Merge branch 'master' into update/docs

docs/mkdocs.yml

@@ -228,7 +228,15 @@ nav:
       - Quickstart: "quickstart.md"
       - Agents: "swarms/agents/index.md"
       - Multi-Agent Architectures: "swarms/structs/index.md"
+    
+    - Protocol:
+      - Overview: "protocol/overview.md"
+      - SIPs: "protocol/sip.md"
       - Feature Set: "swarms/features.md"
+      - Swarms Ecosystem: "swarms/ecosystem.md"
+      - Technical Support: "swarms/support.md"
+    
+    
     - Agents:
       - Overview: "swarms/framework/agents_explained.md"
       - Quickstart: "swarms/agents/index.md"
@@ -319,10 +327,6 @@ nav:
 
         - Communication Structure: "swarms/structs/conversation.md"
 
-    - Protocol:
-      - Overview: "protocol/overview.md"
-      - SIPs: "protocol/sip.md"
-    
     - Tools:
       - Overview: "swarms_tools/overview.md"
       - BaseTool Reference: "swarms/tools/base_tool.md"
@@ -346,10 +350,6 @@ nav:
       - Deploy on Phala: "swarms_cloud/phala_deploy.md"
       # - Deploy on FastAPI: "swarms_cloud/fastapi_deploy.md"
 
-    - More About Us:
-      - Swarms Ecosystem: "swarms/ecosystem.md"
-      - Technical Support: "swarms/support.md"
-
     
   - Examples:
     - Overview: "examples/index.md"
@@ -491,7 +491,7 @@ nav:
       - Understanding Swarms Architecture: "swarms/concept/framework_architecture.md"
       - Development Philosophy & Principles: "swarms/concept/philosophy.md"
 
-  - About Swarms:
+  # - About Swarms:
-    - Vision & Mission: "swarms/concept/vision.md"
+  #   - Vision & Mission: "swarms/concept/vision.md"
-    - Swarm Ecosystem: "swarms/concept/swarm_ecosystem.md"
+  #   - Swarm Ecosystem: "swarms/concept/swarm_ecosystem.md"
-    - Products: "swarms/products.md"
+  #   - Products: "swarms/products.md"

docs/protocol/overview.md

@@ -25,16 +25,12 @@ The Swarms protocol is organized into several key layers, each responsible for a
     
     - Agents can be specialized for different tasks (e.g., reasoning agents, tool agents, judge agents, etc.).
     
-    
     - Example: A `ReasoningAgent` that can analyze data and make decisions, or a `ToolAgent` that wraps external APIs.
     
-    
     - [Quickstart for Agents](https://docs.swarms.world/en/latest/swarms/agents/)
     
-    
     - [Agent API Reference](https://docs.swarms.world/en/latest/swarms/structs/agent/)
 
-
 2. **Tools with Memory (`swarms/tools`, `swarms/utils`)**
     - Tools are modular components that agents use to interact with the outside world, perform computations, or access resources (APIs, 
     databases, files, etc.).
@@ -47,7 +43,6 @@ The Swarms protocol is organized into several key layers, each responsible for a
     
     - [BaseTool Reference](https://docs.swarms.world/en/latest/swarms/tools/base_tool/)
 
-
 3. **Reasoning & Specialized Agents (`swarms/agents`)**
     - These agents build on the base agent class, adding advanced reasoning, self-consistency, and specialized logic for tasks like 
     planning, evaluation, or multi-step workflows.
@@ -63,7 +58,6 @@ The Swarms protocol is organized into several key layers, each responsible for a
     
     - [Agent Judge](https://docs.swarms.world/en/latest/swarms/agents/agent_judge/)
 
-
 4. **Multi-Agent Structures (`swarms/structs`)**
     - Agents are composed into higher-order structures for collaboration, voting, parallelism, and workflow orchestration.
 
@@ -82,7 +76,6 @@ The Swarms protocol is organized into several key layers, each responsible for a
     
     - [Concurrent Workflow](https://docs.swarms.world/en/latest/swarms/structs/concurrentworkflow/)
 
-
 5. **Supporting Components**
 
     - **Communication (`swarms/communication`)**: Provides wrappers for inter-agent communication, database access, message passing, and 
@@ -104,6 +97,34 @@ The Swarms protocol is organized into several key layers, each responsible for a
 
 ---
 
+## Proposing Large Improvements or Enhancements: Swarms Improvement Proposals (SIPs)
+
+For significant changes, new agent architectures, or radical new features, Swarms uses a formal process called **Swarms Improvement Proposals (SIPs)**. SIPs are design documents that describe new features, enhancements, or changes to the Swarms framework. They ensure that major changes are well-documented, discussed, and reviewed by the community before implementation.
+
+**When to use a SIP:**
+
+- Proposing new agent types, swarm patterns, or coordination mechanisms
+
+- Core framework changes or breaking changes
+
+- New integrations (LLM providers, tools, external services)
+
+- Any complex or multi-component feature
+
+**SIP Process Overview:**
+
+1. Discuss your idea in [GitHub Discussions](https://github.com/kyegomez/swarms/discussions)
+
+2. Submit a SIP as a GitHub Issue using the SIP template
+
+3. Engage with the community and iterate on your proposal
+
+4. Undergo review and, if accepted, proceed to implementation
+
+**Learn more:** See the full [SIP Guidelines and Template](https://docs.swarms.world/en/latest/protocol/sip/)
+
+---
+
 ## Detailed Architecture Diagram
 
 The following Mermaid diagram visualizes the protocol flow and the relationship between the main folders in the `swarms/` package:
@@ -409,6 +430,8 @@ For more on the philosophy and architecture, see [Development Philosophy & Princ
 
 - [Understanding Swarms Architecture](https://docs.swarms.world/en/latest/swarms/concept/framework_architecture/)
 
+- [SIP Guidelines and Template](https://docs.swarms.world/en/latest/protocol/sip/)
+
 
 # Conclusion
 

examples/multi_agent/concurrent_examples/streaming_concurrent_workflow.py

@@ -0,0 +1,62 @@
+from swarms import Agent, ConcurrentWorkflow, SwarmRouter
+
+# Initialize market research agent
+market_researcher = Agent(
+    agent_name="Market-Researcher",
+    system_prompt="""You are a market research specialist. Your tasks include:
+    1. Analyzing market trends and patterns
+    2. Identifying market opportunities and threats
+    3. Evaluating competitor strategies
+    4. Assessing customer needs and preferences
+    5. Providing actionable market insights""",
+    model_name="claude-3-5-sonnet-20240620",
+    max_loops=1,
+    streaming_on=True,
+    print_on=False,
+)
+
+# Initialize financial analyst agent
+financial_analyst = Agent(
+    agent_name="Financial-Analyst",
+    system_prompt="""You are a financial analysis expert. Your responsibilities include:
+    1. Analyzing financial statements
+    2. Evaluating investment opportunities
+    3. Assessing risk factors
+    4. Providing financial forecasts
+    5. Recommending financial strategies""",
+    model_name="claude-3-5-sonnet-20240620",
+    max_loops=1,
+    streaming_on=True,
+    print_on=False,
+)
+
+# Initialize technical analyst agent
+technical_analyst = Agent(
+    agent_name="Technical-Analyst",
+    system_prompt="""You are a technical analysis specialist. Your focus areas include:
+    1. Analyzing price patterns and trends
+    2. Evaluating technical indicators
+    3. Identifying support and resistance levels
+    4. Assessing market momentum
+    5. Providing trading recommendations""",
+    model_name="claude-3-5-sonnet-20240620",
+    max_loops=1,
+    streaming_on=True,
+    print_on=False,
+)
+
+# Create list of agents
+agents = [market_researcher, financial_analyst, technical_analyst]
+
+# Initialize the concurrent workflow
+workflow = ConcurrentWorkflow(
+    name="market-analysis-workflow",
+    agents=agents,
+    max_loops=1,
+    show_dashboard=True,
+)
+
+# Run the workflow
+result = workflow.run(
+    "Analyze Tesla (TSLA) stock from market, financial, and technical perspectives"
+)

swarms/structs/agent.py

@@ -996,6 +996,7 @@ class Agent:
         self,
         task: Optional[Union[str, Any]] = None,
         img: Optional[str] = None,
+        streaming_callback: Optional[Callable[[str], None]] = None,
         *args,
         **kwargs,
     ) -> Any:
@@ -1077,6 +1078,7 @@ class Agent:
                                 task=task_prompt,
                                 img=img,
                                 current_loop=loop_count,
+                                streaming_callback=streaming_callback,
                                 *args,
                                 **kwargs,
                             )
@@ -1084,6 +1086,7 @@ class Agent:
                             response = self.call_llm(
                                 task=task_prompt,
                                 current_loop=loop_count,
+                                streaming_callback=streaming_callback,
                                 *args,
                                 **kwargs,
                             )
@@ -2470,6 +2473,7 @@ class Agent:
         task: str,
         img: Optional[str] = None,
         current_loop: int = 0,
+        streaming_callback: Optional[Callable[[str], None]] = None,
         *args,
         **kwargs,
     ) -> str:
@@ -2480,6 +2484,7 @@ class Agent:
             task (str): The task to be performed by the `llm` object.
             img (str, optional): Path or URL to an image file.
             audio (str, optional): Path or URL to an audio file.
+            streaming_callback (Optional[Callable[[str], None]]): Callback function to receive streaming tokens in real-time.
             *args: Variable length argument list.
             **kwargs: Arbitrary keyword arguments.
 
@@ -2515,8 +2520,24 @@ class Agent:
                 if hasattr(
                     streaming_response, "__iter__"
                 ) and not isinstance(streaming_response, str):
+                    # Check if streaming_callback is provided (for ConcurrentWorkflow dashboard integration)
+                    if streaming_callback is not None:
+                        # Real-time callback streaming for dashboard integration
+                        chunks = []
+                        for chunk in streaming_response:
+                            if (
+                                hasattr(chunk, "choices")
+                                and chunk.choices[0].delta.content
+                            ):
+                                content = chunk.choices[
+                                    0
+                                ].delta.content
+                                chunks.append(content)
+                                # Call the streaming callback with the new chunk
+                                streaming_callback(content)
+                        complete_response = "".join(chunks)
                     # Check print_on parameter for different streaming behaviors
-                    if self.print_on is False:
+                    elif self.print_on is False:
                         # Silent streaming - no printing, just collect chunks
                         chunks = []
                         for chunk in streaming_response:
@@ -2599,6 +2620,7 @@ class Agent:
         img: Optional[str] = None,
         imgs: Optional[List[str]] = None,
         correct_answer: Optional[str] = None,
+        streaming_callback: Optional[Callable[[str], None]] = None,
         *args,
         **kwargs,
     ) -> Any:
@@ -2613,6 +2635,7 @@ class Agent:
             task (Optional[str], optional): The task to be executed. Defaults to None.
             img (Optional[str], optional): The image to be processed. Defaults to None.
             imgs (Optional[List[str]], optional): The list of images to be processed. Defaults to None.
+            streaming_callback (Optional[Callable[[str], None]], optional): Callback function to receive streaming tokens in real-time. Defaults to None.
             *args: Additional positional arguments to be passed to the execution method.
             **kwargs: Additional keyword arguments to be passed to the execution method.
 
@@ -2644,6 +2667,7 @@ class Agent:
                 output = self._run(
                     task=task,
                     img=img,
+                    streaming_callback=streaming_callback,
                     *args,
                     **kwargs,
                 )

swarms/structs/concurrent_workflow.py

@@ -1,5 +1,6 @@
 import concurrent.futures
 import os
+import time
 from typing import Callable, List, Optional, Union
 
 from swarms.structs.agent import Agent
@@ -450,8 +451,25 @@ class ConcurrentWorkflow(BaseSwarm):
                     if self.show_dashboard:
                         self.display_agent_dashboard()
 
-                    # Run the agent
+                    # Create a streaming callback for this agent with throttling
-                    output = agent.run(task=task, img=img, imgs=imgs)
+                    last_update_time = [0]  # Use list to allow modification in nested function
+                    update_interval = 0.1  # Update dashboard every 100ms for smooth streaming
+                    
+                    def streaming_callback(chunk: str):
+                        """Update dashboard with streaming content"""
+                        if self.show_dashboard:
+                            # Append the chunk to the agent's current output
+                            current_output = self.agent_statuses[agent.agent_name]["output"]
+                            self.agent_statuses[agent.agent_name]["output"] = current_output + chunk
+                            
+                            # Throttle dashboard updates for better performance
+                            current_time = time.time()
+                            if current_time - last_update_time[0] >= update_interval:
+                                self.display_agent_dashboard()
+                                last_update_time[0] = current_time
+
+                    # Run the agent with streaming callback
+                    output = agent.run(task=task, img=img, imgs=imgs, streaming_callback=streaming_callback)
 
                     # Update status to completed
                     self.agent_statuses[agent.agent_name][

swarms/structs/interactive_groupchat.py

@@ -963,10 +963,16 @@ Remember: You are part of a team. Your response should reflect that you've read,
         to respond to the user query.
         """
         # Filter out invalid agents
-        valid_agents = [name for name in mentioned_agents if name in self.agent_map]
+        valid_agents = [
+            name
+            for name in mentioned_agents
+            if name in self.agent_map
+        ]
 
         if not valid_agents:
-            raise AgentNotFoundError("No valid agents found in the conversation")
+            raise AgentNotFoundError(
+                "No valid agents found in the conversation"
+            )
 
         # Randomly select exactly one agent to respond
         random_agent = random.choice(valid_agents)