pprint_run_response doesn't print if it is ResponseModel in case of stream

[yogin16]

pprint_run_response print blank response if it is Iterable and from structured response. Possibility in case of a Workflow.

/phi/utils/pprint.py

else:
        streaming_response_content: str = ""
        with Live() as live_log:
            status = Status("Working...", spinner="dots")
            live_log.update(status)
            response_timer = Timer()
            response_timer.start()
            for resp in run_response:
                if isinstance(resp, RunResponse) and isinstance(resp.content, str):
                    streaming_response_content += resp.content

                formatted_response = Markdown(streaming_response_content) if markdown else streaming_response_content  # type: ignore
                table = Table(box=ROUNDED, border_style="blue", show_header=False)
                if show_time:
                    table.add_row(f"Response\n({response_timer.elapsed:.1f}s)", formatted_response)  # type: ignore
                else:
                    table.add_row(formatted_response)  # type: ignore
                live_log.update(table)
            response_timer.stop()

when resp.conent is not str and something of BaseModel, it prints blank

[manthanguptaa]

Hey @yogin16, can you help me with your code so that I can reproduce it on my end?

[yogin16]

import json
from typing import Optional, Iterator

from pydantic import BaseModel, Field

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.workflow import Workflow, RunResponse, RunEvent
from phi.storage.workflow.sqlite import SqlWorkflowStorage
from phi.tools.duckduckgo import DuckDuckGo
from phi.utils.pprint import pprint_run_response
from phi.utils.log import logger


class NewsArticle(BaseModel):
    title: str = Field(..., description="Title of the article.")
    url: str = Field(..., description="Link to the article.")
    summary: Optional[str] = Field(..., description="Summary of the article if available.")


class SearchResults(BaseModel):
    articles: list[NewsArticle]

class BlogDraft(BaseModel):
    title: str = Field(..., description="Title of the drafted blog)
    content: str = File(..., description="Blog content in the markdown format)

class BlogPostGenerator(Workflow):
    # Define an Agent that will search the web for a topic
    searcher: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[DuckDuckGo()],
        instructions=["Given a topic, search for the top 5 articles."],
        response_model=SearchResults,
        structured_outputs=True,
    )

    # Define an Agent that will write the blog post
    writer: Agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        instructions=[
            "You will be provided with a topic and a list of top articles on that topic.",
            "Carefully read each article and generate a New York Times worthy blog post on that topic.",
            "Break the blog post into sections and provide key takeaways at the end.",
            "Make sure the title is catchy and engaging.",
            "Always provide sources, do not make up information or sources.",
        ],
        response_model=BlogDraft,
    )

    def run(self, topic: str, use_cache: bool = True) -> Iterator[RunResponse]:
        """This is where the main logic of the workflow is implemented."""

        logger.info(f"Generating a blog post on: {topic}")

        # Step 1: Use the cached blog post if use_cache is True
        if use_cache:
            cached_blog_post = self.get_cached_blog_post(topic)
            if cached_blog_post:
                yield RunResponse(content=cached_blog_post, event=RunEvent.workflow_completed)
                return

        # Step 2: Search the web for articles on the topic
        search_results: Optional[SearchResults] = self.get_search_results(topic)
        # If no search_results are found for the topic, end the workflow
        if search_results is None or len(search_results.articles) == 0:
            yield RunResponse(
                event=RunEvent.workflow_completed,
                content=f"Sorry, could not find any articles on the topic: {topic}",
            )
            return

        # Step 3: Write a blog post
        yield from self.write_blog_post(topic, search_results)

    def get_cached_blog_post(self, topic: str) -> Optional[str]:
        """Get the cached blog post for a topic."""

        logger.info("Checking if cached blog post exists")
        return self.session_state.get("blog_posts", {}).get(topic)

    def add_blog_post_to_cache(self, topic: str, blog_post: Optional[str]):
        """Add a blog post to the cache."""

        logger.info(f"Saving blog post for topic: {topic}")
        self.session_state.setdefault("blog_posts", {})
        self.session_state["blog_posts"][topic] = blog_post

    def get_search_results(self, topic: str) -> Optional[SearchResults]:
        """Get the search results for a topic."""

        MAX_ATTEMPTS = 3

        for attempt in range(MAX_ATTEMPTS):
            try:
                searcher_response: RunResponse = self.searcher.run(topic)

                # Check if we got a valid response
                if not searcher_response or not searcher_response.content:
                    logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS}: Empty searcher response")
                    continue
                # Check if the response is of the expected SearchResults type
                if not isinstance(searcher_response.content, SearchResults):
                    logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS}: Invalid response type")
                    continue

                article_count = len(searcher_response.content.articles)
                logger.info(f"Found {article_count} articles on attempt {attempt + 1}")
                return searcher_response.content

            except Exception as e:
                logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS} failed: {str(e)}")

        logger.error(f"Failed to get search results after {MAX_ATTEMPTS} attempts")
        return None

    def write_blog_post(self, topic: str, search_results: SearchResults) -> Iterator[RunResponse]:
        """Write a blog post on a topic."""

        logger.info("Writing blog post")
        # Prepare the input for the writer
        writer_input = {"topic": topic, "articles": [v.model_dump() for v in search_results.articles]}
        # Run the writer and yield the response
        yield from self.writer.run(json.dumps(writer_input, indent=4), stream=True)
        # Save the blog post in the cache
        self.add_blog_post_to_cache(topic, self.writer.run_response.content)


# Run the workflow if the script is executed directly
if __name__ == "__main__":
    from rich.prompt import Prompt

    # Get topic from user
    topic = Prompt.ask(
        "[bold]Enter a blog post topic[/bold]\n✨",
        default="Why Cats Secretly Run the Internet",
    )

    # Convert the topic to a URL-safe string for use in session_id
    url_safe_topic = topic.lower().replace(" ", "-")

    # Initialize the blog post generator workflow
    # - Creates a unique session ID based on the topic
    # - Sets up SQLite storage for caching results
    generate_blog_post = BlogPostGenerator(
        session_id=f"generate-blog-post-on-{url_safe_topic}",
        storage=SqlWorkflowStorage(
            table_name="generate_blog_post_workflows",
            db_file="tmp/workflows.db",
        ),
    )

    # Execute the workflow with caching enabled
    # Returns an iterator of RunResponse objects containing the generated content
    blog_post: Iterator[RunResponse] = generate_blog_post.run(topic=topic, use_cache=True)

    # Print the response
    pprint_run_response(blog_post)

[manthanguptaa]

@yogin16 the problem is that your run function is returning a tuple instead of RunResponse

[yogin16]

I think that is part of the point. tuple is coming from agent only when using ResponseModel

let me explain more in the detail.

pprint works in following case

import json
from typing import Optional, Iterator

from pydantic import BaseModel, Field

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.workflow import Workflow, RunResponse, RunEvent
from phi.storage.workflow.sqlite import SqlWorkflowStorage
from phi.tools.duckduckgo import DuckDuckGo
from phi.utils.pprint import pprint_run_response
from phi.utils.log import logger


class NewsArticle(BaseModel):
    title: str = Field(..., description="Title of the article.")
    url: str = Field(..., description="Link to the article.")
    summary: Optional[str] = Field(..., description="Summary of the article if available.")


class SearchResults(BaseModel):
    articles: list[NewsArticle]

class BlogDraft(BaseModel):
    title: str = Field(..., description="Title of the drafted blog")
    content: str = Field(..., description="Blog content in the markdown format")

class BlogPostGenerator(Workflow):
    # Define an Agent that will search the web for a topic
    searcher: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[DuckDuckGo()],
        instructions=["Given a topic, search for the top 5 articles."],
        response_model=SearchResults
    )

    # Define an Agent that will write the blog post
    writer: Agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        instructions=[
            "You will be provided with a topic and a list of top articles on that topic.",
            "Carefully read each article and generate a New York Times worthy blog post on that topic.",
            "Break the blog post into sections and provide key takeaways at the end.",
            "Make sure the title is catchy and engaging.",
            "Always provide sources, do not make up information or sources.",
        ],
    )

    def run(self, topic: str, use_cache: bool = True) -> Iterator[RunResponse]:
        """This is where the main logic of the workflow is implemented."""

        logger.info(f"Generating a blog post on: {topic}")

        # Step 1: Use the cached blog post if use_cache is True
        if use_cache:
            cached_blog_post = self.get_cached_blog_post(topic)
            if cached_blog_post:
                yield RunResponse(content=cached_blog_post, event=RunEvent.workflow_completed)
                return

        # Step 2: Search the web for articles on the topic
        search_results: Optional[SearchResults] = self.get_search_results(topic)
        # If no search_results are found for the topic, end the workflow
        if search_results is None or len(search_results.articles) == 0:
            yield RunResponse(
                event=RunEvent.workflow_completed,
                content=f"Sorry, could not find any articles on the topic: {topic}",
            )
            return

        # Step 3: Write a blog post
        yield from self.write_blog_post(topic, search_results)

    def get_cached_blog_post(self, topic: str) -> Optional[str]:
        """Get the cached blog post for a topic."""

        logger.info("Checking if cached blog post exists")
        return self.session_state.get("blog_posts", {}).get(topic)

    def add_blog_post_to_cache(self, topic: str, blog_post: Optional[str]):
        """Add a blog post to the cache."""

        logger.info(f"Saving blog post for topic: {topic}")
        self.session_state.setdefault("blog_posts", {})
        self.session_state["blog_posts"][topic] = blog_post

    def get_search_results(self, topic: str) -> Optional[SearchResults]:
        """Get the search results for a topic."""

        MAX_ATTEMPTS = 3

        for attempt in range(MAX_ATTEMPTS):
            try:
                searcher_response: RunResponse = self.searcher.run(topic)

                # Check if we got a valid response
                if not searcher_response or not searcher_response.content:
                    logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS}: Empty searcher response")
                    continue
                # Check if the response is of the expected SearchResults type
                if not isinstance(searcher_response.content, SearchResults):
                    logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS}: Invalid response type")
                    continue

                article_count = len(searcher_response.content.articles)
                logger.info(f"Found {article_count} articles on attempt {attempt + 1}")
                return searcher_response.content

            except Exception as e:
                logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS} failed: {str(e)}")

        logger.error(f"Failed to get search results after {MAX_ATTEMPTS} attempts")
        return None

    def write_blog_post(self, topic: str, search_results: SearchResults) -> Iterator[RunResponse]:
        """Write a blog post on a topic."""

        logger.info("Writing blog post")
        # Prepare the input for the writer
        writer_input = {"topic": topic, "articles": [v.model_dump() for v in search_results.articles]}
        # Run the writer and yield the response
        writer_response = self.writer.run(json.dumps(writer_input, indent=4), stream=True)
        print("***********")
        print(type(writer_response))
        print("***********")
        yield from writer_response
        # Save the blog post in the cache
        self.add_blog_post_to_cache(topic, self.writer.run_response.content)


# Run the workflow if the script is executed directly
if __name__ == "__main__":
    from rich.prompt import Prompt

    # Get topic from user
    topic = Prompt.ask(
        "[bold]Enter a blog post topic[/bold]\n✨",
        default="Why Cats Secretly Run the Internet",
    )

    # Convert the topic to a URL-safe string for use in session_id
    url_safe_topic = topic.lower().replace(" ", "-")

    # Initialize the blog post generator workflow
    # - Creates a unique session ID based on the topic
    # - Sets up SQLite storage for caching results
    generate_blog_post = BlogPostGenerator(
        session_id=f"generate-blog-post-on-{url_safe_topic}",
        storage=SqlWorkflowStorage(
            table_name="generate_blog_post_workflows",
            db_file="tmp/workflows.db",
        ),
    )

    # Execute the workflow with caching enabled
    # Returns an iterator of RunResponse objects containing the generated content
    blog_post: Iterator[RunResponse] = generate_blog_post.run(topic=topic, use_cache=True)

    print(type(blog_post))
    # Print the response
    pprint_run_response(blog_post)

    # for resp in blog_post:
    #    print(type(resp))
    #    print(resp)
    #    print(resp.content)

Notice writer agent doesn't use ResponseModel in this case

writer: Agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        instructions=[
            "You will be provided with a topic and a list of top articles on that topic.",
            "Carefully read each article and generate a New York Times worthy blog post on that topic.",
            "Break the blog post into sections and provide key takeaways at the end.",
            "Make sure the title is catchy and engaging.",
            "Always provide sources, do not make up information or sources.",
        ],

if the agent was supposed to use the response model, pprint breaks. and here is the example for broken case

i.e., adding this param for the writer agent

writer: Agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        instructions=[
            "You will be provided with a topic and a list of top articles on that topic.",
            "Carefully read each article and generate a New York Times worthy blog post on that topic.",
            "Break the blog post into sections and provide key takeaways at the end.",
            "Make sure the title is catchy and engaging.",
            "Always provide sources, do not make up information or sources.",
        ],
        response_model=BlogDraft,

Here is the complete case with this one line changed:

import json
from typing import Optional, Iterator

from pydantic import BaseModel, Field

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.workflow import Workflow, RunResponse, RunEvent
from phi.storage.workflow.sqlite import SqlWorkflowStorage
from phi.tools.duckduckgo import DuckDuckGo
from phi.utils.pprint import pprint_run_response
from phi.utils.log import logger


class NewsArticle(BaseModel):
    title: str = Field(..., description="Title of the article.")
    url: str = Field(..., description="Link to the article.")
    summary: Optional[str] = Field(..., description="Summary of the article if available.")


class SearchResults(BaseModel):
    articles: list[NewsArticle]

class BlogDraft(BaseModel):
    title: str = Field(..., description="Title of the drafted blog")
    content: str = Field(..., description="Blog content in the markdown format")

class BlogPostGenerator(Workflow):
    # Define an Agent that will search the web for a topic
    searcher: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[DuckDuckGo()],
        instructions=["Given a topic, search for the top 5 articles."],
        response_model=SearchResults
    )

    # Define an Agent that will write the blog post
    writer: Agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        instructions=[
            "You will be provided with a topic and a list of top articles on that topic.",
            "Carefully read each article and generate a New York Times worthy blog post on that topic.",
            "Break the blog post into sections and provide key takeaways at the end.",
            "Make sure the title is catchy and engaging.",
            "Always provide sources, do not make up information or sources.",
        ],
        response_model=BlogDraft,   # ADDED THIS LINE TO GET STRUCTURED OUTPUT
    )

    def run(self, topic: str, use_cache: bool = True) -> Iterator[RunResponse]:
        """This is where the main logic of the workflow is implemented."""

        logger.info(f"Generating a blog post on: {topic}")

        # Step 1: Use the cached blog post if use_cache is True
        if use_cache:
            cached_blog_post = self.get_cached_blog_post(topic)
            if cached_blog_post:
                yield RunResponse(content=cached_blog_post, event=RunEvent.workflow_completed)
                return

        # Step 2: Search the web for articles on the topic
        search_results: Optional[SearchResults] = self.get_search_results(topic)
        # If no search_results are found for the topic, end the workflow
        if search_results is None or len(search_results.articles) == 0:
            yield RunResponse(
                event=RunEvent.workflow_completed,
                content=f"Sorry, could not find any articles on the topic: {topic}",
            )
            return

        # Step 3: Write a blog post
        yield from self.write_blog_post(topic, search_results)

    def get_cached_blog_post(self, topic: str) -> Optional[str]:
        """Get the cached blog post for a topic."""

        logger.info("Checking if cached blog post exists")
        return self.session_state.get("blog_posts", {}).get(topic)

    def add_blog_post_to_cache(self, topic: str, blog_post: Optional[str]):
        """Add a blog post to the cache."""

        logger.info(f"Saving blog post for topic: {topic}")
        self.session_state.setdefault("blog_posts", {})
        self.session_state["blog_posts"][topic] = blog_post

    def get_search_results(self, topic: str) -> Optional[SearchResults]:
        """Get the search results for a topic."""

        MAX_ATTEMPTS = 3

        for attempt in range(MAX_ATTEMPTS):
            try:
                searcher_response: RunResponse = self.searcher.run(topic)

                # Check if we got a valid response
                if not searcher_response or not searcher_response.content:
                    logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS}: Empty searcher response")
                    continue
                # Check if the response is of the expected SearchResults type
                if not isinstance(searcher_response.content, SearchResults):
                    logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS}: Invalid response type")
                    continue

                article_count = len(searcher_response.content.articles)
                logger.info(f"Found {article_count} articles on attempt {attempt + 1}")
                return searcher_response.content

            except Exception as e:
                logger.warning(f"Attempt {attempt + 1}/{MAX_ATTEMPTS} failed: {str(e)}")

        logger.error(f"Failed to get search results after {MAX_ATTEMPTS} attempts")
        return None

    def write_blog_post(self, topic: str, search_results: SearchResults) -> Iterator[RunResponse]:
        """Write a blog post on a topic."""

        logger.info("Writing blog post")
        # Prepare the input for the writer
        writer_input = {"topic": topic, "articles": [v.model_dump() for v in search_results.articles]}
        # Run the writer and yield the response
        writer_response = self.writer.run(json.dumps(writer_input, indent=4), stream=True)
        print("***********")
        print(type(writer_response))
        print("***********")
        yield from writer_response
        # Save the blog post in the cache
        self.add_blog_post_to_cache(topic, self.writer.run_response.content)


# Run the workflow if the script is executed directly
if __name__ == "__main__":
    from rich.prompt import Prompt

    # Get topic from user
    topic = Prompt.ask(
        "[bold]Enter a blog post topic[/bold]\n✨",
        default="Why Cats Secretly Run the Internet",
    )

    # Convert the topic to a URL-safe string for use in session_id
    url_safe_topic = topic.lower().replace(" ", "-")

    # Initialize the blog post generator workflow
    # - Creates a unique session ID based on the topic
    # - Sets up SQLite storage for caching results
    generate_blog_post = BlogPostGenerator(
        session_id=f"generate-blog-post-on-{url_safe_topic}",
        storage=SqlWorkflowStorage(
            table_name="generate_blog_post_workflows",
            db_file="tmp/workflows.db",
        ),
    )

    # Execute the workflow with caching enabled
    # Returns an iterator of RunResponse objects containing the generated content
    blog_post: Iterator[RunResponse] = generate_blog_post.run(topic=topic, use_cache=True)

    print(type(blog_post))
    # Print the response
    # pprint_run_response(blog_post) <- THIS DOESN'T WORK

    for resp in blog_post:
       print(type(resp))
       print(resp)
       print(resp.content)

One would assume that all agent run responses are composable for a workflow as well as pprint_run_response to handle structured response content (like it does in the non streaming case?)