docs: reorganize observability docs and update titles

docs: Add documentation for W&B Weave

docs/changelog.mdx

@@ -4,7 +4,7 @@ description: View the latest updates and changes to CrewAI
 icon: timeline
 ---
 
-<Update label="2024-03-17" description="v0.108.0">
+<Update label="2025-03-17" description="v0.108.0">
   **Features**
   - Converted tabs to spaces in `crew.py` template
   - Enhanced LLM Streaming Response Handling and Event System
@@ -24,7 +24,7 @@ icon: timeline
   - Added documentation for `ApifyActorsTool`
 </Update>
 
-<Update label="2024-03-10" description="v0.105.0">
+<Update label="2025-03-10" description="v0.105.0">
   **Core Improvements & Fixes**
   - Fixed issues with missing template variables and user memory configuration
   - Improved async flow support and addressed agent response formatting
@@ -45,7 +45,7 @@ icon: timeline
   - Fixed typos in prompts and updated Amazon Bedrock model listings
 </Update>
 
-<Update label="2024-02-12" description="v0.102.0">
+<Update label="2025-02-12" description="v0.102.0">
   **Core Improvements & Fixes**
   - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
   - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
@@ -65,7 +65,7 @@ icon: timeline
   - Fixed Various Typos & Formatting Issues
 </Update>
 
-<Update label="2024-01-28" description="v0.100.0">
+<Update label="2025-01-28" description="v0.100.0">
   **Features**
   - Add Composio docs
   - Add SageMaker as a LLM provider
@@ -80,7 +80,7 @@ icon: timeline
   - Improve formatting and clarity in CLI and Composio Tool docs
 </Update>
 
-<Update label="2024-01-20" description="v0.98.0">
+<Update label="2025-01-20" description="v0.98.0">
   **Features**
   - Conversation crew v1
   - Add unique ID to flow states
@@ -101,7 +101,7 @@ icon: timeline
   - Fixed typos, nested pydantic model issue, and docling issues
 </Update>
 
-<Update label="2024-01-04" description="v0.95.0">
+<Update label="2025-01-04" description="v0.95.0">
   **New Features**
   - Adding Multimodal Abilities to Crew
   - Programatic Guardrails
@@ -131,7 +131,7 @@ icon: timeline
   - Suppressed userWarnings from litellm pydantic issues
 </Update>
 
-<Update label="2023-12-05" description="v0.86.0">
+<Update label="2024-12-05" description="v0.86.0">
   **Changes**
   - Remove all references to pipeline and pipeline router
   - Add Nvidia NIM as provider in Custom LLM
@@ -141,7 +141,7 @@ icon: timeline
   - Simplify template crew
 </Update>
 
-<Update label="2023-12-04" description="v0.85.0">
+<Update label="2024-12-04" description="v0.85.0">
   **Features**
   - Added knowledge to agent level
   - Feat/remove langchain
@@ -161,7 +161,7 @@ icon: timeline
   - Improvements to LLM Configuration and Usage
 </Update>
 
-<Update label="2023-11-25" description="v0.83.0">
+<Update label="2024-11-25" description="v0.83.0">
   **New Features**
   - New before_kickoff and after_kickoff crew callbacks
   - Support to pre-seed agents with Knowledge
@@ -178,7 +178,7 @@ icon: timeline
   - Update Docs
 </Update>
 
-<Update label="2023-11-13" description="v0.80.0">
+<Update label="2024-11-13" description="v0.80.0">
   **Fixes**
   - Fixing Tokens callback replacement bug
   - Fixing Step callback issue

docs/concepts/event-listener.mdx

@@ -1,6 +1,7 @@
 ---
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
+icon: spinner
 ---
 
 # Event Listeners

docs/concepts/llms.mdx

@@ -59,7 +59,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         goal: Conduct comprehensive research and analysis
         backstory: A dedicated research professional with years of experience
         verbose: true
-        llm: openai/gpt-4o-mini # your model here 
+        llm: openai/gpt-4o-mini # your model here
         # (see provider configuration examples below for more)
     ```
 
@@ -111,7 +111,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
 ## Provider Configuration Examples
 
 
-CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities. 
+CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
 In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
 
 <AccordionGroup>
@@ -121,7 +121,7 @@ In this section, you'll find detailed examples that help you select, configure,
     ```toml Code
     # Required
     OPENAI_API_KEY=sk-...
-    
+
     # Optional
     OPENAI_API_BASE=<custom-base-url>
     OPENAI_ORGANIZATION=<your-org-id>
@@ -226,7 +226,7 @@ In this section, you'll find detailed examples that help you select, configure,
     AZURE_API_KEY=<your-api-key>
     AZURE_API_BASE=<your-resource-url>
     AZURE_API_VERSION=<api-version>
-    
+
     # Optional
     AZURE_AD_TOKEN=<your-azure-ad-token>
     AZURE_API_TYPE=<your-azure-api-type>
@@ -289,7 +289,7 @@ In this section, you'll find detailed examples that help you select, configure,
     | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
 
   </Accordion>
-  
+
   <Accordion title="Amazon SageMaker">
     ```toml Code
     AWS_ACCESS_KEY_ID=<your-access-key>
@@ -474,7 +474,7 @@ In this section, you'll find detailed examples that help you select, configure,
     WATSONX_URL=<your-url>
     WATSONX_APIKEY=<your-apikey>
     WATSONX_PROJECT_ID=<your-project-id>
-    
+
     # Optional
     WATSONX_TOKEN=<your-token>
     WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
@@ -491,7 +491,7 @@ In this section, you'll find detailed examples that help you select, configure,
 
   <Accordion title="Ollama (Local LLMs)">
     1. Install Ollama: [ollama.ai](https://ollama.ai/)
-    2. Run a model: `ollama run llama2`
+    2. Run a model: `ollama run llama3`
     3. Configure:
 
     ```python Code
@@ -600,7 +600,7 @@ In this section, you'll find detailed examples that help you select, configure,
     ```toml Code
     OPENROUTER_API_KEY=<your-api-key>
     ```
-    
+
     Example usage in your CrewAI project:
     ```python Code
     llm = LLM(
@@ -723,7 +723,7 @@ Learn how to get the most out of your LLM configuration:
         - Small tasks (up to 4K tokens): Standard models
         - Medium tasks (between 4K-32K): Enhanced models
         - Large tasks (over 32K): Large context models
-        
+
         ```python
         # Configure model with appropriate settings
         llm = LLM(
@@ -760,11 +760,11 @@ Learn how to get the most out of your LLM configuration:
     <Warning>
       Most authentication issues can be resolved by checking API key format and environment variable names.
     </Warning>
-    
+
     ```bash
     # OpenAI
     OPENAI_API_KEY=sk-...
-    
+
     # Anthropic
     ANTHROPIC_API_KEY=sk-ant-...
     ```
@@ -773,11 +773,11 @@ Learn how to get the most out of your LLM configuration:
     <Check>
       Always include the provider prefix in model names
     </Check>
-    
+
     ```python
     # Correct
     llm = LLM(model="openai/gpt-4")
-    
+
     # Incorrect
     llm = LLM(model="gpt-4")
     ```
@@ -786,5 +786,10 @@ Learn how to get the most out of your LLM configuration:
     <Tip>
       Use larger context models for extensive tasks
     </Tip>
+    
+    ```python
+    # Large context model
+    llm = LLM(model="openai/gpt-4o")  # 128K tokens
+    ```
   </Tab>
 </Tabs>

docs/docs.json

@@ -97,13 +97,19 @@
               "how-to/kickoff-async",
               "how-to/kickoff-for-each",
               "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks",
+              "how-to/conditional-tasks"
+            ]
+          },
+          {
+            "group": "Agent Monitoring & Observability",
+            "pages": [
+              "how-to/weave-integration",
               "how-to/agentops-observability",
+              "how-to/langfuse-observability",
               "how-to/langtrace-observability",
               "how-to/mlflow-observability",
               "how-to/openlit-observability",
-              "how-to/portkey-observability",
-              "how-to/langfuse-observability"
+              "how-to/portkey-observability"
             ]
           },
           {
@@ -111,6 +117,8 @@
             "pages": [
               "tools/aimindtool",
               "tools/apifyactorstool",
+              "tools/bedrockinvokeagenttool",
+              "tools/bedrockkbretriever",
               "tools/bravesearchtool",
               "tools/browserbaseloadtool",
               "tools/codedocssearchtool",

docs/how-to/agentops-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with AgentOps
+title: AgentOps Integration
 description: Understanding and logging your agent performance with AgentOps.
 icon: paperclip
 ---

docs/how-to/kickoff-for-each.mdx

@@ -39,8 +39,7 @@ analysis_crew = Crew(
     agents=[coding_agent],
     tasks=[data_analysis_task],
     verbose=True,
-    memory=False,
-    respect_context_window=True  # enable by default
+    memory=False
 )
 
 datasets = [

docs/how-to/langfuse-observability.mdx

@@ -1,7 +1,7 @@
 ---
-title: Agent Monitoring with Langfuse
+title: Langfuse Integration
 description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
-icon: magnifying-glass-chart
+icon: vials
 ---
 
 # Integrate Langfuse with CrewAI

docs/how-to/langtrace-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with Langtrace
+title: Langtrace Integration
 description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
 icon: chart-line
 ---

docs/how-to/mlflow-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with MLflow
+title: MLflow Integration
 description: Quickly start monitoring your Agents with MLflow.
 icon: bars-staggered
 ---

docs/how-to/openlit-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with OpenLIT
+title: OpenLIT Integration
 description: Quickly start monitoring your Agents in just a single line of code with OpenTelemetry.
 icon: magnifying-glass-chart
 ---

docs/how-to/portkey-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with Portkey
+title: Portkey Integration
 description: How to use Portkey with CrewAI
 icon: key
 ---

docs/how-to/weave-integration.mdx

@@ -0,0 +1,124 @@
+---
+title: Weave Integration
+description: Learn how to use Weights & Biases (W&B) Weave to track, experiment with, evaluate, and improve your CrewAI applications.
+icon: radar
+---
+
+# Weave Overview
+
+[Weights & Biases (W&B) Weave](https://weave-docs.wandb.ai/) is a framework for tracking, experimenting with, evaluating, deploying, and improving LLM-based applications. 
+
+![Overview of W&B Weave CrewAI tracing usage](/images/weave-tracing.gif)
+
+Weave provides comprehensive support for every stage of your CrewAI application development:
+
+- **Tracing & Monitoring**: Automatically track LLM calls and application logic to debug and analyze production systems
+- **Systematic Iteration**: Refine and iterate on prompts, datasets, and models
+- **Evaluation**: Use custom or pre-built scorers to systematically assess and enhance agent performance
+- **Guardrails**: Protect your agents with pre- and post-safeguards for content moderation and prompt safety
+
+Weave automatically captures traces for your CrewAI applications, enabling you to monitor and analyze your agents' performance, interactions, and execution flow. This helps you build better evaluation datasets and optimize your agent workflows.
+
+## Setup Instructions
+
+<Steps>
+    <Step title="Install required packages">
+      ```shell
+      pip install crewai weave
+      ```
+    </Step>
+    <Step title="Set up W&B Account">
+      Sign up for a [Weights & Biases account](https://wandb.ai) if you haven't already. You'll need this to view your traces and metrics.
+    </Step>
+    <Step title="Initialize Weave in Your Application">
+      Add the following code to your application:
+
+      ```python
+      import weave
+
+      # Initialize Weave with your project name
+      weave.init(project_name="crewai_demo")
+      ```
+      
+      After initialization, Weave will provide a URL where you can view your traces and metrics.
+    </Step>
+    <Step title="Create your Crews/Flows">
+      ```python
+      from crewai import Agent, Task, Crew, LLM, Process
+
+      # Create an LLM with a temperature of 0 to ensure deterministic outputs
+      llm = LLM(model="gpt-4o", temperature=0)
+
+      # Create agents
+      researcher = Agent(
+          role='Research Analyst',
+          goal='Find and analyze the best investment opportunities',
+          backstory='Expert in financial analysis and market research',
+          llm=llm,
+          verbose=True,
+          allow_delegation=False,
+      )
+
+      writer = Agent(
+          role='Report Writer',
+          goal='Write clear and concise investment reports',
+          backstory='Experienced in creating detailed financial reports',
+          llm=llm,
+          verbose=True,
+          allow_delegation=False,
+      )
+
+      # Create tasks
+      research_task = Task(
+          description='Deep research on the {topic}',
+          expected_output='Comprehensive market data including key players, market size, and growth trends.',
+          agent=researcher
+      )
+
+      writing_task = Task(
+          description='Write a detailed report based on the research',
+          expected_output='The report should be easy to read and understand. Use bullet points where applicable.',
+          agent=writer
+      )
+
+      # Create a crew
+      crew = Crew(
+          agents=[researcher, writer],
+          tasks=[research_task, writing_task],
+          verbose=True,
+          process=Process.sequential,
+      )
+
+      # Run the crew
+      result = crew.kickoff(inputs={"topic": "AI in material science"})
+      print(result)
+      ```
+    </Step>
+    <Step title="View Traces in Weave">
+      After running your CrewAI application, visit the Weave URL provided during initialization to view:
+      - LLM calls and their metadata
+      - Agent interactions and task execution flow
+      - Performance metrics like latency and token usage
+      - Any errors or issues that occurred during execution
+
+      <Frame caption="Weave Tracing Dashboard">
+        <img src="/images/weave-tracing.png" alt="Weave tracing example with CrewAI" />
+      </Frame>
+    </Step>
+</Steps>
+
+## Features
+
+- Weave automatically captures all CrewAI operations: agent interactions and task executions; LLM calls with metadata and token usage; tool usage and results.
+- The integration supports all CrewAI execution methods: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
+- Automatic tracing of all [crewAI-tools](https://github.com/crewAIInc/crewAI-tools).
+- Flow feature support with decorator patching (`@start`, `@listen`, `@router`, `@or_`, `@and_`).
+- Track custom guardrails passed to CrewAI `Task` with `@weave.op()`.
+
+For detailed information on what's supported, visit the [Weave CrewAI documentation](https://weave-docs.wandb.ai/guides/integrations/crewai/#getting-started-with-flow).
+
+## Resources
+
+- [📘 Weave Documentation](https://weave-docs.wandb.ai)
+- [📊 Example Weave x CrewAI dashboard](https://wandb.ai/ayut/crewai_demo/weave/traces?cols=%7B%22wb_run_id%22%3Afalse%2C%22attributes.weave.client_version%22%3Afalse%2C%22attributes.weave.os_name%22%3Afalse%2C%22attributes.weave.os_release%22%3Afalse%2C%22attributes.weave.os_version%22%3Afalse%2C%22attributes.weave.source%22%3Afalse%2C%22attributes.weave.sys_version%22%3Afalse%7D&peekPath=%2Fayut%2Fcrewai_demo%2Fcalls%2F0195c838-38cb-71a2-8a15-651ecddf9d89)
+- [🐦 X](https://x.com/weave_wb)

docs/quickstart.mdx

@@ -300,7 +300,7 @@ email_summarizer:
 ```
 
 <Tip>
-  Note how we use the same name for the agent in the `tasks.yaml` (`email_summarizer_task`) file as the method name in the `crew.py` (`email_summarizer_task`) file.
+  Note how we use the same name for the task in the `tasks.yaml` (`email_summarizer_task`) file as the method name in the `crew.py` (`email_summarizer_task`) file.
 </Tip>
 
 ```yaml tasks.yaml

docs/tools/bedrockinvokeagenttool.mdx

@@ -0,0 +1,187 @@
+---
+title: Bedrock Invoke Agent Tool
+description: Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows
+icon: aws
+---
+
+# `BedrockInvokeAgentTool`
+
+The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows.
+
+## Installation
+
+```bash
+uv pip install 'crewai[tools]'
+```
+
+## Requirements
+
+- AWS credentials configured (either through environment variables or AWS CLI)
+- `boto3` and `python-dotenv` packages
+- Access to Amazon Bedrock Agents
+
+## Usage
+
+Here's how to use the tool with a CrewAI agent:
+
+```python {2, 4-8}
+from crewai import Agent, Task, Crew
+from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
+
+# Initialize the tool
+agent_tool = BedrockInvokeAgentTool(
+    agent_id="your-agent-id",
+    agent_alias_id="your-agent-alias-id"
+)
+
+# Create a CrewAI agent that uses the tool
+aws_expert = Agent(
+    role='AWS Service Expert',
+    goal='Help users understand AWS services and quotas',
+    backstory='I am an expert in AWS services and can provide detailed information about them.',
+    tools=[agent_tool],
+    verbose=True
+)
+
+# Create a task for the agent
+quota_task = Task(
+    description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
+    agent=aws_expert
+)
+
+# Create a crew with the agent
+crew = Crew(
+    agents=[aws_expert],
+    tasks=[quota_task],
+    verbose=2
+)
+
+# Run the crew
+result = crew.kickoff()
+print(result)
+```
+
+## Tool Arguments
+
+| Argument | Type | Required | Default | Description |
+|:---------|:-----|:---------|:--------|:------------|
+| **agent_id** | `str` | Yes | None | The unique identifier of the Bedrock agent |
+| **agent_alias_id** | `str` | Yes | None | The unique identifier of the agent alias |
+| **session_id** | `str` | No | timestamp | The unique identifier of the session |
+| **enable_trace** | `bool` | No | False | Whether to enable trace for debugging |
+| **end_session** | `bool` | No | False | Whether to end the session after invocation |
+| **description** | `str` | No | None | Custom description for the tool |
+
+## Environment Variables
+
+```bash
+BEDROCK_AGENT_ID=your-agent-id           # Alternative to passing agent_id
+BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
+AWS_REGION=your-aws-region               # Defaults to us-west-2
+AWS_ACCESS_KEY_ID=your-access-key        # Required for AWS authentication
+AWS_SECRET_ACCESS_KEY=your-secret-key    # Required for AWS authentication
+```
+
+## Advanced Usage
+
+### Multi-Agent Workflow with Session Management
+
+```python {2, 4-22}
+from crewai import Agent, Task, Crew, Process
+from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
+
+# Initialize tools with session management
+initial_tool = BedrockInvokeAgentTool(
+    agent_id="your-agent-id",
+    agent_alias_id="your-agent-alias-id",
+    session_id="custom-session-id"
+)
+
+followup_tool = BedrockInvokeAgentTool(
+    agent_id="your-agent-id",
+    agent_alias_id="your-agent-alias-id",
+    session_id="custom-session-id"
+)
+
+final_tool = BedrockInvokeAgentTool(
+    agent_id="your-agent-id",
+    agent_alias_id="your-agent-alias-id",
+    session_id="custom-session-id",
+    end_session=True
+)
+
+# Create agents for different stages
+researcher = Agent(
+    role='AWS Service Researcher',
+    goal='Gather information about AWS services',
+    backstory='I am specialized in finding detailed AWS service information.',
+    tools=[initial_tool]
+)
+
+analyst = Agent(
+    role='Service Compatibility Analyst',
+    goal='Analyze service compatibility and requirements',
+    backstory='I analyze AWS services for compatibility and integration possibilities.',
+    tools=[followup_tool]
+)
+
+summarizer = Agent(
+    role='Technical Documentation Writer',
+    goal='Create clear technical summaries',
+    backstory='I specialize in creating clear, concise technical documentation.',
+    tools=[final_tool]
+)
+
+# Create tasks
+research_task = Task(
+    description="Find all available AWS services in us-west-2 region.",
+    agent=researcher
+)
+
+analysis_task = Task(
+    description="Analyze which services support IPv6 and their implementation requirements.",
+    agent=analyst
+)
+
+summary_task = Task(
+    description="Create a summary of IPv6-compatible services and their key features.",
+    agent=summarizer
+)
+
+# Create a crew with the agents and tasks
+crew = Crew(
+    agents=[researcher, analyst, summarizer],
+    tasks=[research_task, analysis_task, summary_task],
+    process=Process.sequential,
+    verbose=2
+)
+
+# Run the crew
+result = crew.kickoff()
+```
+
+## Use Cases
+
+### Hybrid Multi-Agent Collaborations
+- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS
+- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally
+- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows
+
+### Data Sovereignty and Compliance
+- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks
+- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account
+- Enable secure multi-agent collaborations where some agents cannot access your organization's private data
+
+### Seamless AWS Service Integration
+- Access any AWS service through Amazon Bedrock Actions without writing complex integration code
+- Enable CrewAI agents to interact with AWS services through natural language requests
+- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more
+
+### Scalable Hybrid Agent Architectures
+- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI
+- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents
+
+### Cross-Organizational Agent Collaboration
+- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents
+- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data
+- Build agent ecosystems that span organizational boundaries while maintaining security and data control

docs/tools/bedrockkbretriever.mdx

@@ -0,0 +1,165 @@
+---
+title: 'Bedrock Knowledge Base Retriever'
+description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
+icon: aws
+---
+
+# `BedrockKBRetrieverTool`
+
+The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
+
+## Installation
+
+```bash
+uv pip install 'crewai[tools]'
+```
+
+## Requirements
+
+- AWS credentials configured (either through environment variables or AWS CLI)
+- `boto3` and `python-dotenv` packages
+- Access to Amazon Bedrock Knowledge Base
+
+## Usage
+
+Here's how to use the tool with a CrewAI agent:
+
+```python {2, 4-17}
+from crewai import Agent, Task, Crew
+from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
+
+# Initialize the tool
+kb_tool = BedrockKBRetrieverTool(
+    knowledge_base_id="your-kb-id",
+    number_of_results=5
+)
+
+# Create a CrewAI agent that uses the tool
+researcher = Agent(
+    role='Knowledge Base Researcher',
+    goal='Find information about company policies',
+    backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
+    tools=[kb_tool],
+    verbose=True
+)
+
+# Create a task for the agent
+research_task = Task(
+    description="Find our company's remote work policy and summarize the key points.",
+    agent=researcher
+)
+
+# Create a crew with the agent
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task],
+    verbose=2
+)
+
+# Run the crew
+result = crew.kickoff()
+print(result)   
+```
+
+## Tool Arguments
+
+| Argument | Type | Required | Default | Description |
+|:---------|:-----|:---------|:---------|:-------------|
+| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
+| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
+| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
+| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
+| **next_token** | `str` | No | None | Token for pagination |
+
+## Environment Variables
+
+```bash
+BEDROCK_KB_ID=your-knowledge-base-id  # Alternative to passing knowledge_base_id
+AWS_REGION=your-aws-region            # Defaults to us-east-1
+AWS_ACCESS_KEY_ID=your-access-key     # Required for AWS authentication
+AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
+```
+
+## Response Format
+
+The tool returns results in JSON format:
+
+```json
+{
+  "results": [
+    {
+      "content": "Retrieved text content",
+      "content_type": "text",
+      "source_type": "S3",
+      "source_uri": "s3://bucket/document.pdf",
+      "score": 0.95,
+      "metadata": {
+        "additional": "metadata"
+      }
+    }
+  ],
+  "nextToken": "pagination-token",
+  "guardrailAction": "NONE"
+}
+```
+
+## Advanced Usage
+
+### Custom Retrieval Configuration
+
+```python
+kb_tool = BedrockKBRetrieverTool(
+    knowledge_base_id="your-kb-id",
+    retrieval_configuration={
+        "vectorSearchConfiguration": {
+            "numberOfResults": 10,
+            "overrideSearchType": "HYBRID"
+        }
+    }
+)
+
+policy_expert = Agent(
+    role='Policy Expert',
+    goal='Analyze company policies in detail',
+    backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
+    tools=[kb_tool]
+)
+```
+
+## Supported Data Sources
+
+- Amazon S3
+- Confluence
+- Salesforce
+- SharePoint
+- Web pages
+- Custom document locations
+- Amazon Kendra
+- SQL databases    
+
+## Use Cases
+
+### Enterprise Knowledge Integration
+- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
+- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
+- Create agents that can answer questions based on your internal documentation while maintaining data security
+
+### Specialized Domain Knowledge
+- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
+- Leverage existing knowledge repositories that are already maintained in your AWS environment
+- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
+
+### Data-Driven Decision Making
+- Ground CrewAI agent responses in your actual company data rather than general knowledge
+- Ensure agents provide recommendations based on your specific business context and documentation
+- Reduce hallucinations by retrieving factual information from your knowledge bases
+
+### Scalable Information Access
+- Access terabytes of organizational knowledge without embedding it all into your models
+- Dynamically query only the relevant information needed for specific tasks
+- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
+
+### Compliance and Governance
+- Ensure CrewAI agents provide responses that align with your company's approved documentation
+- Create auditable trails of information sources used by your agents
+- Maintain control over what information sources your agents can access

pyproject.toml

@@ -17,9 +17,9 @@ dependencies = [
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
     # Telemetry and Monitoring
-    "opentelemetry-api>=1.22.0",
-    "opentelemetry-sdk>=1.22.0",
-    "opentelemetry-exporter-otlp-proto-http>=1.22.0",
+    "opentelemetry-api>=1.30.0",
+    "opentelemetry-sdk>=1.30.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.30.0",
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",

src/crewai/agents/agent_builder/base_agent.py

@@ -25,6 +25,7 @@ from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter
+from crewai.utilities.string_utils import interpolate_only
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -333,9 +334,15 @@ class BaseAgent(ABC, BaseModel):
             self._original_backstory = self.backstory
 
         if inputs:
-            self.role = self._original_role.format(**inputs)
-            self.goal = self._original_goal.format(**inputs)
-            self.backstory = self._original_backstory.format(**inputs)
+            self.role = interpolate_only(
+                input_string=self._original_role, inputs=inputs
+            )
+            self.goal = interpolate_only(
+                input_string=self._original_goal, inputs=inputs
+            )
+            self.backstory = interpolate_only(
+                input_string=self._original_backstory, inputs=inputs
+            )
 
     def set_cache_handler(self, cache_handler: CacheHandler) -> None:
         """Set the cache handler for the agent.

src/crewai/agents/parser.py

@@ -136,7 +136,7 @@ class CrewAgentParser:
 
     def _clean_action(self, text: str) -> str:
         """Clean action string by removing non-essential formatting characters."""
-        return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()
+        return text.strip().strip("*").strip()
 
     def _safe_repair_json(self, tool_input: str) -> str:
         UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]

src/crewai/cli/git.py

@@ -1,4 +1,5 @@
 import subprocess
+from functools import lru_cache
 
 
 class Repository:
@@ -35,6 +36,7 @@ class Repository:
             encoding="utf-8",
         ).strip()
 
+    @lru_cache(maxsize=None)
     def is_git_repo(self) -> bool:
         """Check if the current directory is a git repository."""
         try:

src/crewai/knowledge/storage/knowledge_storage.py

@@ -4,34 +4,13 @@ import io
 import logging
 import os
 import shutil
-import warnings
 from typing import Any, Dict, List, Optional, Union, cast
 
-# Initialize module import status
-CHROMADB_AVAILABLE = False
-
-# Define placeholder types
-class DummyClientAPI:
-    pass
-    
-class DummySettings:
-    pass
-
-# Try to import chromadb-related modules with proper error handling
-try:
-    import chromadb
-    import chromadb.errors
-    from chromadb.api import ClientAPI
-    from chromadb.api.types import OneOrMany
-    from chromadb.config import Settings
-    CHROMADB_AVAILABLE = True
-except (ImportError, AttributeError) as e:
-    warnings.warn(f"Failed to import chromadb: {str(e)}. Knowledge functionality will be limited.")
-    # Use dummy classes when imports fail
-    chromadb = None
-    ClientAPI = DummyClientAPI
-    OneOrMany = Any
-    Settings = DummySettings
+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
@@ -63,9 +42,9 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     search efficiency.
     """
 
-    collection = None  # Type annotation removed to handle case when chromadb is not available
+    collection: Optional[chromadb.Collection] = None
     collection_name: Optional[str] = "knowledge"
-    app = None  # Type annotation removed to handle case when chromadb is not available
+    app: Optional[ClientAPI] = None
 
     def __init__(
         self,
@@ -82,52 +61,37 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
     ) -> List[Dict[str, Any]]:
-        if not CHROMADB_AVAILABLE:
-            logging.warning("Cannot search knowledge as chromadb is not available.")
-            return []
-            
         with suppress_logging():
             if self.collection:
-                try:
-                    fetched = self.collection.query(
-                        query_texts=query,
-                        n_results=limit,
-                        where=filter,
-                    )
-                    results = []
-                    for i in range(len(fetched["ids"][0])):  # type: ignore
-                        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
-                        }
-                        if result["score"] >= score_threshold:
-                            results.append(result)
-                    return results
-                except Exception as e:
-                    logging.error(f"Error during knowledge search: {str(e)}")
-                    return []
+                fetched = self.collection.query(
+                    query_texts=query,
+                    n_results=limit,
+                    where=filter,
+                )
+                results = []
+                for i in range(len(fetched["ids"][0])):  # type: ignore
+                    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
+                    }
+                    if result["score"] >= score_threshold:
+                        results.append(result)
+                return results
             else:
-                logging.warning("Collection not initialized")
-                return []
+                raise Exception("Collection not initialized")
 
     def initialize_knowledge_storage(self):
-        if not CHROMADB_AVAILABLE:
-            logging.warning("Cannot initialize knowledge storage as chromadb is not available.")
-            self.app = None
-            self.collection = None
-            return
-            
+        base_path = os.path.join(db_storage_path(), "knowledge")
+        chroma_client = chromadb.PersistentClient(
+            path=base_path,
+            settings=Settings(allow_reset=True),
+        )
+
+        self.app = chroma_client
+
         try:
-            base_path = os.path.join(db_storage_path(), "knowledge")
-            chroma_client = chromadb.PersistentClient(
-                path=base_path,
-                settings=Settings(allow_reset=True),
-            )
-
-            self.app = chroma_client
-
             collection_name = (
                 f"knowledge_{self.collection_name}"
                 if self.collection_name
@@ -138,46 +102,30 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                     name=collection_name, embedding_function=self.embedder
                 )
             else:
-                logging.warning("Vector Database Client not initialized")
-                self.collection = None
-        except Exception as e:
-            logging.error(f"Failed to create or get collection: {str(e)}")
-            self.app = None
-            self.collection = None
+                raise Exception("Vector Database Client not initialized")
+        except Exception:
+            raise Exception("Failed to create or get collection")
 
     def reset(self):
-        if not CHROMADB_AVAILABLE:
-            logging.warning("Cannot reset knowledge storage as chromadb is not available.")
-            return
-            
-        try:
-            base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
-            if not self.app:
-                self.app = chromadb.PersistentClient(
-                    path=base_path,
-                    settings=Settings(allow_reset=True),
-                )
+        base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
+        if not self.app:
+            self.app = chromadb.PersistentClient(
+                path=base_path,
+                settings=Settings(allow_reset=True),
+            )
 
-            self.app.reset()
-            shutil.rmtree(base_path)
-        except Exception as e:
-            logging.error(f"Error during knowledge reset: {str(e)}")
-        finally:
-            self.app = None
-            self.collection = None
+        self.app.reset()
+        shutil.rmtree(base_path)
+        self.app = None
+        self.collection = None
 
     def save(
         self,
         documents: List[str],
         metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
-        if not CHROMADB_AVAILABLE:
-            logging.warning("Cannot save to knowledge storage as chromadb is not available.")
-            return
-            
         if not self.collection:
-            logging.warning("Collection not initialized")
-            return
+            raise Exception("Collection not initialized")
 
         try:
             # Create a dictionary to store unique documents
@@ -206,46 +154,38 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 filtered_ids.append(doc_id)
 
             # If we have no metadata at all, set it to None
-            final_metadata = None
-            if not all(m is None for m in filtered_metadata):
-                final_metadata = filtered_metadata
+            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
+                None if all(m is None for m in filtered_metadata) else filtered_metadata
+            )
 
             self.collection.upsert(
                 documents=filtered_docs,
                 metadatas=final_metadata,
                 ids=filtered_ids,
             )
+        except chromadb.errors.InvalidDimensionException as e:
+            Logger(verbose=True).log(
+                "error",
+                "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                "red",
+            )
+            raise ValueError(
+                "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                "across all operations with this collection."
+                "Try resetting the collection using `crewai reset-memories -a`"
+            ) from e
         except Exception as e:
-            if hasattr(chromadb, 'errors') and isinstance(e, chromadb.errors.InvalidDimensionException):
-                Logger(verbose=True).log(
-                    "error",
-                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
-                    "red",
-                )
-                logging.error(
-                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
-                    "across all operations with this collection."
-                    "Try resetting the collection using `crewai reset-memories -a`"
-                )
-            else:
-                Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
-                logging.error(f"Failed to upsert documents: {e}")
+            Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
+            raise
 
     def _create_default_embedding_function(self):
-        if not CHROMADB_AVAILABLE:
-            return None
-            
-        try:
-            from chromadb.utils.embedding_functions.openai_embedding_function import (
-                OpenAIEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
 
-            return OpenAIEmbeddingFunction(
-                api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-            )
-        except (ImportError, AttributeError) as e:
-            logging.warning(f"Failed to create default embedding function: {str(e)}")
-            return None
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
 
     def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
         """Set the embedding configuration for the knowledge storage.
@@ -254,12 +194,8 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
                 If None or empty, defaults to the default embedding function.
         """
-        try:
-            self.embedder = (
-                EmbeddingConfigurator().configure_embedder(embedder)
-                if embedder
-                else self._create_default_embedding_function()
-            )
-        except Exception as e:
-            logging.warning(f"Failed to configure embedder: {str(e)}")
-            self.embedder = None
+        self.embedder = (
+            EmbeddingConfigurator().configure_embedder(embedder)
+            if embedder
+            else self._create_default_embedding_function()
+        )

src/crewai/llm.py

@@ -114,6 +114,60 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "Llama-3.2-11B-Vision-Instruct": 16384,
     "Meta-Llama-3.2-3B-Instruct": 4096,
     "Meta-Llama-3.2-1B-Instruct": 16384,
+    # bedrock
+    "us.amazon.nova-pro-v1:0": 300000,
+    "us.amazon.nova-micro-v1:0": 128000,
+    "us.amazon.nova-lite-v1:0": 300000,
+    "us.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "us.anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
+    "us.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "us.anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
+    "us.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "us.anthropic.claude-3-opus-20240229-v1:0": 200000,
+    "us.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "us.meta.llama3-2-11b-instruct-v1:0": 128000,
+    "us.meta.llama3-2-3b-instruct-v1:0": 131000,
+    "us.meta.llama3-2-90b-instruct-v1:0": 128000,
+    "us.meta.llama3-2-1b-instruct-v1:0": 131000,
+    "us.meta.llama3-1-8b-instruct-v1:0": 128000,
+    "us.meta.llama3-1-70b-instruct-v1:0": 128000,
+    "us.meta.llama3-3-70b-instruct-v1:0": 128000,
+    "us.meta.llama3-1-405b-instruct-v1:0": 128000,
+    "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "eu.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "eu.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "eu.meta.llama3-2-3b-instruct-v1:0": 131000,
+    "eu.meta.llama3-2-1b-instruct-v1:0": 131000,
+    "apac.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "apac.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "apac.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "apac.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "amazon.nova-pro-v1:0": 300000,
+    "amazon.nova-micro-v1:0": 128000,
+    "amazon.nova-lite-v1:0": 300000,
+    "anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
+    "anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
+    "anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "anthropic.claude-3-opus-20240229-v1:0": 200000,
+    "anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "anthropic.claude-v2:1": 200000,
+    "anthropic.claude-v2": 100000,
+    "anthropic.claude-instant-v1": 100000,
+    "meta.llama3-1-405b-instruct-v1:0": 128000,
+    "meta.llama3-1-70b-instruct-v1:0": 128000,
+    "meta.llama3-1-8b-instruct-v1:0": 128000,
+    "meta.llama3-70b-instruct-v1:0": 8000,
+    "meta.llama3-8b-instruct-v1:0": 8000,
+    "amazon.titan-text-lite-v1": 4000,
+    "amazon.titan-text-express-v1": 8000,
+    "cohere.command-text-v14": 4000,
+    "ai21.j2-mid-v1": 8191,
+    "ai21.j2-ultra-v1": 8191,
+    "ai21.jamba-instruct-v1:0": 256000,
+    "mistral.mistral-7b-instruct-v0:2": 32000,
+    "mistral.mixtral-8x7b-instruct-v0:1": 32000,
     # mistral
     "mistral-tiny": 32768,
     "mistral-small-latest": 32768,

src/crewai/memory/storage/mem0_storage.py

@@ -1,7 +1,7 @@
 import os
 from typing import Any, Dict, List
 
-from mem0 import MemoryClient
+from mem0 import Memory, MemoryClient
 
 from crewai.memory.storage.interface import Storage
 
@@ -32,13 +32,16 @@ class Mem0Storage(Storage):
         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
-            )
+        # Initialize MemoryClient or Memory based on the presence of the mem0_api_key
+        if mem0_api_key:
+            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)
         else:
-            self.memory = MemoryClient(api_key=mem0_api_key)
+            self.memory = Memory()  # Fallback to Memory if no Mem0 API key is provided
 
     def _sanitize_role(self, role: str) -> str:
         """

src/crewai/memory/storage/rag_storage.py

@@ -60,31 +60,25 @@ class RAGStorage(BaseRAGStorage):
         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:
-            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.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
             )
-
-            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
-                )
-        except (ImportError, AttributeError) as e:
-            import logging
-            logging.warning(f"Failed to initialize chromadb: {str(e)}. Memory functionality will be limited.")
-            self.app = None
-            self.collection = None
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -109,9 +103,6 @@ class RAGStorage(BaseRAGStorage):
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
-        if self.app is None or self.collection is None:
-            logging.warning("Cannot save to memory as chromadb is not available.")
-            return
         try:
             self._generate_embedding(value, metadata)
         except Exception as e:
@@ -124,12 +115,8 @@ class RAGStorage(BaseRAGStorage):
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
     ) -> List[Any]:
-        if not hasattr(self, "app") or not hasattr(self, "collection"):
+        if not hasattr(self, "app"):
             self._initialize_app()
-            
-        if self.app is None or self.collection is None:
-            logging.warning("Cannot search memory as chromadb is not available.")
-            return []
 
         try:
             with suppress_logging():
@@ -154,10 +141,6 @@ class RAGStorage(BaseRAGStorage):
     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()
-            
-        if self.app is None or self.collection is None:
-            logging.warning("Cannot generate embeddings as chromadb is not available.")
-            return
 
         self.collection.add(
             documents=[text],
@@ -177,7 +160,15 @@ class RAGStorage(BaseRAGStorage):
                 # Ignore this specific error
                 pass
             else:
-                logging.error(f"An error occurred while resetting the {self.type} memory: {e}")
-                # Don't raise exception to prevent crashes
-                self.app = None
-                self.collection = None
+                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 (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )

src/crewai/task.py

@@ -2,6 +2,7 @@ import datetime
 import inspect
 import json
 import logging
+import re
 import threading
 import uuid
 from concurrent.futures import Future
@@ -49,6 +50,7 @@ from crewai.utilities.events import (
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
+from crewai.utilities.string_utils import interpolate_only
 
 
 class Task(BaseModel):
@@ -507,7 +509,9 @@ class Task(BaseModel):
             return
 
         try:
-            self.description = self._original_description.format(**inputs)
+            self.description = interpolate_only(
+                input_string=self._original_description, inputs=inputs
+            )
         except KeyError as e:
             raise ValueError(
                 f"Missing required template variable '{e.args[0]}' in description"
@@ -516,7 +520,7 @@ class Task(BaseModel):
             raise ValueError(f"Error interpolating description: {str(e)}") from e
 
         try:
-            self.expected_output = self.interpolate_only(
+            self.expected_output = interpolate_only(
                 input_string=self._original_expected_output, inputs=inputs
             )
         except (KeyError, ValueError) as e:
@@ -524,7 +528,7 @@ class Task(BaseModel):
 
         if self.output_file is not None:
             try:
-                self.output_file = self.interpolate_only(
+                self.output_file = interpolate_only(
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
@@ -555,72 +559,6 @@ class Task(BaseModel):
                 f"\n\n{conversation_instruction}\n\n{conversation_history}"
             )
 
-    def interpolate_only(
-        self,
-        input_string: Optional[str],
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
-    ) -> str:
-        """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
-
-        Args:
-            input_string: The string containing template variables to interpolate.
-                         Can be None or empty, in which case an empty string is returned.
-            inputs: Dictionary mapping template variables to their values.
-                   Supported value types are strings, integers, floats, and dicts/lists
-                   containing only these types and other nested dicts/lists.
-
-        Returns:
-            The interpolated string with all template variables replaced with their values.
-            Empty string if input_string is None or empty.
-
-        Raises:
-            ValueError: If a value contains unsupported types
-        """
-
-        # Validation function for recursive type checking
-        def validate_type(value: Any) -> None:
-            if value is None:
-                return
-            if isinstance(value, (str, int, float, bool)):
-                return
-            if isinstance(value, (dict, list)):
-                for item in value.values() if isinstance(value, dict) else value:
-                    validate_type(item)
-                return
-            raise ValueError(
-                f"Unsupported type {type(value).__name__} in inputs. "
-                "Only str, int, float, bool, dict, and list are allowed."
-            )
-
-        # Validate all input values
-        for key, value in inputs.items():
-            try:
-                validate_type(value)
-            except ValueError as e:
-                raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
-
-        if input_string is None or not input_string:
-            return ""
-        if "{" not in input_string and "}" not in input_string:
-            return input_string
-        if not inputs:
-            raise ValueError(
-                "Inputs dictionary cannot be empty when interpolating variables"
-            )
-        try:
-            escaped_string = input_string.replace("{", "{{").replace("}", "}}")
-
-            for key in inputs.keys():
-                escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}")
-
-            return escaped_string.format(**inputs)
-        except KeyError as e:
-            raise KeyError(
-                f"Template variable '{e.args[0]}' not found in inputs dictionary"
-            ) from e
-        except ValueError as e:
-            raise ValueError(f"Error during string interpolation: {str(e)}") from e
-
     def increment_tools_errors(self) -> None:
         """Increment the tools errors counter."""
         self.tools_errors += 1

src/crewai/telemetry/telemetry.py

@@ -281,8 +281,16 @@ class Telemetry:
         return self._safe_telemetry_operation(operation)
 
     def task_ended(self, span: Span, task: Task, crew: Crew):
-        """Records task execution in a crew."""
+        """Records the completion of a task execution in a crew.
 
+        Args:
+            span (Span): The OpenTelemetry span tracking the task execution
+            task (Task): The task that was completed
+            crew (Crew): The crew context in which the task was executed
+
+        Note:
+            If share_crew is enabled, this will also record the task output
+        """
         def operation():
             if crew.share_crew:
                 self._add_attribute(
@@ -297,8 +305,13 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the repeated usage 'error' of a tool by an agent."""
+        """Records when a tool is used repeatedly, which might indicate an issue.
 
+        Args:
+            llm (Any): The language model being used
+            tool_name (str): Name of the tool being repeatedly used
+            attempts (int): Number of attempts made with this tool
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Repeated Usage")
@@ -317,8 +330,13 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def tool_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the usage of a tool by an agent."""
+        """Records the usage of a tool by an agent.
 
+        Args:
+            llm (Any): The language model being used
+            tool_name (str): Name of the tool being used
+            attempts (int): Number of attempts made with this tool
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Usage")
@@ -337,8 +355,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def tool_usage_error(self, llm: Any):
-        """Records the usage of a tool by an agent."""
+        """Records when a tool usage results in an error.
 
+        Args:
+            llm (Any): The language model being used when the error occurred
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Usage Error")
@@ -357,6 +378,14 @@ class Telemetry:
     def individual_test_result_span(
         self, crew: Crew, quality: float, exec_time: int, model_name: str
     ):
+        """Records individual test results for a crew execution.
+
+        Args:
+            crew (Crew): The crew being tested
+            quality (float): Quality score of the execution
+            exec_time (int): Execution time in seconds
+            model_name (str): Name of the model used
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Crew Individual Test Result")
@@ -383,6 +412,14 @@ class Telemetry:
         inputs: dict[str, Any] | None,
         model_name: str,
     ):
+        """Records the execution of a test suite for a crew.
+
+        Args:
+            crew (Crew): The crew being tested
+            iterations (int): Number of test iterations
+            inputs (dict[str, Any] | None): Input parameters for the test
+            model_name (str): Name of the model used in testing
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Crew Test Execution")
@@ -408,6 +445,7 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def deploy_signup_error_span(self):
+        """Records when an error occurs during the deployment signup process."""
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Deploy Signup Error")
@@ -417,6 +455,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def start_deployment_span(self, uuid: Optional[str] = None):
+        """Records the start of a deployment process.
+
+        Args:
+            uuid (Optional[str]): Unique identifier for the deployment
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Start Deployment")
@@ -428,6 +471,7 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def create_crew_deployment_span(self):
+        """Records the creation of a new crew deployment."""
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Create Crew Deployment")
@@ -437,6 +481,12 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def get_crew_logs_span(self, uuid: Optional[str], log_type: str = "deployment"):
+        """Records the retrieval of crew logs.
+
+        Args:
+            uuid (Optional[str]): Unique identifier for the crew
+            log_type (str, optional): Type of logs being retrieved. Defaults to "deployment".
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Get Crew Logs")
@@ -449,6 +499,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def remove_crew_span(self, uuid: Optional[str] = None):
+        """Records the removal of a crew.
+
+        Args:
+            uuid (Optional[str]): Unique identifier for the crew being removed
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Remove Crew")
@@ -574,6 +629,11 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def flow_creation_span(self, flow_name: str):
+        """Records the creation of a new flow.
+
+        Args:
+            flow_name (str): Name of the flow being created
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Creation")
@@ -584,6 +644,12 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def flow_plotting_span(self, flow_name: str, node_names: list[str]):
+        """Records flow visualization/plotting activity.
+
+        Args:
+            flow_name (str): Name of the flow being plotted
+            node_names (list[str]): List of node names in the flow
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Plotting")
@@ -595,6 +661,12 @@ class Telemetry:
         self._safe_telemetry_operation(operation)
 
     def flow_execution_span(self, flow_name: str, node_names: list[str]):
+        """Records the execution of a flow.
+
+        Args:
+            flow_name (str): Name of the flow being executed
+            node_names (list[str]): List of nodes being executed in the flow
+        """
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Execution")

src/crewai/tools/tool_usage.py

@@ -455,7 +455,7 @@ class ToolUsage:
 
         # Attempt 4: Repair JSON
         try:
-            repaired_input = repair_json(tool_input)
+            repaired_input = repair_json(tool_input, skip_json_loads=True)
             self._printer.print(
                 content=f"Repaired JSON: {repaired_input}", color="blue"
             )

src/crewai/utilities/embedding_configurator.py

@@ -1,40 +1,8 @@
 import os
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Union, cast
+from typing import Any, Dict, Optional, cast
 
-# Initialize with None to indicate module import status
-CHROMADB_AVAILABLE = False
-
-# Define placeholder types for when chromadb is not available
-class EmbeddingFunction:
-    def __call__(self, texts):
-        raise NotImplementedError("Chromadb is not available")
-
-Documents = List[str]
-Embeddings = List[List[float]]
-
-def validate_embedding_function(func):
-    return func
-
-# Try to import chromadb-related modules with proper error handling
-try:
-    from chromadb.api.types import Documents as ChromaDocuments
-    from chromadb.api.types import EmbeddingFunction as ChromaEmbeddingFunction
-    from chromadb.api.types import Embeddings as ChromaEmbeddings
-    from chromadb.utils import (
-        validate_embedding_function as chroma_validate_embedding_function,
-    )
-    
-    # Override our placeholder types with the real ones
-    Documents = ChromaDocuments
-    EmbeddingFunction = ChromaEmbeddingFunction
-    Embeddings = ChromaEmbeddings
-    validate_embedding_function = chroma_validate_embedding_function
-    
-    CHROMADB_AVAILABLE = True
-except (ImportError, AttributeError) as e:
-    # This captures both ImportError and AttributeError (which can happen with NumPy 2.x)
-    warnings.warn(f"Failed to import chromadb: {str(e)}. Embedding functionality will be limited.")
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from chromadb.api.types import validate_embedding_function
 
 
 class EmbeddingConfigurator:
@@ -58,9 +26,6 @@ class EmbeddingConfigurator:
         embedder_config: Optional[Dict[str, Any]] = None,
     ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
-        if not CHROMADB_AVAILABLE:
-            return self._create_unavailable_embedding_function()
-            
         if embedder_config is None:
             return self._create_default_embedding_function()
 
@@ -79,230 +44,143 @@ class EmbeddingConfigurator:
             if provider == "custom"
             else embedding_function(config, model_name)
         )
-        
-    @staticmethod
-    def _create_unavailable_embedding_function():
-        """Creates a fallback embedding function when chromadb is not available."""
-        class UnavailableEmbeddingFunction(EmbeddingFunction):
-            def __call__(self, input):
-                raise ImportError(
-                    "Chromadb is not available due to NumPy compatibility issues. "
-                    "Either downgrade to NumPy<2 or upgrade chromadb and related dependencies."
-                )
-        
-        return UnavailableEmbeddingFunction()
 
     @staticmethod
     def _create_default_embedding_function():
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.openai_embedding_function import (
-                OpenAIEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
 
-            return OpenAIEmbeddingFunction(
-                api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-            )
-        except (ImportError, AttributeError) as e:
-            import warnings
-            warnings.warn(f"Failed to import OpenAIEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
 
     @staticmethod
     def _configure_openai(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.openai_embedding_function import (
-                OpenAIEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
 
-            return OpenAIEmbeddingFunction(
-                api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
-                model_name=model_name,
-                api_base=config.get("api_base", None),
-                api_type=config.get("api_type", None),
-                api_version=config.get("api_version", None),
-                default_headers=config.get("default_headers", None),
-                dimensions=config.get("dimensions", None),
-                deployment_id=config.get("deployment_id", None),
-                organization_id=config.get("organization_id", None),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import OpenAIEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+            model_name=model_name,
+            api_base=config.get("api_base", None),
+            api_type=config.get("api_type", None),
+            api_version=config.get("api_version", None),
+            default_headers=config.get("default_headers", None),
+            dimensions=config.get("dimensions", None),
+            deployment_id=config.get("deployment_id", None),
+            organization_id=config.get("organization_id", None),
+        )
 
     @staticmethod
     def _configure_azure(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.openai_embedding_function import (
-                OpenAIEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
 
-            return OpenAIEmbeddingFunction(
-                api_key=config.get("api_key"),
-                api_base=config.get("api_base"),
-                api_type=config.get("api_type", "azure"),
-                api_version=config.get("api_version"),
-                model_name=model_name,
-                default_headers=config.get("default_headers"),
-                dimensions=config.get("dimensions"),
-                deployment_id=config.get("deployment_id"),
-                organization_id=config.get("organization_id"),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import OpenAIEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key"),
+            api_base=config.get("api_base"),
+            api_type=config.get("api_type", "azure"),
+            api_version=config.get("api_version"),
+            model_name=model_name,
+            default_headers=config.get("default_headers"),
+            dimensions=config.get("dimensions"),
+            deployment_id=config.get("deployment_id"),
+            organization_id=config.get("organization_id"),
+        )
 
     @staticmethod
     def _configure_ollama(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.ollama_embedding_function import (
-                OllamaEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.ollama_embedding_function import (
+            OllamaEmbeddingFunction,
+        )
 
-            return OllamaEmbeddingFunction(
-                url=config.get("url", "http://localhost:11434/api/embeddings"),
-                model_name=model_name,
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import OllamaEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return OllamaEmbeddingFunction(
+            url=config.get("url", "http://localhost:11434/api/embeddings"),
+            model_name=model_name,
+        )
 
     @staticmethod
     def _configure_vertexai(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.google_embedding_function import (
-                GoogleVertexEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
 
-            return GoogleVertexEmbeddingFunction(
-                model_name=model_name,
-                api_key=config.get("api_key"),
-                project_id=config.get("project_id"),
-                region=config.get("region"),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import GoogleVertexEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return GoogleVertexEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+            project_id=config.get("project_id"),
+            region=config.get("region"),
+        )
 
     @staticmethod
     def _configure_google(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.google_embedding_function import (
-                GoogleGenerativeAiEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleGenerativeAiEmbeddingFunction,
+        )
 
-            return GoogleGenerativeAiEmbeddingFunction(
-                model_name=model_name,
-                api_key=config.get("api_key"),
-                task_type=config.get("task_type"),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import GoogleGenerativeAiEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return GoogleGenerativeAiEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+            task_type=config.get("task_type"),
+        )
 
     @staticmethod
     def _configure_cohere(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.cohere_embedding_function import (
-                CohereEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.cohere_embedding_function import (
+            CohereEmbeddingFunction,
+        )
 
-            return CohereEmbeddingFunction(
-                model_name=model_name,
-                api_key=config.get("api_key"),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import CohereEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return CohereEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
 
     @staticmethod
     def _configure_voyageai(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.voyageai_embedding_function import (
-                VoyageAIEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.voyageai_embedding_function import (
+            VoyageAIEmbeddingFunction,
+        )
 
-            return VoyageAIEmbeddingFunction(
-                model_name=model_name,
-                api_key=config.get("api_key"),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import VoyageAIEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return VoyageAIEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
 
     @staticmethod
     def _configure_bedrock(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-                AmazonBedrockEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+            AmazonBedrockEmbeddingFunction,
+        )
 
-            # Allow custom model_name override with backwards compatibility
-            kwargs = {"session": config.get("session")}
-            if model_name is not None:
-                kwargs["model_name"] = model_name
-            return AmazonBedrockEmbeddingFunction(**kwargs)
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import AmazonBedrockEmbeddingFunction: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        # Allow custom model_name override with backwards compatibility
+        kwargs = {"session": config.get("session")}
+        if model_name is not None:
+            kwargs["model_name"] = model_name
+        return AmazonBedrockEmbeddingFunction(**kwargs)
 
     @staticmethod
     def _configure_huggingface(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
-        try:
-            from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-                HuggingFaceEmbeddingServer,
-            )
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+            HuggingFaceEmbeddingServer,
+        )
 
-            return HuggingFaceEmbeddingServer(
-                url=config.get("api_url"),
-            )
-        except (ImportError, AttributeError) as e:
-            warnings.warn(f"Failed to import HuggingFaceEmbeddingServer: {str(e)}")
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+        return HuggingFaceEmbeddingServer(
+            url=config.get("api_url"),
+        )
 
     @staticmethod
     def _configure_watson(config, model_name):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
         try:
             import ibm_watsonx_ai.foundation_models as watson_models
             from ibm_watsonx_ai import Credentials
             from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
         except ImportError as e:
-            warnings.warn(
+            raise ImportError(
                 "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-            )
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            ) from e
 
         class WatsonEmbeddingFunction(EmbeddingFunction):
             def __call__(self, input: Documents) -> Embeddings:
@@ -334,30 +212,25 @@ class EmbeddingConfigurator:
 
     @staticmethod
     def _configure_custom(config):
-        if not CHROMADB_AVAILABLE:
-            return EmbeddingConfigurator._create_unavailable_embedding_function()
-            
         custom_embedder = config.get("embedder")
         if isinstance(custom_embedder, EmbeddingFunction):
             try:
                 validate_embedding_function(custom_embedder)
                 return custom_embedder
             except Exception as e:
-                warnings.warn(f"Invalid custom embedding function: {str(e)}")
-                return EmbeddingConfigurator._create_unavailable_embedding_function()
+                raise ValueError(f"Invalid custom embedding function: {str(e)}")
         elif callable(custom_embedder):
             try:
                 instance = custom_embedder()
                 if isinstance(instance, EmbeddingFunction):
                     validate_embedding_function(instance)
                     return instance
-                warnings.warn("Custom embedder does not create an EmbeddingFunction instance")
-                return EmbeddingConfigurator._create_unavailable_embedding_function()
+                raise ValueError(
+                    "Custom embedder does not create an EmbeddingFunction instance"
+                )
             except Exception as e:
-                warnings.warn(f"Error instantiating custom embedder: {str(e)}")
-                return EmbeddingConfigurator._create_unavailable_embedding_function()
+                raise ValueError(f"Error instantiating custom embedder: {str(e)}")
         else:
-            warnings.warn(
+            raise ValueError(
                 "Custom embedder must be an instance of `EmbeddingFunction` or a callable that creates one"
             )
-            return EmbeddingConfigurator._create_unavailable_embedding_function()

src/crewai/utilities/formatter.py

@@ -1,10 +1,12 @@
-from typing import List
+import re
+from typing import TYPE_CHECKING, List
 
-from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
+if TYPE_CHECKING:
+    from crewai.task import Task
+    from crewai.tasks.task_output import TaskOutput
 
 
-def aggregate_raw_outputs_from_task_outputs(task_outputs: List[TaskOutput]) -> str:
+def aggregate_raw_outputs_from_task_outputs(task_outputs: List["TaskOutput"]) -> str:
     """Generate string context from the task outputs."""
     dividers = "\n\n----------\n\n"
 
@@ -13,7 +15,7 @@ def aggregate_raw_outputs_from_task_outputs(task_outputs: List[TaskOutput]) -> s
     return context
 
 
-def aggregate_raw_outputs_from_tasks(tasks: List[Task]) -> str:
+def aggregate_raw_outputs_from_tasks(tasks: List["Task"]) -> str:
     """Generate string context from the tasks."""
     task_outputs = [task.output for task in tasks if task.output is not None]
 

src/crewai/utilities/planning_handler.py

@@ -96,6 +96,10 @@ class CrewPlanner:
         tasks_summary = []
         for idx, task in enumerate(self.tasks):
             knowledge_list = self._get_agent_knowledge(task)
+            agent_tools = (
+                f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
+                f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
+            )
             task_summary = f"""
                 Task Number {idx + 1} - {task.description}
                 "task_description": {task.description}
@@ -103,10 +107,7 @@ class CrewPlanner:
                 "agent": {task.agent.role if task.agent else "None"}
                 "agent_goal": {task.agent.goal if task.agent else "None"}
                 "task_tools": {task.tools}
-                "agent_tools": %s%s""" % (
-                    f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
-                    f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
-                )
+                "agent_tools": {"".join(agent_tools)}"""
 
             tasks_summary.append(task_summary)
         return " ".join(tasks_summary)

src/crewai/utilities/string_utils.py

@@ -0,0 +1,82 @@
+import re
+from typing import Any, Dict, List, Optional, Union
+
+
+def interpolate_only(
+    input_string: Optional[str],
+    inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
+) -> str:
+    """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
+    Only interpolates placeholders that follow the pattern {variable_name} where
+    variable_name starts with a letter/underscore and contains only letters, numbers, and underscores.
+
+    Args:
+        input_string: The string containing template variables to interpolate.
+                     Can be None or empty, in which case an empty string is returned.
+        inputs: Dictionary mapping template variables to their values.
+               Supported value types are strings, integers, floats, and dicts/lists
+               containing only these types and other nested dicts/lists.
+
+    Returns:
+        The interpolated string with all template variables replaced with their values.
+        Empty string if input_string is None or empty.
+
+    Raises:
+        ValueError: If a value contains unsupported types or a template variable is missing
+    """
+
+    # Validation function for recursive type checking
+    def validate_type(value: Any) -> None:
+        if value is None:
+            return
+        if isinstance(value, (str, int, float, bool)):
+            return
+        if isinstance(value, (dict, list)):
+            for item in value.values() if isinstance(value, dict) else value:
+                validate_type(item)
+            return
+        raise ValueError(
+            f"Unsupported type {type(value).__name__} in inputs. "
+            "Only str, int, float, bool, dict, and list are allowed."
+        )
+
+    # Validate all input values
+    for key, value in inputs.items():
+        try:
+            validate_type(value)
+        except ValueError as e:
+            raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
+
+    if input_string is None or not input_string:
+        return ""
+    if "{" not in input_string and "}" not in input_string:
+        return input_string
+    if not inputs:
+        raise ValueError(
+            "Inputs dictionary cannot be empty when interpolating variables"
+        )
+
+    # The regex pattern to find valid variable placeholders
+    # Matches {variable_name} where variable_name starts with a letter/underscore
+    # and contains only letters, numbers, and underscores
+    pattern = r"\{([A-Za-z_][A-Za-z0-9_]*)\}"
+
+    # Find all matching variables in the input string
+    variables = re.findall(pattern, input_string)
+    result = input_string
+
+    # Check if all variables exist in inputs
+    missing_vars = [var for var in variables if var not in inputs]
+    if missing_vars:
+        raise KeyError(
+            f"Template variable '{missing_vars[0]}' not found in inputs dictionary"
+        )
+
+    # Replace each variable with its value
+    for var in variables:
+        if var in inputs:
+            placeholder = "{" + var + "}"
+            value = str(inputs[var])
+            result = result.replace(placeholder, value)
+
+    return result

tests/task_test.py

@@ -15,6 +15,7 @@ from crewai import Agent, Crew, Process, Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.converter import Converter
+from crewai.utilities.string_utils import interpolate_only
 
 
 def test_task_tool_reflect_agent_tools():
@@ -822,7 +823,7 @@ def test_interpolate_only():
 
     # Test JSON structure preservation
     json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
-    result = task.interpolate_only(
+    result = interpolate_only(
         input_string=json_string,
         inputs={"placeholder": "the data", "nestedVal": "something else"},
     )
@@ -833,20 +834,18 @@ def test_interpolate_only():
 
     # Test normal string interpolation
     normal_string = "Hello {name}, welcome to {place}!"
-    result = task.interpolate_only(
+    result = interpolate_only(
         input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
     )
     assert result == "Hello John, welcome to CrewAI!"
 
     # Test empty string
-    result = task.interpolate_only(input_string="", inputs={"unused": "value"})
+    result = interpolate_only(input_string="", inputs={"unused": "value"})
     assert result == ""
 
     # Test string with no placeholders
     no_placeholders = "Hello, this is a test"
-    result = task.interpolate_only(
-        input_string=no_placeholders, inputs={"unused": "value"}
-    )
+    result = interpolate_only(input_string=no_placeholders, inputs={"unused": "value"})
     assert result == no_placeholders
 
 
@@ -858,7 +857,7 @@ def test_interpolate_only_with_dict_inside_expected_output():
     )
 
     json_string = '{"questions": {"main_question": "What is the user\'s name?", "secondary_question": "What is the user\'s age?"}}'
-    result = task.interpolate_only(
+    result = interpolate_only(
         input_string=json_string,
         inputs={
             "questions": {
@@ -872,18 +871,16 @@ def test_interpolate_only_with_dict_inside_expected_output():
     assert result == json_string
 
     normal_string = "Hello {name}, welcome to {place}!"
-    result = task.interpolate_only(
+    result = interpolate_only(
         input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
     )
     assert result == "Hello John, welcome to CrewAI!"
 
-    result = task.interpolate_only(input_string="", inputs={"unused": "value"})
+    result = interpolate_only(input_string="", inputs={"unused": "value"})
     assert result == ""
 
     no_placeholders = "Hello, this is a test"
-    result = task.interpolate_only(
-        input_string=no_placeholders, inputs={"unused": "value"}
-    )
+    result = interpolate_only(input_string=no_placeholders, inputs={"unused": "value"})
     assert result == no_placeholders
 
 
@@ -1085,12 +1082,12 @@ def test_interpolate_with_list_of_strings():
     # Test simple list of strings
     input_str = "Available items: {items}"
     inputs = {"items": ["apple", "banana", "cherry"]}
-    result = task.interpolate_only(input_str, inputs)
+    result = interpolate_only(input_str, inputs)
     assert result == f"Available items: {inputs['items']}"
 
     # Test empty list
     empty_list_input = {"items": []}
-    result = task.interpolate_only(input_str, empty_list_input)
+    result = interpolate_only(input_str, empty_list_input)
     assert result == "Available items: []"
 
 
@@ -1106,7 +1103,7 @@ def test_interpolate_with_list_of_dicts():
             {"name": "Bob", "age": 25, "skills": ["Java", "Cloud"]},
         ]
     }
-    result = task.interpolate_only("{people}", input_data)
+    result = interpolate_only("{people}", input_data)
 
     parsed_result = eval(result)
     assert isinstance(parsed_result, list)
@@ -1138,7 +1135,7 @@ def test_interpolate_with_nested_structures():
             ],
         }
     }
-    result = task.interpolate_only("{company}", input_data)
+    result = interpolate_only("{company}", input_data)
     parsed = eval(result)
 
     assert parsed["name"] == "TechCorp"
@@ -1161,7 +1158,7 @@ def test_interpolate_with_special_characters():
             "empty": "",
         }
     }
-    result = task.interpolate_only("{special_data}", input_data)
+    result = interpolate_only("{special_data}", input_data)
     parsed = eval(result)
 
     assert parsed["quotes"] == """This has "double" and 'single' quotes"""
@@ -1188,7 +1185,7 @@ def test_interpolate_mixed_types():
             },
         }
     }
-    result = task.interpolate_only("{data}", input_data)
+    result = interpolate_only("{data}", input_data)
     parsed = eval(result)
 
     assert parsed["name"] == "Test Dataset"
@@ -1216,7 +1213,7 @@ def test_interpolate_complex_combination():
             },
         ]
     }
-    result = task.interpolate_only("{report}", input_data)
+    result = interpolate_only("{report}", input_data)
     parsed = eval(result)
 
     assert len(parsed) == 2
@@ -1233,7 +1230,7 @@ def test_interpolate_invalid_type_validation():
 
     # Test with invalid top-level type
     with pytest.raises(ValueError) as excinfo:
-        task.interpolate_only("{data}", {"data": set()})  # type: ignore we are purposely testing this failure
+        interpolate_only("{data}", {"data": set()})  # type: ignore we are purposely testing this failure
 
     assert "Unsupported type set" in str(excinfo.value)
 
@@ -1246,7 +1243,7 @@ def test_interpolate_invalid_type_validation():
         }
     }
     with pytest.raises(ValueError) as excinfo:
-        task.interpolate_only("{data}", {"data": invalid_nested})
+        interpolate_only("{data}", {"data": invalid_nested})
     assert "Unsupported type set" in str(excinfo.value)
 
 
@@ -1265,24 +1262,22 @@ def test_interpolate_custom_object_validation():
 
     # Test with custom object at top level
     with pytest.raises(ValueError) as excinfo:
-        task.interpolate_only("{obj}", {"obj": CustomObject(5)})  # type: ignore we are purposely testing this failure
+        interpolate_only("{obj}", {"obj": CustomObject(5)})  # type: ignore we are purposely testing this failure
     assert "Unsupported type CustomObject" in str(excinfo.value)
 
     # Test with nested custom object in dictionary
     with pytest.raises(ValueError) as excinfo:
-        task.interpolate_only(
-            "{data}", {"data": {"valid": 1, "invalid": CustomObject(5)}}
-        )
+        interpolate_only("{data}", {"data": {"valid": 1, "invalid": CustomObject(5)}})
     assert "Unsupported type CustomObject" in str(excinfo.value)
 
     # Test with nested custom object in list
     with pytest.raises(ValueError) as excinfo:
-        task.interpolate_only("{data}", {"data": [1, "valid", CustomObject(5)]})
+        interpolate_only("{data}", {"data": [1, "valid", CustomObject(5)]})
     assert "Unsupported type CustomObject" in str(excinfo.value)
 
     # Test with deeply nested custom object
     with pytest.raises(ValueError) as excinfo:
-        task.interpolate_only(
+        interpolate_only(
             "{data}", {"data": {"level1": {"level2": [{"level3": CustomObject(5)}]}}}
         )
     assert "Unsupported type CustomObject" in str(excinfo.value)
@@ -1306,7 +1301,7 @@ def test_interpolate_valid_complex_types():
     }
 
     # Should not raise any errors
-    result = task.interpolate_only("{data}", {"data": valid_data})
+    result = interpolate_only("{data}", {"data": valid_data})
     parsed = eval(result)
     assert parsed["name"] == "Valid Dataset"
     assert parsed["stats"]["nested"]["deeper"]["b"] == 2.5
@@ -1319,16 +1314,16 @@ def test_interpolate_edge_cases():
     )
 
     # Test empty dict and list
-    assert task.interpolate_only("{}", {"data": {}}) == "{}"
-    assert task.interpolate_only("[]", {"data": []}) == "[]"
+    assert interpolate_only("{}", {"data": {}}) == "{}"
+    assert interpolate_only("[]", {"data": []}) == "[]"
 
     # Test numeric types
-    assert task.interpolate_only("{num}", {"num": 42}) == "42"
-    assert task.interpolate_only("{num}", {"num": 3.14}) == "3.14"
+    assert interpolate_only("{num}", {"num": 42}) == "42"
+    assert interpolate_only("{num}", {"num": 3.14}) == "3.14"
 
     # Test boolean values (valid JSON types)
-    assert task.interpolate_only("{flag}", {"flag": True}) == "True"
-    assert task.interpolate_only("{flag}", {"flag": False}) == "False"
+    assert interpolate_only("{flag}", {"flag": True}) == "True"
+    assert interpolate_only("{flag}", {"flag": False}) == "False"
 
 
 def test_interpolate_valid_types():
@@ -1346,7 +1341,7 @@ def test_interpolate_valid_types():
         "nested": {"flag": True, "empty": None},
     }
 
-    result = task.interpolate_only("{data}", {"data": valid_data})
+    result = interpolate_only("{data}", {"data": valid_data})
     parsed = eval(result)
 
     assert parsed["active"] is True

tests/test_numpy_compatibility.py

@@ -1,64 +0,0 @@
-import importlib
-import sys
-import warnings
-
-import pytest
-
-
-def test_crew_import_with_numpy():
-    """Test that crewai can be imported even with NumPy compatibility issues."""
-    try:
-        # Force reload to ensure we test our fix
-        if "crewai" in sys.modules:
-            importlib.reload(sys.modules["crewai"])
-        
-        # This should not raise an exception
-        from crewai import Crew
-        assert Crew is not None
-    except Exception as e:
-        pytest.fail(f"Failed to import Crew: {e}")
-
-def test_embedding_configurator_with_numpy():
-    """Test that EmbeddingConfigurator can be imported with NumPy."""
-    try:
-        # Force reload
-        if "crewai.utilities.embedding_configurator" in sys.modules:
-            importlib.reload(sys.modules["crewai.utilities.embedding_configurator"])
-        
-        from crewai.utilities.embedding_configurator import EmbeddingConfigurator
-        configurator = EmbeddingConfigurator()
-        # Test that we can create an embedder (might be unavailable but shouldn't crash)
-        embedder = configurator.configure_embedder()
-        assert embedder is not None
-    except Exception as e:
-        pytest.fail(f"Failed to use EmbeddingConfigurator: {e}")
-
-def test_rag_storage_with_numpy():
-    """Test that RAGStorage can be imported and used with NumPy."""
-    try:
-        # Force reload
-        if "crewai.memory.storage.rag_storage" in sys.modules:
-            importlib.reload(sys.modules["crewai.memory.storage.rag_storage"])
-        
-        from crewai.memory.storage.rag_storage import RAGStorage
-        # Initialize with minimal config to avoid actual DB operations
-        storage = RAGStorage(type="test", crew=None)
-        # Just verify we can create the object without errors
-        assert storage is not None
-    except Exception as e:
-        pytest.fail(f"Failed to use RAGStorage: {e}")
-
-def test_knowledge_storage_with_numpy():
-    """Test that KnowledgeStorage can be imported and used with NumPy."""
-    try:
-        # Force reload
-        if "crewai.knowledge.storage.knowledge_storage" in sys.modules:
-            importlib.reload(sys.modules["crewai.knowledge.storage.knowledge_storage"])
-        
-        from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-        # Initialize with minimal config
-        storage = KnowledgeStorage()
-        # Just verify we can create the object without errors
-        assert storage is not None
-    except Exception as e:
-        pytest.fail(f"Failed to use KnowledgeStorage: {e}")

tests/utilities/test_string_utils.py

@@ -0,0 +1,187 @@
+from typing import Any, Dict, List, Union
+
+import pytest
+
+from crewai.utilities.string_utils import interpolate_only
+
+
+class TestInterpolateOnly:
+    """Tests for the interpolate_only function in string_utils.py."""
+
+    def test_basic_variable_interpolation(self):
+        """Test basic variable interpolation works correctly."""
+        template = "Hello, {name}! Welcome to {company}."
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "name": "Alice",
+            "company": "CrewAI",
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert result == "Hello, Alice! Welcome to CrewAI."
+
+    def test_multiple_occurrences_of_same_variable(self):
+        """Test that multiple occurrences of the same variable are replaced."""
+        template = "{name} is using {name}'s account."
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "name": "Bob"
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert result == "Bob is using Bob's account."
+
+    def test_json_structure_preservation(self):
+        """Test that JSON structures are preserved and not interpolated incorrectly."""
+        template = """
+        Instructions for {agent}:
+
+        Please return the following object:
+
+        {"name": "person's name", "age": 25, "skills": ["coding", "testing"]}
+        """
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "agent": "DevAgent"
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert "Instructions for DevAgent:" in result
+        assert (
+            '{"name": "person\'s name", "age": 25, "skills": ["coding", "testing"]}'
+            in result
+        )
+
+    def test_complex_nested_json(self):
+        """Test with complex JSON structures containing curly braces."""
+        template = """
+        {agent} needs to process:
+        {
+          "config": {
+            "nested": {
+              "value": 42
+            },
+            "arrays": [1, 2, {"inner": "value"}]
+          }
+        }
+        """
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "agent": "DataProcessor"
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert "DataProcessor needs to process:" in result
+        assert '"nested": {' in result
+        assert '"value": 42' in result
+        assert '[1, 2, {"inner": "value"}]' in result
+
+    def test_missing_variable(self):
+        """Test that an error is raised when a required variable is missing."""
+        template = "Hello, {name}!"
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "not_name": "Alice"
+        }
+
+        with pytest.raises(KeyError) as excinfo:
+            interpolate_only(template, inputs)
+
+        assert "template variable" in str(excinfo.value).lower()
+        assert "name" in str(excinfo.value)
+
+    def test_invalid_input_types(self):
+        """Test that an error is raised with invalid input types."""
+        template = "Hello, {name}!"
+        # Using Any for this test since we're intentionally testing an invalid type
+        inputs: Dict[str, Any] = {"name": object()}  # Object is not a valid input type
+
+        with pytest.raises(ValueError) as excinfo:
+            interpolate_only(template, inputs)
+
+        assert "unsupported type" in str(excinfo.value).lower()
+
+    def test_empty_input_string(self):
+        """Test handling of empty or None input string."""
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "name": "Alice"
+        }
+
+        assert interpolate_only("", inputs) == ""
+        assert interpolate_only(None, inputs) == ""
+
+    def test_no_variables_in_template(self):
+        """Test a template with no variables to replace."""
+        template = "This is a static string with no variables."
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "name": "Alice"
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert result == template
+
+    def test_variable_name_starting_with_underscore(self):
+        """Test variables starting with underscore are replaced correctly."""
+        template = "Variable: {_special_var}"
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "_special_var": "Special Value"
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert result == "Variable: Special Value"
+
+    def test_preserves_non_matching_braces(self):
+        """Test that non-matching braces patterns are preserved."""
+        template = (
+            "This {123} and {!var} should not be replaced but {valid_var} should."
+        )
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "valid_var": "works"
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert (
+            result == "This {123} and {!var} should not be replaced but works should."
+        )
+
+    def test_complex_mixed_scenario(self):
+        """Test a complex scenario with both valid variables and JSON structures."""
+        template = """
+        {agent_name} is working on task {task_id}.
+        
+        Instructions:
+        1. Process the data
+        2. Return results as:
+        
+        {
+          "taskId": "{task_id}",
+          "results": {
+            "processed_by": "agent_name",
+            "status": "complete",
+            "values": [1, 2, 3]
+          }
+        }
+        """
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
+            "agent_name": "AnalyticsAgent",
+            "task_id": "T-12345",
+        }
+
+        result = interpolate_only(template, inputs)
+
+        assert "AnalyticsAgent is working on task T-12345" in result
+        assert '"taskId": "T-12345"' in result
+        assert '"processed_by": "agent_name"' in result  # This shouldn't be replaced
+        assert '"values": [1, 2, 3]' in result
+
+    def test_empty_inputs_dictionary(self):
+        """Test that an error is raised with empty inputs dictionary."""
+        template = "Hello, {name}!"
+        inputs: Dict[str, Any] = {}
+
+        with pytest.raises(ValueError) as excinfo:
+            interpolate_only(template, inputs)
+
+        assert "inputs dictionary cannot be empty" in str(excinfo.value).lower()

uv.lock

@@ -715,9 +715,9 @@ requires-dist = [
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "openpyxl", marker = "extra == 'openpyxl'", specifier = ">=3.1.5" },
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-    { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.22.0" },
-    { name = "opentelemetry-sdk", specifier = ">=1.22.0" },
+    { name = "opentelemetry-api", specifier = ">=1.30.0" },
+    { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
+    { name = "opentelemetry-sdk", specifier = ">=1.30.0" },
     { name = "pandas", marker = "extra == 'pandas'", specifier = ">=2.2.3" },
     { name = "pdfplumber", specifier = ">=0.11.4" },
     { name = "pdfplumber", marker = "extra == 'pdfplumber'", specifier = ">=0.11.4" },
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