Merge branch 'main' into brandon/improve-llm-structured-output

2026-01-08 07:38:29 +00:00 · 2025-02-04 13:44:28 -08:00
parent 3de4653023 515478473a
commit 75bd0310f3
15 changed files with 236 additions and 26 deletions
--- a/README.md
+++ b/README.md
@@ -190,7 +190,7 @@ research_task:
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
-    the current year is 2024.
+    the current year is 2025.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher
--- a/docs/concepts/crews.mdx
+++ b/docs/concepts/crews.mdx
@@ -279,9 +279,9 @@ print(result)
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.

 - `kickoff()`: Starts the execution process according to the defined process flow.
- `kickoff_for_each()`: Executes tasks for each agent individually.
+- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
 - `kickoff_async()`: Initiates the workflow asynchronously.
- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.

 ```python Code
 # Start the crew's task execution
--- a/docs/concepts/memory.mdx
+++ b/docs/concepts/memory.mdx
@@ -185,7 +185,7 @@ my_crew = Crew(
    process=Process.sequential,
    memory=True,
    verbose=True,
-    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model="text-embedding-3-small"),
 )
 ```

@@ -224,7 +224,7 @@ my_crew = Crew(
        "provider": "google",
        "config": {
            "api_key": "<YOUR_API_KEY>",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
        }
    }
 )
@@ -247,7 +247,7 @@ my_crew = Crew(
        api_base="YOUR_API_BASE_PATH",
        api_type="azure",
        api_version="YOUR_API_VERSION",
-        model_name="text-embedding-3-small"
+        model="text-embedding-3-small"
    )
 )
 ```
@@ -268,7 +268,7 @@ my_crew = Crew(
        project_id="YOUR_PROJECT_ID",
        region="YOUR_REGION",
        api_key="YOUR_API_KEY",
-        model_name="textembedding-gecko"
+        model="textembedding-gecko"
    )
 )
 ```
@@ -288,7 +288,7 @@ my_crew = Crew(
        "provider": "cohere",
        "config": {
            "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
        }
    }
 )
@@ -308,7 +308,7 @@ my_crew = Crew(
        "provider": "voyageai",
        "config": {
            "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
        }
    }
 )
--- a/docs/concepts/planning.mdx
+++ b/docs/concepts/planning.mdx
@@ -81,8 +81,8 @@ my_crew.kickoff()

 3. **Collect Data:**

-   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
-   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
+   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
+   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.

 4. **Analyze Findings:**

--- a/docs/concepts/tasks.mdx
+++ b/docs/concepts/tasks.mdx
@@ -69,7 +69,7 @@ research_task:
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
-    the current year is 2024.
+    the current year is 2025.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher
@@ -155,7 +155,7 @@ research_task = Task(
    description="""
        Conduct a thorough research about AI Agents.
        Make sure you find any interesting and relevant information given
-        the current year is 2024.
+        the current year is 2025.
    """,
    expected_output="""
        A list with 10 bullet points of the most relevant information about AI Agents
--- a/docs/how-to/human-input-on-execution.mdx
+++ b/docs/how-to/human-input-on-execution.mdx
@@ -60,12 +60,12 @@ writer = Agent(
 # Create tasks for your agents
 task1 = Task(
    description=(
-        "Conduct a comprehensive analysis of the latest advancements in AI in 2024. "
+        "Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
        "Identify key trends, breakthrough technologies, and potential industry impacts. "
        "Compile your findings in a detailed report. "
        "Make sure to check with a human if the draft is good before finalizing your answer."
    ),
-    expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
+    expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
    agent=researcher,
    human_input=True
 )
@@ -76,7 +76,7 @@ task2 = Task(
        "Your post should be informative yet accessible, catering to a tech-savvy audience. "
        "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
    ),
-    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
+    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
    agent=writer,
    human_input=True
 )
--- a/docs/how-to/mlflow-observability.mdx
+++ b/docs/how-to/mlflow-observability.mdx
@@ -0,0 +1,206 @@
+---
+title: Agent Monitoring with MLflow
+description: Quickly start monitoring your Agents with MLflow.
+icon: bars-staggered
+---
+
+# MLflow Overview
+
+[MLflow](https://mlflow.org/) is an open-source platform to assist machine learning practitioners and teams in handling the complexities of the machine learning process.
+
+It provides a tracing feature that enhances LLM observability in your Generative AI applications by capturing detailed information about the execution of your application’s services. 
+Tracing provides a way to record the inputs, outputs, and metadata associated with each intermediate step of a request, enabling you to easily pinpoint the source of bugs and unexpected behaviors.
+
+![Overview of MLflow crewAI tracing usage](/images/mlflow-tracing.gif)
+
+### Features
+
+- **Tracing Dashboard**: Monitor activities of your crewAI agents with detailed dashboards that include inputs, outputs and metadata of spans.
+- **Automated Tracing**: A fully automated integration with crewAI, which can be enabled by running `mlflow.crewai.autolog()`. 
+- **Manual Trace Instrumentation with minor efforts**: Customize trace instrumentation through MLflow's high-level fluent APIs such as decorators, function wrappers and context managers.
+- **OpenTelemetry Compatibility**: MLflow Tracing supports exporting traces to an OpenTelemetry Collector, which can then be used to export traces to various backends such as Jaeger, Zipkin, and AWS X-Ray.
+- **Package and Deploy Agents**: Package and deploy your crewAI agents to an inference server with a variety of deployment targets.
+- **Securely Host LLMs**: Host multiple LLM from various providers in one unified endpoint through MFflow gateway.
+- **Evaluation**: Evaluate your crewAI agents with a wide range of metrics using a convenient API `mlflow.evaluate()`.
+
+## Setup Instructions
+
+<Steps>
+    <Step title="Install MLflow package">
+      ```shell
+      # The crewAI integration is available in mlflow>=2.19.0
+      pip install mlflow
+      ```
+    </Step>
+    <Step title="Start MFflow tracking server">
+      ```shell
+      # This process is optional, but it is recommended to use MLflow tracking server for better visualization and broader features.
+      mlflow server
+      ```
+    </Step>
+    <Step title="Initialize MLflow in Your Application">
+      Add the following two lines to your application code:
+
+      ```python
+      import mlflow
+
+      mlflow.crewai.autolog()
+
+      # Optional: Set a tracking URI and an experiment name if you have a tracking server
+      mlflow.set_tracking_uri("http://localhost:5000")
+      mlflow.set_experiment("CrewAI")
+      ```
+      
+      Example Usage for tracing CrewAI Agents:
+
+      ```python
+      from crewai import Agent, Crew, Task
+      from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+      from crewai_tools import SerperDevTool, WebsiteSearchTool
+
+      from textwrap import dedent
+
+      content = "Users name is John. He is 30 years old and lives in San Francisco."
+      string_source = StringKnowledgeSource(
+          content=content, metadata={"preference": "personal"}
+      )
+
+      search_tool = WebsiteSearchTool()
+
+
+      class TripAgents:
+          def city_selection_agent(self):
+              return Agent(
+                  role="City Selection Expert",
+                  goal="Select the best city based on weather, season, and prices",
+                  backstory="An expert in analyzing travel data to pick ideal destinations",
+                  tools=[
+                      search_tool,
+                  ],
+                  verbose=True,
+              )
+
+          def local_expert(self):
+              return Agent(
+                  role="Local Expert at this city",
+                  goal="Provide the BEST insights about the selected city",
+                  backstory="""A knowledgeable local guide with extensive information
+              about the city, it's attractions and customs""",
+                  tools=[search_tool],
+                  verbose=True,
+              )
+
+
+      class TripTasks:
+          def identify_task(self, agent, origin, cities, interests, range):
+              return Task(
+                  description=dedent(
+                      f"""
+                      Analyze and select the best city for the trip based
+                      on specific criteria such as weather patterns, seasonal
+                      events, and travel costs. This task involves comparing
+                      multiple cities, considering factors like current weather
+                      conditions, upcoming cultural or seasonal events, and
+                      overall travel expenses.
+                      Your final answer must be a detailed
+                      report on the chosen city, and everything you found out
+                      about it, including the actual flight costs, weather
+                      forecast and attractions.
+
+                      Traveling from: {origin}
+                      City Options: {cities}
+                      Trip Date: {range}
+                      Traveler Interests: {interests}
+                  """
+                  ),
+                  agent=agent,
+                  expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
+              )
+
+          def gather_task(self, agent, origin, interests, range):
+              return Task(
+                  description=dedent(
+                      f"""
+                      As a local expert on this city you must compile an
+                      in-depth guide for someone traveling there and wanting
+                      to have THE BEST trip ever!
+                      Gather information about key attractions, local customs,
+                      special events, and daily activity recommendations.
+                      Find the best spots to go to, the kind of place only a
+                      local would know.
+                      This guide should provide a thorough overview of what
+                      the city has to offer, including hidden gems, cultural
+                      hotspots, must-visit landmarks, weather forecasts, and
+                      high level costs.
+                      The final answer must be a comprehensive city guide,
+                      rich in cultural insights and practical tips,
+                      tailored to enhance the travel experience.
+
+                      Trip Date: {range}
+                      Traveling from: {origin}
+                      Traveler Interests: {interests}
+                  """
+                  ),
+                  agent=agent,
+                  expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
+              )
+
+
+      class TripCrew:
+          def __init__(self, origin, cities, date_range, interests):
+              self.cities = cities
+              self.origin = origin
+              self.interests = interests
+              self.date_range = date_range
+
+          def run(self):
+              agents = TripAgents()
+              tasks = TripTasks()
+
+              city_selector_agent = agents.city_selection_agent()
+              local_expert_agent = agents.local_expert()
+
+              identify_task = tasks.identify_task(
+                  city_selector_agent,
+                  self.origin,
+                  self.cities,
+                  self.interests,
+                  self.date_range,
+              )
+              gather_task = tasks.gather_task(
+                  local_expert_agent, self.origin, self.interests, self.date_range
+              )
+
+              crew = Crew(
+                  agents=[city_selector_agent, local_expert_agent],
+                  tasks=[identify_task, gather_task],
+                  verbose=True,
+                  memory=True,
+                  knowledge={
+                      "sources": [string_source],
+                      "metadata": {"preference": "personal"},
+                  },
+              )
+
+              result = crew.kickoff()
+              return result
+
+
+      trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
+      result = trip_crew.run()
+
+      print(result)
+      ```
+      Refer to [MLflow Tracing Documentation](https://mlflow.org/docs/latest/llms/tracing/index.html) for more configurations and use cases.
+    </Step>
+    <Step title="Visualize Activities of Agents">
+      Now traces for your crewAI agents are captured by MLflow. 
+      Let's visit MLflow tracking server to view the traces and get insights into your Agents.
+
+      Open `127.0.0.1:5000` on your browser to visit MLflow tracking server. 
+      <Frame caption="MLflow Tracing Dashboard">
+        <img src="/images/mlflow1.png" alt="MLflow tracing example with crewai" />
+      </Frame>
+    </Step>
+</Steps> 
+
--- a/docs/images/mlflow-tracing.gif
+++ b/docs/images/mlflow-tracing.gif
--- a/docs/images/mlflow1.png
+++ b/docs/images/mlflow1.png
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -101,6 +101,7 @@
        "how-to/conditional-tasks",
        "how-to/agentops-observability",
        "how-to/langtrace-observability",
+        "how-to/mlflow-observability",
        "how-to/openlit-observability",
        "how-to/portkey-observability"
      ]
--- a/docs/quickstart.mdx
+++ b/docs/quickstart.mdx
@@ -58,7 +58,7 @@ Follow the steps below to get crewing! 🚣‍♂️
      description: >
        Conduct a thorough research about {topic}
        Make sure you find any interesting and relevant information given
-        the current year is 2024.
+        the current year is 2025.
      expected_output: >
        A list with 10 bullet points of the most relevant information about {topic}
      agent: researcher
@@ -195,10 +195,10 @@ Follow the steps below to get crewing! 🚣‍♂️

  <CodeGroup>
    ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2024
+    # Comprehensive Report on the Rise and Impact of AI Agents in 2025

    ## 1. Introduction to AI Agents
-    In 2024, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.

    ## 2. Benefits of AI Agents
    AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
@@ -252,7 +252,7 @@ Follow the steps below to get crewing! 🚣‍♂️
    To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.

    ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 2024. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
    ```
  </CodeGroup>
  </Step>
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -152,6 +152,7 @@ nav:
    - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
    - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
    - Agent Monitoring with OpenLIT: 'how-to/openlit-Observability.md'
+    - Agent Monitoring with MLflow: 'how-to/mlflow-Observability.md'
  - Tools Docs:
    - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
    - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'
--- a/src/crewai/utilities/embedding_configurator.py
+++ b/src/crewai/utilities/embedding_configurator.py
@@ -141,9 +141,11 @@ class EmbeddingConfigurator:
            AmazonBedrockEmbeddingFunction,
        )

-        return AmazonBedrockEmbeddingFunction(
-            session=config.get("session"),
-        )
+        # 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):
--- a/tests/config/tasks.yaml
+++ b/tests/config/tasks.yaml
@@ -2,7 +2,7 @@ research_task:
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
-    the current year is 2024.
+    the current year is 2025.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher
--- a/tests/task_test.py
+++ b/tests/task_test.py
@@ -723,14 +723,14 @@ def test_interpolate_inputs():
    )

    task.interpolate_inputs_and_add_conversation_history(
-        inputs={"topic": "AI", "date": "2024"}
+        inputs={"topic": "AI", "date": "2025"}
    )
    assert (
        task.description
        == "Give me a list of 5 interesting ideas about AI to explore for an article, what makes them unique and interesting."
    )
    assert task.expected_output == "Bullet point list of 5 interesting ideas about AI."
-    assert task.output_file == "/tmp/AI/output_2024.txt"
+    assert task.output_file == "/tmp/AI/output_2025.txt"

    task.interpolate_inputs_and_add_conversation_history(
        inputs={"topic": "ML", "date": "2025"}