fix(agents,examples): spawn-safety + main-guards, model ids, server URLs; add run-all harness

GPTAssistantAgent's assistant tool becomes a module-level picklable callable (spawn-safe). Examples: add if-__main__ guards to stateful/looping workers (75/76/82/83/84), hoist <locals> tools to module level (92, 16g) and make 82 spawn-safe (env-shared IPC + client rebuild), use settings.llm_model in 58, read server URL from env in the schedule examples, add missing 'import sys' (72/73). Adds run_all_examples.py harness and gitignores its generated report + coding-agent output.

Author: v1r3n

.gitignore

@@ -3,6 +3,10 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+# Example-run artifacts (generated by run_all_examples.py / coding-agent examples)
+examples/agents/run_report.html
+examples/agents/codingexamples/
+
 # C extensions
 *.so
 

examples/agents/16g_credentials_framework_passthrough.py

@@ -24,24 +24,28 @@
 
 import os
 
+from langchain_core.tools import tool as lc_tool
+
 from conductor.ai.agents import AgentRuntime
 from settings import settings
 
 
+# Module-level tool (spawn-safe: importable by qualified name). A <locals>
+# tool defined inside the factory can't be resolved by a spawned worker.
+@lc_tool
+def check_github_auth() -> str:
+    """Check if GitHub authentication is available."""
+    token = os.environ.get("GITHUB_TOKEN", "")
+    if token:
+        return f"GitHub token is set (starts with {token[:4]}...)"
+    return "GitHub token is NOT set"
+
+
 def create_langgraph_agent():
     """Create a simple LangGraph agent with a tool that uses GITHUB_TOKEN."""
-    from langchain_core.tools import tool as lc_tool
     from langchain_openai import ChatOpenAI
     from langgraph.prebuilt import create_react_agent
 
-    @lc_tool
-    def check_github_auth() -> str:
-        """Check if GitHub authentication is available."""
-        token = os.environ.get("GITHUB_TOKEN", "")
-        if token:
-            return f"GitHub token is set (starts with {token[:4]}...)"
-        return "GitHub token is NOT set"
-
     # Parse provider/model format
     model_str = settings.llm_model
     if "/" in model_str:

examples/agents/58_scatter_gather.py

@@ -50,7 +50,7 @@ def search_knowledge_base(query: str) -> dict:
 
 researcher = Agent(
     name="researcher",
-    model="anthropic/claude-sonnet-4-20250514",
+    model=settings.llm_model,
     instructions=(
         "You are a country analyst. You will be given the name of a country. "
         "Use the search_knowledge_base tool ONCE to research that country, then "

examples/agents/72_client_reconnect.py

@@ -26,6 +26,7 @@
 import json
 import os
 import signal
+import sys
 import time
 from pathlib import Path
 

examples/agents/73_worker_restart_recovery.py

@@ -24,6 +24,7 @@
 import json
 import os
 import signal
+import sys
 import time
 from datetime import UTC, datetime
 from pathlib import Path

examples/agents/75_wait_for_message.py

@@ -57,18 +57,26 @@ def execute_task(task: str) -> str:
     ),
 )
 
-with AgentRuntime() as runtime:
-    handle = runtime.start(agent, "Start listening for messages.")
-    print(f"Agent started: {handle.execution_id}")
-    print("Sending messages...\n")
-
-    for msg in ["summarize quarterly report", "draft release notes", "check system health"]:
-        time.sleep(2)
-        print(f"  -> sending: {msg!r}")
-        runtime.send_message(handle.execution_id, {"task": msg})
-
-    # Let the agent process all messages (~5-6s per message)
-    time.sleep(30)
-    handle.stop()
-    handle.join(timeout=30)
-    print("\nDone.")
+def main() -> None:
+    with AgentRuntime() as runtime:
+        handle = runtime.start(agent, "Start listening for messages.")
+        print(f"Agent started: {handle.execution_id}")
+        print("Sending messages...\n")
+
+        for msg in ["summarize quarterly report", "draft release notes", "check system health"]:
+            time.sleep(2)
+            print(f"  -> sending: {msg!r}")
+            runtime.send_message(handle.execution_id, {"task": msg})
+
+        # Let the agent process all messages (~5-6s per message)
+        time.sleep(30)
+        handle.stop()
+        handle.join(timeout=30)
+        print("\nDone.")
+
+
+# Guard the runtime block: spawned tool workers re-import this module, and
+# without the guard they would re-run the orchestration (multiprocessing's
+# "Safe importing of main module" error).
+if __name__ == "__main__":
+    main()

examples/agents/76_wait_for_message_streaming.py

@@ -63,38 +63,46 @@ def respond(answer: str) -> str:
     "Write a one-line Python function that reverses a string",
 ]
 
-with AgentRuntime() as runtime:
-    handle = runtime.start(agent, "Begin. Wait for your first instruction.")
-    print(f"Agent started: {handle.execution_id}\n")
-
-    # Push messages from a background thread while we stream events on the main thread.
-    # Wait long enough between sends for the agent to finish processing each message.
-    # No sleep after the last send — handle.stream() on the main thread is already the
-    # barrier: it blocks until DONE, which only fires once the workflow reaches a
-    # terminal state (after stop() sets the flag and the current iteration completes).
-    def sender():
-        for task in TASKS:
-            time.sleep(8)
-            print(f"\n  [caller] sending -> {task!r}")
-            runtime.send_message(handle.execution_id, {"task": task})
-        handle.stop()
-
-    threading.Thread(target=sender, daemon=True).start()
-
-    for event in handle.stream():
-        if event.type == EventType.THINKING:
-            print(f"  [thinking] {event.content}")
-
-        elif event.type == EventType.TOOL_CALL and event.tool_name == "respond":
-            args = event.args or {}
-            print(f"  [answer] {args.get('answer', '')}")
-
-        elif event.type == EventType.WAITING:
-            print(f"  [waiting] {event.content}")
-
-        elif event.type == EventType.ERROR:
-            print(f"  [error] {event.content}")
-
-        elif event.type == EventType.DONE:
-            print(f"\nAgent finished: {event.output}")
-            break
+def main() -> None:
+    with AgentRuntime() as runtime:
+        handle = runtime.start(agent, "Begin. Wait for your first instruction.")
+        print(f"Agent started: {handle.execution_id}\n")
+
+        # Push messages from a background thread while we stream events on the main thread.
+        # Wait long enough between sends for the agent to finish processing each message.
+        # No sleep after the last send — handle.stream() on the main thread is already the
+        # barrier: it blocks until DONE, which only fires once the workflow reaches a
+        # terminal state (after stop() sets the flag and the current iteration completes).
+        def sender():
+            for task in TASKS:
+                time.sleep(8)
+                print(f"\n  [caller] sending -> {task!r}")
+                runtime.send_message(handle.execution_id, {"task": task})
+            handle.stop()
+
+        threading.Thread(target=sender, daemon=True).start()
+
+        for event in handle.stream():
+            if event.type == EventType.THINKING:
+                print(f"  [thinking] {event.content}")
+
+            elif event.type == EventType.TOOL_CALL and event.tool_name == "respond":
+                args = event.args or {}
+                print(f"  [answer] {args.get('answer', '')}")
+
+            elif event.type == EventType.WAITING:
+                print(f"  [waiting] {event.content}")
+
+            elif event.type == EventType.ERROR:
+                print(f"  [error] {event.content}")
+
+            elif event.type == EventType.DONE:
+                print(f"\nAgent finished: {event.output}")
+                break
+
+
+# Guard the runtime block: spawned tool workers re-import this module, and
+# without the guard they would re-run the orchestration (multiprocessing's
+# "Safe importing of main module" error).
+if __name__ == "__main__":
+    main()