fix: Add empty thought since its required

fixing LiteLLM callback replacement bug

docs/concepts/llms.mdx

@@ -25,7 +25,100 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. Custom LLM Objects
+### 2. Updating YAML files
+
+You can update the `agents.yml` file to refer to the LLM you want to use:
+
+```yaml Code
+researcher:
+    role: Research Specialist
+    goal: Conduct comprehensive research and analysis to gather relevant information,
+        synthesize findings, and produce well-documented insights.
+    backstory: A dedicated research professional with years of experience in academic
+        investigation, literature review, and data analysis, known for thorough and
+        methodical approaches to complex research questions.
+    verbose: true
+    llm: openai/gpt-4o
+    # llm: azure/gpt-4o-mini
+    # llm: gemini/gemini-pro
+    # llm: anthropic/claude-3-5-sonnet-20240620
+    # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+    # llm: mistral/mistral-large-latest
+    # llm: ollama/llama3:70b
+    # llm: groq/llama-3.2-90b-vision-preview
+    # llm: watsonx/meta-llama/llama-3-1-70b-instruct
+    # ...
+```
+
+Keep in mind that you will need to set certain ENV vars depending on the model you are
+using to account for the credentials or set a custom LLM object like described below.
+Here are some of the required ENV vars for some of the LLM integrations:
+
+<AccordionGroup>
+    <Accordion title="OpenAI">
+    ```python Code
+    OPENAI_API_KEY=<your-api-key>
+    OPENAI_API_BASE=<optional-custom-base-url>
+    OPENAI_MODEL_NAME=<openai-model-name>
+    OPENAI_ORGANIZATION=<your-org-id>       # OPTIONAL
+    OPENAI_API_BASE=<openaiai-api-base>     # OPTIONAL
+    ```
+    </Accordion>
+
+    <Accordion title="Anthropic">
+    ```python Code
+    ANTHROPIC_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Google">
+    ```python Code
+    GEMINI_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Azure">
+    ```python Code
+    AZURE_API_KEY=<your-api-key>                    # "my-azure-api-key"
+    AZURE_API_BASE=<your-resource-url>              # "https://example-endpoint.openai.azure.com"
+    AZURE_API_VERSION=<api-version>                 # "2023-05-15"
+    AZURE_AD_TOKEN=<your-azure-ad-token>            # Optional
+    AZURE_API_TYPE=<your-azure-api-type>            # Optional
+    ```
+    </Accordion>
+
+    <Accordion title="AWS Bedrock">
+    ```python Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+    </Accordion>
+
+    <Accordion title="Mistral">
+    ```python Code
+    MISTRAL_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Groq">
+    ```python Code
+    GROQ_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="IBM watsonx.ai">
+    ```python Code
+    WATSONX_URL=<your-url>                          # (required) Base URL of your WatsonX instance
+    WATSONX_APIKEY=<your-apikey>                    # (required) IBM cloud API key
+    WATSONX_TOKEN=<your-token>                      # (required) IAM auth token (alternative to APIKEY)
+    WATSONX_PROJECT_ID=<your-project-id>            # (optional) Project ID of your WatsonX instance
+    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>     # (optional) ID of deployment space for deployed models
+    ```
+    </Accordion>
+</AccordionGroup>
+
+### 3. Custom LLM Objects
 
 Pass a custom LLM implementation or object from another library.
 
@@ -102,7 +195,7 @@ When configuring an LLM for your agent, you have access to a wide range of param
 
 These are examples of how to configure LLMs for your agent.
 
-<AccordionGroup>    
+<AccordionGroup>
     <Accordion title="OpenAI">
 
     ```python Code
@@ -133,10 +226,10 @@ These are examples of how to configure LLMs for your agent.
         model="cerebras/llama-3.1-70b",
         api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...) 
+    agent = Agent(llm=llm, ...)
     ```
     </Accordion>
- 
+
     <Accordion title="Ollama (Local LLMs)">
 
     CrewAI supports using Ollama for running open-source models locally:
@@ -150,7 +243,7 @@ These are examples of how to configure LLMs for your agent.
 
     agent = Agent(
         llm=LLM(
-            model="ollama/llama3.1", 
+            model="ollama/llama3.1",
             base_url="http://localhost:11434"
         ),
         ...
@@ -164,7 +257,7 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="groq/llama3-8b-8192", 
+        model="groq/llama3-8b-8192",
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -189,7 +282,7 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",  
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -224,6 +317,29 @@ These are examples of how to configure LLMs for your agent.
     </Accordion>
 
     <Accordion title="IBM watsonx.ai">
+    You can use IBM Watson by seeting the following ENV vars:
+
+    ```python Code
+    WATSONX_URL=<your-url>
+    WATSONX_APIKEY=<your-apikey>
+    WATSONX_PROJECT_ID=<your-project-id>
+    ```
+
+    You can then define your agents llms by updating the `agents.yml`
+
+    ```yaml Code
+    researcher:
+        role: Research Specialist
+        goal: Conduct comprehensive research and analysis to gather relevant information,
+            synthesize findings, and produce well-documented insights.
+        backstory: A dedicated research professional with years of experience in academic
+            investigation, literature review, and data analysis, known for thorough and
+            methodical approaches to complex research questions.
+        verbose: true
+        llm: watsonx/meta-llama/llama-3-1-70b-instruct
+    ```
+
+    You can also set up agents more dynamically as a base level LLM instance, like bellow:
 
     ```python Code
     from crewai import LLM
@@ -247,7 +363,7 @@ These are examples of how to configure LLMs for your agent.
         api_key="your-api-key-here",
         base_url="your_api_endpoint"
     )
-    agent = Agent(llm=llm, ...) 
+    agent = Agent(llm=llm, ...)
     ```
     </Accordion>
 </AccordionGroup>

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.76.9"
+version = "0.79.4"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
@@ -16,7 +16,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.13.4",
+    "crewai-tools>=0.14.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -37,7 +37,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.13.4"]
+tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
 
 [tool.uv]
@@ -52,7 +52,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.13.4",
+    "crewai-tools>=0.14.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/__init__.py

@@ -14,5 +14,5 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.76.9"
+__version__ = "0.79.4"
 __all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

src/crewai/agent.py

@@ -123,6 +123,11 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
+        unnacepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
@@ -146,39 +151,44 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
+            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+
             # Iterate over all environment variables to find matching API keys or use defaults
             for provider, env_vars in ENV_VARS.items():
-                for env_var in env_vars:
-                    # Check if the environment variable is set
-                    if "key_name" in env_var:
-                        env_value = os.environ.get(env_var["key_name"])
-                        if env_value:
-                            # Map key names containing "API_KEY" to "api_key"
-                            key_name = (
-                                "api_key"
-                                if "API_KEY" in env_var["key_name"]
-                                else env_var["key_name"]
-                            )
-                            # Map key names containing "API_BASE" to "api_base"
-                            key_name = (
-                                "api_base"
-                                if "API_BASE" in env_var["key_name"]
-                                else key_name
-                            )
-                            # Map key names containing "API_VERSION" to "api_version"
-                            key_name = (
-                                "api_version"
-                                if "API_VERSION" in env_var["key_name"]
-                                else key_name
-                            )
-                            llm_params[key_name] = env_value
-                    # Check for default values if the environment variable is not set
-                    elif env_var.get("default", False):
-                        for key, value in env_var.items():
-                            if key not in ["prompt", "key_name", "default"]:
-                                # Only add default if the key is already set in os.environ
-                                if key in os.environ:
-                                    llm_params[key] = value
+                if provider == set_provider:
+                    for env_var in env_vars:
+                        if env_var["key_name"] in unnacepted_attributes:
+                            continue
+                        # Check if the environment variable is set
+                        if "key_name" in env_var:
+                            env_value = os.environ.get(env_var["key_name"])
+                            if env_value:
+                                # Map key names containing "API_KEY" to "api_key"
+                                key_name = (
+                                    "api_key"
+                                    if "API_KEY" in env_var["key_name"]
+                                    else env_var["key_name"]
+                                )
+                                # Map key names containing "API_BASE" to "api_base"
+                                key_name = (
+                                    "api_base"
+                                    if "API_BASE" in env_var["key_name"]
+                                    else key_name
+                                )
+                                # Map key names containing "API_VERSION" to "api_version"
+                                key_name = (
+                                    "api_version"
+                                    if "API_VERSION" in env_var["key_name"]
+                                    else key_name
+                                )
+                                llm_params[key_name] = env_value
+                        # Check for default values if the environment variable is not set
+                        elif env_var.get("default", False):
+                            for key, value in env_var.items():
+                                if key not in ["prompt", "key_name", "default"]:
+                                    # Only add default if the key is already set in os.environ
+                                    if key in os.environ:
+                                        llm_params[key] = value
 
             self.llm = LLM(**llm_params)
         else:

src/crewai/agents/crew_agent_executor.py

@@ -145,25 +145,26 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         formatted_answer.result = action_result
                         self._show_logs(formatted_answer)
 
-                        if self.step_callback:
-                            self.step_callback(formatted_answer)
+                    if self.step_callback:
+                        self.step_callback(formatted_answer)
 
-                        if self._should_force_answer():
-                            if self.have_forced_answer:
-                                return AgentFinish(
-                                    output=self._i18n.errors(
-                                        "force_final_answer_error"
-                                    ).format(formatted_answer.text),
-                                    text=formatted_answer.text,
-                                )
-                            else:
-                                formatted_answer.text += (
-                                    f'\n{self._i18n.errors("force_final_answer")}'
-                                )
-                                self.have_forced_answer = True
-                        self.messages.append(
-                            self._format_msg(formatted_answer.text, role="assistant")
-                        )
+                    if self._should_force_answer():
+                        if self.have_forced_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=self._i18n.errors(
+                                    "force_final_answer_error"
+                                ).format(formatted_answer.text),
+                                text=formatted_answer.text,
+                            )
+                        else:
+                            formatted_answer.text += (
+                                f'\n{self._i18n.errors("force_final_answer")}'
+                            )
+                            self.have_forced_answer = True
+                    self.messages.append(
+                        self._format_msg(formatted_answer.text, role="assistant")
+                    )
 
         except OutputParserException as e:
             self.messages.append({"role": "user", "content": e.error})
@@ -332,9 +333,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration][
-                        "improved_output"
-                    ] = result.output
+                    training_data[agent_id][train_iteration]["improved_output"] = (
+                        result.output
+                    )
                     training_handler.save(training_data)
                 else:
                     self._logger.log(
@@ -385,4 +386,5 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return CrewAgentParser(agent=self.agent).parse(answer)
 
     def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
+        prompt = prompt.rstrip()
         return {"role": role, "content": prompt}

src/crewai/cli/run_crew.py

@@ -24,7 +24,6 @@ def run_crew() -> None:
             f"Please run `crewai update` to update your pyproject.toml to use uv.",
             fg="red",
         )
-        print()
 
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.9,<1.0.0"
+    "crewai[tools]>=0.79.4,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.9,<1.0.0",
+    "crewai[tools]>=0.79.4,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.76.9,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.79.4,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

src/crewai/cli/templates/pipeline_router/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = ["Your Name <you@example.com>"]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.9,<1.0.0"
+    "crewai[tools]>=0.79.4,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.9"
+    "crewai[tools]>=0.79.4"
 ]
 

src/crewai/llm.py

@@ -118,12 +118,12 @@ class LLM:
 
         litellm.drop_params = True
         litellm.set_verbose = False
-        litellm.callbacks = callbacks
+        self.set_callbacks(callbacks)
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
-                litellm.callbacks = callbacks
+                self.set_callbacks(callbacks)
 
             try:
                 params = {
@@ -181,3 +181,15 @@ class LLM:
     def get_context_window_size(self) -> int:
         # Only using 75% of the context window size to avoid cutting the message in the middle
         return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
+
+    def set_callbacks(self, callbacks: List[Any]):
+        callback_types = [type(callback) for callback in callbacks]
+        for callback in litellm.success_callback[:]:
+            if type(callback) in callback_types:
+                litellm.success_callback.remove(callback)
+
+        for callback in litellm._async_success_callback[:]:
+            if type(callback) in callback_types:
+                litellm._async_success_callback.remove(callback)
+
+        litellm.callbacks = callbacks

src/crewai/memory/contextual/contextual_memory.py

@@ -34,7 +34,6 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in stm_results]
         )
-        print("formatted_results stm", formatted_results)
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
     def _fetch_ltm_context(self, task) -> Optional[str]:
@@ -54,8 +53,6 @@ class ContextualMemory:
         formatted_results = list(dict.fromkeys(formatted_results))
         formatted_results = "\n".join([f"- {result}" for result in formatted_results])  # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
 
-        print("formatted_results ltm", formatted_results)
-
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
     def _fetch_entity_context(self, query) -> str:
@@ -67,5 +64,4 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
-        print("formatted_results em", formatted_results)
         return f"Entities:\n{formatted_results}" if em_results else ""

tests/cassettes/test_llm_callback_replacement.yaml

@@ -0,0 +1,205 @@
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+      host:
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+      user-agent:
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+      x-stainless-arch:
+      - x64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - Linux
+      x-stainless-package-version:
+      - 1.52.1
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
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+    uri: https://api.openai.com/v1/chat/completions
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tests/crew_test.py

@@ -1280,10 +1280,10 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
     assert result.raw == "Howdy!"
 
     assert result.token_usage == UsageMetrics(
-        total_tokens=2626,
-        prompt_tokens=2482,
-        completion_tokens=144,
-        successful_requests=5,
+        total_tokens=1673,
+        prompt_tokens=1562,
+        completion_tokens=111,
+        successful_requests=3,
     )
 
 

tests/llm_test.py

@@ -0,0 +1,30 @@
+import pytest
+
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
+from crewai.llm import LLM
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_callback_replacement():
+    llm = LLM(model="gpt-4o-mini")
+
+    calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
+    calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
+
+    llm.call(
+        messages=[{"role": "user", "content": "Hello, world!"}],
+        callbacks=[calc_handler_1],
+    )
+    usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
+
+    llm.call(
+        messages=[{"role": "user", "content": "Hello, world from another agent!"}],
+        callbacks=[calc_handler_2],
+    )
+    usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
+
+    # The first handler should not have been updated
+    assert usage_metrics_1.successful_requests == 1
+    assert usage_metrics_2.successful_requests == 1
+    assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()

uv.lock

@@ -604,7 +604,7 @@ wheels = [
 
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 name = "crewai"
-version = "0.76.9"
+version = "0.79.4"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -665,8 +665,8 @@ requires-dist = [
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     { name = "chromadb", specifier = ">=0.4.24" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", specifier = ">=0.13.4" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.13.4" },
+    { name = "crewai-tools", specifier = ">=0.14.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
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@@ -688,7 +688,7 @@ requires-dist = [
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 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.13.4" },
+    { name = "crewai-tools", specifier = ">=0.14.0" },
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@@ -707,7 +707,7 @@ dev = [
 
 [[package]]
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-version = "0.13.4"
+version = "0.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 [[package]]