improve type hintings

docs/concepts/cli.mdx

@@ -161,7 +161,6 @@ The CLI will initially prompt for API keys for the following services:
 * Groq
 * Anthropic
 * Google Gemini
-* SambaNova
 
 When you select a provider, the CLI will prompt you to enter your API key.
 

docs/concepts/llms.mdx

@@ -146,19 +146,6 @@ Here's a detailed breakdown of supported models and their capabilities, you can
       Groq is known for its fast inference speeds, making it suitable for real-time applications.
     </Tip>
   </Tab>
-  <Tab title="SambaNova">
-    | Model | Context Window | Best For |
-    |-------|---------------|-----------|
-    | Llama 3.1 70B/8B | Up to 131,072 tokens | High-performance, large context tasks |
-    | Llama 3.1 405B | 8,192 tokens | High-performance and output quality |
-    | Llama 3.2 Series | 8,192 tokens | General-purpose tasks, multimodal |
-    | Llama 3.3 70B | Up to 131,072 tokens | High-performance and output quality|
-    | Qwen2 familly | 8,192 tokens | High-performance and output quality |
-
-    <Tip>
-      [SambaNova](https://cloud.sambanova.ai/) has several models with fast inference speed at full precision.
-    </Tip>
-  </Tab>
   <Tab title="Others">
     | Provider | Context Window | Key Features |
     |----------|---------------|--------------|

docs/concepts/memory.mdx

@@ -134,23 +134,6 @@ crew = Crew(
 )
 ```
 
-## Memory Configuration Options
-If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
-
-```python Code
-from crewai import Crew
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
-    },
-)
-```
 
 ## Additional Embedding Providers
 

docs/how-to/llm-connections.mdx

@@ -32,7 +32,6 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Cloudflare Workers AI
 - DeepInfra
 - Groq
-- SambaNova
 - [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
 - And many more!
 

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -26,7 +26,7 @@ class CrewAgentExecutorMixin:
 
     def _should_force_answer(self) -> bool:
         """Determine if a forced answer is required based on iteration count."""
-        return self.iterations >= self.max_iter
+        return (self.iterations >= self.max_iter) and not self.have_forced_answer
 
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""

src/crewai/cli/constants.py

@@ -85,12 +85,6 @@ ENV_VARS = {
             "key_name": "CEREBRAS_API_KEY",
         },
     ],
-    "sambanova": [
-        {
-            "prompt": "Enter your SambaNovaCloud API key (press Enter to skip)",
-            "key_name": "SAMBANOVA_API_KEY",
-        }
-    ],
 }
 
 
@@ -104,7 +98,6 @@ PROVIDERS = [
     "bedrock",
     "azure",
     "cerebras",
-    "sambanova",
 ]
 
 MODELS = {
@@ -163,19 +156,6 @@ MODELS = {
         "bedrock/mistral.mistral-7b-instruct-v0:2",
         "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
     ],
-    "sambanova": [
-        "sambanova/Meta-Llama-3.3-70B-Instruct",
-        "sambanova/QwQ-32B-Preview",
-        "sambanova/Qwen2.5-72B-Instruct",
-        "sambanova/Qwen2.5-Coder-32B-Instruct",
-        "sambanova/Meta-Llama-3.1-405B-Instruct",
-        "sambanova/Meta-Llama-3.1-70B-Instruct",
-        "sambanova/Meta-Llama-3.1-8B-Instruct",
-        "sambanova/Llama-3.2-90B-Vision-Instruct",
-        "sambanova/Llama-3.2-11B-Vision-Instruct",
-        "sambanova/Meta-Llama-3.2-3B-Instruct",
-        "sambanova/Meta-Llama-3.2-1B-Instruct",
-    ],
 }
 
 DEFAULT_LLM_MODEL = "gpt-4o-mini"

src/crewai/cli/templates/crew/config/tasks.yaml

@@ -2,7 +2,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is {current_year}.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher

src/crewai/cli/templates/crew/main.py

@@ -2,8 +2,6 @@
 import sys
 import warnings
 
-from datetime import datetime
-
 from {{folder_name}}.crew import {{crew_name}}
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -18,8 +16,7 @@ def run():
     Run the crew.
     """
     inputs = {
-        'topic': 'AI LLMs',
-        'current_year': str(datetime.now().year)
+        'topic': 'AI LLMs'
     }
     
     try:

src/crewai/llm.py

@@ -76,18 +76,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
     "llama-3.3-70b-versatile": 128000,
     "llama-3.3-70b-instruct": 128000,
-    #sambanova
-    "Meta-Llama-3.3-70B-Instruct": 131072,
-    "QwQ-32B-Preview": 8192,
-    "Qwen2.5-72B-Instruct": 8192,
-    "Qwen2.5-Coder-32B-Instruct": 8192,
-    "Meta-Llama-3.1-405B-Instruct": 8192,
-    "Meta-Llama-3.1-70B-Instruct": 131072,
-    "Meta-Llama-3.1-8B-Instruct": 131072,
-    "Llama-3.2-90B-Vision-Instruct": 16384,
-    "Llama-3.2-11B-Vision-Instruct": 16384,
-    "Meta-Llama-3.2-3B-Instruct": 4096,
-    "Meta-Llama-3.2-1B-Instruct": 16384,
 }
 
 DEFAULT_CONTEXT_WINDOW_SIZE = 8192

src/crewai/memory/storage/mem0_storage.py

@@ -27,18 +27,10 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        config = self.memory_config.get("config", {})
-        mem0_api_key = config.get("api_key") or os.getenv("MEM0_API_KEY")
-        mem0_org_id = config.get("org_id")
-        mem0_project_id = config.get("project_id")
-
-        # Initialize MemoryClient with available parameters
-        if mem0_org_id and mem0_project_id:
-            self.memory = MemoryClient(
-                api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
-            )
-        else:
-            self.memory = MemoryClient(api_key=mem0_api_key)
+        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
+            "MEM0_API_KEY"
+        )
+        self.memory = MemoryClient(api_key=mem0_api_key)
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -65,7 +57,7 @@ class Mem0Storage(Storage):
                 metadata={"type": "long_term", **metadata},
             )
         elif self.memory_type == "entities":
-            entity_name = self._get_agent_name()
+            entity_name = None
             self.memory.add(
                 value, user_id=entity_name, metadata={"type": "entity", **metadata}
             )