[feat][auto-equip-respond][auto inject respond tool into groupchat

  agents][bugf][router-groupchat-params][forward output type and
  verbose to groupchat][feat][groupchat-examples][add dynamic and
  router groupchat examples][docs][example-readmes][list new examples
  in readmes][improvement][version-bump][bump package version to
  12.0.3]

Author: kyegomez

dynamic_groupchat_example.py

@@ -1,14 +1,14 @@
 import json
 
-from swarms import RESPOND_TOOL, Agent, GroupChat
+from swarms import Agent, GroupChat
 
 a1 = Agent(
     agent_name="Researcher",
     system_prompt="You are a research-minded agent who values evidence.",
     model_name="gpt-5.5",
     max_loops=1,
     persistent_memory=False,
-    tools_list_dictionary=[RESPOND_TOOL],
+    print_on=True,
     output_type="final",
 )
 
@@ -17,7 +17,7 @@
     system_prompt="You push back on weak claims and ask sharp questions.",
     model_name="claude-haiku-4-5",
     max_loops=1,
-    tools_list_dictionary=[RESPOND_TOOL],
+    print_on=True,
     persistent_memory=False,
     output_type="final",
 )
@@ -27,8 +27,8 @@
     system_prompt="You turn ideas into concrete next steps.",
     model_name="gpt-5.5",
     max_loops=1,
-    tools_list_dictionary=[RESPOND_TOOL],
     persistent_memory=False,
+    print_on=True,
     output_type="final",
 )
 
@@ -38,7 +38,9 @@
     threshold=0.6,
     output_type="dict",
 )
+
 result = chat.run(
     "Should we use vector databases or knowledge graphs for agent memory?"
 )
+
 print(json.dumps(result, indent=4))

examples/multi_agent/groupchat/README.md

@@ -4,6 +4,7 @@ This directory contains examples demonstrating the dynamic `GroupChat` — an as
 
 ## Examples
 
+- [dynamic_groupchat_example.py](dynamic_groupchat_example.py) - Minimal dynamic groupchat walkthrough
 - [enhanced_collaboration_example.py](enhanced_collaboration_example.py) - Analyst / researcher / strategist scenarios
 - [medical_panel_example.py](medical_panel_example.py) - Panel of medical specialists discussing a complex multi-system case
 - [quantum_physics_swarm.py](quantum_physics_swarm.py) - Condensed-matter physics research team

examples/multi_agent/groupchat/dynamic_groupchat_example.py

@@ -0,0 +1,46 @@
+import json
+
+from swarms import RESPOND_TOOL, Agent, GroupChat
+
+a1 = Agent(
+    agent_name="Researcher",
+    system_prompt="You are a research-minded agent who values evidence.",
+    model_name="gpt-5.5",
+    max_loops=1,
+    persistent_memory=False,
+    # tools_list_dictionary=[RESPOND_TOOL],
+    output_type="final",
+)
+
+a2 = Agent(
+    agent_name="Skeptic",
+    system_prompt="You push back on weak claims and ask sharp questions.",
+    model_name="claude-haiku-4-5",
+    max_loops=1,
+    # tools_list_dictionary=[RESPOND_TOOL],
+    persistent_memory=False,
+    output_type="final",
+)
+
+a3 = Agent(
+    agent_name="Builder",
+    system_prompt="You turn ideas into concrete next steps.",
+    model_name="gpt-5.5",
+    max_loops=1,
+    tools_list_dictionary=[RESPOND_TOOL],
+    persistent_memory=False,
+    output_type="final",
+)
+
+chat = GroupChat(
+    agents=[a1, a2, a3],
+    max_loops=4,
+    threshold=0.6,
+    output_type="dict",
+)
+
+result = chat.run(
+    "Should we use vector databases or knowledge graphs for agent memory?"
+)
+
+print(json.dumps(result, indent=4))

examples/multi_agent/swarm_router/README.md

@@ -4,6 +4,7 @@ This directory contains examples demonstrating swarm routing patterns for direct
 
 ## Examples
 
+- [groupchat_swarm_router_example.py](groupchat_swarm_router_example.py) - Self-selecting groupchat via SwarmRouter
 - [heavy_swarm_router_example.py](heavy_swarm_router_example.py) - Router for heavy swarms
 - [market_analysis_swarm_router_concurrent.py](market_analysis_swarm_router_concurrent.py) - Concurrent market analysis router
 - [sr_moa_example.py](sr_moa_example.py) - Swarm router with MOA

groupchat_swarm_router_example.py …router/groupchat_swarm_router_example.pygroupchat_swarm_router_example.py renamed to examples/multi_agent/swarm_router/groupchat_swarm_router_example.py groupchat_swarm_router_example.py renamed to examples/multi_agent/swarm_router/groupchat_swarm_router_example.py

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "12.0.2"
+version = "12.0.3"
 description = "Swarms - TGSC"
 license = "Apache-2.0"
 authors = ["Kye Gomez <kye@swarms.world>"]

pyproject.toml

@@ -195,6 +195,7 @@ def __init__(
         output_type: str = "str-all-except-first",
         verbose: bool = False,
         print_on: bool = True,
+        auto_equip: bool = True,
     ):
         """Initialize the dynamic groupchat runtime.
 
@@ -222,12 +223,40 @@ def __init__(
         self.output_type = output_type
         self.verbose = verbose
         self.print_on = print_on
+        self.auto_equip = auto_equip
 
         self.conversation = Conversation(time_enabled=True)
 
         if len(self.agents) < 2:
             raise ValueError("GroupChat requires at least 2 agents.")
 
+        if self.auto_equip:
+            self._ensure_respond_tool()
+
+    def _ensure_respond_tool(self) -> None:
+        """Inject ``RESPOND_TOOL`` into agents that do not already carry it.
+
+        Without the ``respond`` tool an agent's speaking decision can never be
+        parsed, so it would sit silent for the whole chat. The agent's LLM
+        client bakes ``tools_list_dictionary`` in at construction time, so
+        after appending the schema the client is rebuilt via
+        ``llm_handling()``.
+        """
+        for agent in self.agents:
+            tools = agent.tools_list_dictionary or []
+            if any(
+                tool.get("function", {}).get("name") == "respond"
+                for tool in tools
+                if isinstance(tool, dict)
+            ):
+                continue
+            agent.tools_list_dictionary = [*tools, RESPOND_TOOL]
+            agent.llm = agent.llm_handling()
+            self._log(
+                "info",
+                f"Injected respond tool into {agent.agent_name}",
+            )
+
     def _other_agents(self, exclude: str) -> str:
         """Return a comma-separated list of peers visible to one agent."""
         return ", ".join(

swarms/structs/groupchat.py

@@ -708,6 +708,8 @@ def _create_group_chat(self, *args, **kwargs):
             description=self.description,
             agents=self.agents,
             max_loops=self.max_loops,
+            output_type=self.output_type,
+            verbose=self.verbose,
             *args,
             **kwargs,
         )