#     dspy.settings.configure(lm=lm, trace=[])
+#     dspy.configure(lm=lm, trace=[])
 
 #     class SimpleModule(dspy.Module):
 #         def __init__(self):
@@ -56,7 +56,7 @@
 # # def test_retry_forward_with_typed_predictor():
 # #     # First we make a mistake, then we fix it
 # #     lm = DummyLM([{"output": '{"answer":"red"}'}, {"output": '{"answer":"blue"}'}])
-# #     dspy.settings.configure(lm=lm, trace=[])
+# #     dspy.configure(lm=lm, trace=[])
 
 # #     class AnswerQuestion(dspy.Signature):
 # #         """Answer questions with succinct responses."""
diff --git a/tests/primitives/test_base_module.py b/tests/primitives/test_base_module.py
index f92315a90d..a8960cfda9 100644
--- a/tests/primitives/test_base_module.py
+++ b/tests/primitives/test_base_module.py
@@ -105,7 +105,7 @@ class MySignature(dspy.Signature):
     ]
     trainset = [dspy.Example(**example).with_inputs("current_date", "target_date") for example in trainset]
 
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM([{"date_diff": "1", "reasoning": "n/a"}, {"date_diff": "2", "reasoning": "n/a"}] * 10)
     )
 
@@ -247,7 +247,7 @@ def emit(self, record):
 
 @pytest.mark.llm_call
 def test_single_module_call_with_usage_tracker(lm_for_test):
-    dspy.settings.configure(lm=dspy.LM(lm_for_test, cache=False), track_usage=True)
+    dspy.configure(lm=dspy.LM(lm_for_test, cache=False), track_usage=True)
 
     predict = dspy.ChainOfThought("question -> answer")
     output = predict(question="What is the capital of France?")
@@ -259,7 +259,7 @@ def test_single_module_call_with_usage_tracker(lm_for_test):
     assert lm_usage[lm_for_test]["total_tokens"] > 0
 
     # Test no usage being tracked when cache is enabled
-    dspy.settings.configure(lm=dspy.LM(lm_for_test, cache=True), track_usage=True)
+    dspy.configure(lm=dspy.LM(lm_for_test, cache=True), track_usage=True)
     for _ in range(2):
         output = predict(question="What is the capital of France?")
 
@@ -268,7 +268,7 @@ def test_single_module_call_with_usage_tracker(lm_for_test):
 
 @pytest.mark.llm_call
 def test_multi_module_call_with_usage_tracker(lm_for_test):
-    dspy.settings.configure(lm=dspy.LM(lm_for_test, cache=False), track_usage=True)
+    dspy.configure(lm=dspy.LM(lm_for_test, cache=False), track_usage=True)
 
     class MyProgram(dspy.Module):
         def __init__(self):
@@ -301,12 +301,12 @@ def __init__(self, lm):
             self.predict2 = dspy.ChainOfThought("question, answer -> score")
 
         def __call__(self, question: str) -> Prediction:
-            with dspy.settings.context(lm=self.lm):
+            with dspy.context(lm=self.lm):
                 answer = self.predict1(question=question)
                 score = self.predict2(question=question, answer=answer)
                 return score
 
-    dspy.settings.configure(track_usage=True)
+    dspy.configure(track_usage=True)
     program1 = MyProgram(lm=dspy.LM("openai/gpt-4o-mini", cache=False))
     program2 = MyProgram(lm=dspy.LM("openai/gpt-3.5-turbo", cache=False))
 
@@ -356,7 +356,7 @@ async def test_usage_tracker_async_parallel():
             program.acall(question="What is the capital of France?"),
             program.acall(question="What is the capital of France?"),
         ]
-        with dspy.settings.context(
+        with dspy.context(
             lm=dspy.LM("openai/gpt-4o-mini", cache=False), track_usage=True, adapter=dspy.JSONAdapter()
         ):
             results = await asyncio.gather(*coroutines)
@@ -404,7 +404,7 @@ def forward(self, question: str, **kwargs) -> Prediction:
             ],
             model="openai/gpt-4o-mini",
         )
-        dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.JSONAdapter())
+        dspy.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.JSONAdapter())
         program = MyProgram()
         program(question="What is the capital of France?")
 
@@ -421,7 +421,7 @@ def forward(self, question: str, **kwargs) -> Prediction:
 
         assert program.history[0]["outputs"] == ["{'reasoning': 'Paris is the capital of France', 'answer': 'Paris'}"]
 
-        dspy.settings.configure(disable_history=True)
+        dspy.configure(disable_history=True)
 
         program(question="What is the capital of France?")
         # No history is recorded when history is disabled.
@@ -429,7 +429,7 @@ def forward(self, question: str, **kwargs) -> Prediction:
         assert len(program.cot.history) == 2
         assert len(program.cot.predict.history) == 2
 
-        dspy.settings.configure(disable_history=False)
+        dspy.configure(disable_history=False)
 
         program(question="What is the capital of France?")
         # History is recorded again when history is enabled.
@@ -452,7 +452,7 @@ def forward(self, question: str, **kwargs) -> Prediction:
             choices=[Choices(message=Message(content="{'reasoning': 'N/A', 'answer': 'Holy crab!'}"))],
             model="openai/gpt-4o-mini",
         )
-        dspy.settings.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.JSONAdapter())
+        dspy.configure(lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.JSONAdapter())
         program = MyProgram()
 
         parallelizer = dspy.Parallel()
diff --git a/tests/primitives/test_module.py b/tests/primitives/test_module.py
index abd3f21a9d..166658673c 100644
--- a/tests/primitives/test_module.py
+++ b/tests/primitives/test_module.py
@@ -38,7 +38,7 @@ def test_predictors():
 
 def test_forward():
     program = HopModule()
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             {
                 "What is 1+1?": {"query": "let me check"},
diff --git a/tests/reliability/utils.py b/tests/reliability/utils.py
index c713072402..3dfe9fba7d 100644
--- a/tests/reliability/utils.py
+++ b/tests/reliability/utils.py
@@ -80,7 +80,7 @@ def judge_dspy_configuration(**extra_judge_config):
     if judge_params is None:
         raise ValueError(f"No LiteLLM configuration found for judge model: {JUDGE_MODEL_NAME}")
 
-    with dspy.settings.context(lm=dspy.LM(**judge_params, **extra_judge_config), adapter=adapter):
+    with dspy.context(lm=dspy.LM(**judge_params, **extra_judge_config), adapter=adapter):
         yield
 
 
diff --git a/tests/signatures/test_adapter_image.py b/tests/signatures/test_adapter_image.py
index 07c8154e77..c0fe94371a 100644
--- a/tests/signatures/test_adapter_image.py
+++ b/tests/signatures/test_adapter_image.py
@@ -69,7 +69,7 @@ def count_patterns(obj, pattern):
 def setup_predictor(signature, expected_output):
     """Helper to set up a predictor with DummyLM"""
     lm = DummyLM([expected_output])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     return dspy.Predict(signature), lm
 
 
diff --git a/tests/signatures/test_signature.py b/tests/signatures/test_signature.py
index 9e63743b9d..292401bb75 100644
--- a/tests/signatures/test_signature.py
+++ b/tests/signatures/test_signature.py
@@ -189,7 +189,7 @@ class SubSignature(Signature):
 
 def test_multiline_instructions():
     lm = DummyLM([{"output": "short answer"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     class MySignature(Signature):
         """First line
@@ -418,7 +418,7 @@ class CustomType(pydantic.BaseModel):
     assert test_signature.input_fields["input"].annotation == CustomType
 
     lm = DummyLM([{"output": "processed"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     custom_obj = CustomType(value="test")
     pred = dspy.Predict(test_signature)(input=custom_obj)
@@ -432,7 +432,7 @@ def test_custom_type_from_different_module():
     assert test_signature.input_fields["input"].annotation == Path
 
     lm = DummyLM([{"output": "/test/path"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     path_obj = Path("/test/path")
     pred = dspy.Predict(test_signature)(input=path_obj)
@@ -568,7 +568,7 @@ class CustomType(pydantic.BaseModel):
     assert sig.output_fields["output"].annotation == Union[int, str]
 
     lm = DummyLM([{"output": "processed"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     custom_obj = CustomType(value="test")
     pred = dspy.Predict(sig)(input=custom_obj)
diff --git a/tests/teleprompt/test_bootstrap.py b/tests/teleprompt/test_bootstrap.py
index 17b58aedaf..75193eb24a 100644
--- a/tests/teleprompt/test_bootstrap.py
+++ b/tests/teleprompt/test_bootstrap.py
@@ -43,7 +43,7 @@ def test_compile_with_predict_instances():
     teacher = SimpleModule("input -> output")
 
     lm = DummyLM(["Initial thoughts", "Finish[blue]"])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     # Initialize BootstrapFewShot and compile the student
     bootstrap = BootstrapFewShot(metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1)
@@ -58,7 +58,7 @@ def test_bootstrap_effectiveness():
     student = SimpleModule("input -> output")
     teacher = SimpleModule("input -> output")
     lm = DummyLM([{"output": "blue"}, {"output": "Ring-ding-ding-ding-dingeringeding!"}], follow_examples=True)
-    dspy.settings.configure(lm=lm, trace=[])
+    dspy.configure(lm=lm, trace=[])
 
     bootstrap = BootstrapFewShot(metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1)
     compiled_student = bootstrap.compile(student, teacher=teacher, trainset=trainset)
@@ -99,7 +99,7 @@ def forward(self, **kwargs):
             {"output": "Initial thoughts"},  # Simulate initial teacher's prediction
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     bootstrap = BootstrapFewShot(
         metric=simple_metric,
@@ -125,7 +125,7 @@ def test_validation_set_usage():
             {"output": "Finish[blue]"},  # Expected output for both training and validation
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     bootstrap = BootstrapFewShot(metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1)
     compiled_student = bootstrap.compile(student, teacher=teacher, trainset=trainset)
diff --git a/tests/teleprompt/test_bootstrap_finetune.py b/tests/teleprompt/test_bootstrap_finetune.py
index 59c685902f..2eb8614560 100644
--- a/tests/teleprompt/test_bootstrap_finetune.py
+++ b/tests/teleprompt/test_bootstrap_finetune.py
@@ -42,7 +42,7 @@ def test_compile_with_predict_instances():
     teacher = SimpleModule("input -> output")
 
     lm = DummyLM([{"output": "blue"}, {"output": "Ring-ding-ding-ding-dingeringeding!"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     # Set LM for both student and teacher
     student.set_lm(lm)
@@ -65,7 +65,7 @@ def test_error_handling_missing_lm():
     """Test error handling when predictor doesn't have an LM assigned."""
 
     lm = DummyLM([{"output": "test"}])
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     student = SimpleModule("input -> output")
     # Intentionally NOT setting LM for the student module
diff --git a/tests/teleprompt/test_copro_optimizer.py b/tests/teleprompt/test_copro_optimizer.py
index c031d3c7a3..b1dd870e8d 100644
--- a/tests/teleprompt/test_copro_optimizer.py
+++ b/tests/teleprompt/test_copro_optimizer.py
@@ -39,7 +39,7 @@ def forward(self, **kwargs):
 
 def test_signature_optimizer_optimization_process():
     optimizer = COPRO(metric=simple_metric, breadth=2, depth=1, init_temperature=1.4)
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             [
                 {
@@ -69,7 +69,7 @@ def test_signature_optimizer_statistics_tracking():
     optimizer = COPRO(metric=simple_metric, breadth=2, depth=1, init_temperature=1.4)
     optimizer.track_stats = True  # Enable statistics tracking
 
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             [
                 {
@@ -105,7 +105,7 @@ def test_optimization_and_output_verification():
             {"reasoning": "france", "output": "Paris"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
     optimizer = COPRO(metric=simple_metric, breadth=2, depth=1, init_temperature=1.4)
 
     student = SimpleModule("input -> output")
@@ -125,7 +125,7 @@ def test_optimization_and_output_verification():
 
 
 def test_statistics_tracking_during_optimization():
-    dspy.settings.configure(
+    dspy.configure(
         lm=DummyLM(
             [
                 {"proposed_instruction": "Optimized Prompt", "proposed_prefix_for_output_field": "Optimized Prefix"},
diff --git a/tests/teleprompt/test_gepa.py b/tests/teleprompt/test_gepa.py
index da9a74da82..7a379e6ab4 100644
--- a/tests/teleprompt/test_gepa.py
+++ b/tests/teleprompt/test_gepa.py
@@ -96,7 +96,7 @@ def test_basic_workflow(use_mlflow, mock_mlflow):
     reflection_lm_history = data["reflection_lm"]
 
     lm_main = DictDummyLM(lm_history)
-    dspy.settings.configure(lm=lm_main)
+    dspy.configure(lm=lm_main)
     reflection_lm = DictDummyLM(reflection_lm_history)
 
     optimizer = dspy.GEPA(
@@ -165,7 +165,7 @@ def all_component_selector(state, trajectories, subsample_scores, candidate_idx,
     lm_main = DictDummyLM(lm_history)
     reflection_lm = DictDummyLM(reflection_lm_history)
 
-    dspy.settings.configure(lm=lm_main)
+    dspy.configure(lm=lm_main)
     optimizer = dspy.GEPA(
         metric=metric,
         reflection_lm=reflection_lm,
diff --git a/tests/teleprompt/test_gepa_instruction_proposer.py b/tests/teleprompt/test_gepa_instruction_proposer.py
index 1efeb5c5ac..56af5e2070 100644
--- a/tests/teleprompt/test_gepa_instruction_proposer.py
+++ b/tests/teleprompt/test_gepa_instruction_proposer.py
@@ -107,7 +107,7 @@ def test_reflection_lm_gets_structured_images():
             {"label": "vertebrate"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     gepa = dspy.GEPA(
         metric=lambda gold, pred, trace=None, pred_name=None, pred_trace=None: 0.3,
@@ -172,7 +172,7 @@ def __call__(self, candidate, reflective_dataset, components_to_update):
             {"label": "mode"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     # Test the full flexibility: no reflection_lm provided to GEPA at all!
     # The updated GEPA core library now allows this when using custom proposers
@@ -266,7 +266,7 @@ def __call__(
             {"label": "herbivore"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     reflection_lm = DummyLM(
         [
@@ -323,7 +323,7 @@ def test_default_proposer():
             {"label": "herbivore"},
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     reflection_lm = DummyLM(
         [
diff --git a/tests/teleprompt/test_knn_fewshot.py b/tests/teleprompt/test_knn_fewshot.py
index db72c58e52..a6a451425e 100644
--- a/tests/teleprompt/test_knn_fewshot.py
+++ b/tests/teleprompt/test_knn_fewshot.py
@@ -49,7 +49,7 @@ def _test_knn_few_shot_compile(setup_knn_few_shot):
 
     # Setup DummyLM with a response for a query similar to one of the training examples
     lm = DummyLM(["Madrid", "10"])
-    dspy.settings.configure(lm=lm)  # Responses for the capital of Spain and the result of 5+5)
+    dspy.configure(lm=lm)  # Responses for the capital of Spain and the result of 5+5)
 
     knn_few_shot = setup_knn_few_shot
     trainset = knn_few_shot.KNN.trainset
diff --git a/tests/teleprompt/test_random_search.py b/tests/teleprompt/test_random_search.py
index 7da4069aaa..3041be0d33 100644
--- a/tests/teleprompt/test_random_search.py
+++ b/tests/teleprompt/test_random_search.py
@@ -29,7 +29,7 @@ def test_basic_workflow():
             "Finish[blue]",  # Expected output for both training and validation
         ]
     )
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     optimizer = BootstrapFewShotWithRandomSearch(metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1)
     trainset = [
diff --git a/tests/utils/test_saving.py b/tests/utils/test_saving.py
index 57047fd2bd..85754ace7b 100644
--- a/tests/utils/test_saving.py
+++ b/tests/utils/test_saving.py
@@ -60,7 +60,7 @@ class MySignature(dspy.Signature):
 @pytest.mark.extra
 def test_save_compiled_model(tmp_path):
     predict = dspy.Predict("question->answer")
-    dspy.settings.configure(lm=DummyLM([{"answer": "blue"}, {"answer": "white"}] * 10))
+    dspy.configure(lm=DummyLM([{"answer": "blue"}, {"answer": "white"}] * 10))
 
     trainset = [
         {"question": "What is the color of the sky?", "answer": "blue"},
diff --git a/tests/utils/test_settings.py b/tests/utils/test_settings.py
index f2bd93ec26..410309f4a1 100644
--- a/tests/utils/test_settings.py
+++ b/tests/utils/test_settings.py
@@ -160,7 +160,7 @@ def bar1():
         # In this case, foo1 (async) calls bar1 (sync), and bar1 uses the async task from foo1.
         with pytest.raises(RuntimeError) as e:
             dspy.configure(lm=dspy.LM("openai/gpt-4o"))
-        assert "dspy.settings.configure(...) can only be called from the same async" in str(e.value)
+        assert "dspy.configure(...) can only be called from the same async" in str(e.value)
 
     async def foo1():
         bar1()
@@ -170,7 +170,7 @@ async def foo2():
         # `dspy.configure` is disallowed in different async tasks from the initial one.
         with pytest.raises(RuntimeError) as e:
             dspy.configure(lm=dspy.LM("openai/gpt-4o"))
-        assert "dspy.settings.configure(...) can only be called from the same async" in str(e.value)
+        assert "dspy.configure(...) can only be called from the same async" in str(e.value)
         await asyncio.sleep(0.1)
 
     async def foo3():
diff --git a/tests/utils/test_usage_tracker.py b/tests/utils/test_usage_tracker.py
index 71c08ee651..574d1f7400 100644
--- a/tests/utils/test_usage_tracker.py
+++ b/tests/utils/test_usage_tracker.py
@@ -137,7 +137,7 @@ def test_track_usage_with_multiple_models():
 
 def test_track_usage_context_manager(lm_for_test):
     lm = dspy.LM(lm_for_test, cache=False)
-    dspy.settings.configure(lm=lm)
+    dspy.configure(lm=lm)
 
     predict = dspy.ChainOfThought("question -> answer")
     with track_usage() as tracker: