Fix result_as_answer handling in ToolUsage class

Co-Authored-By: Joe Moura <joao@crewai.com>

docs/concepts/cli.mdx

@@ -179,78 +179,7 @@ def crew(self) -> Crew:
 ```
 </Note>
 
-### 10. Deploy
-
-Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
-
-- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
-    ```shell Terminal
-    crewai signup
-    ```
-    If you already have an account, you can login with:
-    ```shell Terminal
-    crewai login
-    ```
-
-- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
-    ```shell Terminal
-    crewai deploy create
-    ```
-    - Reads your local project configuration.
-    - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
-    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
-
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
-    ```shell Terminal
-    crewai deploy push
-    ``` 
-    - Initiates the deployment process on the CrewAI Enterprise platform.
-    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
-
-- **Deployment Status**: You can check the status of your deployment with:
-    ```shell Terminal
-    crewai deploy status
-    ```
-    This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
-
-- **Deployment Logs**: You can check the logs of your deployment with:
-    ```shell Terminal
-    crewai deploy logs
-    ```
-    This streams the deployment logs to your terminal.
-
-- **List deployments**: You can list all your deployments with:
-    ```shell Terminal
-    crewai deploy list
-    ```
-    This lists all your deployments.
-
-- **Delete a deployment**: You can delete a deployment with:
-    ```shell Terminal
-    crewai deploy remove
-    ```
-    This deletes the deployment from the CrewAI Enterprise platform.
-
-- **Help Command**: You can get help with the CLI with:
-    ```shell Terminal
-    crewai deploy --help
-    ```
-    This shows the help message for the CrewAI Deploy CLI.
-
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
-
-### 11. API Keys
+### 10. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
 

docs/docs.json

@@ -8,9 +8,6 @@
     "dark": "#C94C3C"
   },
   "favicon": "favicon.svg",
-  "contextual": {
-    "options": ["copy", "view", "chatgpt", "claude"]
-  },
   "navigation": {
     "tabs": [
       {
@@ -247,12 +244,7 @@
     "prompt": "Search CrewAI docs"
   },
   "seo": {
-    "indexing": "all"
-  },
-  "errors": {
-    "404": {
-      "redirect": true
-    }
+    "indexing": "navigable"
   },
   "footer": {
     "socials": {

docs/tools/codeinterpretertool.mdx

@@ -8,29 +8,11 @@ icon: code-simple
 
 ## Description
 
-The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
+The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
 
-There are several ways to use this tool:
-
-### Docker Container (Recommended)
-
-This is the primary option. The code runs in a secure, isolated Docker container, ensuring safety regardless of its content.
-Make sure Docker is installed and running on your system. If you don’t have it, you can install it from [here](https://docs.docker.com/get-docker/).
-
-### Sandbox environment
-
-If Docker is unavailable — either not installed or not accessible for any reason — the code will be executed in a restricted Python environment - called sandbox.
-This environment is very limited, with strict restrictions on many modules and built-in functions.
-
-### Unsafe Execution
-
-**NOT RECOMMENDED FOR PRODUCTION** 
-This mode allows execution of any Python code, including dangerous calls to `sys, os..` and similar modules. [Check out](/tools/codeinterpretertool#enabling-unsafe-mode) how to enable this mode
-
-## Logging
-
-The `CodeInterpreterTool` logs the selected execution strategy to STDOUT
+## Requirements
 
+- Docker must be installed and running on your system. If you don't have it, you can install it from [here](https://docs.docker.com/get-docker/).
 
 ## Installation
 
@@ -92,32 +74,18 @@ programmer_agent = Agent(
 )
 ```
 
-### Enabling `unsafe_mode`
-
-```python Code
-from crewai_tools import CodeInterpreterTool
-
-code = """
-import os
-os.system("ls -la")
-"""
-
-CodeInterpreterTool(unsafe_mode=True).run(code=code)
-```
-
 ## Parameters
 
 The `CodeInterpreterTool` accepts the following parameters during initialization:
 
 - **user_dockerfile_path**: Optional. Path to a custom Dockerfile to use for the code interpreter container.
 - **user_docker_base_url**: Optional. URL to the Docker daemon to use for running the container.
-- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container or sandbox. Default is `False`. Use with caution!
-- **default_image_tag**: Optional. Default Docker image tag. Default is `code-interpreter:latest`
+- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is `False`. Use with caution!
 
 When using the tool with an agent, the agent will need to provide:
 
 - **code**: Required. The Python 3 code to execute.
-- **libraries_used**: Optional. A list of libraries used in the code that need to be installed. Default is `[]`
+- **libraries_used**: Required. A list of libraries used in the code that need to be installed.
 
 ## Agent Integration Example
 
@@ -184,7 +152,7 @@ class CodeInterpreterTool(BaseTool):
         if self.unsafe_mode:
             return self.run_code_unsafe(code, libraries_used)
         else:
-            return self.run_code_safety(code, libraries_used)
+            return self.run_code_in_docker(code, libraries_used)
 ```
 
 The tool performs the following steps:
@@ -200,9 +168,8 @@ The tool performs the following steps:
 By default, the `CodeInterpreterTool` runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:
 
 1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
-2. If the Docker container is unavailable and the code needs to run safely, it will be executed in a sandbox environment. For security reasons, installing arbitrary libraries is not allowed
-3. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
-4. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
+2. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
+3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
 
 ## Conclusion
 

src/crewai/cli/constants.py

@@ -122,16 +122,7 @@ PROVIDERS = [
 ]
 
 MODELS = {
-    "openai": [
-        "gpt-4",
-        "gpt-4.1",
-        "gpt-4.1-mini-2025-04-14",
-        "gpt-4.1-nano-2025-04-14",
-        "gpt-4o",
-        "gpt-4o-mini",
-        "o1-mini",
-        "o1-preview",
-    ],
+    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
     "anthropic": [
         "claude-3-5-sonnet-20240620",
         "claude-3-sonnet-20240229",
@@ -141,17 +132,8 @@ MODELS = {
     "gemini": [
         "gemini/gemini-1.5-flash",
         "gemini/gemini-1.5-pro",
-        "gemini/gemini-2.0-flash-lite-001",
-        "gemini/gemini-2.0-flash-001",
-        "gemini/gemini-2.0-flash-thinking-exp-01-21",
-        "gemini/gemini-2.5-flash-preview-04-17",
-        "gemini/gemini-2.5-pro-exp-03-25",
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
-        "gemini/gemma-3-1b-it",
-        "gemini/gemma-3-4b-it",
-        "gemini/gemma-3-12b-it",
-        "gemini/gemma-3-27b-it",
     ],
     "nvidia_nim": [
         "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",

src/crewai/knowledge/storage/knowledge_storage.py

@@ -4,15 +4,13 @@ import io
 import logging
 import os
 import shutil
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
+from typing import Any, Dict, List, Optional, Union
 
-# Type checking imports that don't cause runtime imports
-if TYPE_CHECKING:
-    import chromadb
-    import chromadb.errors
-    from chromadb.api import ClientAPI
-    from chromadb.api.types import OneOrMany
-    from chromadb.config import Settings
+import chromadb
+import chromadb.errors
+from chromadb.api import ClientAPI
+from chromadb.api.types import OneOrMany
+from chromadb.config import Settings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.utilities import EmbeddingConfigurator
@@ -45,9 +43,9 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     search efficiency.
     """
 
-    collection: Optional[Any] = None
+    collection: Optional[chromadb.Collection] = None
     collection_name: Optional[str] = "knowledge"
-    app: Optional[Any] = None
+    app: Optional[ClientAPI] = None
 
     def __init__(
         self,
@@ -86,10 +84,6 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 raise Exception("Collection not initialized")
 
     def initialize_knowledge_storage(self):
-        # Import chromadb here to avoid importing at module level
-        import chromadb
-        from chromadb.config import Settings
-        
         base_path = os.path.join(db_storage_path(), "knowledge")
         chroma_client = chromadb.PersistentClient(
             path=base_path,
@@ -115,10 +109,6 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             raise Exception("Failed to create or get collection")
 
     def reset(self):
-        # Import chromadb here to avoid importing at module level
-        import chromadb
-        from chromadb.config import Settings
-        
         base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
         if not self.app:
             self.app = chromadb.PersistentClient(
@@ -136,11 +126,6 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         documents: List[str],
         metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
-        # Import chromadb here to avoid importing at module level
-        import chromadb
-        import chromadb.errors
-        from chromadb.api.types import OneOrMany
-        
         if not self.collection:
             raise Exception("Collection not initialized")
 
@@ -171,7 +156,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 filtered_ids.append(doc_id)
 
             # If we have no metadata at all, set it to None
-            final_metadata: Optional[OneOrMany['chromadb.Metadata']] = (
+            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
                 None if all(m is None for m in filtered_metadata) else filtered_metadata
             )
 

src/crewai/llm.py

@@ -81,26 +81,14 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gpt-4o": 128000,
     "gpt-4o-mini": 128000,
     "gpt-4-turbo": 128000,
-    "gpt-4.1": 1047576,  # Based on official docs
-    "gpt-4.1-mini-2025-04-14": 1047576,
-    "gpt-4.1-nano-2025-04-14": 1047576,
     "o1-preview": 128000,
     "o1-mini": 128000,
     "o3-mini": 200000,  # Based on official o3-mini specifications
     # gemini
     "gemini-2.0-flash": 1048576,
-    "gemini-2.0-flash-thinking-exp-01-21": 32768,
-    "gemini-2.0-flash-lite-001": 1048576,
-    "gemini-2.0-flash-001": 1048576,
-    "gemini-2.5-flash-preview-04-17": 1048576,
-    "gemini-2.5-pro-exp-03-25": 1048576,
     "gemini-1.5-pro": 2097152,
     "gemini-1.5-flash": 1048576,
     "gemini-1.5-flash-8b": 1048576,
-    "gemini/gemma-3-1b-it": 32000,
-    "gemini/gemma-3-4b-it": 128000,
-    "gemini/gemma-3-12b-it": 128000,
-    "gemini/gemma-3-27b-it": 128000,
     # deepseek
     "deepseek-chat": 128000,
     # groq

src/crewai/memory/storage/rag_storage.py

@@ -4,18 +4,13 @@ import logging
 import os
 import shutil
 import uuid
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional
 
-# Type checking imports that don't cause runtime imports
-if TYPE_CHECKING:
-    import chromadb
-    from chromadb.api import ClientAPI
-    from chromadb.config import Settings
+from chromadb.api import ClientAPI
 
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.chromadb import sanitize_collection_name
-from crewai.utilities.logger import Logger
+from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
 
 
@@ -42,45 +37,76 @@ class RAGStorage(BaseRAGStorage):
     search efficiency.
     """
 
-    collection: Optional[Any] = None
-    collection_name: Optional[str] = "memory"
-    app: Optional[Any] = None
+    app: ClientAPI | None = None
 
     def __init__(
-        self,
-        type: str = "memory",
-        allow_reset: bool = True,
-        embedder_config: Optional[Dict[str, Any]] = None,
-        crew: Any = None,
-        collection_name: Optional[str] = None,
+        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
     ):
         super().__init__(type, allow_reset, embedder_config, crew)
-        self.collection_name = collection_name or type
-        self._set_embedder_config(embedder_config)
+        agents = crew.agents if crew else []
+        agents = [self._sanitize_role(agent.role) for agent in agents]
+        agents = "_".join(agents)
+        self.agents = agents
+        self.storage_file_name = self._build_storage_file_name(type, agents)
 
-    def save(
-        self,
-        value: Any,
-        metadata: Dict[str, Any],
-    ) -> None:
-        with suppress_logging():
-            if not self.collection:
-                self._initialize_app()
+        self.type = type
 
-            if isinstance(value, list):
-                documents = value
-                metadatas = [metadata] * len(value) if metadata else None
-                ids = [str(uuid.uuid4()) for _ in range(len(documents))]
-            else:
-                documents = [value]
-                metadatas = [metadata] if metadata else None
-                ids = [str(uuid.uuid4())]
+        self.allow_reset = allow_reset
+        self.path = path
+        self._initialize_app()
 
-            self.collection.add(
-                documents=documents,
-                metadatas=metadatas,
-                ids=ids,
+    def _set_embedder_config(self):
+        configurator = EmbeddingConfigurator()
+        self.embedder_config = configurator.configure_embedder(self.embedder_config)
+
+    def _initialize_app(self):
+        import chromadb
+        from chromadb.config import Settings
+
+        self._set_embedder_config()
+        chroma_client = chromadb.PersistentClient(
+            path=self.path if self.path else self.storage_file_name,
+            settings=Settings(allow_reset=self.allow_reset),
+        )
+
+        self.app = chroma_client
+
+        try:
+            self.collection = self.app.get_collection(
+                name=self.type, embedding_function=self.embedder_config
             )
+        except Exception:
+            self.collection = self.app.create_collection(
+                name=self.type, embedding_function=self.embedder_config
+            )
+
+    def _sanitize_role(self, role: str) -> str:
+        """
+        Sanitizes agent roles to ensure valid directory names.
+        """
+        return role.replace("\n", "").replace(" ", "_").replace("/", "_")
+
+    def _build_storage_file_name(self, type: str, file_name: str) -> str:
+        """
+        Ensures file name does not exceed max allowed by OS
+        """
+        base_path = f"{db_storage_path()}/{type}"
+
+        if len(file_name) > MAX_FILE_NAME_LENGTH:
+            logging.warning(
+                f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters."
+            )
+            file_name = file_name[:MAX_FILE_NAME_LENGTH]
+
+        return f"{base_path}/{file_name}"
+
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        if not hasattr(self, "app") or not hasattr(self, "collection"):
+            self._initialize_app()
+        try:
+            self._generate_embedding(value, metadata)
+        except Exception as e:
+            logging.error(f"Error during {self.type} save: {str(e)}")
 
     def search(
         self,
@@ -89,96 +115,54 @@ class RAGStorage(BaseRAGStorage):
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
     ) -> List[Any]:
-        with suppress_logging():
-            if not hasattr(self, "collection") or not self.collection:
-                self._initialize_app()
+        if not hasattr(self, "app"):
+            self._initialize_app()
 
-            if isinstance(query, str):
-                query = [query]
+        try:
+            with suppress_logging():
+                response = self.collection.query(query_texts=query, n_results=limit)
 
-            fetched = self.collection.query(
-                query_texts=query,
-                n_results=limit,
-                where=filter,
-            )
             results = []
-            for i in range(len(fetched["ids"][0])):  # type: ignore
+            for i in range(len(response["ids"][0])):
                 result = {
-                    "id": fetched["ids"][0][i],  # type: ignore
-                    "metadata": fetched["metadatas"][0][i],  # type: ignore
-                    "context": fetched["documents"][0][i],  # type: ignore
-                    "score": fetched["distances"][0][i],  # type: ignore
+                    "id": response["ids"][0][i],
+                    "metadata": response["metadatas"][0][i],
+                    "context": response["documents"][0][i],
+                    "score": response["distances"][0][i],
                 }
                 if result["score"] >= score_threshold:
                     results.append(result)
+
             return results
+        except Exception as e:
+            logging.error(f"Error during {self.type} search: {str(e)}")
+            return []
 
-    def _initialize_app(self):
-        # Import chromadb here to avoid importing at module level
-        import chromadb
-        from chromadb.config import Settings
-        
-        base_path = os.path.join(db_storage_path(), "memory")
-        chroma_client = chromadb.PersistentClient(
-            path=base_path,
-            settings=Settings(allow_reset=self.allow_reset),
-        )
-
-        self.app = chroma_client
-
-        try:
-            collection_name = (
-                f"memory_{self.collection_name}"
-                if self.collection_name
-                else "memory"
-            )
-            if self.app:
-                self.collection = self.app.get_or_create_collection(
-                    name=sanitize_collection_name(collection_name),
-                    embedding_function=self.embedder,
-                )
-            else:
-                raise Exception("Vector Database Client not initialized")
-        except Exception:
-            raise Exception("Failed to create or get collection")
-
-    def initialize_rag_storage(self):
-        self._initialize_app()
-
-    def reset(self) -> None:
-        # Import chromadb here to avoid importing at module level
-        import chromadb
-        from chromadb.config import Settings
-        
-        base_path = os.path.join(db_storage_path(), "memory")
-        if not self.app:
-            self.app = chromadb.PersistentClient(
-                path=base_path,
-                settings=Settings(allow_reset=True),
-            )
-
-        self.app.reset()
-        shutil.rmtree(base_path)
-        self.app = None
-        self.collection = None
-
-    def _generate_embedding(
-        self, text: str, metadata: Optional[Dict[str, Any]] = None
-    ) -> Any:
-        if not hasattr(self, "collection") or not self.collection:
+    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None:  # type: ignore
+        if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
 
-        id = str(uuid.uuid4())
         self.collection.add(
             documents=[text],
             metadatas=[metadata or {}],
-            ids=[id],
+            ids=[str(uuid.uuid4())],
         )
-        return id
 
-    def _sanitize_role(self, role: str) -> str:
-        """Sanitize role name for use in file names."""
-        return role.lower().replace(" ", "_").replace("\n", "").replace("/", "_")
+    def reset(self) -> None:
+        try:
+            if self.app:
+                self.app.reset()
+                shutil.rmtree(f"{db_storage_path()}/{self.type}")
+                self.app = None
+                self.collection = None
+        except Exception as e:
+            if "attempt to write a readonly database" in str(e):
+                # Ignore this specific error
+                pass
+            else:
+                raise Exception(
+                    f"An error occurred while resetting the {self.type} memory: {e}"
+                )
 
     def _create_default_embedding_function(self):
         from chromadb.utils.embedding_functions.openai_embedding_function import (
@@ -188,20 +172,3 @@ class RAGStorage(BaseRAGStorage):
         return OpenAIEmbeddingFunction(
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )
-
-    def _set_embedder_config(self, embedder_config: Optional[Dict[str, Any]] = None) -> None:
-        """Set the embedding configuration for the RAG storage.
-
-        Args:
-            embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
-                If None or empty, defaults to the default embedding function.
-        """
-        self.embedder = (
-            EmbeddingConfigurator().configure_embedder(embedder_config)
-            if embedder_config
-            else self._create_default_embedding_function()
-        )
-
-    def _build_storage_file_name(self, role_name: str) -> str:
-        """Build storage file name from role name."""
-        return f"{self._sanitize_role(role_name)}_memory"

src/crewai/tools/tool_usage.py

@@ -75,6 +75,7 @@ class ToolUsage:
         agent: Optional[Union["BaseAgent", "LiteAgent"]] = None,
         action: Any = None,
         fingerprint_context: Optional[Dict[str, str]] = None,
+        original_tools: List[Any] = [],
     ) -> None:
         self._i18n: I18N = agent.i18n if agent else I18N()
         self._printer: Printer = Printer()
@@ -86,6 +87,7 @@ class ToolUsage:
         self.tools_description = render_text_description_and_args(tools)
         self.tools_names = get_tool_names(tools)
         self.tools_handler = tools_handler
+        self.original_tools = original_tools
         self.tools = tools
         self.task = task
         self.action = action
@@ -191,13 +193,16 @@ class ToolUsage:
             )  # type: ignore
             from_cache = result is not None
 
+        original_tool = None
+        if hasattr(self, 'original_tools') and self.original_tools:
+            original_tool = next(
+                (ot for ot in self.original_tools if ot.name == tool.name),
+                None
+            )
+        
         available_tool = next(
-            (
-                available_tool
-                for available_tool in self.tools
-                if available_tool.name == tool.name
-            ),
-            None,
+            (at for at in self.tools if at.name == tool.name),
+            None
         )
 
         if result is None:
@@ -259,10 +264,11 @@ class ToolUsage:
 
             if self.tools_handler:
                 should_cache = True
-                if (
-                    hasattr(available_tool, "cache_function")
-                    and available_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
-                ):
+                if original_tool and hasattr(original_tool, "cache_function") and original_tool.cache_function:
+                    should_cache = original_tool.cache_function(
+                        calling.arguments, result
+                    )
+                elif available_tool and hasattr(available_tool, "cache_function") and available_tool.cache_function:
                     should_cache = available_tool.cache_function(  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
                         calling.arguments, result
                     )
@@ -290,10 +296,10 @@ class ToolUsage:
             result=result,
         )
 
-        if (
-            hasattr(available_tool, "result_as_answer")
-            and available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
-        ):
+        if original_tool and hasattr(original_tool, "result_as_answer") and original_tool.result_as_answer:
+            result_as_answer = original_tool.result_as_answer
+            data["result_as_answer"] = result_as_answer
+        elif available_tool and hasattr(available_tool, "result_as_answer") and available_tool.result_as_answer:
             result_as_answer = available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
             data["result_as_answer"] = result_as_answer  # type: ignore
 

src/crewai/utilities/embedding_configurator.py

@@ -1,10 +1,8 @@
 import os
-from typing import TYPE_CHECKING, Any, Dict, Optional, cast
+from typing import Any, Dict, Optional, cast
 
-# Type checking imports that don't cause runtime imports
-if TYPE_CHECKING:
-    from chromadb import Documents, EmbeddingFunction, Embeddings
-    from chromadb.api.types import EmbeddingFunction
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from chromadb.api.types import validate_embedding_function
 
 
 class EmbeddingConfigurator:
@@ -26,12 +24,8 @@ class EmbeddingConfigurator:
     def configure_embedder(
         self,
         embedder_config: Optional[Dict[str, Any]] = None,
-    ) -> 'EmbeddingFunction':
+    ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
-        # Import here to avoid importing chromadb at module level
-        from chromadb import EmbeddingFunction
-        from chromadb.api.types import validate_embedding_function
-        
         if embedder_config is None:
             return self._create_default_embedding_function()
 
@@ -188,9 +182,6 @@ class EmbeddingConfigurator:
                 "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
             ) from e
 
-        # Import chromadb types here to avoid importing at module level
-        from chromadb import Documents, EmbeddingFunction, Embeddings
-
         class WatsonEmbeddingFunction(EmbeddingFunction):
             def __call__(self, input: Documents) -> Embeddings:
                 if isinstance(input, str):
@@ -221,10 +212,6 @@ class EmbeddingConfigurator:
 
     @staticmethod
     def _configure_custom(config):
-        # Import here to avoid importing chromadb at module level
-        from chromadb import EmbeddingFunction
-        from chromadb.api.types import validate_embedding_function
-        
         custom_embedder = config.get("embedder")
         if isinstance(custom_embedder, EmbeddingFunction):
             try:

src/crewai/utilities/tool_utils.py

@@ -60,6 +60,7 @@ def execute_tool_and_check_finality(
             task=task,
             agent=agent,
             action=agent_action,
+            original_tools=tools,  # Pass original tools to ensure custom tools work
         )
 
         # Parse tool calling

tests/cassettes/test_gemini_models[gemini-gemini-2.0-flash-001].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/62RTU+EMBCG7/0VTY9kIQUT1vXqx0njRokxUQ8jDNAILaFdoyH8dwssbNGrTdo0
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-        zsiPAu7nFejS5zxkpCc/6so6tIECAAA=
-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Tue, 22 Apr 2025 14:25:05 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=1219
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
-      - nosniff
-      X-Frame-Options:
-      - SAMEORIGIN
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemini_models[gemini-gemini-2.0-flash-lite-001].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-lite-001:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/61RTU+EMBC98yuanhdSiMjq1Y+Txo0SY6J7GGGARmhJ2zUawn+3wMIWvdpDM5n3
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-        +UUNuvJtd/QZC6nXez+t5iqFggIAAA==
-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Tue, 22 Apr 2025 14:25:06 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=1090
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
-      - nosniff
-      X-Frame-Options:
-      - SAMEORIGIN
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemini_models[gemini-gemini-2.0-flash-thinking-exp-01-21].yaml

@@ -1,58 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-thinking-exp-01-21:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/22QQWuEMBCF7/6KkKNsFt1DKb2221vp0koplD0M66jDxkSSWbCI/71Rq+vS5pCE
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-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Tue, 22 Apr 2025 14:25:04 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=764
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
-      - nosniff
-      X-Frame-Options:
-      - SAMEORIGIN
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemini_models[gemini-gemini-2.5-flash-preview-04-17].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-preview-04-17:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/2WQT0+EMBDF7/0UTY9k2ewaDerVPzfjRokxMR4mMEBjaUk76BrCd7fAslvcHppm
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-        O2bXiSICAAA=
-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Tue, 22 Apr 2025 14:25:28 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=20971
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
-      - nosniff
-      X-Frame-Options:
-      - SAMEORIGIN
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemini_models[gemini-gemini-2.5-pro-exp-03-25].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro-exp-03-25:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/12QT2uEMBDF7/kUIUepi2tZaHttu7fSpZVSKD0MOquhmkgygkX87o26cWM9SJj3
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-    headers:
-      Alt-Svc:
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-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Tue, 22 Apr 2025 14:25:30 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=2418
-      Transfer-Encoding:
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-      - X-Origin
-      - Referer
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-      X-Frame-Options:
-      - SAMEORIGIN
-      X-XSS-Protection:
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-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemma3[gemini-gemma-3-12b-it].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemma-3-12b-it:generateContent
-  response:
-    body:
-      string: !!binary |
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-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Tue, 22 Apr 2025 14:25:39 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=3835
-      Transfer-Encoding:
-      - chunked
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-      - SAMEORIGIN
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemma3[gemini-gemma-3-1b-it].yaml

@@ -1,60 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemma-3-1b-it:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/2VRy07DQAy85yusPUZtBSoIxJWHxAFRQYSQKAc3cVqL7DrKuqKlqsRv8Ht8CZuk
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-      Content-Encoding:
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-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
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-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=1535
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
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-      X-Frame-Options:
-      - SAMEORIGIN
-      X-XSS-Protection:
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-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gemma3[gemini-gemma-3-27b-it].yaml

@@ -1,60 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemma-3-27b-it:generateContent
-  response:
-    body:
-      string: !!binary |
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@@ -1,101 +0,0 @@
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tests/cassettes/test_gpt_4_1[gpt-4.1].yaml

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tests/llm_test.py

@@ -256,52 +256,6 @@ def test_validate_call_params_no_response_format():
     llm._validate_call_params()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
-@pytest.mark.parametrize(
-    "model",
-    [
-        "gemini/gemini-2.0-flash-thinking-exp-01-21",
-        "gemini/gemini-2.0-flash-001",
-        "gemini/gemini-2.0-flash-lite-001",
-        "gemini/gemini-2.5-flash-preview-04-17",
-        "gemini/gemini-2.5-pro-exp-03-25",
-    ],
-)
-def test_gemini_models(model):
-    llm = LLM(model=model)
-    result = llm.call("What is the capital of France?")
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
-@pytest.mark.parametrize(
-    "model",
-    [
-        "gemini/gemma-3-1b-it",
-        "gemini/gemma-3-4b-it",
-        "gemini/gemma-3-12b-it",
-        "gemini/gemma-3-27b-it",
-    ],
-)
-def test_gemma3(model):
-    llm = LLM(model=model)
-    result = llm.call("What is the capital of France?")
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.parametrize(
-    "model", ["gpt-4.1", "gpt-4.1-mini-2025-04-14", "gpt-4.1-nano-2025-04-14"]
-)
-def test_gpt_4_1(model):
-    llm = LLM(model=model)
-    result = llm.call("What is the capital of France?")
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_o3_mini_reasoning_effort_high():
     llm = LLM(

tests/tools/test_custom_tool_invocation.py

@@ -0,0 +1,77 @@
+from unittest.mock import MagicMock
+
+import pytest
+from pydantic import BaseModel, Field
+
+from crewai import Agent, Task
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.agents.parser import AgentAction
+from crewai.agents.tools_handler import ToolsHandler
+from crewai.tools import BaseTool
+from crewai.utilities.i18n import I18N
+from crewai.utilities.tool_utils import execute_tool_and_check_finality
+
+
+class TestToolInput(BaseModel):
+    test_param: str = Field(..., description="A test parameter")
+
+
+class TestCustomTool(BaseTool):
+    name: str = "Test Custom Tool"
+    description: str = "A test tool to verify custom tool invocation"
+    args_schema: type[BaseModel] = TestToolInput
+
+    def _run(self, test_param: str) -> str:
+        return f"Tool executed with param: {test_param}"
+
+
+def test_custom_tool_invocation():
+    custom_tool = TestCustomTool()
+    
+    mock_agent = MagicMock()
+    mock_task = MagicMock()
+    mock_llm = MagicMock()
+    mock_crew = MagicMock()
+    tools_handler = ToolsHandler()
+    
+    executor = CrewAgentExecutor(
+        llm=mock_llm,
+        task=mock_task,
+        crew=mock_crew,
+        agent=mock_agent,
+        prompt={},
+        max_iter=5,
+        tools=[custom_tool],
+        tools_names="Test Custom Tool",
+        stop_words=[],
+        tools_description="A test tool to verify custom tool invocation",
+        tools_handler=tools_handler,
+        original_tools=[custom_tool]
+    )
+    
+    action = AgentAction(
+        tool="Test Custom Tool",
+        tool_input={"test_param": "test_value"},
+        thought="I'll use the custom tool",
+        text="I'll use the Test Custom Tool to get a result"
+    )
+    
+    i18n = I18N()
+    
+    mock_agent.key = "test_agent"
+    mock_agent.role = "test_role"
+    
+    result = execute_tool_and_check_finality(
+        agent_action=action,
+        tools=[custom_tool],
+        i18n=i18n,
+        agent_key=mock_agent.key,
+        agent_role=mock_agent.role,
+        tools_handler=tools_handler,
+        task=mock_task,
+        agent=mock_agent,
+        function_calling_llm=mock_llm
+    )
+    
+    assert "Tool executed with param: test_value" in result.result
+    assert result.result_as_answer is False

tests/utilities/test_embedding_configurator.py

@@ -1,49 +0,0 @@
-import importlib
-import sys
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-
-class TestEmbeddingConfiguratorImports:
-    """Test that ChromaDB is not imported at module level."""
-    
-    def test_no_chromadb_import_at_module_level(self):
-        """Test that chromadb is not imported when the module is imported."""
-        for module_name in list(sys.modules.keys()):
-            if module_name.startswith('crewai.utilities.embedding_configurator'):
-                del sys.modules[module_name]
-        
-        mock_chromadb = MagicMock()
-        
-        chromadb_imported = [False]
-        
-        def mock_import(name, *args, **kwargs):
-            if name == 'chromadb':
-                chromadb_imported[0] = True
-                return mock_chromadb
-            return importlib.__import__(name, *args, **kwargs)
-        
-        with patch('builtins.__import__', side_effect=mock_import):
-            from crewai.utilities import embedding_configurator
-            
-            assert not chromadb_imported[0], "chromadb was imported at module level"
-    
-    def test_chromadb_import_in_configure_embedder(self):
-        """Test that chromadb is imported when configure_embedder is called."""
-        for module_name in list(sys.modules.keys()):
-            if module_name.startswith('crewai.utilities.embedding_configurator'):
-                del sys.modules[module_name]
-        
-        from crewai.utilities.embedding_configurator import EmbeddingConfigurator
-        
-        mock_chromadb = MagicMock()
-        
-        def mock_import(name, *args, **kwargs):
-            if name == 'chromadb':
-                raise ImportError("Mock import error for chromadb")
-            return importlib.__import__(name, *args, **kwargs)
-        
-        with patch('builtins.__import__', side_effect=mock_import):
-            with pytest.raises(ImportError, match="Mock import error for chromadb"):
-                EmbeddingConfigurator().configure_embedder()

tests/utilities/test_knowledge_planning.py

@@ -29,7 +29,7 @@ def mock_knowledge_source():
     """
     return StringKnowledgeSource(content=content)
 
-@patch('chromadb.PersistentClient')
+@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
 def test_knowledge_included_in_planning(mock_chroma):
     """Test that verifies knowledge sources are properly included in planning."""
     # Mock ChromaDB collection

tests/utilities/test_planning_handler.py

@@ -100,7 +100,7 @@ class InternalCrewPlanner:
         # Knowledge field should not be present when empty
         assert '"agent_knowledge"' not in tasks_summary
 
-    @patch('chromadb.PersistentClient')
+    @patch('crewai.knowledge.storage.knowledge_storage.chromadb')
     def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
         """Test task summary generation with both knowledge and tools present."""
         # Mock ChromaDB collection

uv.lock

@@ -1,18 +1,32 @@
 version = 1
+revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
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+    "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
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+    "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
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+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
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+version = "0.1.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx" },
+]
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+]
+
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 version = "1.13.3"
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+version = "8.1.8"
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 dependencies = [
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+version = "0.114.0"
 source = { editable = "." }
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     { name = "appdirs" },
@@ -604,6 +630,7 @@ dependencies = [
     { name = "click" },
     { name = "instructor" },
     { name = "json-repair" },
+    { name = "json5" },
     { name = "jsonref" },
     { name = "litellm" },
     { name = "openai" },
@@ -625,9 +652,15 @@ dependencies = [
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 ]
+aisuite = [
+    { name = "aisuite" },
+]
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+embeddings = [
+    { name = "tiktoken" },
+]
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@@ -660,8 +693,8 @@ dev = [
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     { name = "pytest" },
     { name = "pytest-asyncio" },
+    { name = "pytest-recording" },
     { name = "pytest-subprocess" },
-    { name = "pytest-vcr" },
     { name = "python-dotenv" },
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@@ -669,25 +702,27 @@ dev = [
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+    { name = "aisuite", marker = "extra == 'aisuite'", specifier = ">=0.1.10" },
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-    { name = "litellm", specifier = ">=1.44.22" },
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+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
+]
+dependencies = [
+    { name = "pyyaml", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "urllib3", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "wrapt", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "yarl", marker = "platform_python_implementation != 'PyPy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/25/d3/856e06184d4572aada1dd559ddec3bedc46df1f2edc5ab2c91121a2cccdb/vcrpy-7.0.0.tar.gz", hash = "sha256:176391ad0425edde1680c5b20738ea3dc7fb942520a48d2993448050986b3a50", size = 85502 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/5d/1f15b252890c968d42b348d1e9b0aa12d5bf3e776704178ec37cceccdb63/vcrpy-7.0.0-py2.py3-none-any.whl", hash = "sha256:55791e26c18daa363435054d8b35bd41a4ac441b6676167635d1b37a71dbe124", size = 42321 },
+]
+
 [[package]]
 name = "virtualenv"
 version = "20.27.0"
@@ -5550,18 +5550,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ff/21/abdedb4cdf6ff41ebf01a74087740a709e2edb146490e4d9beea054b0b7a/wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1", size = 23362 },
 ]
 
-[[package]]
-name = "wsproto"
-version = "1.2.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "h11" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/c9/4a/44d3c295350d776427904d73c189e10aeae66d7f555bb2feee16d1e4ba5a/wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065", size = 53425 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/58/e860788190eba3bcce367f74d29c4675466ce8dddfba85f7827588416f01/wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736", size = 24226 },
-]
-
 [[package]]
 name = "xlsxwriter"
 version = "3.2.0"