Drop prints

src/crewai/agents/agent_builder/utilities/base_output_converter.py

@@ -31,11 +31,11 @@ class OutputConverter(BaseModel, ABC):
     )
 
     @abstractmethod
-    def to_pydantic(self, current_attempt=1):
+    def to_pydantic(self, current_attempt=1) -> BaseModel:
         """Convert text to pydantic."""
         pass
 
     @abstractmethod
-    def to_json(self, current_attempt=1):
+    def to_json(self, current_attempt=1) -> dict:
         """Convert text to json."""
         pass

src/crewai/llm.py

@@ -26,9 +26,9 @@ from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
-    from litellm import Choices, get_supported_openai_params
+    from litellm import Choices
     from litellm.types.utils import ModelResponse
-    from litellm.utils import supports_response_schema
+    from litellm.utils import get_supported_openai_params, supports_response_schema
 
 
 from crewai.traces.unified_trace_controller import trace_llm_call
@@ -449,7 +449,7 @@ class LLM:
     def supports_function_calling(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)
-            return "response_format" in params
+            return params is not None and "tools" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False
@@ -457,7 +457,7 @@ class LLM:
     def supports_stop_words(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)
-            return "stop" in params
+            return params is not None and "stop" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False

src/crewai/utilities/converter.py

@@ -20,11 +20,11 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
-    def to_pydantic(self, current_attempt=1):
+    def to_pydantic(self, current_attempt=1) -> BaseModel:
         """Convert text to pydantic."""
         try:
             if self.llm.supports_function_calling():
-                return self._create_instructor().to_pydantic()
+                result = self._create_instructor().to_pydantic()
             else:
                 response = self.llm.call(
                     [
@@ -32,18 +32,40 @@ class Converter(OutputConverter):
                         {"role": "user", "content": self.text},
                     ]
                 )
-                return self.model.model_validate_json(response)
+                try:
+                    # Try to directly validate the response JSON
+                    result = self.model.model_validate_json(response)
+                except ValidationError:
+                    # If direct validation fails, attempt to extract valid JSON
+                    result = handle_partial_json(response, self.model, False, None)
+                    # Ensure result is a BaseModel instance
+                    if not isinstance(result, BaseModel):
+                        if isinstance(result, dict):
+                            result = self.model.parse_obj(result)
+                        elif isinstance(result, str):
+                            try:
+                                parsed = json.loads(result)
+                                result = self.model.parse_obj(parsed)
+                            except Exception as parse_err:
+                                raise ConverterError(
+                                    f"Failed to convert partial JSON result into Pydantic: {parse_err}"
+                                )
+                        else:
+                            raise ConverterError(
+                                "handle_partial_json returned an unexpected type."
+                            )
+            return result
         except ValidationError as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to the following validation error: {e}"
+                f"Failed to convert text into a Pydantic model due to validation error: {e}"
             )
         except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to the following error: {e}"
+                f"Failed to convert text into a Pydantic model due to error: {e}"
             )
 
     def to_json(self, current_attempt=1):
@@ -197,11 +219,15 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
     if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
         instructions += (
-            f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
+            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
+            f"The JSON must follow this schema exactly:\n```json\n{model_schema}\n```"
         )
     else:
         model_description = generate_model_description(model)
-        instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
+        instructions += (
+            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
+            f"The JSON must follow this format exactly:\n{model_description}"
+        )
     return instructions
 
 

tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml

@@ -1,869 +1,36 @@
 interactions:
 - request:
-    body: '{"model": "llama3.2:3b", "prompt": "### User:\nName: Alice Llama, Age:
-      30\n\n### System:\nProduce JSON OUTPUT ONLY! Adhere to this format {\"name\":
-      \"function_name\", \"arguments\":{\"argument_name\": \"argument_value\"}} The
-      following functions are available to you:\n{''type'': ''function'', ''function'':
-      {''name'': ''SimpleModel'', ''description'': ''Correctly extracted `SimpleModel`
-      with all the required parameters with correct types'', ''parameters'': {''properties'':
-      {''name'': {''title'': ''Name'', ''type'': ''string''}, ''age'': {''title'':
-      ''Age'', ''type'': ''integer''}}, ''required'': [''age'', ''name''], ''type'':
-      ''object''}}}\n\n\n", "options": {}, "stream": false, "format": "json"}'
+    body: '{"model": "llama3.2:3b", "prompt": "### System:\nPlease convert the following
+      text into valid JSON.\n\nOutput ONLY the valid JSON and nothing else.\n\nThe
+      JSON must follow this format exactly:\n{\n  \"name\": str,\n  \"age\": int\n}\n\n###
+      User:\nName: Alice Llama, Age: 30\n\n", "options": {"stop": []}, "stream": false}'
     headers:
-      accept:
+      Accept:
       - '*/*'
-      accept-encoding:
+      Accept-Encoding:
       - gzip, deflate
-      connection:
+      Connection:
       - keep-alive
-      content-length:
-      - '657'
-      host:
-      - localhost:11434
-      user-agent:
-      - litellm/1.57.4
+      Content-Length:
+      - '321'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - python-requests/2.32.2
     method: POST
     uri: http://localhost:11434/api/generate
   response:
-    content: '{"model":"llama3.2:3b","created_at":"2025-01-15T20:47:11.926411Z","response":"{\"name\":
-      \"SimpleModel\", \"arguments\":{\"name\": \"Alice Llama\", \"age\": 30}}","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,2724,512,678,25,30505,445,81101,11,13381,25,220,966,271,14711,744,512,1360,13677,4823,32090,27785,0,2467,6881,311,420,3645,5324,609,794,330,1723,1292,498,330,16774,23118,14819,1292,794,330,14819,3220,32075,578,2768,5865,527,2561,311,499,512,13922,1337,1232,364,1723,518,364,1723,1232,5473,609,1232,364,16778,1747,518,364,4789,1232,364,34192,398,28532,1595,16778,1747,63,449,682,279,2631,5137,449,4495,4595,518,364,14105,1232,5473,13495,1232,5473,609,1232,5473,2150,1232,364,678,518,364,1337,1232,364,928,25762,364,425,1232,5473,2150,1232,364,17166,518,364,1337,1232,364,11924,8439,2186,364,6413,1232,2570,425,518,364,609,4181,364,1337,1232,364,1735,23742,3818,128009,128006,78191,128007,271,5018,609,794,330,16778,1747,498,330,16774,23118,609,794,330,62786,445,81101,498,330,425,794,220,966,3500],"total_duration":3374470708,"load_duration":1075750500,"prompt_eval_count":167,"prompt_eval_duration":1871000000,"eval_count":24,"eval_duration":426000000}'
+    body:
+      string: '{"model":"llama3.2:3b","created_at":"2025-02-18T21:52:20.737635Z","response":"{\"name\":
+        \"Alice Llama\", \"age\": 30}","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,5618,5625,279,2768,1495,1139,2764,4823,382,5207,27785,279,2764,4823,323,4400,775,382,791,4823,2011,1833,420,3645,7041,512,517,220,330,609,794,610,345,220,330,425,794,528,198,633,14711,2724,512,678,25,30505,445,81101,11,13381,25,220,966,271,128009,128006,78191,128007,271,5018,609,794,330,62786,445,81101,498,330,425,794,220,966,92],"total_duration":2438288750,"load_duration":819026959,"prompt_eval_count":82,"prompt_eval_duration":1368000000,"eval_count":15,"eval_duration":249000000}'
     headers:
       Content-Length:
-      - '1263'
+      - '761'
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Wed, 15 Jan 2025 20:47:12 GMT
-    http_version: HTTP/1.1
-    status_code: 200
-- request:
-    body: '{"name": "llama3.2:3b"}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '23'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-      user-agent:
-      - litellm/1.57.4
-    method: POST
-    uri: http://localhost:11434/api/show
-  response:
-    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
-      Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms
-      and conditions for use, reproduction, distribution \\nand modification of the
-      Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications,
-      manuals and documentation accompanying Llama 3.2\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D
-      or \u201Cyou\u201D means you, or your employer or any other person or entity
-      (if you are \\nentering into this Agreement on such person or entity\u2019s
-      behalf), of the age required under\\napplicable laws, rules or regulations to
-      provide legal consent and that has legal authority\\nto bind your employer or
-      such other person or entity if you are entering in this Agreement\\non their
-      behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models
-      and software and algorithms, including\\nmachine-learning model code, trained
-      model weights, inference-enabling code, training-enabling code,\\nfine-tuning
-      enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama
-      Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation
-      (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
-      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
-      or, \\nif you are an entity, your principal place of business is in the EEA
-      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
-      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
-      or distributing any portion or element of the Llama Materials,\\nyou agree to
-      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
-      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
-      and royalty-free limited license under Meta\u2019s intellectual property or
-      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
-      distribute, copy, create derivative works \\nof, and make modifications to the
-      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
-      you distribute or make available the Llama Materials (or any derivative works
-      thereof), \\nor a product or service (including another AI model) that contains
-      any of them, you shall (A) provide\\na copy of this Agreement with any such
-      Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
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-      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
-      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
-      or made available, you shall also include \u201CLlama\u201D\\nat the beginning
-      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
-      or any derivative works thereof, from a Licensee as part\\nof an integrated
-      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
-      \       iii. You must retain in all copies of the Llama Materials that you distribute
-      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
-      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
-      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
-      \       iv. Your use of the Llama Materials must comply with applicable laws
-      and regulations\\n(including trade compliance laws and regulations) and adhere
-      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
-      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
-      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
-      monthly active users\\nof the products or services made available by or for
-      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
-      active users in the preceding calendar month, you must request \\na license
-      from Meta, which Meta may grant to you in its sole discretion, and you are not
-      authorized to\\nexercise any of the rights under this Agreement unless or until
-      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
-      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
-      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
-      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
-      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
-      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
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-      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
-      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
-      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
-      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
-      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
-      \   a. No trademark licenses are granted under this Agreement, and in connection
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-      owned by or associated with the other or any of its affiliates, \\nexcept as
-      required for reasonable and customary use in describing and redistributing the
-      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
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-      \   claim is filed or instituted. You will indemnify and hold harmless Meta
-      from and against any claim by any third\\n    party arising out of or related
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-      to any multimodal models included in Llama 3.2, the rights granted under Section
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-      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
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-      \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
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-      if .Tools }}When you receive a tool call response, use the output to format
-      an answer to the orginal user question.\\n\\nYou are a helpful assistant with
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-      - Wed, 15 Jan 2025 20:47:12 GMT
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+      - Tue, 18 Feb 2025 21:52:21 GMT
+    status:
+      code: 200
+      message: OK
 version: 1

tests/utilities/test_converter.py

@@ -1,4 +1,5 @@
 import json
+import os
 from typing import Dict, List, Optional
 from unittest.mock import MagicMock, Mock, patch
 
@@ -220,10 +221,13 @@ def test_get_conversion_instructions_gpt():
         supports_function_calling.return_value = True
         instructions = get_conversion_instructions(SimpleModel, llm)
         model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        assert (
-            instructions
-            == f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
+        expected_instructions = (
+            "Please convert the following text into valid JSON.\n\n"
+            "Output ONLY the valid JSON and nothing else.\n\n"
+            "The JSON must follow this schema exactly:\n```json\n"
+            f"{model_schema}\n```"
         )
+        assert instructions == expected_instructions
 
 
 def test_get_conversion_instructions_non_gpt():
@@ -346,12 +350,17 @@ def test_convert_with_instructions():
     assert output.age == 30
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
+# Skip tests that call external APIs when running in CI/CD
+skip_external_api = pytest.mark.skipif(
+    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
+)
+
+
+@skip_external_api
+@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
 def test_converter_with_llama3_2_model():
     llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
-
     sample_text = "Name: Alice Llama, Age: 30"
-
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
         llm=llm,
@@ -359,19 +368,17 @@ def test_converter_with_llama3_2_model():
         model=SimpleModel,
         instructions=instructions,
     )
-
     output = converter.to_pydantic()
-
     assert isinstance(output, SimpleModel)
     assert output.name == "Alice Llama"
     assert output.age == 30
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
+@skip_external_api
+@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
 def test_converter_with_llama3_1_model():
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     sample_text = "Name: Alice Llama, Age: 30"
-
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
         llm=llm,
@@ -379,14 +386,19 @@ def test_converter_with_llama3_1_model():
         model=SimpleModel,
         instructions=instructions,
     )
-
     output = converter.to_pydantic()
-
     assert isinstance(output, SimpleModel)
     assert output.name == "Alice Llama"
     assert output.age == 30
 
 
+# Skip tests that call external APIs when running in CI/CD
+skip_external_api = pytest.mark.skipif(
+    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
+)
+
+
+@skip_external_api
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_nested_model():
     llm = LLM(model="gpt-4o-mini")
@@ -563,7 +575,7 @@ def test_converter_with_ambiguous_input():
     with pytest.raises(ConverterError) as exc_info:
         output = converter.to_pydantic()
 
-    assert "validation error" in str(exc_info.value).lower()
+    assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
 
 
 # Tests for function calling support