docs agent streaming with formatting name fix

docs/examples/agent_stream.md

@@ -0,0 +1,62 @@
+# Agent with Streaming
+
+The Swarms framework provides powerful real-time streaming capabilities for agents, allowing you to see responses being generated token by token as they're produced by the language model. This creates a more engaging and interactive experience, especially useful for long-form content generation, debugging, or when you want to provide immediate feedback to users.
+
+## Installation
+
+Install the swarms package using pip:
+
+```bash
+pip install -U swarms
+```
+
+## Basic Setup
+
+1. First, set up your environment variables:
+
+```python
+WORKSPACE_DIR="agent_workspace"
+OPENAI_API_KEY=""
+```
+
+## Step by Step
+
+- Install and put your keys in `.env`
+
+- Turn on streaming in `Agent()` with `streaming_on=True`
+
+- Optional: If you want to pretty print it, you can do `print_on=True`; if not, it will print normally
+
+## Code
+
+```python
+from swarms import Agent
+
+# Enable real-time streaming
+agent = Agent(
+    agent_name="StoryAgent",
+    model_name="gpt-4o-mini",
+    streaming_on=True,  # 🔥 This enables real streaming!
+    max_loops=1,
+    print_on=True,  # By default, it's False for raw streaming!
+)
+
+# This will now stream in real-time with a beautiful UI!
+response = agent.run("Tell me a detailed story about humanity colonizing the stars")
+print(response)
+```
+
+## Connect With Us
+
+If you'd like technical support, join our Discord below and stay updated on our Twitter for new updates!
+
+| Platform | Link | Description |
+|----------|------|-------------|
+| 📚 Documentation | [docs.swarms.world](https://docs.swarms.world) | Official documentation and guides |
+| 📝 Blog | [Medium](https://medium.com/@kyeg) | Latest updates and technical articles |
+| 💬 Discord | [Join Discord](https://discord.gg/jM3Z6M9uMq) | Live chat and community support |
+| 🐦 Twitter | [@kyegomez](https://twitter.com/kyegomez) | Latest news and announcements |
+| 👥 LinkedIn | [The Swarm Corporation](https://www.linkedin.com/company/the-swarm-corporation) | Professional network and updates |
+| 📺 YouTube | [Swarms Channel](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) | Tutorials and demos |
+| 🎫 Events | [Sign up here](https://lu.ma/5p2jnc2v) | Join our community events |
+

docs/mkdocs.yml

@@ -311,6 +311,7 @@ nav:
     # - PreBuilt Templates: "examples/templates_index.md"
     - Customizing Agents:
       - Basic Agent: "swarms/examples/basic_agent.md"
+      - Agent with Streaming: "examples/agent_stream.md"
       - Agents with Callable Tools: "swarms/examples/agent_with_tools.md"
       # - Agent With MCP Integration: "swarms/examples/agent_with_mcp.md"
       - Agent Output Types: "swarms/examples/agent_output_types.md"

stream_example.py

@@ -10,4 +10,5 @@ agent = Agent(
 )
 
 # This will now stream in real-time with beautiful UI!
-response = agent.run("Tell me a detailed story...")
+response = agent.run("Tell me a detailed story about Humanity colonizing the stars")
+print(response)

swarm_router_test.py

@@ -1,81 +0,0 @@
-import json
-from swarms import Agent, SwarmRouter
-
-# Agent 1: Risk Metrics Calculator
-risk_metrics_agent = Agent(
-    agent_name="Risk-Metrics-Calculator",
-    agent_description="Calculates key risk metrics like VaR, Sharpe ratio, and volatility",
-    system_prompt="""You are a risk metrics specialist. Calculate and explain:
-    - Value at Risk (VaR)
-    - Sharpe ratio
-    - Volatility
-    - Maximum drawdown
-    - Beta coefficient
-    
-    Provide clear, numerical results with brief explanations.""",
-    max_loops=1,
-    # model_name="gpt-4o-mini",
-    random_model_enabled=True,
-    dynamic_temperature_enabled=True,
-    output_type="str-all-except-first",
-    max_tokens=4096,
-)
-
-# Agent 2: Portfolio Risk Analyzer
-portfolio_risk_agent = Agent(
-    agent_name="Portfolio-Risk-Analyzer",
-    agent_description="Analyzes portfolio diversification and concentration risk",
-    system_prompt="""You are a portfolio risk analyst. Focus on:
-    - Portfolio diversification analysis
-    - Concentration risk assessment
-    - Correlation analysis
-    - Sector/asset allocation risk
-    - Liquidity risk evaluation
-    
-    Provide actionable insights for risk reduction.""",
-    max_loops=1,
-    # model_name="gpt-4o-mini",
-    random_model_enabled=True,
-    dynamic_temperature_enabled=True,
-    output_type="str-all-except-first",
-    max_tokens=4096,
-)
-
-# Agent 3: Market Risk Monitor
-market_risk_agent = Agent(
-    agent_name="Market-Risk-Monitor",
-    agent_description="Monitors market conditions and identifies risk factors",
-    system_prompt="""You are a market risk monitor. Identify and assess:
-    - Market volatility trends
-    - Economic risk factors
-    - Geopolitical risks
-    - Interest rate risks
-    - Currency risks
-    
-    Provide current risk alerts and trends.""",
-    max_loops=1,
-    # model_name="gpt-4o-mini",
-    random_model_enabled=True,
-    dynamic_temperature_enabled=True,
-    output_type="str-all-except-first",
-    max_tokens=4096,
-)
-
-
-swarm = SwarmRouter(
-    agents=[
-        risk_metrics_agent,
-        portfolio_risk_agent,
-    ],
-    max_loops=1,
-    swarm_type="MixtureOfAgents",
-    output_type="final",
-)
-
-
-# swarm.run(
-#     "Calculate VaR and Sharpe ratio for a portfolio with 15% annual return and 20% volatility"
-# )
-
-
-print(f"Swarm config: {json.dumps(swarm.to_dict(), indent=4)}")

swarms/structs/agent.py

@@ -1061,12 +1061,16 @@ class Agent:
                             response = self.call_llm(
                                 task=task_prompt,
                                 img=img,
+                                current_loop=loop_count,
                                 *args,
                                 **kwargs,
                             )
                         else:
                             response = self.call_llm(
-                                task=task_prompt, *args, **kwargs
+                                task=task_prompt,
+                                current_loop=loop_count,
+                                *args,
+                                **kwargs,
                             )
 
                         # Parse the response from the agent with the output type
@@ -2463,7 +2467,12 @@ class Agent:
         return None
 
     def call_llm(
-        self, task: str, img: Optional[str] = None, *args, **kwargs
+        self,
+        task: str,
+        img: Optional[str] = None,
+        current_loop: int = 0,
+        *args,
+        **kwargs,
     ) -> str:
         """
         Calls the appropriate method on the `llm` object based on the given task.
@@ -2486,7 +2495,7 @@ class Agent:
 
         try:
             # Set streaming parameter in LLM if streaming is enabled
-            if self.streaming_on and hasattr(self.llm, 'stream'):
+            if self.streaming_on and hasattr(self.llm, "stream"):
                 original_stream = self.llm.stream
                 self.llm.stream = True
 
@@ -2495,22 +2504,37 @@ class Agent:
                         task=task, img=img, *args, **kwargs
                     )
                 else:
-                    streaming_response = self.llm.run(task=task, *args, **kwargs)
+                    streaming_response = self.llm.run(
+                        task=task, *args, **kwargs
+                    )
 
                 # If we get a streaming response, handle it with the new streaming panel
-                if hasattr(streaming_response, '__iter__') and not isinstance(streaming_response, str):
+                if hasattr(
+                    streaming_response, "__iter__"
+                ) and not isinstance(streaming_response, str):
                     # Check print_on parameter for different streaming behaviors
                     if self.print_on is False:
                         # Show raw streaming text without formatting panels
                         chunks = []
-                        print(f"\n{self.agent_name}: ", end="", flush=True)
+                        print(
+                            f"\n{self.agent_name}: ",
+                            end="",
+                            flush=True,
+                        )
                         for chunk in streaming_response:
-                            if hasattr(chunk, 'choices') and chunk.choices[0].delta.content:
-                                content = chunk.choices[0].delta.content
-                                print(content, end="", flush=True)  # Print raw streaming text
+                            if (
+                                hasattr(chunk, "choices")
+                                and chunk.choices[0].delta.content
+                            ):
+                                content = chunk.choices[
+                                    0
+                                ].delta.content
+                                print(
+                                    content, end="", flush=True
+                                )  # Print raw streaming text
                                 chunks.append(content)
                         print()  # New line after streaming completes
-                        complete_response = ''.join(chunks)
+                        complete_response = "".join(chunks)
                     else:
                         # Collect chunks for conversation saving
                         collected_chunks = []
@@ -2521,15 +2545,17 @@ class Agent:
                             # Optional: Save each chunk to conversation in real-time
                             # This creates a more detailed conversation history
                             if self.verbose:
-                                logger.debug(f"Streaming chunk received: {chunk[:50]}...")
+                                logger.debug(
+                                    f"Streaming chunk received: {chunk[:50]}..."
+                                )
 
                         # Use the streaming panel to display and collect the response
                         complete_response = formatter.print_streaming_panel(
                             streaming_response,
-                            title=f"🤖 {self.agent_name} Streaming Response",
+                            title=f"🤖 Agent: {self.agent_name} Loops: {current_loop}",
                             style="bold cyan",
                             collect_chunks=True,
-                            on_chunk_callback=on_chunk_received
+                            on_chunk_callback=on_chunk_received,
                         )
 
                     # Restore original stream setting

swarms/utils/formatter.py

@@ -10,6 +10,23 @@ from rich.table import Table
 from rich.text import Text
 
 
+def choose_random_color():
+    import random
+
+    colors = [
+        "red",
+        "green",
+        "blue",
+        "yellow",
+        "magenta",
+        "cyan",
+        "white",
+    ]
+    random_color = random.choice(colors)
+
+    return random_color
+
+
 class Formatter:
     """
     A class for formatting and printing rich text to the console.
@@ -32,18 +49,8 @@ class Formatter:
             title (str, optional): The title of the panel. Defaults to "".
             style (str, optional): The style of the panel. Defaults to "bold blue".
         """
-        import random
+        random_color = choose_random_color()
         
-        colors = [
-            "red",
-            "green",
-            "blue",
-            "yellow",
-            "magenta",
-            "cyan",
-            "white",
-        ]
-        random_color = random.choice(colors)
         panel = Panel(
             content, title=title, style=f"bold {random_color}"
         )
@@ -149,7 +156,7 @@ class Formatter:
         self,
         streaming_response,
         title: str = "🤖 Agent Streaming Response",
-        style: str = "bold cyan",
+        style: str = choose_random_color(),
         collect_chunks: bool = False,
         on_chunk_callback: Optional[Callable] = None,
     ) -> str:
@@ -167,6 +174,7 @@ class Formatter:
         Returns:
             str: The complete accumulated response text.
         """
+
         def create_streaming_panel(text_obj, is_complete=False):
             """Create panel with proper text wrapping using Rich's built-in capabilities"""
             panel_title = f"[bold cyan]{title}[/bold cyan]"
@@ -197,11 +205,15 @@ class Formatter:
         with Live(
             create_streaming_panel(streaming_text),
             console=self.console,
-            refresh_per_second=20
+            refresh_per_second=20,
         ) as live:
             try:
                 for part in streaming_response:
-                    if hasattr(part, 'choices') and part.choices and part.choices[0].delta.content:
+                    if (
+                        hasattr(part, "choices")
+                        and part.choices
+                        and part.choices[0].delta.content
+                    ):
                         # Add ONLY the new chunk to the Text object
                         chunk = part.choices[0].delta.content
                         streaming_text.append(chunk, style="white")
@@ -216,15 +228,29 @@ class Formatter:
                             on_chunk_callback(chunk)
 
                         # Update display with new text - Rich handles all wrapping automatically
-                        live.update(create_streaming_panel(streaming_text, is_complete=False))
+                        live.update(
+                            create_streaming_panel(
+                                streaming_text, is_complete=False
+                            )
+                        )
 
                 # Final update to show completion
-                live.update(create_streaming_panel(streaming_text, is_complete=True))
+                live.update(
+                    create_streaming_panel(
+                        streaming_text, is_complete=True
+                    )
+                )
 
             except Exception as e:
                 # Handle any streaming errors gracefully
-                streaming_text.append(f"\n[Error: {str(e)}]", style="bold red")
-                live.update(create_streaming_panel(streaming_text, is_complete=True))
+                streaming_text.append(
+                    f"\n[Error: {str(e)}]", style="bold red"
+                )
+                live.update(
+                    create_streaming_panel(
+                        streaming_text, is_complete=True
+                    )
+                )
 
         return complete_response