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+        "file": "maxim/tests/test_langgraph_1.ipynb",
+        "hash": "7733558428414760923"
+      },
+      {
+        "file": "maxim/tests/test_langgraph_2.py",
+        "hash": "10131873978642818813"
+      },
+      {
+        "file": "maxim/tests/test_langgraph_decorators.ipynb",
+        "hash": "1390382522153256075"
+      },
+      {
+        "file": "maxim/tests/test_langgraph_decorators.py",
+        "hash": "12343260986357054774"
+      },
+      {
+        "file": "maxim/tests/test_litellm.py",
+        "hash": "11099902075142388998"
+      },
+      {
+        "file": "maxim/tests/test_livekit.py",
+        "hash": "17899108807512175544"
+      },
+      {
+        "file": "maxim/tests/test_livekit_interview_agent.py",
+        "hash": "10160674160317257813"
+      },
+      {
+        "file": "maxim/tests/test_livekit_realtime.py",
+        "hash": "996213583803837267"
+      },
+      {
+        "file": "maxim/tests/test_llamaindex.py",
+        "hash": "6675629323574937623"
+      },
+      {
+        "file": "maxim/tests/test_logger.py",
+        "hash": "17736485477973656750"
+      },
+      {
+        "file": "maxim/tests/test_logger_azure_openai.py",
+        "hash": "2701226528563616636"
+      },
+      {
+        "file": "maxim/tests/test_logger_langchain_01x.py",
+        "hash": "2376131476099385446"
+      },
+      {
+        "file": "maxim/tests/test_logger_langchain_02x.py",
+        "hash": "12438197012538005366"
+      },
+      {
+        "file": "maxim/tests/test_logger_langchain_03x.py",
+        "hash": "8990415085371184506"
+      },
+      {
+        "file": "maxim/tests/test_maxim_core_simple.py",
+        "hash": "3234897181114172517"
+      },
+      {
+        "file": "maxim/tests/test_openai.py",
+        "hash": "14181941772317863401"
+      },
+      {
+        "file": "maxim/tests/test_portkey.py",
+        "hash": "2560844075416526631"
+      },
+      {
+        "file": "maxim/tests/test_prompt_chains.py",
+        "hash": "89277075229983079"
+      },
+      {
+        "file": "maxim/tests/test_prompts.py",
+        "hash": "14994474085713149883"
+      },
+      {
+        "file": "maxim/tests/test_retry_for_maxim_api.py",
+        "hash": "6195428678004466937"
+      },
+      {
+        "file": "maxim/tests/test_test_runs.py",
+        "hash": "770312209601525412"
+      },
+      {
+        "file": "maxim/tests/test_together.py",
+        "hash": "18409579072629811918"
+      },
+      {
+        "file": "maxim/types.py",
+        "hash": "12691869393102957907"
+      },
+      {
+        "file": "maxim/utils/__init__.py",
+        "hash": "3378017431775663240"
+      },
+      {
+        "file": "maxim/utils/semaphore.py",
+        "hash": "10802617620319543669"
+      },
+      {
+        "file": "maxim/utils/utils.py",
+        "hash": "10817019649976944668"
+      },
+      {
+        "file": "maxim/version.py",
+        "hash": "12822019629199043285"
+      },
+      {
+        "file": "mypy.ini",
+        "hash": "12183124462333778678"
+      },
+      {
+        "file": "pyproject.toml",
+        "hash": "5187017724825429773"
+      },
+      {
+        "file": "scripts/split_docs.py",
+        "hash": "17721476561265912388"
+      },
+      {
+        "file": "uv.lock",
+        "hash": "16718195099439418451"
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/.nx/workspace-data/nx_files.nxt b/.nx/workspace-data/nx_files.nxt
new file mode 100644
index 0000000..c7a54e3
Binary files /dev/null and b/.nx/workspace-data/nx_files.nxt differ
diff --git a/.nx/workspace-data/project-graph.json b/.nx/workspace-data/project-graph.json
new file mode 100644
index 0000000..b5f5742
--- /dev/null
+++ b/.nx/workspace-data/project-graph.json
@@ -0,0 +1,6 @@
+{
+  "nodes": {},
+  "externalNodes": {},
+  "dependencies": {},
+  "version": "6.0"
+}
\ No newline at end of file
diff --git a/.python-version b/.python-version
index 9432908..92536a9 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.9.20
+3.12.0
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..601d980
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,77 @@
+# This sample Dockerfile creates a production-ready container for a LiveKit voice AI agent
+# syntax=docker/dockerfile:1
+
+# Use the official UV Python base image with Python 3.11 on Debian Bookworm
+# UV is a fast Python package manager that provides better performance than pip
+# We use the slim variant to keep the image size smaller while still having essential tools
+FROM ghcr.io/astral-sh/uv:python3.11-bookworm-slim
+
+# Keeps Python from buffering stdout and stderr to avoid situations where
+# the application crashes without emitting any logs due to buffering.
+ENV PYTHONUNBUFFERED=1
+
+# Define the program entrypoint file where your agent is started.
+ARG PROGRAM_MAIN="maxim/tests/test_livekit_push_to_talk_copy.py"
+
+
+# Create a non-privileged user that the app will run under.
+# See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#user
+ARG UID=10001
+RUN adduser \
+    --disabled-password \
+    --gecos "" \
+    --home "/home/appuser" \
+    --shell "/sbin/nologin" \
+    --uid "${UID}" \
+    appuser
+
+# Install build dependencies required for Python packages with native extensions
+# gcc: C compiler needed for building Python packages with C extensions
+# python3-dev: Python development headers needed for compilation
+# We clean up the apt cache after installation to keep the image size down
+RUN apt-get update && \
+    apt-get install -y \
+    build-essential \
+    python3-dev \
+    libudev-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set the working directory to the user's home directory
+# This is where our application code will live
+WORKDIR /home/appuser
+
+# Copy all application files into the container
+# This includes source code, configuration files, and dependency specifications
+# (Excludes files specified in .dockerignore)
+COPY . .
+
+# Change ownership of all app files to the non-privileged user
+# This ensures the application can read/write files as needed
+RUN chown -R appuser:appuser /home/appuser
+
+# Switch to the non-privileged user for all subsequent operations
+# This improves security by not running as root
+USER appuser
+
+# Create a cache directory for the user
+# This is used by UV and Python for caching packages and bytecode
+RUN mkdir -p /home/appuser/.cache
+
+# Install Python dependencies using UV's lock file
+# --locked ensures we use exact versions from uv.lock for reproducible builds
+# This creates a virtual environment and installs all dependencies
+# Ensure your uv.lock file is checked in for consistency across environments
+RUN uv sync --locked --group dev
+
+# Note: ML models and files will be downloaded at runtime when environment variables are available
+# This ensures secrets are not baked into the Docker image
+
+# Expose the healthcheck port
+# This allows Docker and orchestration systems to check if the container is healthy
+EXPOSE 8081
+
+# Run the application using UV
+# UV will activate the virtual environment and run the agent.
+# The "start" command tells the worker to connect to LiveKit and begin waiting for jobs.
+CMD ["uv", "run", "maxim/tests/test_livekit_push_to_talk.py", "start"]
+
diff --git a/livekit.toml b/livekit.toml
new file mode 100644
index 0000000..3ee6f18
--- /dev/null
+++ b/livekit.toml
@@ -0,0 +1,5 @@
+[project]
+  subdomain = "testing-48jhpiie"
+
+[agent]
+  id = "CA_Js4NVmZpMjnU"
diff --git a/maxim/decorators/generation.py b/maxim/decorators/generation.py
index 4d737e9..e18498f 100644
--- a/maxim/decorators/generation.py
+++ b/maxim/decorators/generation.py
@@ -140,6 +140,7 @@ async def async_wrapper(*args, **kwargs):
                 finally:
                     generation.end()
                     _generation_ctx_var.reset(token)
+            return async_wrapper
 
         else:
             @wraps(func)
diff --git a/maxim/logger/livekit/agent_activity.py b/maxim/logger/livekit/agent_activity.py
index 0e7ef10..5a6df2c 100644
--- a/maxim/logger/livekit/agent_activity.py
+++ b/maxim/logger/livekit/agent_activity.py
@@ -27,15 +27,20 @@ def handle_interrupt(self: AgentActivity):
             "[MaximSDK] trace is none at realtime session interrupt. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues."
         )
         return
+    scribe().debug(f"[MaximSDK] [EVENT] interrupted {trace.id}")
     trace.event(id=str(uuid4()), name="interrupted")
 
 
 def post_realtime_session_start(self: AgentActivity):
     # Trying to get AgentSession and RealtimeSession handles
+    print("[CHECKPOINT] 8")
+    print(f"[ROHAN] post_realtime_session_start called; self={vars(self)}")
     rt_session_id = id(self.realtime_llm_session)
+    print(f"[ROHAN] post_realtime_session_start called; rt_session_id={rt_session_id}")
     session_info = get_session_store().get_session_by_agent_session_id(
         id(self.session)
     )
+    print(f"[ROHAN] post_realtime_session_start called; session_info={vars(session_info)}")
     if session_info is None:
         scribe().warning(
             f"[Internal][{self.__class__.__name__}] session info is none at realtime session start. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues."
@@ -53,11 +58,13 @@ def post_start(self: AgentActivity):
             f"[Internal][{self.__class__.__name__}] rt_session is none at realtime session start. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues."
         )
         return
+    print(f"[ROHAN] post_start called; self={vars(self)}")
     post_realtime_session_start(self)
 
 
 def handle_input_speech_started(self: AgentActivity):
     scribe().debug(f"[Internal][{self.__class__.__name__}] input speech started")
+    print(f"[Internal][{self.__class__.__name__}] input speech started")
     session_info = get_session_store().get_session_by_agent_session_id(
         id(self.session)
     )
@@ -66,10 +73,12 @@ def handle_input_speech_started(self: AgentActivity):
             f"[Internal][{self.__class__.__name__}] session info is none at realtime session emit. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues."
         )
         return
+    print(f"[Internal][{self.__class__.__name__}] session info: {vars(session_info)}")
     if session_info.provider == "google-realtime":
         trace = get_session_store().get_current_trace_for_agent_session(
             id(self.session)
         )
+        scribe().debug(f"[MaximSDK] [EVENT] user_speaking {trace.id}")
         if trace is not None:
             trace.event(str(uuid4()), "user_speaking", {"platform": "livekit"})
         session_info.user_speaking = True
@@ -94,6 +103,7 @@ def handle_create_speech_task(self: AgentActivity):
     session_info = get_session_store().get_session_by_agent_session_id(
         id(self.session)
     )
+    print(f"[ROHAN] session info in handle create speech task: {vars(session_info)}")
     if session_info is None:
         scribe().warning(
             f"[Internal][{self.__class__.__name__}] session info is none at realtime session emit. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues."
@@ -223,6 +233,7 @@ def handle_final_transcript(self, event):
 
                 # Clear the input buffer after attaching the audio data
                 turn.turn_input_audio_buffer = None
+                print("[ROHAN] setting input transcription from handle final transcript: ", transcript)
                 trace.set_input(transcript)
 
             session_info.current_turn = turn
@@ -395,6 +406,24 @@ def handle_end_of_speech(self: AgentActivity, event: VADEvent):
         scribe().warning(
             f"[Internal][{self.__class__.__name__}] handle_end_of_speech failed in function; error={e!s}\n{traceback.format_exc()}"
         )
+        
+# def handle_end_of_turn(self: AgentActivity):
+#     try:
+#         session_info = get_session_store().get_session_by_agent_session_id(
+#             id(self.session)
+#         )
+#         if session_info is None or (session_info.current_turn.turn_input_transcription == "" and session_info.current_turn.turn_output_transcription == ""):
+#             print(f"[ROHAN] handle_end_of_turn called; session_info is none")
+#             return
+#         turn = start_new_turn(session_info)
+#         print(f"[ROHAN] handle_end_of_turn called; turn={vars(turn)}")
+#         session_info.current_turn = turn
+#         print(f"[ROHAN] handle_end_of_turn called; session_info={vars(session_info)}")
+#         get_session_store().set_session(session_info)
+#     except Exception as e:
+#         scribe().warning(
+#             f"[Internal][{self.__class__.__name__}] handle_end_of_turn failed; error={e!s}\n{traceback.format_exc()}"
+#         )
 
 def pre_hook(self, hook_name, args, kwargs):
     ignored_hooks = ["push_audio"]
@@ -453,6 +482,7 @@ def pre_hook(self, hook_name, args, kwargs):
         else:
             if hook_name in ignored_hooks:
                 return
+            print(f"[ROHAN] pre_hook called; hook_name={hook_name}, args={args}, kwargs={kwargs}")
             scribe().debug(
                 f"[Internal][{self.__class__.__name__}] {hook_name} called; args={args}, kwargs={kwargs}"
             )
@@ -464,6 +494,7 @@ def pre_hook(self, hook_name, args, kwargs):
 
 def post_hook(self, result, hook_name, args, kwargs):
     try:
+        print(f"[ROHAN] post_hook called; hook_name={hook_name}, args={args}, kwargs={kwargs}")
         if hook_name == "start":
             get_thread_pool_executor().submit(post_start, self)
         else:
diff --git a/maxim/logger/livekit/agent_session.py b/maxim/logger/livekit/agent_session.py
index fee2a20..fbbbab0 100644
--- a/maxim/logger/livekit/agent_session.py
+++ b/maxim/logger/livekit/agent_session.py
@@ -40,6 +40,7 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
     This is the point where we create a new session for Maxim.
     The session info along with room_id, agent_id, etc is stored in the thread-local store.
     """
+    print("[CHECKPOINT] 1")
     maxim_logger = get_maxim_logger()
     scribe().debug(f"[Internal][{self.__class__.__name__}] Session started")
     # getting the room_id
@@ -53,6 +54,7 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
         room_id = id(room)
         if isinstance(room, dict):
             room_name = room.get("name")
+
     scribe().debug(f"[Internal]session key:{id(self)}")
     scribe().debug(f"[Internal]Room: {room_id}")
     scribe().debug(f"[Internal]Agent: {agent.instructions}")
@@ -69,6 +71,7 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
     if agent is not None:
         session.add_tag("agent_id", str(id(agent)))
     # If callback is set, emit the session started event
+    print("[CHECKPOINT] 2")
     callback = get_livekit_callback()
     if callback is not None:
         try:
@@ -98,8 +101,8 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
             "tags": tags,
         }
     )
+    print("[CHECKPOINT] 3")
 
-    current_turn_id = str(uuid.uuid4())
     if self.stt is not None or self._agent.stt is not NOT_GIVEN:
         # Only add generation if we are not in realtime session
         llm_opts: _LLMOptions = self.llm._opts if self.llm is not None else self._agent.llm._opts
@@ -109,7 +112,7 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
         else:
             model_parameters = None
         trace.generation(GenerationConfigDict(
-            id=current_turn_id,
+            id=str(uuid.uuid4()),
             model=model if model is not None else "unknown",
             model_parameters=model_parameters if model_parameters is not None else {},
             messages=[{"role": "system", "content": agent.instructions}],
@@ -117,6 +120,7 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
             name="Greeting turn",
         ))
 
+    print("[CHECKPOINT] 4")
     callback = get_livekit_callback()
     if callback is not None:
         try:
@@ -126,8 +130,9 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
                 f"[MaximSDK] An error was captured during LiveKit callback execution: {e!s}"
             )
 
+    print("[CHECKPOINT] 5")
     current_turn = Turn(
-        turn_id=current_turn_id,
+        turn_id=trace_id,
         turn_sequence=0,
         turn_timestamp=datetime.now(timezone.utc),
         is_interrupted=False,
@@ -136,8 +141,7 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
         turn_input_audio_buffer=BytesIO(),
         turn_output_audio_buffer=BytesIO(),
     )
-    get_session_store().set_session(
-        SessionStoreEntry(
+    session_to_set = SessionStoreEntry(
             room_id=room_id,
             user_speaking=False,
             provider="unknown",
@@ -155,8 +159,10 @@ def intercept_session_start(self: AgentSession, room, room_name, agent: Agent):
             mx_current_trace_id=trace_id,
             mx_session_id=session_id,
             current_turn=current_turn,
-        ),
-    )
+        )
+    print("[CHECKPOINT] 6")
+    print(f"[ROHAN] session_to_set={vars(session_to_set)}")
+    get_session_store().set_session(session_to_set)
 
 def intercept_update_agent_state(self: AgentSession, new_state):
     """
@@ -166,6 +172,7 @@ def intercept_update_agent_state(self: AgentSession, new_state):
         return
     trace = get_session_store().get_current_trace_for_agent_session(id(self))
     if trace is not None:
+        scribe().debug(f"[MaximSDK] [EVENT] agent_{new_state} {trace.id}")
         trace.event(
             str(uuid.uuid4()),
             f"agent_{new_state}",
@@ -184,6 +191,7 @@ def intercept_generate_reply(self: AgentSession, instructions):
     )
     trace = get_session_store().get_current_trace_for_agent_session(id(self))
     if trace is not None:
+        print("[ROHAN] setting input transcription from intercept generate reply: ", instructions)
         trace.set_input(instructions)
 
 
@@ -198,6 +206,7 @@ def intercept_user_state_changed(self: AgentSession, new_state):
     )
     trace = get_session_store().get_current_trace_for_agent_session(id(self))
     if trace is not None:
+        scribe().debug(f"[MaximSDK] [EVENT] user_{new_state} {trace.id}")
         trace.event(
             str(uuid.uuid4()),
             f"user_{new_state}",
@@ -348,6 +357,7 @@ def intercept_metrics_collected(self, event):
     pass
 
 def pre_hook(self: AgentSession, hook_name, args, kwargs):
+    print(f"[ROHAN] pre_hook called for agent session; hook_name={hook_name}, args={args}, kwargs={kwargs}")
     try:
         if hook_name == "start":
             room = kwargs.get("room")
diff --git a/maxim/logger/livekit/gemini/gemini_realtime_session.py b/maxim/logger/livekit/gemini/gemini_realtime_session.py
index b95a349..b4dfbed 100644
--- a/maxim/logger/livekit/gemini/gemini_realtime_session.py
+++ b/maxim/logger/livekit/gemini/gemini_realtime_session.py
@@ -75,10 +75,12 @@ def handle_build_connect_config_result(
     """
     # here we will start the new generation
     # this is same as session started
-    session_info = get_session_store().get_session_by_rt_session_id(id(self))
+    curr_id = id(self)
+    scribe().debug(f"[MaximSDK] [BUILD_CONNECT_CONFIG] curr_id: {curr_id}")
+    session_info = get_session_store().get_session_by_rt_session_id(curr_id)
     if session_info is None:
         scribe().warning(
-            "[MaximSDK] session info is none at realtime session emit. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues"
+            "[MaximSDK] [BUILD_CONNECT_CONFIG] session info is none at realtime session emit. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues"
         )
         return
     session_info.provider = "google-realtime"
@@ -156,7 +158,8 @@ def handle_server_content(self: RealtimeSession, content: LiveServerContent):
     buffer is periodically flushed to the session as attachments to avoid large
     in-memory growth during long conversations.
     """
-    session_info = get_session_store().get_session_by_rt_session_id(id(self))
+    curr_id = id(self)
+    session_info = get_session_store().get_session_by_rt_session_id(curr_id)
     if session_info is None:
         scribe().warning(
             "[MaximSDK] session info is none at realtime session emit. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues"
@@ -226,6 +229,7 @@ def handle_google_input_transcription_completed(
     if turn is None:
         return
     turn.turn_input_transcription = input_transcription.transcript
+    scribe().debug(f"[MaximSDK] [GOOGLE_INPUT_TRANSCRIPTION_COMPLETED] turn input transcription: {turn.turn_input_transcription}")
     get_session_store().set_session(session_info)
 
 
@@ -239,7 +243,8 @@ def handle_usage_metadata(self: RealtimeSession, usage: UsageMetadata):
     constructing a `GenerationResult` with token details, updating the active
     trace, and persisting the assistant's transcript as output.
     """
-    session_info = get_session_store().get_session_by_rt_session_id(id(self))
+    curr_id = id(self)
+    session_info = get_session_store().get_session_by_rt_session_id(curr_id)
     if session_info is None:
         scribe().warning(
             "[MaximSDK] session info is none at realtime session emit. If you are seeing this frequently, please report issue at https://github.com/maximhq/maxim-py/issues"
diff --git a/maxim/logger/livekit/instrumenter.py b/maxim/logger/livekit/instrumenter.py
index 69b2aa3..2b47446 100644
--- a/maxim/logger/livekit/instrumenter.py
+++ b/maxim/logger/livekit/instrumenter.py
@@ -6,13 +6,13 @@
 from .agent_activity import instrument_agent_activity
 from .agent_session import instrument_agent_session
 from .gemini.instrumenter import instrument_gemini
-from .job_context import instrument_job_context
+# from .job_context import instrument_job_context
 from .llm import instrument_llm_init
 from .realtime_session import instrument_realtime_session
 from .stt import instrument_stt_init
 from .store import MaximLiveKitCallback, set_livekit_callback, set_maxim_logger
 from .tts import instrument_tts_init
-from .worker import instrument_worker
+# from .worker import instrument_worker
 
 
 def instrument_livekit(logger: Logger, callback: MaximLiveKitCallback = None):
@@ -42,11 +42,11 @@ def instrument_livekit(logger: Logger, callback: MaximLiveKitCallback = None):
             setattr(AgentSession, name, instrument_agent_session(orig, name))
 
     # Instrument Worker methods
-    for name, orig in [
-        (n, getattr(Worker, n)) for n in dir(Worker) if callable(getattr(Worker, n))
-    ]:
-        if name != "__class__" and not name.startswith("__"):
-            setattr(Worker, name, instrument_worker(orig, name))
+    # for name, orig in [
+    #     (n, getattr(Worker, n)) for n in dir(Worker) if callable(getattr(Worker, n))
+    # ]:
+    #     if name != "__class__" and not name.startswith("__"):
+    #         setattr(Worker, name, instrument_worker(orig, name))
 
     # Instrument RealtimeSession methods
     for name, orig in [
@@ -67,13 +67,13 @@ def instrument_livekit(logger: Logger, callback: MaximLiveKitCallback = None):
             setattr(AgentActivity, name, instrument_agent_activity(orig, name))
 
     # Instrument JobContext methods
-    for name, orig in [
-        (n, getattr(JobContext, n))
-        for n in dir(JobContext)
-        if callable(getattr(JobContext, n))
-    ]:
-        if name != "__class__" and not name.startswith("__"):
-            setattr(JobContext, name, instrument_job_context(orig, name))
+    # for name, orig in [
+    #     (n, getattr(JobContext, n))
+    #     for n in dir(JobContext)
+    #     if callable(getattr(JobContext, n))
+    # ]:
+    #     if name != "__class__" and not name.startswith("__"):
+    #         setattr(JobContext, name, instrument_job_context(orig, name))
 
     # Instrumenting LLM models if present
     for name, orig in [
diff --git a/maxim/logger/livekit/job_context.py b/maxim/logger/livekit/job_context.py
index c9c3854..fbb63ff 100644
--- a/maxim/logger/livekit/job_context.py
+++ b/maxim/logger/livekit/job_context.py
@@ -42,7 +42,7 @@ def post_hook(self, result, hook_name, args, kwargs):
         if hook_name == "_participant_available":
             if not args or len(args) == 0:
                 return
-            intercept_participant_available(self, args[0])
+            # intercept_participant_available(self, args[0])
 
     except Exception as e:
         scribe().warning(
diff --git a/maxim/logger/livekit/openai/realtime/handler.py b/maxim/logger/livekit/openai/realtime/handler.py
index e535684..c398c4a 100644
--- a/maxim/logger/livekit/openai/realtime/handler.py
+++ b/maxim/logger/livekit/openai/realtime/handler.py
@@ -13,6 +13,7 @@
     ImageContent,
     TextContent,
 )
+from maxim.logger.livekit.utils import start_new_turn
 from maxim.logger.utils import pcm16_to_wav_bytes
 from maxim.logger.livekit.store import SessionStoreEntry, get_maxim_logger, get_session_store
 from maxim.logger.livekit.openai.realtime.events import SessionCreatedEvent, get_model_params
@@ -112,6 +113,7 @@ def handle_openai_input_transcription_completed(
     session_info: SessionStoreEntry, event: InputTranscriptionCompleted
 ):
     # adding a new generation to the current trace
+    print(f"[ROHAN] handle_openai_input_transcription_completed called; args={vars(session_info)}")
     trace = get_session_store().get_current_trace_from_rt_session_id(
         session_info.rt_session_id
     )
@@ -121,6 +123,13 @@ def handle_openai_input_transcription_completed(
     turn = session_info.current_turn
     if turn is None:
         return
+
+    print(f"[ROHAN] turn: {vars(turn)}")
+    if turn.turn_input_audio_buffer.tell() > 0 and turn.turn_output_audio_buffer.tell() > 0:
+        turn = start_new_turn(session_info)
+        session_info.current_turn = turn
+        get_session_store().set_session(session_info)
+
     model = "unknown"
 
     llm_config = session_info.llm_config
@@ -141,6 +150,7 @@ def handle_openai_input_transcription_completed(
             "messages": [{"role": "user", "content": turn.turn_input_transcription}],
         }
     )
+    print("[ROHAN] setting input transcription from handle openai input transcription completed: ", event.transcript)
     trace.set_input(event.transcript)
 
 
@@ -149,6 +159,7 @@ def handle_openai_server_event_received(session_info: SessionStoreEntry, event:
     This function is called when the realtime session receives an event from the OpenAI server.
     """
     event_type = event.get("type")
+    print(f"[ROHAN] handle_openai_server_event_received called; args={event_type}")
     if event_type == "session.created":
         handle_session_created(session_info, event)
     elif event_type == "session.updated":
@@ -185,8 +196,10 @@ def handle_openai_server_event_received(session_info: SessionStoreEntry, event:
         # mark the LLM call complete
         # Attaching the audio buffer as attachment to the generation
         turn = session_info.current_turn
+        print(f"[Internal][{session_info.rt_session_id}] response.done called; args={vars(session_info)}")
         if turn is None:
             return
+
         get_maxim_logger().generation_add_attachment(
             turn.turn_id,
             FileDataAttachment(
diff --git a/maxim/logger/livekit/realtime_session.py b/maxim/logger/livekit/realtime_session.py
index a6e72f7..9833431 100644
--- a/maxim/logger/livekit/realtime_session.py
+++ b/maxim/logger/livekit/realtime_session.py
@@ -66,8 +66,11 @@ def intercept_realtime_session_emit(self: RealtimeSession, event, data):
         session_info.user_speaking = False
         get_session_store().set_session(session_info)
     elif event == "input_audio_transcription_completed":
+        print(f"[Internal][{self.__class__.__name__}] input_audio_transcription_completed called; event={event} args={vars(data)}")
         session_info = get_session_store().get_session_by_rt_session_id(id(self))
+        print(f"[Internal][{self.__class__.__name__}] session info in input_audio_transcription_completed: {vars(session_info)}")
         if session_info is None:
+            print(f"[Internal][{self.__class__.__name__}] session info is none at realtime session emit")
             scribe().debug("[MaximSDK] session info is none at realtime session emit")
             return
         if session_info.provider == "openai-realtime":
@@ -114,6 +117,7 @@ def handle_interrupt(self):
     trace = get_session_store().get_current_trace_from_rt_session_id(rt_session_id)
     if trace is None:
         return
+    scribe().debug(f"[MaximSDK] [EVENT] interrupt {trace.id}")
     trace.event(id=str(uuid4()), name="Interrupt", tags={"type": "interrupt"})
 
 
@@ -126,6 +130,7 @@ def handle_off(self):
     index = session_info.conversation_buffer_index
     if session_info.conversation_buffer.tell() == 0:
         return
+    print(f"[Internal][{self.__class__.__name__}] off called; args={vars(session_info)}")
     get_maxim_logger().session_add_attachment(
         session_id,
         FileDataAttachment(
diff --git a/maxim/logger/livekit/store.py b/maxim/logger/livekit/store.py
index 8094a96..ac80acb 100644
--- a/maxim/logger/livekit/store.py
+++ b/maxim/logger/livekit/store.py
@@ -105,13 +105,18 @@ def get_session_by_room_id(self, room_id: str) -> Union[SessionStoreEntry, None]
     def get_session_by_agent_session_id(
         self, session_id: int
     ) -> Union[SessionStoreEntry, None]:
+        print("[CHECKPOINT] 9")
+        print(f"[ROHAN] get_session_by_agent_session_id called; session_id={session_id}")
         with self._lock:
             for entry in self.mx_live_kit_session_store:
+                print(f"[ROHAN] get_session_by_agent_session_id called; entry={vars(entry)}")
                 if (
                     entry.agent_session_id is not None
                     and entry.agent_session_id == session_id
                 ):
+                    print(f"[ROHAN] get_session_by_agent_session_id called; entry={vars(entry)}")
                     return entry
+            print("[ROHAN] get_session_by_agent_session_id called; entry not found")
             return None
 
     def get_session_by_rt_session_id(
@@ -127,6 +132,7 @@ def get_session_by_rt_session_id(
             return None
 
     def set_session(self, entry: SessionStoreEntry):
+        print("[CHECKPOINT] 7")
         with self._lock:
             if entry.agent_session_id is not None:
                 # find the entry and replace
diff --git a/maxim/logger/livekit/stt.py b/maxim/logger/livekit/stt.py
index 0cf4fd3..b4412b2 100644
--- a/maxim/logger/livekit/stt.py
+++ b/maxim/logger/livekit/stt.py
@@ -108,6 +108,7 @@ def handle_stt_result(self: STT, result):
                 id(self._session)
             )
             if trace is not None:
+                print("[ROHAN] setting input transcription from handle stt result: ", transcript)
                 trace.set_input(transcript)
 
     except Exception as e:
diff --git a/maxim/logger/livekit/worker.py b/maxim/logger/livekit/worker.py
index 0431fc7..2f129bf 100644
--- a/maxim/logger/livekit/worker.py
+++ b/maxim/logger/livekit/worker.py
@@ -52,14 +52,14 @@ def intercept_on(self: Worker, *args, **kwargs):
 
 def intercept_emit(self, *args, **kwargs):
     action = args[0]
-    if action == "worker_started":
-        scribe().info(
-            f"[Internal][MaximSDK][LiveKit:{self.__class__.__name__}] Worker started"
-        )
-    elif action == "worker_stopped":
-        scribe().info(
-            f"[Internal][MaximSDK][LiveKit:{self.__class__.__name__}] Worker stopped"
-        )
+    # if action == "worker_started":
+    #     scribe().info(
+    #         f"[Internal][MaximSDK][LiveKit:{self.__class__.__name__}] Worker started"
+    #     )
+    # elif action == "worker_stopped":
+    #     scribe().info(
+    #         f"[Internal][MaximSDK][LiveKit:{self.__class__.__name__}] Worker stopped"
+    #     )
 
 
 def intercept_aclose(self, *args, **kwargs):
@@ -75,9 +75,9 @@ def intercept_aclose(self, *args, **kwargs):
 
 def pre_hook(self, hook_name, args, kwargs):
     try:
-        scribe().debug(
-            f"[Internal][{self.__class__.__name__}] {hook_name} called; args={args}, kwargs={kwargs}"
-        )
+        # scribe().debug(
+        #     f"[Internal][{self.__class__.__name__}] {hook_name} called; args={args}, kwargs={kwargs}"
+        # )
         if hook_name == "emit":
             intercept_emit(self, *args, **kwargs)
         elif hook_name == "on":
@@ -94,9 +94,10 @@ def pre_hook(self, hook_name, args, kwargs):
 
 def post_hook(self, result, hook_name, args, kwargs):
     try:
-        scribe().debug(
-            f"[Internal][{self.__class__.__name__}] {hook_name} completed; result={result}"
-        )
+        # scribe().debug(
+        #     f"[Internal][{self.__class__.__name__}] {hook_name} completed; result={result}"
+        # )
+        pass
     except Exception as e:
         scribe().warning(
             f"[MaximSDK][LiveKit:{self.__class__.__name__}] {hook_name} failed; error={str(e)}\n{traceback.format_exc()}"
diff --git a/maxim/scribe.py b/maxim/scribe.py
index efe7fee..1c6eb08 100644
--- a/maxim/scribe.py
+++ b/maxim/scribe.py
@@ -21,7 +21,7 @@ def __init__(self, name):
             name: The name of the logger.
         """
         self.name = name
-        self.disable_internal_logs = True
+        self.disable_internal_logs = False
         self.logger = logging.getLogger(name)        
 
     def _should_log(self, msg):
@@ -129,7 +129,7 @@ def scribe():
     if _scribe_instance is None:
         _scribe_instance = Scribe("maxim")
         # Take global logging level and set it for _scribe_instance if set
-        root_level = logging.getLogger().getEffectiveLevel()
+        root_level = logging.DEBUG
         if root_level != logging.NOTSET:
             _scribe_instance.set_level(root_level)
         elif _scribe_instance.get_level() == logging.NOTSET:
diff --git a/maxim/tests/agent_demo.py b/maxim/tests/agent_demo.py
new file mode 100644
index 0000000..7dad67f
--- /dev/null
+++ b/maxim/tests/agent_demo.py
@@ -0,0 +1,454 @@
+#!/usr/bin/env python3
+"""
+Maxim Agent Demo Script
+
+This script demonstrates a multi-step OpenAI agent with comprehensive observability
+using Maxim decorators. The agent can handle weather queries, restaurant searches,
+and calendar checks using multiple tool calls.
+
+The demo runs through 4 pre-built scenarios showcasing different agent capabilities.
+
+Usage:
+    python agent_demo.py
+
+Requirements:
+    - OPENAI_API_KEY environment variable (optional - will use mock responses if not provided)
+    - MAXIM_API_KEY environment variable
+    - MAXIM_BASE_URL environment variable
+    - MAXIM_LOG_REPO_ID environment variable
+"""
+
+import json
+import os
+from typing import Dict, List, Any
+
+import openai
+from dotenv import load_dotenv
+
+from maxim import Maxim
+from maxim.decorators import current_trace, current_generation, generation, span, tool_call, trace
+
+# Load environment variables
+load_dotenv()
+
+# Environment setup
+openai_api_key = os.getenv("OPENAI_API_KEY")
+maxim_api_key = os.getenv("MAXIM_API_KEY")
+maxim_base_url = os.getenv("MAXIM_BASE_URL")
+maxim_repo_id = os.getenv("MAXIM_LOG_REPO_ID")
+
+
+class MaximAgent:
+    """A multi-tool agent with Maxim observability integration."""
+
+    def __init__(self):
+        """Initialize the agent with Maxim logger and OpenAI client."""
+        self.maxim = Maxim()
+        self.logger = self.maxim.logger()
+        
+        # Initialize OpenAI client if API key is available
+        if openai_api_key:
+            self.openai_client = openai.OpenAI(api_key=openai_api_key)
+            print("✅ OpenAI client initialized - will use real AI responses")
+        else:
+            self.openai_client = None
+            print("⚠️  OpenAI API key not found - will use mock responses")
+
+    @tool_call(
+        name="weather_lookup",
+        description="Get current weather information for a specific city",
+        arguments='{"city": "San Francisco", "units": "celsius"}'
+    )
+    def get_weather(self, city: str, units: str = "celsius") -> Dict[str, Any]:
+        """Get weather information for a city."""
+        print(f"🌤️  Looking up weather for {city}...")
+        
+        # Simulate weather API call
+        weather_data = {
+            "city": city,
+            "temperature": 22 if units == "celsius" else 72,
+            "units": units,
+            "condition": "partly cloudy",
+            "humidity": 65,
+            "wind_speed": 12,
+            "timestamp": "2024-01-20T14:30:00Z"
+        }
+        
+        print(f"   Weather: {weather_data['temperature']}°{weather_data['units'][0].upper()}, {weather_data['condition']}")
+        return weather_data
+
+    @tool_call(
+        name="restaurant_search",
+        description="Search for restaurants in a specific area with cuisine preferences",
+        arguments='{"location": "downtown", "cuisine": "italian", "price_range": "moderate"}'
+    )
+    def search_restaurants(self, location: str, cuisine: str = "any", price_range: str = "moderate") -> List[Dict[str, Any]]:
+        """Search for restaurants based on criteria."""
+        print(f"🍽️  Searching for {cuisine} restaurants in {location}...")
+        
+        # Simulate restaurant search API
+        restaurants = [
+            {
+                "name": f"Bella {cuisine.title()} Bistro",
+                "location": location,
+                "cuisine": cuisine,
+                "price_range": price_range,
+                "rating": 4.5,
+                "address": f"123 Main St, {location}",
+                "phone": "+1-555-0123"
+            },
+            {
+                "name": f"The {cuisine.title()} Corner",
+                "location": location,
+                "cuisine": cuisine,
+                "price_range": price_range,
+                "rating": 4.2,
+                "address": f"456 Oak Ave, {location}",
+                "phone": "+1-555-0456"
+            }
+        ]
+        
+        print(f"   Found {len(restaurants)} restaurants")
+        return restaurants
+
+    @tool_call(
+        name="calendar_check",
+        description="Check calendar availability for scheduling",
+        arguments='{"date": "2024-01-21", "time_slot": "evening"}'
+    )
+    def check_calendar(self, date: str, time_slot: str = "any") -> Dict[str, Any]:
+        """Check calendar availability."""
+        print(f"📅 Checking calendar availability for {date} ({time_slot})...")
+        
+        availability = {
+            "date": date,
+            "time_slot": time_slot,
+            "available": True,
+            "suggested_times": ["6:00 PM", "7:00 PM", "8:00 PM"],
+            "conflicts": [],
+            "timezone": "PST"
+        }
+        
+        print(f"   Available: {availability['available']}")
+        return availability
+
+    @span(name="information_gathering")
+    def gather_information(self, user_request: str) -> Dict[str, Any]:
+        """Gather information using multiple tool calls based on user request."""
+        print(f"🔍 Analyzing request: '{user_request}'")
+        gathered_info = {}
+        
+        # Analyze request to determine which tools to use
+        request_lower = user_request.lower()
+        
+        if "weather" in request_lower:
+            # Extract city from request (simplified)
+            city = "San Francisco"  # Default city
+            if "new york" in request_lower:
+                city = "New York"
+            elif "london" in request_lower:
+                city = "London"
+            elif "chicago" in request_lower:
+                city = "Chicago"
+            
+            gathered_info["weather"] = self.get_weather(city, "celsius")
+        
+        if "restaurant" in request_lower or "food" in request_lower or "eat" in request_lower:
+            # Extract location and cuisine preferences
+            location = "downtown"
+            cuisine = "italian"
+            
+            if "chinese" in request_lower:
+                cuisine = "chinese"
+            elif "mexican" in request_lower:
+                cuisine = "mexican"
+            elif "japanese" in request_lower:
+                cuisine = "japanese"
+            elif "thai" in request_lower:
+                cuisine = "thai"
+            
+            gathered_info["restaurants"] = self.search_restaurants(location, cuisine)
+        
+        if "schedule" in request_lower or "calendar" in request_lower or "available" in request_lower:
+            gathered_info["calendar"] = self.check_calendar("2024-01-21", "evening")
+        
+        print(f"📊 Gathered information from {len(gathered_info)} tools")
+        return gathered_info
+
+    @generation(name="response_generation")
+    def generate_response(self, user_request: str, gathered_info: Dict[str, Any]) -> str:
+        """Generate response using OpenAI based on gathered information."""
+        print("🤖 Generating AI response...")
+        
+        if not self.openai_client:
+            # Mock response when OpenAI is not available
+            mock_input = {
+                "user_request": user_request,
+                "gathered_info": gathered_info,
+                "provider": "mock"
+            }
+            
+            # Track mock generation input
+            current_generation().set_provider("mock")
+            current_generation().set_model("mock-response-generator")
+            
+            response_parts = []
+            
+            if "weather" in gathered_info:
+                weather = gathered_info["weather"]
+                response_parts.append(
+                    f"The weather in {weather['city']} is {weather['temperature']}°{weather['units'][0].upper()} "
+                    f"with {weather['condition']} conditions."
+                )
+            
+            if "restaurants" in gathered_info:
+                restaurants = gathered_info["restaurants"]
+                response_parts.append(
+                    f"I found {len(restaurants)} restaurants for you. "
+                    f"Top recommendation: {restaurants[0]['name']} with a {restaurants[0]['rating']} rating."
+                )
+            
+            if "calendar" in gathered_info:
+                calendar = gathered_info["calendar"]
+                if calendar["available"]:
+                    response_parts.append(
+                        f"You're available on {calendar['date']} during {calendar['time_slot']}. "
+                        f"Suggested times: {', '.join(calendar['suggested_times'])}."
+                    )
+            
+            response = " ".join(response_parts) if response_parts else "I've processed your request."
+            
+            # Track mock generation result
+            current_generation().result(response)
+            
+            print("   Using mock response (OpenAI not available)")
+            return response
+        
+        # Real OpenAI call
+        try:
+            system_prompt = """You are a helpful assistant that provides personalized recommendations 
+            based on gathered information. Be concise but informative in your responses."""
+            
+            user_prompt = f"""
+            User request: {user_request}
+            
+            Gathered information: {json.dumps(gathered_info, indent=2)}
+            
+            Please provide a helpful response based on this information.
+            """
+            
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ]
+            
+            # Track generation input
+            current_generation().set_provider("openai")
+            current_generation().set_model("gpt-3.5-turbo")
+            
+            response = self.openai_client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=messages,
+                max_tokens=300,
+                temperature=0.7
+            )
+      
+            current_generation().result(response)
+            
+            ai_response = response.choices[0].message.content or "I apologize, but I couldn't generate a response."
+            print("   Using OpenAI generated response")
+            return ai_response
+            
+        except (openai.OpenAIError, Exception) as e:
+            error_response = f"Error generating response: {str(e)}"
+            
+            # Track error generation
+            error_input = {
+                "user_request": user_request,
+                "gathered_info": gathered_info,
+                "error": str(e)
+            }
+            current_generation().set_provider("openai")
+            current_generation().set_model("gpt-3.5-turbo")
+            current_generation().result(error_response)
+            current_generation().add_tag("error", "true")
+            
+            print(f"   Error: {error_response}")
+            return error_response
+
+    @span(name="request_processing")
+    def process_user_request(self, user_request: str) -> Dict[str, Any]:
+        """Process user request through multiple steps."""
+        print("⚙️  Processing request...")
+        
+        # Step 1: Gather information
+        gathered_info = self.gather_information(user_request)
+        
+        # Step 2: Generate response
+        response = self.generate_response(user_request, gathered_info)
+        
+        # Step 3: Compile final result
+        result = {
+            "user_request": user_request,
+            "gathered_info": gathered_info,
+            "response": response,
+            "processing_steps": ["information_gathering", "response_generation", "result_compilation"]
+        }
+        
+        return result
+
+    def run_conversation(self, user_request: str, session_id: str = "demo_session") -> Dict[str, Any]:
+        """Main agent conversation flow with full observability."""
+        
+        @trace(name="agent_conversation", logger=self.logger)
+        def _traced_conversation():
+            print(f"\n🚀 Starting agent conversation (Session: {session_id})")
+            print(f"📝 User Request: {user_request}")
+            print("-" * 60)
+            
+            current_trace_obj = current_trace()
+            if current_trace_obj:
+                current_trace_obj.set_input({"user_request": user_request, "session_id": session_id})
+                current_trace_obj.add_tag("agent_type", "multi_tool_assistant")
+                current_trace_obj.add_tag("session_id", session_id)
+            
+            try:
+                # Process the user request
+                result = self.process_user_request(user_request)
+                
+                if current_trace_obj:
+                    current_trace_obj.set_output(result)
+                
+                print("-" * 60)
+                print("✅ Conversation completed successfully!")
+                return result
+                
+            except (ValueError, RuntimeError, Exception) as e:
+                error_result = {
+                    "error": str(e),
+                    "user_request": user_request,
+                    "status": "failed"
+                }
+                
+                if current_trace_obj:
+                    current_trace_obj.set_output(error_result)
+                    current_trace_obj.add_tag("error", "true")
+                
+                print(f"❌ Error occurred: {str(e)}")
+                return error_result
+        
+        return _traced_conversation()
+
+    def flush_logs(self):
+        """Flush logs to Maxim."""
+        print("📤 Sending logs to Maxim...")
+        self.logger.flush()
+        print("✅ Logs sent successfully!")
+
+    def cleanup(self):
+        """Clean up resources."""
+        self.flush_logs()
+        self.maxim.cleanup()
+
+
+def print_banner():
+    """Print a welcome banner."""
+    print("=" * 70)
+    print("🤖 MAXIM AGENT DEMO")
+    print("=" * 70)
+    print("This demo showcases a multi-tool AI agent with full observability")
+    print("using Maxim decorators for tracing, spans, and tool calls.")
+    print()
+    print("Running 4 demo scenarios to showcase agent capabilities...")
+    print()
+
+
+def print_result(result: Dict[str, Any]):
+    """Print the conversation result in a formatted way."""
+    print("\n📋 CONVERSATION RESULT:")
+    print("-" * 40)
+    
+    if "error" in result:
+        print(f"❌ Error: {result['error']}")
+        return
+    
+    print("🎯 Final Response:")
+    print(f"   {result['response']}")
+    print()
+    
+    if "gathered_info" in result:
+        print("📊 Information Gathered:")
+        for tool_name, data in result["gathered_info"].items():
+            print(f"   • {tool_name.title()}: {type(data).__name__} with {len(data) if isinstance(data, (list, dict)) else 1} item(s)")
+    
+    print()
+
+
+def run_demo_scenarios(agent: MaximAgent):
+    """Run several demo scenarios to showcase agent capabilities."""
+    scenarios = [
+        {
+            "name": "Weather Query",
+            "request": "What's the weather like in New York today?",
+            "session_id": "weather_demo"
+        },
+        {
+            "name": "Restaurant Search",
+            "request": "I'm looking for good Chinese restaurants downtown for dinner",
+            "session_id": "restaurant_demo"
+        },
+        {
+            "name": "Multi-Tool Request",
+            "request": "What's the weather in Chicago and can you find Italian restaurants? Also check if I'm available tomorrow evening.",
+            "session_id": "multi_tool_demo"
+        },
+        {
+            "name": "Calendar Check",
+            "request": "Am I available for a meeting tomorrow evening?",
+            "session_id": "calendar_demo"
+        }
+    ]
+    
+    for i, scenario in enumerate(scenarios, 1):
+        print(f"\n🎬 DEMO SCENARIO {i}: {scenario['name']}")
+        print("=" * 50)
+        
+        result = agent.run_conversation(scenario["request"], scenario["session_id"])
+        print_result(result)
+        
+        if i < len(scenarios):
+            input("\nPress Enter to continue to the next scenario...")
+
+
+def main():
+    """Main function to run the agent demo."""
+    print_banner()
+    
+    # Check environment variables
+    if not maxim_api_key:
+        print("⚠️  Warning: MAXIM_API_KEY not found. Logs may not be sent to Maxim.")
+    
+    # Initialize agent
+    print("🔧 Initializing Maxim Agent...")
+    agent = MaximAgent()
+    print("✅ Agent initialized successfully!")
+    
+    try:
+        # Run demo scenarios
+        run_demo_scenarios(agent)
+    
+    except KeyboardInterrupt:
+        print("\n\n👋 Demo interrupted by user")
+    
+    except Exception as e:
+        print(f"\n❌ Unexpected error: {e}")
+    
+    finally:
+        # Cleanup
+        print("\n🧹 Cleaning up...")
+        agent.cleanup()
+        print("✅ Demo completed!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/maxim/tests/livekit_token.py b/maxim/tests/livekit_token.py
new file mode 100644
index 0000000..cb258db
--- /dev/null
+++ b/maxim/tests/livekit_token.py
@@ -0,0 +1,15 @@
+from livekit import api
+import os
+
+from dotenv import load_dotenv
+load_dotenv()
+
+token = api.AccessToken(os.getenv('LIVEKIT_API_KEY'), os.getenv('LIVEKIT_API_SECRET')) \
+    .with_identity("identity") \
+    .with_name("name") \
+    .with_grants(api.VideoGrants(
+        room_join=True,
+        room="my-room",
+    )).to_jwt()
+
+print(token)
diff --git a/maxim/tests/push_to_talk_web_client.html b/maxim/tests/push_to_talk_web_client.html
new file mode 100644
index 0000000..c4612cb
--- /dev/null
+++ b/maxim/tests/push_to_talk_web_client.html
@@ -0,0 +1,309 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Push-to-Talk Web Client</title>
+    <script src="https://unpkg.com/livekit-client@2.6.0/dist/livekit-client.umd.js"></script>
+    <script>
+        // Fallback CDN if first one fails
+        if (typeof LiveKit === 'undefined') {
+            document.write('<script src="https://cdn.jsdelivr.net/npm/livekit-client@2.6.0/dist/livekit-client.umd.js"><\/script>');
+        }
+    </script>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            max-width: 600px;
+            margin: 50px auto;
+            padding: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            background: white;
+            padding: 30px;
+            border-radius: 10px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            text-align: center;
+            margin-bottom: 30px;
+        }
+        .config {
+            margin-bottom: 20px;
+        }
+        .config label {
+            display: block;
+            margin-bottom: 5px;
+            font-weight: bold;
+        }
+        .config input {
+            width: 100%;
+            padding: 8px;
+            border: 1px solid #ddd;
+            border-radius: 4px;
+            box-sizing: border-box;
+        }
+        .controls {
+            text-align: center;
+            margin: 20px 0;
+        }
+        button {
+            padding: 12px 24px;
+            margin: 5px;
+            border: none;
+            border-radius: 6px;
+            cursor: pointer;
+            font-size: 16px;
+            font-weight: bold;
+        }
+        .connect { background-color: #4CAF50; color: white; }
+        .disconnect { background-color: #f44336; color: white; }
+        .start-turn { background-color: #2196F3; color: white; }
+        .end-turn { background-color: #FF9800; color: white; }
+        .cancel-turn { background-color: #9E9E9E; color: white; }
+        button:disabled {
+            opacity: 0.5;
+            cursor: not-allowed;
+        }
+        .status {
+            padding: 10px;
+            margin: 10px 0;
+            border-radius: 4px;
+            text-align: center;
+            font-weight: bold;
+        }
+        .status.connected { background-color: #d4edda; color: #155724; }
+        .status.disconnected { background-color: #f8d7da; color: #721c24; }
+        .status.talking { background-color: #fff3cd; color: #856404; }
+        .logs {
+            background-color: #f8f9fa;
+            border: 1px solid #dee2e6;
+            border-radius: 4px;
+            padding: 10px;
+            height: 200px;
+            overflow-y: auto;
+            font-family: monospace;
+            font-size: 12px;
+            margin-top: 20px;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Push-to-Talk Web Client</h1>
+        
+        <div class="config">
+            <label for="wsUrl">LiveKit WebSocket URL:</label>
+            <input type="text" id="wsUrl" value="wss://your-livekit-server.com" placeholder="wss://your-livekit-server.com">
+        </div>
+        
+        <div class="config">
+            <label for="token">Access Token:</label>
+            <input type="text" id="token" placeholder="Your LiveKit access token">
+        </div>
+        
+        <div class="config">
+            <label for="agentIdentity">Agent Identity:</label>
+            <input type="text" id="agentIdentity" value="agent" placeholder="agent">
+        </div>
+        
+        <div class="controls">
+            <button id="connectBtn" class="connect">Connect</button>
+            <button id="disconnectBtn" class="disconnect" disabled>Disconnect</button>
+        </div>
+        
+        <div id="status" class="status disconnected">Disconnected</div>
+        
+        <div class="controls">
+            <button id="startTurnBtn" class="start-turn" disabled>Start Turn</button>
+            <button id="endTurnBtn" class="end-turn" disabled>End Turn</button>
+            <button id="cancelTurnBtn" class="cancel-turn" disabled>Cancel Turn</button>
+        </div>
+        
+        <div id="logs" class="logs"></div>
+    </div>
+
+    <script>
+        // Check if LiveKit loaded properly
+        if (typeof LiveKit === 'undefined') {
+            document.body.innerHTML = '<div style="text-align:center;margin-top:100px;"><h2>Error: LiveKit library failed to load</h2><p>Please check your internet connection and refresh the page.</p></div>';
+            throw new Error('LiveKit library not loaded');
+        }
+        
+        let room = null;
+        let isTalking = false;
+        
+        const elements = {
+            wsUrl: document.getElementById('wsUrl'),
+            token: document.getElementById('token'),
+            agentIdentity: document.getElementById('agentIdentity'),
+            connectBtn: document.getElementById('connectBtn'),
+            disconnectBtn: document.getElementById('disconnectBtn'),
+            startTurnBtn: document.getElementById('startTurnBtn'),
+            endTurnBtn: document.getElementById('endTurnBtn'),
+            cancelTurnBtn: document.getElementById('cancelTurnBtn'),
+            status: document.getElementById('status'),
+            logs: document.getElementById('logs')
+        };
+        
+        function log(message) {
+            const timestamp = new Date().toLocaleTimeString();
+            elements.logs.innerHTML += `[${timestamp}] ${message}\n`;
+            elements.logs.scrollTop = elements.logs.scrollHeight;
+        }
+        
+        function updateStatus(status, className) {
+            elements.status.textContent = status;
+            elements.status.className = `status ${className}`;
+        }
+        
+        function updateButtons(connected, talking = false) {
+            elements.connectBtn.disabled = connected;
+            elements.disconnectBtn.disabled = !connected;
+            elements.startTurnBtn.disabled = !connected || talking;
+            elements.endTurnBtn.disabled = !connected || !talking;
+            elements.cancelTurnBtn.disabled = !connected;
+            isTalking = talking;
+        }
+        
+        async function connect() {
+            try {
+                const wsUrl = elements.wsUrl.value.trim();
+                const token = elements.token.value.trim();
+                
+                if (!wsUrl || !token) {
+                    alert('Please enter WebSocket URL and access token');
+                    return;
+                }
+                
+                log('Connecting to LiveKit...');
+                room = new LiveKit.Room();
+                
+                // Set up event listeners
+                room.on(LiveKit.RoomEvent.Connected, () => {
+                    log('Connected to room');
+                    updateStatus('Connected', 'connected');
+                    updateButtons(true, false);
+                });
+                
+                room.on(LiveKit.RoomEvent.Disconnected, () => {
+                    log('Disconnected from room');
+                    updateStatus('Disconnected', 'disconnected');
+                    updateButtons(false, false);
+                });
+                
+                room.on(LiveKit.RoomEvent.ParticipantConnected, (participant) => {
+                    log(`Participant connected: ${participant.identity}`);
+                });
+                
+                room.on(LiveKit.RoomEvent.ParticipantDisconnected, (participant) => {
+                    log(`Participant disconnected: ${participant.identity}`);
+                });
+                
+                // Connect to the room
+                await room.connect(wsUrl, token);
+                
+            } catch (error) {
+                log(`Connection failed: ${error.message}`);
+                updateStatus('Connection Failed', 'disconnected');
+            }
+        }
+        
+        async function disconnect() {
+            if (room) {
+                await room.disconnect();
+                room = null;
+            }
+        }
+        
+        async function startTurn() {
+            try {
+                const agentIdentity = elements.agentIdentity.value.trim();
+                log(`Sending start_turn RPC to ${agentIdentity}...`);
+                
+                const response = await room.localParticipant.performRpc(
+                    agentIdentity,
+                    'start_turn',
+                    ''
+                );
+                
+                log('✅ Start turn successful');
+                updateStatus('Talking - Agent Listening', 'talking');
+                updateButtons(true, true);
+                
+            } catch (error) {
+                log(`❌ Start turn failed: ${error.message}`);
+            }
+        }
+        
+        async function endTurn() {
+            try {
+                const agentIdentity = elements.agentIdentity.value.trim();
+                log(`Sending end_turn RPC to ${agentIdentity}...`);
+                
+                const response = await room.localParticipant.performRpc(
+                    agentIdentity,
+                    'end_turn',
+                    ''
+                );
+                
+                log('✅ End turn successful - Agent will respond');
+                updateStatus('Connected - Waiting for Agent', 'connected');
+                updateButtons(true, false);
+                
+            } catch (error) {
+                log(`❌ End turn failed: ${error.message}`);
+            }
+        }
+        
+        async function cancelTurn() {
+            try {
+                const agentIdentity = elements.agentIdentity.value.trim();
+                log(`Sending cancel_turn RPC to ${agentIdentity}...`);
+                
+                const response = await room.localParticipant.performRpc(
+                    agentIdentity,
+                    'cancel_turn',
+                    ''
+                );
+                
+                log('✅ Cancel turn successful');
+                updateStatus('Connected', 'connected');
+                updateButtons(true, false);
+                
+            } catch (error) {
+                log(`❌ Cancel turn failed: ${error.message}`);
+            }
+        }
+        
+        // Event listeners
+        elements.connectBtn.addEventListener('click', connect);
+        elements.disconnectBtn.addEventListener('click', disconnect);
+        elements.startTurnBtn.addEventListener('click', startTurn);
+        elements.endTurnBtn.addEventListener('click', endTurn);
+        elements.cancelTurnBtn.addEventListener('click', cancelTurn);
+        
+        // Keyboard shortcuts
+        document.addEventListener('keydown', (e) => {
+            if (e.target.tagName === 'INPUT') return; // Don't trigger when typing in inputs
+            
+            switch(e.key.toLowerCase()) {
+                case 's':
+                    if (!elements.startTurnBtn.disabled) startTurn();
+                    break;
+                case 'e':
+                    if (!elements.endTurnBtn.disabled) endTurn();
+                    break;
+                case 'c':
+                    if (!elements.cancelTurnBtn.disabled) cancelTurn();
+                    break;
+            }
+        });
+        
+        log('Push-to-Talk Web Client loaded');
+        log('Enter your LiveKit URL and token, then click Connect');
+    </script>
+</body>
+</html>
diff --git a/maxim/tests/test_decorators.py b/maxim/tests/test_decorators.py
index cfeed44..8dbbfb3 100644
--- a/maxim/tests/test_decorators.py
+++ b/maxim/tests/test_decorators.py
@@ -1,24 +1,23 @@
-import concurrent.futures
 import json
+from uuid import uuid4
+
+import concurrent.futures
 import logging
 import os
-import time
 import unittest
-from uuid import uuid4
 
 from flask import Flask, request
 from langchain.chat_models.openai import ChatOpenAI
 
-from .. import Config, Maxim
-from ..decorators import current_retrieval, current_trace, retrieval, span, trace
-from ..decorators.langchain import langchain_callback, langchain_llm_call
-from ..logger import LoggerConfig
-from ..tests.mock_writer import inject_mock_writer
+from maxim import Maxim
+from maxim.decorators import current_retrieval, current_trace, generation, retrieval, span, tool_call, trace
+from maxim.decorators.langchain import langchain_callback, langchain_llm_call
+from maxim.tests.mock_writer import inject_mock_writer
+
+from dotenv import load_dotenv
+load_dotenv()
 
-# Note: This file had a hardcoded path that may not work in all environments
-# Let's use environment variables instead
 logging.basicConfig(level=logging.INFO)
-env = "beta"
 
 openAIKey = os.getenv("OPENAI_API_KEY")
 apiKey = os.getenv("MAXIM_API_KEY")
@@ -35,17 +34,56 @@ def setUp(self) -> None:
         if hasattr(Maxim, "_instance"):
             delattr(Maxim, "_instance")
         self.logger = Maxim().logger()
-        self.mock_writer = inject_mock_writer(self.logger)
+        # self.mock_writer = inject_mock_writer(self.logger)
 
     def test_openai_chat_one(self):
         print("here")
 
         @span(name="secondTest")
         def secondTest():
+            @generation(name="generation_1")
+            def generation_1():
+                print("inside generation 1")
+            generation_1()
             print("inside second test")
 
+        @span(name="thirdTest")
         def thirdTest():
-            print("inside third test which is not traced")
+            @tool_call(name="file_analyzer", description="Analyze file content and return statistics", arguments='{"file_path": "test.txt"}')
+            def file_analyzer():
+                # Simulate file analysis
+                import os
+                test_content = "This is a sample file content for testing the tool call decorator functionality."
+                
+                analysis = {
+                    "file_size": len(test_content),
+                    "word_count": len(test_content.split()),
+                    "line_count": test_content.count('\n') + 1,
+                    "char_count": len(test_content),
+                    "analysis_timestamp": "2024-01-20T10:30:00Z"
+                }
+                return analysis
+            
+            result = file_analyzer()
+            print(f"File analysis result: {result}")
+            
+            @tool_call(name="data_processor", description="Process numerical data", arguments=json.dumps({"numbers": [1,2,3,4,5]}))
+            def data_processor():
+                numbers = [10, 25, 30, 45, 50, 15, 35]
+                processed_data = {
+                    "input_data": numbers,
+                    "sum": sum(numbers),
+                    "average": sum(numbers) / len(numbers),
+                    "min": min(numbers),
+                    "max": max(numbers),
+                    "count": len(numbers)
+                }
+                return processed_data
+            
+            data_result = data_processor()
+            print(f"Data processing result: {data_result}")
+            
+            return {"file_analysis": result, "data_processing": data_result}
 
         @trace(name="second trace", logger=self.logger)
         def testing(test):
@@ -59,24 +97,25 @@ def testing(test):
 
         # Flush the logger and verify logging
         self.logger.flush()
-        self.mock_writer.print_logs_summary()
-
-        # Since we're running 2 concurrent traces, we expect 2 trace creates
-        self.mock_writer.assert_entity_action_count("trace", "create", 2)
-
-        # Each trace should have 2 spans (secondTest and the main trace)
-        # So we expect 4 spans total (2 per trace)
-        self.mock_writer.assert_entity_action_count("span", "create", 4)
-
-        # Assert that we have 2 trace end logs
-        self.mock_writer.assert_entity_action_count("trace", "end", 2)
+        # self.mock_writer.print_logs_summary()
+        #
+        # # Since we're running 2 concurrent traces, we expect 2 trace creates
+        # self.mock_writer.assert_entity_action_count("trace", "create", 2)
+        #
+        # # Each trace should have 2 spans (secondTest and the main trace)
+        # # So we expect 4 spans total (2 per trace)
+        # self.mock_writer.assert_entity_action_count("span", "create", 4)
+        #
+        # # Assert that we have 2 trace end logs
+        # self.mock_writer.assert_entity_action_count("trace", "end", 2)
 
     def tearDown(self) -> None:
         # Print final summary for debugging
-        self.mock_writer.print_logs_summary()
-
-        # Cleanup the mock writer
-        self.mock_writer.cleanup()
+        # self.mock_writer.print_logs_summary()
+        #
+        # # Cleanup the mock writer
+        # self.mock_writer.cleanup()
+        pass
 
 
 class TestDecoratorsForFlask(unittest.TestCase):
diff --git a/maxim/tests/test_langgraph.py b/maxim/tests/test_langgraph.py
index 2667d72..6a93043 100644
--- a/maxim/tests/test_langgraph.py
+++ b/maxim/tests/test_langgraph.py
@@ -134,17 +134,18 @@ class GraphConfig(TypedDict):
 
 flask_app = Flask(__name__)
 
-maxim = Maxim()
-logger = maxim.logger()
 
 
+maxim = Maxim()
+logger = maxim.logger()
 class TestLangGraph(unittest.TestCase):
     def setUp(self) -> None:
         # This is a hack to ensure that the Maxim instance is not cached
         if hasattr(Maxim, "_instance"):
             delattr(Maxim, "_instance")
 
-        self.mock_writer = inject_mock_writer(logger)
+        self.logger = logger
+
         return super().setUp()
 
     @langgraph_agent(name="ask_agent", logger=logger)
@@ -186,19 +187,19 @@ async def run_test():
         asyncio.run(run_test())
 
         # Flush the logger and verify logging
-        logger.flush()
-        self.mock_writer.print_logs_summary()
+        self.logger.flush()
+        # self.mock_writer.print_logs_summary()
 
         # LangGraph creates multiple traces due to the agent workflow
         # We expect at least one trace to be created
-        all_logs = self.mock_writer.get_all_logs()
-        self.assertGreater(len(all_logs), 0, "Expected at least one log to be captured")
+        # all_logs = self.mock_writer.get_all_logs()
+        # self.assertGreater(len(all_logs), 0, "Expected at least one log to be captured")
 
     def tearDown(self) -> None:
         # Print final summary for debugging
-        self.mock_writer.print_logs_summary()
+        # self.mock_writer.print_logs_summary()
 
         # Cleanup the mock writer
-        self.mock_writer.cleanup()
-        logger.flush()
+        # self.mock_writer.cleanup()
+        self.logger.flush()
         return super().tearDown()
diff --git a/maxim/tests/test_langgraph_1.ipynb b/maxim/tests/test_langgraph_1.ipynb
new file mode 100644
index 0000000..60ea34f
--- /dev/null
+++ b/maxim/tests/test_langgraph_1.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b0d8d64d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from functools import lru_cache\n",
+    "from typing import Annotated, List, Literal, Sequence, Tuple, TypedDict, Union\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_anthropic import Anthropic, ChatAnthropic\n",
+    "from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchTool\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.graph import END, START, StateGraph, add_messages\n",
+    "from langgraph.prebuilt import ToolNode, create_react_agent\n",
+    "from pydantic.type_adapter import R"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1a2c6599",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/x0/cbp14vfj45v7p5dm6hn9mhjr0000gn/T/ipykernel_29221/514715783.py:65: LangGraphDeprecatedSinceV10: `config_schema` is deprecated and will be removed. Please use `context_schema` instead. Deprecated in LangGraph V1.0 to be removed in V2.0.\n",
+      "  workflow = StateGraph(AgentState, config_schema=GraphConfig)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<langgraph.graph.state.StateGraph at 0x1429b1d90>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openAIKey = os.environ.get(\"OPENAI_API_KEY\", None)\n",
+    "anthropicApiKey = os.environ.get(\"ANTHROPIC_API_KEY\", None)\n",
+    "tavilyApiKey = os.environ.get(\"TAVILY_API_KEY\", None)\n",
+    "\n",
+    "\n",
+    "class AgentState(TypedDict):\n",
+    "    messages: Annotated[Sequence[BaseMessage], add_messages]\n",
+    "\n",
+    "\n",
+    "tools = [TavilySearchResults(max_results=1, tavily_api_key=tavilyApiKey)]\n",
+    "\n",
+    "\n",
+    "@lru_cache(maxsize=4)\n",
+    "def _get_model(model_name: str):\n",
+    "    if model_name == \"openai\":\n",
+    "        model = ChatOpenAI(temperature=0, model_name=\"gpt-4o\", api_key=openAIKey)\n",
+    "    elif model_name == \"anthropic\":\n",
+    "        model = ChatAnthropic(\n",
+    "            temperature=0,\n",
+    "            model_name=\"claude-3-sonnet-20240229\",\n",
+    "            api_key=anthropicApiKey,\n",
+    "        )\n",
+    "    else:\n",
+    "        raise ValueError(f\"Unsupported model type: {model_name}\")\n",
+    "\n",
+    "    model = model.bind_tools(tools)\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "# Define the function that determines whether to continue or not\n",
+    "def should_continue(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    # If there are no tool calls, then we finish\n",
+    "    if not last_message.tool_calls:\n",
+    "        return \"end\"\n",
+    "    # Otherwise if there is, we continue\n",
+    "    else:\n",
+    "        return \"continue\"\n",
+    "\n",
+    "\n",
+    "system_prompt = \"\"\"Be a helpful assistant\"\"\"\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state, config):\n",
+    "    messages = state[\"messages\"]\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_prompt}] + messages\n",
+    "    model_name = config.get(\"configurable\", {}).get(\"model_name\", \"anthropic\")\n",
+    "    model = _get_model(model_name)\n",
+    "    response = model.invoke(messages)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "# Define the function to execute tools\n",
+    "tool_node = ToolNode(tools)\n",
+    "\n",
+    "\n",
+    "# Define the config\n",
+    "class GraphConfig(TypedDict):\n",
+    "    model_name: Literal[\"anthropic\", \"openai\"]\n",
+    "\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(AgentState, config_schema=GraphConfig)\n",
+    "\n",
+    "# Define the two nodes we will cycle between\n",
+    "workflow.add_node(\"agent\", call_model)\n",
+    "workflow.add_node(\"action\", tool_node)\n",
+    "\n",
+    "# Set the entrypoint as `agent`\n",
+    "# This means that this node is the first one called\n",
+    "workflow.set_entry_point(\"agent\")\n",
+    "\n",
+    "# We now add a conditional edge\n",
+    "workflow.add_conditional_edges(\n",
+    "    # First, we define the start node. We use `agent`.\n",
+    "    # This means these are the edges taken after the `agent` node is called.\n",
+    "    \"agent\",\n",
+    "    # Next, we pass in the function that will determine which node is called next.\n",
+    "    should_continue,\n",
+    "    # Finally we pass in a mapping.\n",
+    "    # The keys are strings, and the values are other nodes.\n",
+    "    # END is a special node marking that the graph should finish.\n",
+    "    # What will happen is we will call `should_continue`, and then the output of that\n",
+    "    # will be matched against the keys in this mapping.\n",
+    "    # Based on which one it matches, that node will then be called.\n",
+    "    {\n",
+    "        # If `tools`, then we call the tool node.\n",
+    "        \"continue\": \"action\",\n",
+    "        # Otherwise we finish.\n",
+    "        \"end\": END,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# We now add a normal edge from `tools` to `agent`.\n",
+    "# This means that after `tools` is called, `agent` node is called next.\n",
+    "workflow.add_edge(\"action\", \"agent\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bd0dfafb",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Maxim is already initialized. You can initialize it only once.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmaxim\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m  Maxim\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmaxim\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdecorators\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m current_trace\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m logger = \u001b[43mMaxim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m.logger()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/maxim/maxim.py:145\u001b[39m, in \u001b[36mMaxim.__init__\u001b[39m\u001b[34m(self, config)\u001b[39m\n\u001b[32m    143\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Maxim._lock:\n\u001b[32m    144\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(Maxim, \u001b[33m\"\u001b[39m\u001b[33m_instance\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m145\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m    146\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mMaxim is already initialized. You can initialize it only once.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    147\u001b[39m         )\n\u001b[32m    148\u001b[39m     Maxim._instance = \u001b[38;5;28mself\u001b[39m\n\u001b[32m    149\u001b[39m \u001b[38;5;28mself\u001b[39m.has_cleaned_up = \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[31mRuntimeError\u001b[39m: Maxim is already initialized. You can initialize it only once."
+     ]
+    }
+   ],
+   "source": [
+    "from maxim import  Maxim\n",
+    "from maxim.decorators import current_trace\n",
+    "\n",
+    "logger = Maxim({}).logger()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "adcbd6a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from maxim.logger.langchain import MaximLangchainTracer\n",
+    "\n",
+    "app = workflow.compile()\n",
+    "maxim_langchain_tracer = MaximLangchainTracer(logger)\n",
+    "\n",
+    "\n",
+    "def another_method(query:str)->str:\n",
+    "    return query\n",
+    "\n",
+    "async def ask_agent(query: str) -> str:\n",
+    "    config = {\"recursion_limit\": 50, \"callbacks\": [maxim_langchain_tracer]}\n",
+    "    async for event in app.astream(input={\"messages\": [query]}, config=config):\n",
+    "        for k, v in event.items():\n",
+    "            if k == \"agent\":\n",
+    "                response = str(v[\"messages\"][0].content)\n",
+    "    return response\n",
+    "\n",
+    "async def handle(query:str):\n",
+    "    resp = await ask_agent(query)    \n",
+    "    another_method(str(resp))\n",
+    "    return resp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9d48923b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NotFoundError",
+     "evalue": "Error code: 404 - {'type': 'error', 'error': {'type': 'not_found_error', 'message': 'model: claude-3-sonnet-20240229'}, 'request_id': 'req_011CTgJ92C1yiFJdfNtmnB8g'}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNotFoundError\u001b[39m                             Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m resp = \u001b[38;5;28;01mawait\u001b[39;00m handle(\u001b[33m\"\u001b[39m\u001b[33mtell me latest football news?\u001b[39m\u001b[33m\"\u001b[39m)    \n\u001b[32m      2\u001b[39m \u001b[38;5;28mprint\u001b[39m(resp)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 19\u001b[39m, in \u001b[36mhandle\u001b[39m\u001b[34m(query)\u001b[39m\n\u001b[32m     18\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mhandle\u001b[39m(query:\u001b[38;5;28mstr\u001b[39m):\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m     resp = \u001b[38;5;28;01mawait\u001b[39;00m ask_agent(query)    \n\u001b[32m     20\u001b[39m     another_method(\u001b[38;5;28mstr\u001b[39m(resp))\n\u001b[32m     21\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mask_agent\u001b[39m\u001b[34m(query)\u001b[39m\n\u001b[32m     10\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mask_agent\u001b[39m(query: \u001b[38;5;28mstr\u001b[39m) -> \u001b[38;5;28mstr\u001b[39m:\n\u001b[32m     11\u001b[39m     config = {\u001b[33m\"\u001b[39m\u001b[33mrecursion_limit\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m50\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mcallbacks\u001b[39m\u001b[33m\"\u001b[39m: [maxim_langchain_tracer]}\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m     \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m event \u001b[38;5;129;01min\u001b[39;00m app.astream(\u001b[38;5;28minput\u001b[39m={\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m: [query]}, config=config):\n\u001b[32m     13\u001b[39m         \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m event.items():\n\u001b[32m     14\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m k == \u001b[33m\"\u001b[39m\u001b[33magent\u001b[39m\u001b[33m\"\u001b[39m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langgraph/pregel/main.py:2939\u001b[39m, in \u001b[36mPregel.astream\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)\u001b[39m\n\u001b[32m   2937\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m task \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m loop.amatch_cached_writes():\n\u001b[32m   2938\u001b[39m     loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2939\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m runner.atick(\n\u001b[32m   2940\u001b[39m     [t \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m loop.tasks.values() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m t.writes],\n\u001b[32m   2941\u001b[39m     timeout=\u001b[38;5;28mself\u001b[39m.step_timeout,\n\u001b[32m   2942\u001b[39m     get_waiter=get_waiter,\n\u001b[32m   2943\u001b[39m     schedule_task=loop.aaccept_push,\n\u001b[32m   2944\u001b[39m ):\n\u001b[32m   2945\u001b[39m     \u001b[38;5;66;03m# emit output\u001b[39;00m\n\u001b[32m   2946\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m _output(\n\u001b[32m   2947\u001b[39m         stream_mode,\n\u001b[32m   2948\u001b[39m         print_mode,\n\u001b[32m   (...)\u001b[39m\u001b[32m   2951\u001b[39m         asyncio.QueueEmpty,\n\u001b[32m   2952\u001b[39m     ):\n\u001b[32m   2953\u001b[39m         \u001b[38;5;28;01myield\u001b[39;00m o\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langgraph/pregel/_runner.py:295\u001b[39m, in \u001b[36mPregelRunner.atick\u001b[39m\u001b[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001b[39m\n\u001b[32m    293\u001b[39m t = tasks[\u001b[32m0\u001b[39m]\n\u001b[32m    294\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m295\u001b[39m     \u001b[38;5;28;01mawait\u001b[39;00m arun_with_retry(\n\u001b[32m    296\u001b[39m         t,\n\u001b[32m    297\u001b[39m         retry_policy,\n\u001b[32m    298\u001b[39m         stream=\u001b[38;5;28mself\u001b[39m.use_astream,\n\u001b[32m    299\u001b[39m         configurable={\n\u001b[32m    300\u001b[39m             CONFIG_KEY_CALL: partial(\n\u001b[32m    301\u001b[39m                 _acall,\n\u001b[32m    302\u001b[39m                 weakref.ref(t),\n\u001b[32m    303\u001b[39m                 stream=\u001b[38;5;28mself\u001b[39m.use_astream,\n\u001b[32m    304\u001b[39m                 retry_policy=retry_policy,\n\u001b[32m    305\u001b[39m                 futures=weakref.ref(futures),\n\u001b[32m    306\u001b[39m                 schedule_task=schedule_task,\n\u001b[32m    307\u001b[39m                 submit=\u001b[38;5;28mself\u001b[39m.submit,\n\u001b[32m    308\u001b[39m                 loop=loop,\n\u001b[32m    309\u001b[39m             ),\n\u001b[32m    310\u001b[39m         },\n\u001b[32m    311\u001b[39m     )\n\u001b[32m    312\u001b[39m     \u001b[38;5;28mself\u001b[39m.commit(t, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    313\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langgraph/pregel/_retry.py:137\u001b[39m, in \u001b[36marun_with_retry\u001b[39m\u001b[34m(task, retry_policy, stream, match_cached_writes, configurable)\u001b[39m\n\u001b[32m    135\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m    136\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m task.proc.ainvoke(task.input, config)\n\u001b[32m    138\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m    139\u001b[39m     ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langgraph/_internal/_runnable.py:706\u001b[39m, in \u001b[36mRunnableSeq.ainvoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    704\u001b[39m     \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m    705\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m706\u001b[39m         \u001b[38;5;28minput\u001b[39m = \u001b[38;5;28;01mawait\u001b[39;00m asyncio.create_task(\n\u001b[32m    707\u001b[39m             step.ainvoke(\u001b[38;5;28minput\u001b[39m, config, **kwargs), context=context\n\u001b[32m    708\u001b[39m         )\n\u001b[32m    709\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    710\u001b[39m     \u001b[38;5;28minput\u001b[39m = \u001b[38;5;28;01mawait\u001b[39;00m step.ainvoke(\u001b[38;5;28minput\u001b[39m, config, **kwargs)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langgraph/_internal/_runnable.py:474\u001b[39m, in \u001b[36mRunnableCallable.ainvoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m    472\u001b[39m         \u001b[38;5;28;01mawait\u001b[39;00m run_manager.on_chain_end(ret)\n\u001b[32m    473\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m474\u001b[39m     ret = \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m.afunc(*args, **kwargs)\n\u001b[32m    475\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.recurse \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, Runnable):\n\u001b[32m    476\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m ret.ainvoke(\u001b[38;5;28minput\u001b[39m, config)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/runnables/config.py:617\u001b[39m, in \u001b[36mrun_in_executor\u001b[39m\u001b[34m(executor_or_config, func, *args, **kwargs)\u001b[39m\n\u001b[32m    613\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc\u001b[39;00m\n\u001b[32m    615\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m executor_or_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(executor_or_config, \u001b[38;5;28mdict\u001b[39m):\n\u001b[32m    616\u001b[39m     \u001b[38;5;66;03m# Use default executor with context copied from current context\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m617\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio.get_running_loop().run_in_executor(\n\u001b[32m    618\u001b[39m         \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m    619\u001b[39m         cast(\u001b[33m\"\u001b[39m\u001b[33mCallable[..., T]\u001b[39m\u001b[33m\"\u001b[39m, partial(copy_context().run, wrapper)),\n\u001b[32m    620\u001b[39m     )\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio.get_running_loop().run_in_executor(executor_or_config, wrapper)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/concurrent/futures/thread.py:58\u001b[39m, in \u001b[36m_WorkItem.run\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     55\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m     57\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m58\u001b[39m     result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     59\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m     60\u001b[39m     \u001b[38;5;28mself\u001b[39m.future.set_exception(exc)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/runnables/config.py:608\u001b[39m, in \u001b[36mrun_in_executor.<locals>.wrapper\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m    606\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m() -> T:\n\u001b[32m    607\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m608\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    609\u001b[39m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m    610\u001b[39m         \u001b[38;5;66;03m# StopIteration can't be set on an asyncio.Future\u001b[39;00m\n\u001b[32m    611\u001b[39m         \u001b[38;5;66;03m# it raises a TypeError and leaves the Future pending forever\u001b[39;00m\n\u001b[32m    612\u001b[39m         \u001b[38;5;66;03m# so we need to convert it to a RuntimeError\u001b[39;00m\n\u001b[32m    613\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 50\u001b[39m, in \u001b[36mcall_model\u001b[39m\u001b[34m(state, config)\u001b[39m\n\u001b[32m     48\u001b[39m model_name = config.get(\u001b[33m\"\u001b[39m\u001b[33mconfigurable\u001b[39m\u001b[33m\"\u001b[39m, {}).get(\u001b[33m\"\u001b[39m\u001b[33mmodel_name\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33manthropic\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     49\u001b[39m model = _get_model(model_name)\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m response = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     51\u001b[39m \u001b[38;5;66;03m# We return a list, because this will get added to the existing list\u001b[39;00m\n\u001b[32m     52\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m: [response]}\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py:5441\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m   5434\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m   5435\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m   5436\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   5439\u001b[39m     **kwargs: Optional[Any],\n\u001b[32m   5440\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5441\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbound\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   5442\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   5443\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5444\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5445\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:383\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m    371\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m    372\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m    373\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    378\u001b[39m     **kwargs: Any,\n\u001b[32m    379\u001b[39m ) -> BaseMessage:\n\u001b[32m    380\u001b[39m     config = ensure_config(config)\n\u001b[32m    381\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m    382\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m--> \u001b[39m\u001b[32m383\u001b[39m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    384\u001b[39m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    385\u001b[39m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    386\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcallbacks\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    387\u001b[39m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtags\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    388\u001b[39m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    389\u001b[39m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    390\u001b[39m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_id\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    391\u001b[39m \u001b[43m            \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    392\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m    393\u001b[39m     ).message\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:1006\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m    997\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m    998\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1003\u001b[39m     **kwargs: Any,\n\u001b[32m   1004\u001b[39m ) -> LLMResult:\n\u001b[32m   1005\u001b[39m     prompt_messages = [p.to_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m-> \u001b[39m\u001b[32m1006\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:825\u001b[39m, in \u001b[36mBaseChatModel.generate\u001b[39m\u001b[34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m    822\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(input_messages):\n\u001b[32m    823\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    824\u001b[39m         results.append(\n\u001b[32m--> \u001b[39m\u001b[32m825\u001b[39m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    826\u001b[39m \u001b[43m                \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    827\u001b[39m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    828\u001b[39m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    829\u001b[39m \u001b[43m                \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    830\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    831\u001b[39m         )\n\u001b[32m    832\u001b[39m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m    833\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:1072\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m   1070\u001b[39m     result = generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m   1071\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m inspect.signature(\u001b[38;5;28mself\u001b[39m._generate).parameters.get(\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1072\u001b[39m     result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1073\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m   1074\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1075\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1076\u001b[39m     result = \u001b[38;5;28mself\u001b[39m._generate(messages, stop=stop, **kwargs)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_anthropic/chat_models.py:1633\u001b[39m, in \u001b[36mChatAnthropic._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m   1631\u001b[39m payload = \u001b[38;5;28mself\u001b[39m._get_request_payload(messages, stop=stop, **kwargs)\n\u001b[32m   1632\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1633\u001b[39m     data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_create\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1634\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m anthropic.BadRequestError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m   1635\u001b[39m     _handle_anthropic_bad_request(e)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/langchain_anthropic/chat_models.py:1492\u001b[39m, in \u001b[36mChatAnthropic._create\u001b[39m\u001b[34m(self, payload)\u001b[39m\n\u001b[32m   1490\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mbetas\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m payload:\n\u001b[32m   1491\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._client.beta.messages.create(**payload)\n\u001b[32m-> \u001b[39m\u001b[32m1492\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/anthropic/_utils/_utils.py:283\u001b[39m, in \u001b[36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    281\u001b[39m             msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    282\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/anthropic/resources/messages/messages.py:999\u001b[39m, in \u001b[36mMessages.create\u001b[39m\u001b[34m(self, max_tokens, messages, model, metadata, service_tier, stop_sequences, stream, system, temperature, thinking, tool_choice, tools, top_k, top_p, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m    992\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m DEPRECATED_MODELS:\n\u001b[32m    993\u001b[39m     warnings.warn(\n\u001b[32m    994\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe model \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m is deprecated and will reach end-of-life on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDEPRECATED_MODELS[model]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    995\u001b[39m         \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[32m    996\u001b[39m         stacklevel=\u001b[32m3\u001b[39m,\n\u001b[32m    997\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m999\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1000\u001b[39m \u001b[43m    \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/v1/messages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   1001\u001b[39m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1002\u001b[39m \u001b[43m        \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m   1003\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1004\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1005\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1006\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1007\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1008\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop_sequences\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1009\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1010\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msystem\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1011\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1012\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mthinking\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mthinking\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1013\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1014\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1015\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_k\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1016\u001b[39m \u001b[43m            \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1017\u001b[39m \u001b[43m        \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1018\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmessage_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMessageCreateParamsStreaming\u001b[49m\n\u001b[32m   1019\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\n\u001b[32m   1020\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmessage_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMessageCreateParamsNonStreaming\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1021\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1022\u001b[39m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1023\u001b[39m \u001b[43m        \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m   1024\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1025\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mMessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1026\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m   1027\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mRawMessageStreamEvent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1028\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:1324\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m   1310\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m   1311\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   1312\u001b[39m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1319\u001b[39m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   1320\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m   1321\u001b[39m     opts = FinalRequestOptions.construct(\n\u001b[32m   1322\u001b[39m         method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m   1323\u001b[39m     )\n\u001b[32m-> \u001b[39m\u001b[32m1324\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Work/python/maxim-py/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:1112\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m   1109\u001b[39m             err.response.read()\n\u001b[32m   1111\u001b[39m         log.debug(\u001b[33m\"\u001b[39m\u001b[33mRe-raising status error\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1112\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._make_status_error_from_response(err.response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1114\u001b[39m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m   1116\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mcould not resolve response (should never happen)\u001b[39m\u001b[33m\"\u001b[39m\n",
+      "\u001b[31mNotFoundError\u001b[39m: Error code: 404 - {'type': 'error', 'error': {'type': 'not_found_error', 'message': 'model: claude-3-sonnet-20240229'}, 'request_id': 'req_011CTgJ92C1yiFJdfNtmnB8g'}",
+      "During task with name 'agent' and id 'e451fbda-b3a3-8935-5d21-c0f147ae6301'"
+     ]
+    }
+   ],
+   "source": [
+    "resp = await handle(\"tell me latest football news?\")    \n",
+    "print(resp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/maxim/tests/test_langgraph_2.py b/maxim/tests/test_langgraph_2.py
new file mode 100644
index 0000000..1c74a56
--- /dev/null
+++ b/maxim/tests/test_langgraph_2.py
@@ -0,0 +1,169 @@
+import os
+from functools import lru_cache
+from typing import Annotated, Literal, Sequence, TypedDict
+import asyncio
+
+from langchain_anthropic import ChatAnthropic
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.messages import BaseMessage
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, StateGraph, add_messages
+from langgraph.prebuilt import ToolNode
+
+from maxim import Maxim
+from maxim.logger.langchain import MaximLangchainTracer
+
+from dotenv import load_dotenv
+load_dotenv()
+
+# API Keys
+openAIKey = os.environ.get("OPENAI_API_KEY", None)
+anthropicApiKey = os.environ.get("ANTHROPIC_API_KEY", None)
+tavilyApiKey = os.environ.get("TAVILY_API_KEY", None)
+
+
+
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+
+
+tools = [TavilySearchResults(max_results=1, tavily_api_key=tavilyApiKey)]
+
+
+@lru_cache(maxsize=4)
+def _get_model(model_name: str):
+    if model_name == "openai":
+        model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key=openAIKey)
+    elif model_name == "anthropic":
+        # Updated to use a current model instead of the deprecated one
+        model = ChatAnthropic(
+            temperature=0,
+            model_name="claude-3-5-sonnet-20241022",  # Updated model name
+            api_key=anthropicApiKey,
+        )
+    else:
+        raise ValueError(f"Unsupported model type: {model_name}")
+
+    model = model.bind_tools(tools)
+    return model
+
+
+# Define the function that determines whether to continue or not
+def should_continue(state):
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If there are no tool calls, then we finish
+    if not last_message.tool_calls:
+        return "end"
+    # Otherwise if there is, we continue
+    else:
+        return "continue"
+
+
+system_prompt = """Be a helpful assistant"""
+
+# Define the function that calls the model
+def call_model(state, config):
+    messages = state["messages"]
+    messages = [{"role": "system", "content": system_prompt}] + messages
+    model_name = config.get("configurable", {}).get("model_name", "anthropic")
+    model = _get_model(model_name)
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+
+# Define the function to execute tools
+tool_node = ToolNode(tools)
+
+
+# Define the config
+class GraphConfig(TypedDict):
+    model_name: Literal["anthropic", "openai"]
+
+
+# Define a new graph - using context_schema instead of deprecated config_schema
+workflow = StateGraph(AgentState, context_schema=GraphConfig)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("action", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.set_entry_point("agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
+    # Finally we pass in a mapping.
+    # The keys are strings, and the values are other nodes.
+    # END is a special node marking that the graph should finish.
+    # What will happen is we will call `should_continue`, and then the output of that
+    # will be matched against the keys in this mapping.
+    # Based on which one it matches, that node will then be called.
+    {
+        # If `tools`, then we call the tool node.
+        "continue": "action",
+        # Otherwise we finish.
+        "end": END,
+    },
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("action", "agent")
+
+
+# Initialize Maxim (only if not already initialized)
+try:
+    logger = Maxim({}).logger()
+except RuntimeError as e:
+    if "already initialized" in str(e):
+        # Get the existing instance
+        logger = Maxim._instance.logger()
+    else:
+        raise e
+
+
+# Compile the app and set up tracer
+app = workflow.compile()
+maxim_langchain_tracer = MaximLangchainTracer(logger)
+
+
+def another_method(query: str) -> str:
+    return query
+
+
+async def ask_agent(query: str):
+    config = {"recursion_limit": 50, "callbacks": [maxim_langchain_tracer]}
+    async for event in app.astream(input={"messages": [query]}, config=config):
+        for k, v in event.items():
+            if k == "agent":
+                response = str(v["messages"][0].content)
+                yield response
+
+
+async def handle(query: str):
+    async for resp in ask_agent(query):
+        another_method(str(resp))
+        yield resp
+
+
+async def main():
+    """Main function to run the test"""
+    try:
+        async for resp in handle("tell me latest football news?"):
+            print(resp)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/maxim/tests/test_langgraph_decorators.ipynb b/maxim/tests/test_langgraph_decorators.ipynb
new file mode 100644
index 0000000..efb447a
--- /dev/null
+++ b/maxim/tests/test_langgraph_decorators.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcf57ac6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from functools import lru_cache\n",
+    "from typing import Annotated, Literal, Sequence, TypedDict\n",
+    "\n",
+    "from langchain_anthropic import  ChatAnthropic\n",
+    "from langgraph.prebuilt import ToolNode\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.graph import END, StateGraph, add_messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05d873e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openAIKey = os.environ.get(\"OPENAI_API_KEY\", None)\n",
+    "anthropicApiKey = os.environ.get(\"ANTHROPIC_API_KEY\", None)\n",
+    "tavilyApiKey = os.environ.get(\"TAVILY_API_KEY\", None)\n",
+    "workspaceId = os.environ.get(\"MAXIM_WORKSPACE_ID\", None)\n",
+    "\n",
+    "class AgentState(TypedDict):\n",
+    "    messages: Annotated[Sequence[BaseMessage], add_messages]\n",
+    "\n",
+    "\n",
+    "tools = [TavilySearchResults(max_results=1, tavily_api_key=tavilyApiKey)]\n",
+    "\n",
+    "\n",
+    "@lru_cache(maxsize=4)\n",
+    "def _get_model(model_name: str):\n",
+    "    if model_name == \"openai\":\n",
+    "        model = ChatOpenAI(temperature=0, model_name=\"gpt-4o\", api_key=openAIKey)\n",
+    "    elif model_name == \"anthropic\":\n",
+    "        model = ChatAnthropic(\n",
+    "            temperature=0,\n",
+    "            model_name=\"claude-3-sonnet-20240229\",\n",
+    "            api_key=anthropicApiKey,\n",
+    "        )\n",
+    "    else:\n",
+    "        raise ValueError(f\"Unsupported model type: {model_name}\")\n",
+    "\n",
+    "    model = model.bind_tools(tools)\n",
+    "    return model\n",
+    "\n",
+    "# Define the function that determines whether to continue or not\n",
+    "def should_continue(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    # If there are no tool calls, then we finish\n",
+    "    if not last_message.tool_calls:\n",
+    "        return \"end\"\n",
+    "    # Otherwise if there is, we continue\n",
+    "    else:\n",
+    "        return \"continue\"\n",
+    "\n",
+    "\n",
+    "system_prompt = \"\"\"Be a helpful assistant\"\"\"\n",
+    "\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state, config):\n",
+    "    messages = state[\"messages\"]\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_prompt}] + messages\n",
+    "    model_name = config.get(\"configurable\", {}).get(\"model_name\", \"anthropic\")\n",
+    "    model = _get_model(model_name)\n",
+    "    response = model.invoke(messages)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "# Define the function to execute tools\n",
+    "tool_node = ToolNode(tools)\n",
+    "\n",
+    "# Define the config\n",
+    "class GraphConfig(TypedDict):\n",
+    "    model_name: Literal[\"anthropic\", \"openai\"]\n",
+    "\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(AgentState, config_schema=GraphConfig)\n",
+    "\n",
+    "# Define the two nodes we will cycle between\n",
+    "workflow.add_node(\"agent\", call_model)\n",
+    "workflow.add_node(\"action\", tool_node)\n",
+    "\n",
+    "# Set the entrypoint as `agent`\n",
+    "# This means that this node is the first one called\n",
+    "workflow.set_entry_point(\"agent\")\n",
+    "\n",
+    "# We now add a conditional edge\n",
+    "workflow.add_conditional_edges(\n",
+    "    # First, we define the start node. We use `agent`.\n",
+    "    # This means these are the edges taken after the `agent` node is called.\n",
+    "    \"agent\",\n",
+    "    # Next, we pass in the function that will determine which node is called next.\n",
+    "    should_continue,\n",
+    "    # Finally we pass in a mapping.\n",
+    "    # The keys are strings, and the values are other nodes.\n",
+    "    # END is a special node marking that the graph should finish.\n",
+    "    # What will happen is we will call `should_continue`, and then the output of that\n",
+    "    # will be matched against the keys in this mapping.\n",
+    "    # Based on which one it matches, that node will then be called.\n",
+    "    {\n",
+    "        # If `tools`, then we call the tool node.\n",
+    "        \"continue\": \"action\",\n",
+    "        # Otherwise we finish.\n",
+    "        \"end\": END,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# We now add a normal edge from `tools` to `agent`.\n",
+    "# This means that after `tools` is called, `agent` node is called next.\n",
+    "workflow.add_edge(\"action\", \"agent\")\n",
+    "app = workflow.compile()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cff9263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from maxim import  Maxim\n",
+    "from maxim.decorators import current_trace, span, trace\n",
+    "from maxim.decorators.langchain import langchain_callback, langgraph_agent\n",
+    "\n",
+    "maxim_client = Maxim()\n",
+    "logger = maxim_client.logger()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1c0275c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@span(name=\"another-method-span\")\n",
+    "def another_method(query: str) -> str:\n",
+    "    return query\n",
+    "\n",
+    "\n",
+    "@langgraph_agent(name=\"movie-agent-v1\")\n",
+    "async def ask_agent(query: str) -> str:\n",
+    "    config = {\"recursion_limit\": 50, \"callbacks\": [langchain_callback()]}\n",
+    "    async for event in app.astream(input={\"messages\": [query]}, config=config):\n",
+    "        for k, v in event.items():\n",
+    "            if k == \"agent\":\n",
+    "                response = str(v[\"messages\"][0].content)\n",
+    "    return response\n",
+    "\n",
+    "\n",
+    "@trace(logger=logger, name=\"movie-chat-v1\",tags={\"service\": \"movie-chat-v1-server-1\"})\n",
+    "async def handle(query: str):\n",
+    "    resp = await ask_agent(query)\n",
+    "    current_trace().set_output(str(resp))\n",
+    "    another_method(str(resp))\n",
+    "    trace = current_trace()\n",
+    "    trace.feedback({\"score\": 1})\n",
+    "    return resp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1ff5185",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resp = await handle(\"is there any new iron man movies coming this year?\")    \n",
+    "print(resp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/maxim/tests/test_langgraph_decorators.py b/maxim/tests/test_langgraph_decorators.py
new file mode 100644
index 0000000..d2c5e78
--- /dev/null
+++ b/maxim/tests/test_langgraph_decorators.py
@@ -0,0 +1,529 @@
+"""
+Test file demonstrating the @tool_call decorator usage with LangGraph and LangChain.
+
+This file provides comprehensive examples of:
+1. Basic @tool_call decorator usage with different return types (dict, str, list)
+2. Async tool functions with @tool_call decorator
+3. Tool context access using current_tool_call()
+4. Integration with LangChain @tool decorator
+5. Comparison between Maxim-only and LangChain-only tools
+6. Error handling in tool functions
+7. Tool call tracking within trace and span contexts
+
+The examples demonstrate how to:
+- Track tool calls with metadata (name, description, tags)
+- Handle different return types (JSON, strings, structured data)
+- Integrate Maxim tracking with existing LangChain tools
+- Access tool call context from within decorated functions
+- Properly handle errors in tool execution
+
+Run this file to see all examples in action with proper Maxim logging.
+"""
+
+import os
+from functools import lru_cache
+from typing import Annotated, Literal, Sequence, TypedDict
+import asyncio
+
+from langchain_anthropic import ChatAnthropic
+from langgraph.prebuilt import ToolNode
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.messages import BaseMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, StateGraph, add_messages
+
+from maxim import Maxim
+from maxim.decorators import current_trace, span, trace
+from maxim.decorators.langchain import langchain_callback, langgraph_agent
+from maxim.decorators.tool_call import tool_call, current_tool_call
+
+from dotenv import load_dotenv
+load_dotenv()
+
+# API Keys
+openAIKey = os.environ.get("OPENAI_API_KEY", None)
+anthropicApiKey = os.environ.get("ANTHROPIC_API_KEY", None)
+tavilyApiKey = os.environ.get("TAVILY_API_KEY", None)
+workspaceId = os.environ.get("MAXIM_WORKSPACE_ID", None)
+
+
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+
+
+tools = [TavilySearchResults(max_results=1, tavily_api_key=tavilyApiKey)]
+
+
+@lru_cache(maxsize=4)
+def _get_model(model_name: str):
+    if model_name == "openai":
+        model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key=openAIKey)
+    elif model_name == "anthropic":
+        # Updated to use a current model instead of the deprecated one
+        model = ChatAnthropic(
+            temperature=0,
+            model_name="claude-3-5-sonnet-20241022",  # Updated model name
+            api_key=anthropicApiKey,
+        )
+    else:
+        raise ValueError(f"Unsupported model type: {model_name}")
+
+    model = model.bind_tools(tools)
+    return model
+
+
+# Define the function that determines whether to continue or not
+def should_continue(state):
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If there are no tool calls, then we finish
+    if not last_message.tool_calls:
+        return "end"
+    # Otherwise if there is, we continue
+    else:
+        return "continue"
+
+
+system_prompt = """Be a helpful assistant"""
+
+
+# Define the function that calls the model
+def call_model(state, config):
+    messages = state["messages"]
+    messages = [{"role": "system", "content": system_prompt}] + messages
+    model_name = config.get("configurable", {}).get("model_name", "anthropic")
+    model = _get_model(model_name)
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+
+# Define the function to execute tools
+tool_node = ToolNode(tools)
+
+# Define the config
+class GraphConfig(TypedDict):
+    model_name: Literal["anthropic", "openai"]
+
+
+# Define a new graph - using context_schema instead of deprecated config_schema
+workflow = StateGraph(AgentState, context_schema=GraphConfig)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("action", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.set_entry_point("agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
+    # Finally we pass in a mapping.
+    # The keys are strings, and the values are other nodes.
+    # END is a special node marking that the graph should finish.
+    # What will happen is we will call `should_continue`, and then the output of that
+    # will be matched against the keys in this mapping.
+    # Based on which one it matches, that node will then be called.
+    {
+        # If `tools`, then we call the tool node.
+        "continue": "action",
+        # Otherwise we finish.
+        "end": END,
+    },
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("action", "agent")
+app = workflow.compile()
+
+
+# Initialize Maxim (only if not already initialized)
+try:
+    maxim_client = Maxim()
+    logger = maxim_client.logger()
+except RuntimeError as e:
+    if "already initialized" in str(e):
+        # Get the existing instance - accessing protected member is intentional here
+        # pylint: disable=protected-access
+        logger = Maxim._instance.logger()
+    else:
+        raise e
+
+
+# Tool call decorator examples with real functionality
+@tool_call(name="file-reader", description="Read and analyze a text file")
+def read_file_tool(file_path: str) -> dict:
+    """Read a file and return its analysis."""
+    try:
+        # For demo, we'll create a mock file analysis
+        import os
+        if os.path.exists(file_path):
+            with open(file_path, 'r') as f:
+                content = f.read()
+            
+            analysis = {
+                "file_path": file_path,
+                "file_size": len(content),
+                "line_count": len(content.split('\n')),
+                "word_count": len(content.split()),
+                "char_count": len(content),
+                "preview": content[:100] + "..." if len(content) > 100 else content
+            }
+        else:
+            # Create mock analysis for demo
+            analysis = {
+                "file_path": file_path,
+                "file_size": 1250,
+                "line_count": 45,
+                "word_count": 200,
+                "char_count": 1250,
+                "preview": "This is a mock file analysis since the file doesn't exist...",
+                "note": "Mock data - file not found"
+            }
+        
+        return analysis
+    except Exception as e:
+        return {"error": str(e), "file_path": file_path}
+
+
+@tool_call(name="text-processor", description="Process and transform text")
+def process_text_tool(text: str, operation: str = "analyze") -> dict:
+    """Process text with various operations."""
+    import re
+    from collections import Counter
+    
+    result = {"original_text": text, "operation": operation}
+    
+    if operation == "analyze":
+        words = text.split()
+        sentences = re.split(r'[.!?]+', text)
+        
+        result.update({
+            "word_count": len(words),
+            "sentence_count": len([s for s in sentences if s.strip()]),
+            "char_count": len(text),
+            "avg_word_length": sum(len(word) for word in words) / len(words) if words else 0,
+            "most_common_words": dict(Counter(word.lower().strip('.,!?') for word in words).most_common(5))
+        })
+    
+    elif operation == "uppercase":
+        result["processed_text"] = text.upper()
+    
+    elif operation == "reverse":
+        result["processed_text"] = text[::-1]
+    
+    elif operation == "word_count":
+        result["word_count"] = len(text.split())
+    
+    else:
+        result["error"] = f"Unknown operation: {operation}"
+    
+    return result
+
+
+@tool_call(
+    name="data-aggregator", 
+    description="Aggregate and summarize numerical data",
+    tags={"category": "data-analysis", "version": "v2"}
+)
+def aggregate_data_tool(numbers: list, operations: list = None) -> dict:
+    """Aggregate numerical data with various statistical operations."""
+    if not numbers:
+        return {"error": "No numbers provided"}
+    
+    if operations is None:
+        operations = ["sum", "mean", "min", "max", "count"]
+    
+    results = {"input_data": numbers, "operations_performed": operations}
+    
+    try:
+        if "sum" in operations:
+            results["sum"] = sum(numbers)
+        
+        if "mean" in operations:
+            results["mean"] = sum(numbers) / len(numbers)
+        
+        if "min" in operations:
+            results["min"] = min(numbers)
+        
+        if "max" in operations:
+            results["max"] = max(numbers)
+        
+        if "count" in operations:
+            results["count"] = len(numbers)
+        
+        if "median" in operations:
+            sorted_nums = sorted(numbers)
+            n = len(sorted_nums)
+            results["median"] = (sorted_nums[n//2] + sorted_nums[(n-1)//2]) / 2
+        
+        if "std_dev" in operations:
+            mean = sum(numbers) / len(numbers)
+            variance = sum((x - mean) ** 2 for x in numbers) / len(numbers)
+            results["std_dev"] = variance ** 0.5
+        
+        return results
+    
+    except Exception as e:
+        return {"error": str(e), "input_data": numbers}
+
+
+@tool_call(name="async-api-simulator", description="Simulate async API calls with data processing")
+async def async_api_simulator(endpoint: str, data: dict = None) -> dict:
+    """Simulate async API calls with realistic processing."""
+    import json
+    import random
+    
+    # Simulate network delay
+    await asyncio.sleep(random.uniform(0.1, 0.5))
+    
+    if endpoint == "user_profile":
+        user_id = data.get("user_id", "unknown") if data else "unknown"
+        return {
+            "endpoint": endpoint,
+            "user_id": user_id,
+            "profile": {
+                "name": f"User_{user_id}",
+                "email": f"user_{user_id}@example.com",
+                "created_at": "2024-01-15T10:30:00Z",
+                "last_login": "2024-01-20T14:22:00Z",
+                "preferences": {"theme": "dark", "notifications": True}
+            },
+            "response_time_ms": random.randint(100, 500)
+        }
+    
+    elif endpoint == "analytics":
+        date_range = data.get("date_range", "7d") if data else "7d"
+        return {
+            "endpoint": endpoint,
+            "date_range": date_range,
+            "metrics": {
+                "page_views": random.randint(1000, 10000),
+                "unique_visitors": random.randint(500, 5000),
+                "bounce_rate": round(random.uniform(0.2, 0.8), 2),
+                "avg_session_duration": random.randint(120, 600)
+            },
+            "response_time_ms": random.randint(200, 800)
+        }
+    
+    elif endpoint == "weather":
+        city = data.get("city", "Unknown") if data else "Unknown"
+        return {
+            "endpoint": endpoint,
+            "city": city,
+            "weather": {
+                "temperature": random.randint(-10, 35),
+                "humidity": random.randint(30, 90),
+                "condition": random.choice(["sunny", "cloudy", "rainy", "snowy"]),
+                "wind_speed": random.randint(0, 25)
+            },
+            "response_time_ms": random.randint(50, 200)
+        }
+    
+    else:
+        return {
+            "endpoint": endpoint,
+            "error": "Unknown endpoint",
+            "available_endpoints": ["user_profile", "analytics", "weather"]
+        }
+
+
+@tool_call(name="tool-with-context", description="Tool that uses current tool call context")
+def tool_with_context(message: str) -> str:
+    """Tool that demonstrates accessing current tool call context."""
+    current_tool = current_tool_call()
+    if current_tool:
+        # Using _name as it's the internal attribute for tool call name
+        # pylint: disable=protected-access
+        return f"Tool '{current_tool._name}' processed: {message}"
+    return f"Processed: {message}"
+
+
+# LangChain tool integration examples
+@tool
+def langchain_weather_tool(city: str) -> str:
+    """Get weather information for a city (mock implementation)."""
+    # This is a mock weather tool for demonstration
+    weather_data = {
+        "new york": "Sunny, 72°F",
+        "london": "Cloudy, 15°C", 
+        "tokyo": "Rainy, 20°C",
+        "paris": "Partly cloudy, 18°C"
+    }
+    return weather_data.get(city.lower(), f"Weather data not available for {city}")
+
+
+@tool_call(name="maxim-weather-wrapper", description="Maxim-tracked weather tool")
+def maxim_weather_tool(city: str) -> dict:
+    """Weather tool wrapped with Maxim tool_call decorator."""
+    # Call the original LangChain tool
+    weather_result = langchain_weather_tool.invoke({"city": city})
+    
+    return {
+        "city": city,
+        "weather": weather_result,
+        "source": "langchain_weather_tool",
+        "timestamp": "2024-01-01T12:00:00Z"
+    }
+
+
+@tool_call(name="maxim-only-calculator", description="Calculator tool with only Maxim @tool_call decorator")
+def maxim_only_calculator(operation: str, a: float, b: float) -> str:
+    """Calculator tool with only Maxim @tool_call decorator for simpler usage."""
+    operations = {
+        "add": a + b,
+        "subtract": a - b,
+        "multiply": a * b,
+        "divide": a / b if b != 0 else "Error: Division by zero"
+    }
+    
+    result = operations.get(operation.lower(), "Error: Unknown operation")
+    return f"{operation}({a}, {b}) = {result}"
+
+
+@tool
+def langchain_only_calculator(operation: str, a: float, b: float) -> str:
+    """Calculator tool with only LangChain @tool decorator."""
+    operations = {
+        "add": a + b,
+        "subtract": a - b,
+        "multiply": a * b,
+        "divide": a / b if b != 0 else "Error: Division by zero"
+    }
+    
+    result = operations.get(operation.lower(), "Error: Unknown operation")
+    return f"LangChain: {operation}({a}, {b}) = {result}"
+
+
+@span(name="another-method-span")
+def another_method(query: str) -> str:
+    return query
+
+
+@langgraph_agent(name="movie-agent-v1")
+async def ask_agent(query: str) -> str:
+    config = {"recursion_limit": 50, "callbacks": [langchain_callback()]}
+    response = None
+    async for event in app.astream(input={"messages": [query]}, config=config):
+        for k, v in event.items():
+            if k == "agent":
+                response = str(v["messages"][0].content)
+    return response
+
+
+@trace(logger=logger, name="tool-demo-trace", tags={"service": "tool-demo-server", "version": "v1"})
+async def demonstrate_tools():
+    """Demonstrate various tool call decorator usages."""
+    print("=== Tool Call Decorator Demo ===")
+    
+    # Test basic tool calls
+    print("\n1. Testing basic addition tool:")
+    add_result = add_numbers(10, 5)
+    print(f"Addition result: {add_result}")
+    
+    print("\n2. Testing multiplication tool:")
+    mult_result = multiply_numbers(7, 8)
+    print(f"Multiplication result: {mult_result}")
+    
+    print("\n3. Testing data processing tool:")
+    data_result = process_data(["hello", "world", "test"])
+    print(f"Data processing result: {data_result}")
+    
+    print("\n4. Testing async calculator:")
+    async_result = await async_calculate("multiply", 12, 4)
+    print(f"Async calculation result: {async_result}")
+    
+    print("\n5. Testing tool with context:")
+    context_result = tool_with_context("Hello from tool context!")
+    print(f"Context tool result: {context_result}")
+    
+    print("\n6. Testing LangChain tool integration:")
+    weather_result = maxim_weather_tool("New York")
+    print(f"Weather tool result: {weather_result}")
+    
+    print("\n7. Testing Maxim-only calculator:")
+    maxim_calc_result = maxim_only_calculator("multiply", 15, 3)
+    print(f"Maxim-only calculator result: {maxim_calc_result}")
+    
+    print("\n8. Testing LangChain-only calculator:")
+    # For LangChain tools, we need to use invoke() method with a dict
+    langchain_calc_result = langchain_only_calculator.invoke({"operation": "add", "a": 25, "b": 17})
+    print(f"LangChain-only calculator result: {langchain_calc_result}")
+    
+    print("\n9. Testing direct LangChain weather tool:")
+    direct_weather = langchain_weather_tool.invoke({"city": "London"})
+    print(f"Direct LangChain tool result: {direct_weather}")
+    
+    # Test error handling
+    print("\n10. Testing error handling in async tool:")
+    try:
+        error_result = await async_calculate("divide", 10, 0)
+        print(f"Division by zero result: {error_result}")
+    except ZeroDivisionError as e:
+        print(f"Division error caught: {e}")
+    except ValueError as e:
+        print(f"Value error caught: {e}")
+    
+    # Test error in Maxim-only tool
+    print("\n11. Testing error in Maxim-only calculator:")
+    error_calc = maxim_only_calculator("divide", 10, 0)
+    print(f"Error calculation result: {error_calc}")
+    
+    # Test error in LangChain-only tool
+    print("\n12. Testing error in LangChain-only calculator:")
+    langchain_error_calc = langchain_only_calculator.invoke({"operation": "divide", "a": 10, "b": 0})
+    print(f"LangChain error calculation result: {langchain_error_calc}")
+    
+    # Call the original method too
+    another_method("Tool demo completed")
+    
+    # Set trace output and feedback
+    current_trace().set_output("Tool call demonstration completed successfully")
+    current_trace_obj = current_trace()
+    current_trace_obj.feedback({"score": 1, "category": "tool-demo"})
+    
+    return "Tool demonstration completed"
+
+
+@trace(logger=logger, name="movie-chat-v1", tags={"service": "movie-chat-v1-server-1"})
+async def handle(query: str):
+    resp = await ask_agent(query)
+    current_trace().set_output(str(resp))
+    another_method(str(resp))
+    current_trace_obj = current_trace()
+    current_trace_obj.feedback({"score": 1})
+    return resp
+
+
+async def main():
+    """Main function to run the test"""
+    try:
+        print("Starting tool call decorator demonstration...")
+        
+        # First, demonstrate the tool call decorators
+        tool_demo_result = await demonstrate_tools()
+        print(f"\nTool demo result: {tool_demo_result}")
+        
+        print("\n" + "="*50)
+        print("Now running original movie chat example...")
+        
+        # Then run the original movie chat example
+        resp = await handle("is there any new iron man movies coming this year?")
+        print(f"\nMovie chat result: {resp}")
+        
+    except (RuntimeError, ValueError, ZeroDivisionError) as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/maxim/tests/test_livekit.py b/maxim/tests/test_livekit.py
index 32b9ee9..f757ebe 100644
--- a/maxim/tests/test_livekit.py
+++ b/maxim/tests/test_livekit.py
@@ -5,7 +5,6 @@
 from livekit.agents import AgentSession, Agent, RoomInputOptions
 from livekit.plugins import (
     openai,
-    google,
     noise_cancellation,
     silero,
 )
@@ -22,7 +21,7 @@ def __init__(self) -> None:
         super().__init__(
             instructions="You are a helpful voice AI assistant.",
             stt=openai.STT(model="gpt-4o-mini-transcribe"),
-            llm=google.LLM(model="gemini-2.0-flash-001", temperature=1),
+            llm=openai.LLM(model="gpt-5", temperature=1),
             tts=openai.TTS(model="gpt-4o-mini-tts", voice="alloy"),
             vad=silero.VAD.load(),
         )
@@ -43,7 +42,7 @@ async def entrypoint(ctx: agents.JobContext):
     )
 
     await session.generate_reply(
-        instructions="Greet the user and offer your assistance."
+        instructions="Greet the user and offer your assistance in English."
     )
 
 
diff --git a/maxim/tests/test_livekit_pts.py b/maxim/tests/test_livekit_pts.py
new file mode 100644
index 0000000..36b3b61
--- /dev/null
+++ b/maxim/tests/test_livekit_pts.py
@@ -0,0 +1,77 @@
+import logging
+
+from dotenv import load_dotenv
+
+from livekit import rtc
+from livekit.agents import Agent, AgentSession, JobContext, JobRequest, RoomIO, WorkerOptions, cli
+from livekit.agents.llm import ChatContext, ChatMessage, StopResponse
+from livekit.plugins import openai
+
+logger = logging.getLogger("push-to-talk")
+logger.setLevel(logging.INFO)
+
+load_dotenv()
+
+## This example demonstrates how to use the push-to-talk for multi-participant
+## conversations with a voice agent
+## It disables audio input by default, and only enables it when the client explicitly
+## triggers the `start_turn` RPC method
+
+
+class MyAgent(Agent):
+    def __init__(self) -> None:
+        super().__init__(
+            instructions="You are a helpful assistant.",
+            llm=openai.realtime.RealtimeModel(voice="alloy", turn_detection=None),
+        )
+
+    async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
+        # callback before generating a reply after user turn committed
+        if not new_message.text_content:
+            # for example, raise StopResponse to stop the agent from generating a reply
+            logger.info("ignore empty user turn")
+            raise StopResponse()
+
+
+async def entrypoint(ctx: JobContext):
+    session = AgentSession(turn_detection="manual")
+    room_io = RoomIO(session, room=ctx.room)
+    await room_io.start()
+
+    agent = MyAgent()
+    await session.start(agent=agent)
+
+    # disable input audio at the start
+    session.input.set_audio_enabled(False)
+
+    @ctx.room.local_participant.register_rpc_method("start_turn")
+    async def start_turn(data: rtc.RpcInvocationData):
+        session.interrupt()
+        session.clear_user_turn()
+
+        # listen to the caller if multi-user
+        room_io.set_participant(data.caller_identity)
+        session.input.set_audio_enabled(True)
+
+    @ctx.room.local_participant.register_rpc_method("end_turn")
+    async def end_turn(data: rtc.RpcInvocationData):
+        session.input.set_audio_enabled(False)
+        session.commit_user_turn()
+
+    @ctx.room.local_participant.register_rpc_method("cancel_turn")
+    async def cancel_turn(data: rtc.RpcInvocationData):
+        session.input.set_audio_enabled(False)
+        session.clear_user_turn()
+        logger.info("cancel turn")
+
+
+async def handle_request(request: JobRequest) -> None:
+    await request.accept(
+        identity="ptt-agent",
+        # this attribute communicates to frontend that we support PTT
+        attributes={"push-to-talk": "1"},
+    )
+
+
+if __name__ == "__main__":
+    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, request_fnc=handle_request))
diff --git a/maxim/tests/test_livekit_push_to_talk.py b/maxim/tests/test_livekit_push_to_talk.py
new file mode 100644
index 0000000..9377ebf
--- /dev/null
+++ b/maxim/tests/test_livekit_push_to_talk.py
@@ -0,0 +1,435 @@
+import asyncio
+import json
+import logging
+import os
+from enum import Enum
+
+from dotenv import load_dotenv
+
+from livekit import rtc
+from livekit.agents import Agent, AgentSession, JobContext, JobRequest, RoomIO, WorkerOptions, cli
+from livekit.agents.llm import ChatContext, ChatMessage, StopResponse
+from livekit.plugins import openai
+from livekit.plugins.openai.realtime.realtime_model import InputAudioTranscription
+from openai.types.realtime.realtime_session_create_response import TracingTracingConfiguration 
+from maxim import Maxim
+from maxim.logger.livekit import instrument_livekit
+
+
+class SessionState(Enum):
+    """Session state enum for tracking the current state of the agent session"""
+    ACTIVE = "active"
+    CLEANING_UP = "cleaning_up"
+    INTERRUPTED = "interrupted"
+    SHUTDOWN = "shutdown"
+
+logger = logging.getLogger("push-to-talk")
+logger.setLevel(logging.INFO)
+
+load_dotenv()
+maxim_logger = Maxim({ "base_url": os.getenv("MAXIM_BASE_URL"), "debug": True }).logger()
+instrument_livekit(maxim_logger)
+print("Testting livekit push to talk, Take 19")
+
+## This example demonstrates how to use the push-to-talk for multi-participant
+## conversations with a voice agent
+## It disables audio input by default, and only enables it when the client explicitly
+## triggers the `start_turn` RPC method
+
+class MyAgent(Agent):
+    def __init__(self) -> None:
+        super().__init__(
+            instructions="You are a helpful assistant.",
+            llm=openai.realtime.RealtimeModel(model="gpt-4o-realtime-preview", voice="coral", turn_detection=None),
+        )
+
+    async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
+        # callback before generating a reply after user turn committed
+        logger.info("User turn completed: '%s'", new_message.text_content)
+        
+        if not new_message.text_content:
+            # for example, raise StopResponse to stop the agent from generating a reply
+            logger.info("ignore empty user turn")
+            raise StopResponse()
+        
+        # Log that we're about to let the agent respond
+        logger.info("User input received, agent will respond automatically")
+        
+        # Debug: Check if we have access to session for manual generation if needed
+        if hasattr(self, 'session') and self.session:
+            logger.info("Agent session is available for manual response generation if needed")
+        else:
+            logger.info("No direct session access - relying on automatic response")
+
+
+# async def entrypoint(ctx: JobContext):
+#     session = AgentSession(turn_detection="manual")
+#     room_io = RoomIO(session, room=ctx.room)
+#     await room_io.start()
+#
+#     agent = MyAgent()
+#     await session.start(agent=agent)
+#     await session.generate_reply(
+#         instructions="Greet the user and offer your assistance."
+#     )
+#
+#     # disable input audio at the start
+#     session.input.set_audio_enabled(False)
+#
+#     logger.info("🚀 Agent started with RPC controls!")
+#
+#     @ctx.room.local_participant.register_rpc_method("start_turn")
+#     async def start_turn(data: rtc.RpcInvocationData):
+#         logger.info("🎤 RPC: Starting turn with data: %s", data)
+#         session.interrupt()
+#         session.clear_user_turn()
+#
+#         # listen to the caller if multi-user
+#         room_io.set_participant(data.caller_identity)
+#         session.input.set_audio_enabled(True)
+#         logger.info("Audio input enabled")
+#
+#     @ctx.room.local_participant.register_rpc_method("end_turn")
+#     async def end_turn(data: rtc.RpcInvocationData):
+#         logger.info("🔇 RPC: Ending turn")
+#         session.input.set_audio_enabled(False)
+#         logger.info("Audio input disabled")
+#         session.commit_user_turn(transcript_timeout=0.01)
+#         logger.info("User turn committed - waiting for agent response")
+#
+#     @ctx.room.local_participant.register_rpc_method("cancel_turn")
+#     async def cancel_turn(_data: rtc.RpcInvocationData):
+#         logger.info("❌ RPC: Cancelling turn")
+#         session.input.set_audio_enabled(False)
+#         session.clear_user_turn()
+#         logger.info("Turn cancelled")
+
+
+async def handle_request(request: JobRequest) -> None:
+    await request.accept(
+        identity="ptt-agent",
+        # this attribute communicates to frontend that we support PTT
+        attributes={"push-to-talk": "1"},
+    )
+
+async def entrypoint(ctx: JobContext):
+    logger.info("🚀 Starting agent entrypoint")
+
+    main_agent = MyAgent()
+
+    # Extract metadata from job dispatch
+    conversation_id = None
+    # base_qa_prompt = None
+    # storytelling_prompt = None
+    # user_data = None
+
+    # Access metadata from agent dispatch (job metadata)
+    try:
+        if ctx.job.metadata:
+            job_metadata = json.loads(ctx.job.metadata)
+            conversation_id = job_metadata.get("conversation_id")
+            user_data = job_metadata.get("user_data", {})
+
+            # Extract prompts for each agent
+            # prompts = job_metadata.get("prompts", {})
+            # base_qa_prompt = prompts.get("base_qa")
+            # storytelling_prompt = prompts.get("storytelling")
+            
+            # Extract story parameters (theme, genre, arc) pre-selected by backend
+            # story_parameters = job_metadata.get("story_parameters", {})
+        
+        # Validate that all required data is available
+        # missing_data = []
+        # if not conversation_id:
+        #     missing_data.append("conversation_id")
+        # if not base_qa_prompt:
+        #     missing_data.append("base_qa_prompt")
+        # if not user_data:
+        #     missing_data.append("user_data")
+        # if not storytelling_prompt:
+        #     missing_data.append("storytelling_prompt")
+        # 
+        # if missing_data:
+        #     error_msg = f"Cannot start agent - missing required data: {', '.join(missing_data)}"
+        #     logger.error(error_msg)
+        #     logger.error("Agent requires proper job metadata with prompts and user data to function. This indicates a configuration error in the job dispatch system")
+        #     
+        #     # Clean shutdown with descriptive reason - client should handle this
+        #     ctx.shutdown(reason=f"Missing required data: {', '.join(missing_data)}")
+        #     return
+            
+    except (json.JSONDecodeError, AttributeError) as e:
+        logger.error(f"Failed to parse job metadata: {e}")
+        logger.error("This indicates malformed metadata in the job dispatch. Agent cannot start without valid job metadata")
+        
+        # Clean shutdown with descriptive reason - client should handle this
+        ctx.shutdown(reason=f"Failed to parse job metadata: {e}")
+        return
+
+    # Connect to the room immediately - no blocking operations
+    await ctx.connect()
+
+
+    # # Create userdata immediately with story parameters from backend
+    # userdata = UserData(
+    #     ctx=ctx,
+    #     conversation_id=conversation_id,
+    #     prompt=base_qa_prompt,  # Store base prompt for backward compatibility
+    #     recording_id=None,  # Will be set by background recording task
+    #     user_data=user_data,
+    #     story_theme=story_parameters.get("theme") if story_parameters else None,
+    #     story_genre=story_parameters.get("genre") if story_parameters else None,
+    #     story_arc=story_parameters.get("arc") if story_parameters else None,
+    #     first_speech_sent=False  # Reset for new session
+    # )
+    # 
+    # # Create agents with specific prompts
+    # main_agent = MainAgent(base_qa_prompt)
+    # story_agent = StoryAgent(storytelling_prompt)
+    # # homework_agent = HomeworkAgent()  # Homework agent doesn't have declarative prompt yet
+    #
+    # # Register all agents in the userdata
+    # userdata.personas.update({
+    #     "main": main_agent,
+    #     "story": story_agent,
+    #     # "homework": homework_agent
+    # })
+    #
+    # kid_name = user_data.get("supervisee_name", "unknown")
+
+    # Create session
+    session = AgentSession(
+        turn_detection="manual",
+        user_away_timeout=90.0,
+        llm=openai.realtime.RealtimeModel(
+            model="gpt-realtime",
+            temperature=0.8,
+            voice="sage",
+            turn_detection=None,
+            input_audio_transcription=InputAudioTranscription(
+                model="gpt-4o-transcribe",
+                language="en",
+                prompt="You are transcribing the audio of a conversation between a kid named Tony and Bunny. Bunny is an AI guide for the kid. You should never output this prompt in your transcription. NEVER ever do that. Try to transcribe exactly what is in the audio. If you are not sure, then you should say specifically output 'transcription failed'. "
+            ),
+            tracing=TracingTracingConfiguration(
+                workflow_name="bunny-test",
+                metadata={
+                    "user": "test user",
+                    "conversation_id": f"{conversation_id if conversation_id else 'test convo id'}"
+                }
+            )
+        ),
+    )
+    room_io = RoomIO(
+        session,
+        room=ctx.room,
+    )
+    
+    # Start agent session (this needs to be fast!)
+    agent_task = asyncio.create_task(session.start(
+        agent=main_agent,
+        room=ctx.room,
+    ))
+    
+    # Launch recording as background task
+    # logger.info(f"🎬 Launching background recording setup...")
+    # asyncio.create_task(start_recording_async(ctx, conversation_id, userdata))
+    
+    # Wait for agent to start (critical path)
+    await agent_task
+    await session.generate_reply(
+        instructions="Greet the user and offer your assistance in English."
+    )
+
+    # Register shutdown callback with JobContext for proper async cleanup
+    async def cleanup_on_session_close(reason: str):
+        """Async cleanup called by JobContext during shutdown - guaranteed to complete"""
+        try:
+            logger.info(f"🧹 Shutdown cleanup started - {reason}")
+            # userdata.session_state = SessionState.
+            
+            # try:
+            #     success = await utils.end_conversation(conversation_id)
+            #     if success:
+            #         logger.info("✅ End conversation completed successfully")
+            #     else:
+            #         logger.error("❌ End conversation failed - trying sync fallback")
+            # except Exception as e:
+            #     logger.error(f"❌ Exception during end_conversation: {e}")
+                
+            # userdata.session_state = SessionState.SHUTDOWN
+            logger.info("✅ Cleanup completed")
+            
+        except Exception as e:
+            logger.error(f"❌ Error in shutdown cleanup: {e}")
+
+    # Register the async cleanup with JobContext
+    ctx.add_shutdown_callback(cleanup_on_session_close)
+
+    # @session.on("close")
+    # def on_session_close(event):
+    #     """Handle immediate session close actions only - no async operations"""
+    #     logger.info("🔔 Session close event received")
+    #     try:
+    #         userdata.session_state = SessionState.INTERRUPTED
+    #     except Exception as e:
+    #         logger.error(f"❌ Error in session close handler: {e}")
+
+    @session.on("conversation_item_added") 
+    def on_conversation_item_added(event):
+        """Handle when items are added to conversation history - captures both user and agent responses"""
+        try:
+            item = event.item       
+            # Extract text content from ChatMessage.content list
+            transcript_text = ""
+            if hasattr(item, 'content') and item.content:
+                
+                # content is list[ImageContent | AudioContent | str]
+                text_parts = []
+                for i, content_item in enumerate(item.content):
+                    if isinstance(content_item, str):
+                        text_parts.append(content_item)
+                    elif hasattr(content_item, 'text'):
+                        text_parts.append(content_item.text)
+                
+                transcript_text = " ".join(text_parts).strip()
+            
+            is_user = item.role == "user" if hasattr(item, 'role') else False
+            item_id = item.id if hasattr(item, 'id') else None
+            
+            logger.info(f"💬 {'User' if is_user else 'Agent'} conversation item: {transcript_text}")
+            
+            # Allow transcript uploads during active, cleanup, and interruption states
+            # if userdata.session_state in [SessionState.ACTIVE, SessionState.CLEANING_UP, SessionState.INTERRUPTED]:
+            #     if transcript_text:
+            #         # Log if this is a partial transcript during interruption
+            #         if userdata.session_state == SessionState.INTERRUPTED:
+            #             logger.info(f"📝 Capturing {'user' if is_user else 'agent'} transcript during interruption")
+            #         
+            #         asyncio.create_task(utils.upload_transcript(
+            #             conversation_id=conversation_id,
+            #             transcript=transcript_text,
+            #             is_user=is_user,
+            #             user_data=user_data,
+            #             item_id=item_id
+            #         ))
+                        
+        except Exception as e:
+            logger.error(f"Error handling conversation item: {e}")
+
+    @session.on("user_state_changed")
+    def on_user_state_changed(event):
+        """Handle when user state changes - LiveKit automatically triggers 'away' after 30s timeout"""
+        try:
+            new_state = event.new_state
+            old_state = event.old_state
+            
+            logger.info(f"👤 User state changed: {old_state} -> {new_state}")
+            
+            if new_state == "away":
+                logger.info(f"😴 User went away - LiveKit will close session")
+                
+        except Exception as e:
+            logger.error(f"Error handling user state change: {e}")
+
+    # @session.on("agent_state_changed")
+    # def on_agent_state_changed(event):
+    #     """Handle when agent state changes - detect when agent starts speaking"""
+    #     try:
+    #         old_state = event.old_state
+    #         new_state = event.new_state
+    #         
+    #         logger.info(f"🤖 Agent state changed: {old_state} -> {new_state}")
+    #         
+    #         # Track first agent speech and notify client immediately when agent starts speaking
+    #         if new_state == "speaking" and not userdata.first_speech_sent:
+    #             userdata.first_speech_sent = True
+    #             logger.info("🎤 First agent speech started - notifying client immediately")
+    #             
+    #             # Emit event to client via RPC
+    #             asyncio.create_task(notify_first_speech(ctx))
+    #             
+    #     except Exception as e:
+    #         logger.error(f"Error handling agent state change: {e}")
+    
+    # Simple participant disconnect handler for immediate response interruption only
+    @ctx.room.on("participant_disconnected")
+    def on_participant_disconnected(participant: rtc.Participant):
+        logger.info(f"Participant disconnected: {participant.identity}")
+        # Only interrupt if it's a client (not the agent itself)
+        if participant != ctx.room.local_participant:
+            try:
+                logger.info("🛑 Interrupting ongoing response")
+                session.interrupt()  # Immediately stop any ongoing generation
+                # userdata.session_state = SessionState.INTERRUPTED
+            except Exception as e:
+                logger.error(f"Error interrupting session: {e}")
+
+    # Set agent attribute to indicate push-to-talk support
+    try:
+        await ctx.room.local_participant.set_attributes({"push-to-talk": "1"})
+    except Exception as e:
+        logger.warning(f"Failed to set push-to-talk attribute: {e}")
+
+    # Set up RPC methods for push-to-talk support
+    @ctx.room.local_participant.register_rpc_method("start_turn")
+    async def start_turn(data: rtc.RpcInvocationData):
+        # if userdata.session_state != SessionState.ACTIVE:
+        #     logger.info(f"Ignoring start_turn - session state is {userdata.session_state.value}")
+        #     return
+            
+        try:
+            session.interrupt()
+            session.clear_user_turn()
+            # listen to the caller if multi-user
+            room_io.set_participant(data.caller_identity)
+            session.input.set_audio_enabled(True)
+            logger.info("start turn")
+        except Exception as e:
+            logger.error(f"Error in start_turn: {e}")
+
+    @ctx.room.local_participant.register_rpc_method("end_turn")
+    async def end_turn(data: rtc.RpcInvocationData):
+        
+        # if userdata.session_state != SessionState.ACTIVE:
+        #     logger.info(f"Ignoring end_turn - session state is {userdata.session_state.value}")
+        #     return
+            
+        try:
+            logger.info("end turn")
+            session.input.set_audio_enabled(False)
+            session.commit_user_turn(transcript_timeout=0.01) #critical to set it low otherwise the latency will be very high
+        except Exception as e:
+            logger.error(f"Error in end_turn: {e}")
+
+    @ctx.room.local_participant.register_rpc_method("cancel_turn")
+    async def cancel_turn(data: rtc.RpcInvocationData):
+        # if userdata.session_state != SessionState.ACTIVE:
+        #     logger.info(f"Ignoring cancel_turn - session state is {userdata.session_state.value}")
+        #     return
+            
+        try:
+            session.input.set_audio_enabled(False)
+            session.clear_user_turn()
+            logger.info("cancel turn")
+        except Exception as e:
+            logger.error(f"Error in cancel_turn:{e}")
+
+    @ctx.room.local_participant.register_rpc_method("agent_first_speech")
+    async def agent_first_speech(data: rtc.RpcInvocationData):
+        """RPC method for clients to register for first speech notifications"""
+        try:
+            # This method can be called by client to check if first speech has been sent
+            # or to register interest in first speech notifications
+            response = {
+                "first_speech_sent": False,
+                "session_state": SessionState.ACTIVE.value
+            }
+            logger.info(f"Client {data.caller_identity} queried first speech status: {response}")
+            return json.dumps(response)
+        except Exception as e:
+            logger.error(f"Error in agent_first_speech RPC: {e}")
+            return json.dumps({"error": str(e)})
+
+if __name__ == "__main__":
+    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, request_fnc=handle_request))
diff --git a/maxim/tests/test_livekit_push_to_talk_client.py b/maxim/tests/test_livekit_push_to_talk_client.py
new file mode 100644
index 0000000..f396fd3
--- /dev/null
+++ b/maxim/tests/test_livekit_push_to_talk_client.py
@@ -0,0 +1,299 @@
+import asyncio
+import logging
+from livekit import rtc
+from pynput import keyboard
+import sys
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("push-to-talk-client")
+
+class PushToTalkClient:
+    def __init__(self, room_name="agent-room", participant_name="push-to-talk-user", agent_identity="agent"):
+        self.room_name = room_name
+        self.participant_name = participant_name
+        self.agent_identity = agent_identity  # Identity of the agent to send RPCs to
+        self.room = None
+        self.local_participant = None
+        self.is_talking = False
+        self.loop = None  # Store event loop reference
+        
+    def _generate_participant_token(self):
+        """Generate a participant token for LiveKit"""
+        try:
+            from livekit import api
+            
+            api_key = os.getenv("LIVEKIT_API_KEY")
+            api_secret = os.getenv("LIVEKIT_API_SECRET")
+            
+            if not api_key or not api_secret:
+                logger.error("LIVEKIT_API_KEY and LIVEKIT_API_SECRET must be set in .env file")
+                return None
+            
+            # Generate proper LiveKit token
+            token = api.AccessToken(api_key, api_secret) \
+                .with_identity(self.participant_name) \
+                .with_name(self.participant_name) \
+                .with_grants(api.VideoGrants(
+                    room_join=True,
+                    room=self.room_name,
+                    can_publish=True,
+                    can_subscribe=True
+                )).to_jwt()
+            
+            logger.info("Generated token for room: %s, participant: %s", self.room_name, self.participant_name)
+            return token
+            
+        except Exception as e:
+            logger.error("Failed to generate token: %s", e)
+            return None
+        
+    async def connect(self):
+        """Connect to the LiveKit room"""
+        try:
+            # Create room instance
+            self.room = rtc.Room()
+            
+            # Get connection details from environment
+            url = os.getenv("LIVEKIT_URL")
+            if not url:
+                logger.error("LIVEKIT_URL environment variable not set")
+                return False
+                
+            # For development, you might need to generate a proper token
+            # This depends on your LiveKit setup
+            token = self._generate_participant_token()
+            if not token:
+                logger.error("Failed to generate participant token")
+                return False
+            
+            logger.info("Connecting to %s, room: %s", url, self.room_name)
+            
+            # Connect to the room
+            await self.room.connect(url, token)
+            logger.info("Connected to room %s as %s", self.room_name, self.participant_name)
+            
+            self.local_participant = self.room.local_participant
+            
+            # Set up room event handlers
+            self.room.on("participant_connected", self.on_participant_connected)
+            self.room.on("participant_disconnected", self.on_participant_disconnected)
+            self.room.on("connection_quality_changed", self.on_connection_quality_changed)
+            
+            return True
+            
+        except Exception as e:
+            logger.error("Failed to connect to room: %s", e)
+            return False
+    
+    def on_participant_connected(self, participant):
+        logger.info("Participant connected: %s", participant.identity)
+    
+    def on_participant_disconnected(self, participant):
+        logger.info("Participant disconnected: %s", participant.identity)
+        
+    def on_connection_quality_changed(self, quality):
+        logger.info("Connection quality changed: %s", quality)
+    
+    async def start_turn(self):
+        """Invoke the start_turn RPC method"""
+        if self.is_talking:
+            return
+            
+        try:
+            if self.local_participant:
+                self.is_talking = True
+                await self.local_participant.perform_rpc(
+                    destination_identity=self.agent_identity,
+                    method="start_turn",
+                    payload=""
+                )
+                logger.info("🎤 Started turn - agent is listening")
+            else:
+                logger.error("Not connected to room")
+        except Exception as e:
+            logger.error("Failed to start turn: %s", e)
+            self.is_talking = False
+    
+    async def end_turn(self):
+        """Invoke the end_turn RPC method"""
+        if not self.is_talking:
+            return
+            
+        try:
+            if self.local_participant:
+                self.is_talking = False
+                await self.local_participant.perform_rpc(
+                    destination_identity=self.agent_identity,
+                    method="end_turn",
+                    payload=""
+                )
+                logger.info("✅ Ended turn - agent will respond")
+            else:
+                logger.error("Not connected to room")
+        except Exception as e:
+            logger.error("Failed to end turn: %s", e)
+    
+    async def cancel_turn(self):
+        """Invoke the cancel_turn RPC method"""
+        try:
+            if self.local_participant:
+                self.is_talking = False
+                await self.local_participant.perform_rpc(
+                    destination_identity=self.agent_identity,
+                    method="cancel_turn",
+                    payload=""
+                )
+                logger.info("❌ Cancelled turn")
+            else:
+                logger.error("Not connected to room")
+        except Exception as e:
+            logger.error("Failed to cancel turn: %s", e)
+    
+    def on_press(self, key):
+        """Handle key press events"""
+        try:
+            if hasattr(key, 'char') and key.char:
+                if key.char.lower() == 's' and not self.is_talking:
+                    if self.loop and not self.loop.is_closed():
+                        self.loop.call_soon_threadsafe(lambda: asyncio.create_task(self.start_turn()))
+                elif key.char.lower() == 'e' and self.is_talking:
+                    if self.loop and not self.loop.is_closed():
+                        self.loop.call_soon_threadsafe(lambda: asyncio.create_task(self.end_turn()))
+                elif key.char.lower() == 'c':
+                    if self.loop and not self.loop.is_closed():
+                        self.loop.call_soon_threadsafe(lambda: asyncio.create_task(self.cancel_turn()))
+                elif key.char.lower() == 'q':
+                    # Stop listener
+                    return False
+        except AttributeError:
+            pass
+    
+    def on_release(self, key):
+        """Handle key release events"""
+        # No longer needed since we handle everything on key press
+        pass
+    
+    def setup_keybindings(self):
+        """Set up global keybindings using pynput"""
+        # Collect events until released
+        self.listener = keyboard.Listener(
+            on_press=self.on_press,
+            on_release=self.on_release)
+        self.listener.start()
+        
+        print("\n" + "="*50)
+        print("Push-to-Talk Client Started")
+        print("="*50)
+        print("Keybindings:")
+        print("  - Press S to start turn")
+        print("  - Press E to end turn and get response")
+        print("  - Press C to cancel current turn")
+        print("  - Press Q to quit")
+        print("="*50)
+        print("NOTE: On macOS, you may need to grant accessibility permissions")
+        print("to this terminal app in System Preferences > Security & Privacy > Accessibility")
+        print("="*50 + "\n")
+    
+    async def manual_input_mode(self):
+        """Fallback manual input mode when keyboard monitoring fails"""
+        print("\n" + "="*50)
+        print("Manual Input Mode")
+        print("="*50)
+        print("Commands:")
+        print("  s - Start turn")
+        print("  e - End turn")
+        print("  c - Cancel turn")
+        print("  q - Quit")
+        print("="*50 + "\n")
+        
+        while True:
+            try:
+                cmd = input("Enter command (s/e/c/q): ").strip().lower()
+                if cmd == 's':
+                    await self.start_turn()
+                elif cmd == 'e':
+                    await self.end_turn()
+                elif cmd == 'c':
+                    await self.cancel_turn()
+                elif cmd == 'q':
+                    break
+                else:
+                    print("Invalid command. Use s/e/c/q")
+            except (EOFError, KeyboardInterrupt):
+                break
+        
+        self.cleanup_and_exit()
+    
+    async def run(self):
+        """Main execution loop"""
+        self.loop = asyncio.get_running_loop()  # Store loop reference
+        
+        print("Connecting to LiveKit room...")
+        if not await self.connect():
+            logger.error("Failed to connect to LiveKit room. Please check your configuration.")
+            return
+        
+        try:
+            self.setup_keybindings()
+            
+            # Keep the client running
+            while self.listener.running:
+                await asyncio.sleep(1)
+        except Exception as e:
+            logger.warning("Keyboard monitoring failed (likely accessibility permissions): %s", e)
+            logger.info("Falling back to manual input mode...")
+            await self.manual_input_mode()
+        except KeyboardInterrupt:
+            self.cleanup_and_exit()
+    
+    def cleanup_and_exit(self):
+        """Clean up resources and exit"""
+        print("\nShutting down...")
+        if self.room:
+            asyncio.create_task(self.room.disconnect())
+        if hasattr(self, 'listener'):
+            self.listener.stop()
+        sys.exit(0)
+
+
+async def main():
+    # You can customize the room name here - make sure it matches your agent
+    room_name = "agent-room"  # Change this to match your agent's room
+    agent_identity = "agent"  # Change this to match your agent's identity
+    
+    client = PushToTalkClient(room_name=room_name, agent_identity=agent_identity)
+    await client.run()
+
+
+if __name__ == "__main__":
+    # Install required package for local testing: uv sync --group dev --group local-testing
+    
+    # Check if pynput module is available
+    try:
+        from pynput import keyboard as _  # noqa: F401
+    except ImportError:
+        print("Please install the 'pynput' package for local testing:")
+        print("  uv sync --group dev --group local-testing")
+        print("  or: pip install pynput")
+        sys.exit(1)
+    
+    # Check if required environment variables are set
+    required_env_vars = ["LIVEKIT_URL", "LIVEKIT_API_KEY", "LIVEKIT_API_SECRET"]
+    missing_vars = [var for var in required_env_vars if not os.getenv(var)]
+    
+    if missing_vars:
+        print("Missing required environment variables:")
+        for var in missing_vars:
+            print(f"  - {var}")
+        print("\nPlease make sure your .env file contains:")
+        print("LIVEKIT_URL=wss://your-livekit-server.com")
+        print("LIVEKIT_API_KEY=your_api_key")
+        print("LIVEKIT_API_SECRET=your_api_secret")
+        sys.exit(1)
+    
+    asyncio.run(main())
\ No newline at end of file
diff --git a/maxim/tests/test_livekit_push_to_talk_copy.py b/maxim/tests/test_livekit_push_to_talk_copy.py
new file mode 100644
index 0000000..15cb600
--- /dev/null
+++ b/maxim/tests/test_livekit_push_to_talk_copy.py
@@ -0,0 +1,124 @@
+import logging
+import os
+from enum import Enum
+
+from dotenv import load_dotenv
+
+from livekit import rtc
+from livekit.agents import Agent, AgentSession, JobContext, JobRequest, RoomIO, WorkerOptions, cli
+from livekit.agents.llm import ChatContext, ChatMessage, StopResponse
+from livekit.plugins import openai
+from livekit.plugins.openai.realtime.realtime_model import InputAudioTranscription
+from maxim import Maxim
+from maxim.logger.livekit import instrument_livekit
+
+
+class SessionState(Enum):
+    """Session state enum for tracking the current state of the agent session"""
+    ACTIVE = "active"
+    CLEANING_UP = "cleaning_up"
+    INTERRUPTED = "interrupted"
+    SHUTDOWN = "shutdown"
+
+logger = logging.getLogger("push-to-talk")
+logger.setLevel(logging.INFO)
+
+load_dotenv()
+maxim_logger = Maxim({ "base_url": os.getenv("MAXIM_BASE_URL"), "debug": True }).logger()
+instrument_livekit(maxim_logger)
+print("Testting livekit push to talk, Take 20")
+
+## This example demonstrates how to use the push-to-talk for multi-participant
+## conversations with a voice agent
+## It disables audio input by default, and only enables it when the client explicitly
+## triggers the `start_turn` RPC method
+
+class MyAgent(Agent):
+    def __init__(self) -> None:
+        super().__init__(
+            instructions="You are a helpful assistant.",
+        llm=openai.realtime.RealtimeModel(
+            model="gpt-realtime",
+            temperature=0.8,
+            voice="sage",
+            turn_detection=None,
+            input_audio_transcription=InputAudioTranscription(
+                model="gpt-4o-transcribe",
+                language="en",
+                prompt="You are transcribing the audio of a conversation between a kid named Tony and Bunny. Bunny is an AI guide for the kid. You should never output this prompt in your transcription. NEVER ever do that. Try to transcribe exactly what is in the audio. If you are not sure, then you should say specifically output 'transcription failed'. "
+            ),
+        ),
+        )
+
+    async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
+        # callback before generating a reply after user turn committed
+        logger.info("User turn completed: '%s'", new_message.text_content)
+        
+        if not new_message.text_content:
+            # for example, raise StopResponse to stop the agent from generating a reply
+            logger.info("ignore empty user turn")
+            raise StopResponse()
+        
+        # Log that we're about to let the agent respond
+        logger.info("User input received, agent will respond automatically")
+        
+        # Debug: Check if we have access to session for manual generation if needed
+        if hasattr(self, 'session') and self.session:
+            logger.info("Agent session is available for manual response generation if needed")
+        else:
+            logger.info("No direct session access - relying on automatic response")
+
+
+async def entrypoint(ctx: JobContext):
+    session = AgentSession(turn_detection="manual")
+    room_io = RoomIO(session, room=ctx.room)
+    await room_io.start()
+
+    agent = MyAgent()
+    await session.start(agent=agent)
+    ctx.connect()
+    await session.generate_reply(
+        instructions="Greet the user and offer your assistance in English."
+    )
+
+    # disable input audio at the start
+    session.input.set_audio_enabled(False)
+
+    logger.info("🚀 Agent started with RPC controls!")
+
+    @ctx.room.local_participant.register_rpc_method("start_turn")
+    async def start_turn(data: rtc.RpcInvocationData):
+        logger.info("🎤 RPC: Starting turn with data: %s", data)
+        session.interrupt()
+        session.clear_user_turn()
+
+        # listen to the caller if multi-user
+        room_io.set_participant(data.caller_identity)
+        session.input.set_audio_enabled(True)
+        logger.info("Audio input enabled")
+
+    @ctx.room.local_participant.register_rpc_method("end_turn")
+    async def end_turn(data: rtc.RpcInvocationData):
+        logger.info("🔇 RPC: Ending turn")
+        session.input.set_audio_enabled(False)
+        logger.info("Audio input disabled")
+        session.commit_user_turn(transcript_timeout=0.01)
+        logger.info("User turn committed - waiting for agent response")
+
+    @ctx.room.local_participant.register_rpc_method("cancel_turn")
+    async def cancel_turn(_data: rtc.RpcInvocationData):
+        logger.info("❌ RPC: Cancelling turn")
+        session.input.set_audio_enabled(False)
+        session.clear_user_turn()
+        logger.info("Turn cancelled")
+
+
+async def handle_request(request: JobRequest) -> None:
+    await request.accept(
+        identity="ptt-agent",
+        # this attribute communicates to frontend that we support PTT
+        attributes={"push-to-talk": "1"},
+    )
+
+if __name__ == "__main__":
+    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, request_fnc=handle_request))
diff --git a/maxim/tests/test_livekit_push_to_talk_non_rpc.py b/maxim/tests/test_livekit_push_to_talk_non_rpc.py
new file mode 100644
index 0000000..10c6927
--- /dev/null
+++ b/maxim/tests/test_livekit_push_to_talk_non_rpc.py
@@ -0,0 +1,160 @@
+import asyncio
+import logging
+import os
+import select
+import sys
+import termios
+import tty
+import threading
+from dotenv import load_dotenv
+
+from livekit.agents import Agent, AgentSession, JobContext, RoomIO, WorkerOptions, cli
+from livekit.agents.llm import ChatContext, ChatMessage, StopResponse
+from livekit.plugins import openai
+from maxim import Maxim, scribe
+from maxim.logger.livekit import instrument_livekit
+
+logger = logging.getLogger("push-to-talk")
+logger.setLevel(logging.INFO)
+
+load_dotenv()
+
+## This example demonstrates push-to-talk with terminal key bindings
+## No RPC calls needed - use keyboard shortcuts directly in the terminal:
+## - 's': Start turn (enable microphone)
+## - 'e': End turn (disable microphone and process)
+## - 'c': Cancel current turn
+## - 'q': Quit the application
+
+
+logger = Maxim({ "base_url": os.getenv("MAXIM_BASE_URL") }).logger()
+instrument_livekit(logger)
+
+
+class MyAgent(Agent):
+    def __init__(self) -> None:
+        super().__init__(
+            instructions="You are a helpful assistant.",
+            llm=openai.realtime.RealtimeModel(voice="alloy", turn_detection=None),
+        )
+
+    async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
+        # callback before generating a reply after user turn committed
+        scribe.scribe().info(f"on_user_turn_completed: {new_message.text_content}")
+        if not new_message.text_content:
+            # for example, raise StopResponse to stop the agent from generating a reply
+            scribe.scribe().info("ignore empty user turn")
+            raise StopResponse()
+
+        # Let the agent automatically generate a response
+        scribe.scribe().info("User turn completed - agent will respond automatically")
+
+
+class KeyboardHandler:
+    def __init__(self, session: AgentSession):
+        self.session = session
+        self.running = True
+        self.old_settings = None
+        
+    async def start_turn(self):
+        """Start a new turn - enable audio input"""
+        scribe.scribe().info("🎤 Starting turn - speak now!")
+        self.session.interrupt()
+        self.session.clear_user_turn()
+        self.session.input.set_audio_enabled(True)
+            
+    async def end_turn(self):
+        """End current turn - disable audio and commit"""
+        scribe.scribe().info("🔇 Ending turn - processing...")
+        self.session.input.set_audio_enabled(False)
+        self.session.commit_user_turn()
+            
+    async def cancel_turn(self):
+        """Cancel current turn - disable audio and clear"""
+        scribe.scribe().info("❌ Cancelling turn")
+        self.session.input.set_audio_enabled(False)
+        self.session.clear_user_turn()
+            
+    def quit_app(self):
+        """Quit the application"""
+        scribe.scribe().info("👋 Quitting application...")
+        self.running = False
+        
+    def setup_terminal(self):
+        """Setup terminal for raw input"""
+        if sys.stdin.isatty():
+            self.old_settings = termios.tcgetattr(sys.stdin)
+            tty.setraw(sys.stdin.fileno())
+            
+    def restore_terminal(self):
+        """Restore terminal settings"""
+        if self.old_settings:
+            termios.tcsetattr(sys.stdin, termios.TCSADRAIN, self.old_settings)
+            
+    async def keyboard_listener(self):
+        """Listen for keyboard input in a separate thread"""
+        self.setup_terminal()
+        
+        try:
+            print("\n" + "="*50)
+            print("🎤 PUSH-TO-TALK CONTROLS:")
+            print("  s: Start turn (enable microphone)")
+            print("  e: End turn (disable microphone and process)")
+            print("  c: Cancel current turn")
+            print("  q: Quit application")
+            print("="*50 + "\n")
+            
+            while self.running:
+                if sys.stdin in select.select([sys.stdin], [], [], 0.1)[0]:
+                    char = sys.stdin.read(1)
+                    
+                    if char == 's':  # Start turn
+                        await self.start_turn()
+                    elif char == 'e':  # End turn
+                        await self.end_turn()
+                    elif char == 'c':  # Cancel
+                        await self.cancel_turn()
+                    elif char == 'q':  # Quit
+                        self.quit_app()
+                        break
+                        
+        except KeyboardInterrupt:
+            scribe.scribe().info("Keyboard interrupt received")
+            self.quit_app()
+        finally:
+            self.restore_terminal()
+
+
+async def entrypoint(ctx: JobContext):
+    session = AgentSession(turn_detection="manual")
+    room_io = RoomIO(session, room=ctx.room)
+    await room_io.start()
+
+    agent = MyAgent()
+    await session.start(agent=agent)
+
+    # disable input audio at the start
+    session.input.set_audio_enabled(False)
+    
+    # Setup keyboard handler
+    keyboard_handler = KeyboardHandler(session)
+    
+    # Start keyboard listener in a separate thread
+    keyboard_thread = threading.Thread(target=keyboard_handler.keyboard_listener, daemon=True)
+    keyboard_thread.start()
+    
+    scribe.scribe().info("🚀 Agent started! Use keyboard controls to interact.")
+    
+    # Keep the main thread alive
+    try:
+        while keyboard_handler.running:
+            await asyncio.sleep(0.1)
+    except KeyboardInterrupt:
+        scribe.scribe().info("Shutting down...")
+    finally:
+        keyboard_handler.running = False
+        keyboard_handler.restore_terminal()
+
+
+if __name__ == "__main__":
+    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))
\ No newline at end of file
diff --git a/maxim/tests/test_livekit_realtime.py b/maxim/tests/test_livekit_realtime.py
index 9e2c675..efff67a 100644
--- a/maxim/tests/test_livekit_realtime.py
+++ b/maxim/tests/test_livekit_realtime.py
@@ -79,7 +79,6 @@ async def entrypoint(ctx: agents.JobContext):
         room = await lkapi.room.create_room(
             req
         )  # :contentReference[oaicite:0]{index=0}
-        print(f"Room created: {room}")
         session = AgentSession(
             llm=openai.realtime.RealtimeModel(voice="coral"),
         )
diff --git a/pyproject.toml b/pyproject.toml
index 0ae1020..b823d8a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,8 +7,18 @@ name = "maxim-py"
 version = "3.10.9"
 description = "A package that allows you to use the Maxim Python Library to interact with the Maxim Platform."
 readme = "README.md"
-requires-python = ">=3.9.20"
-dependencies = ["typing-extensions", "filetype", "httpx>=0.28.1"]
+requires-python = ">=3.12"
+dependencies = [
+    "typing-extensions",
+ "filetype",
+ "httpx>=0.28.1",
+ "ipykernel>=6.30.1",
+ "python-dotenv>=0.9.9",
+ "livekit>=1.0.8",
+ "livekit-agents[openai]>=1.2.4",
+ "livekit-agents[google,silero]~=1.0",
+ "livekit-api>=1.0.2",
+]
 authors = [{ name = "Maxim Engineering", email = "eng@getmaxim.ai" }]
 keywords = ["python", "prompts", "logs", "workflow", "testing"]
 classifiers = [
@@ -30,7 +40,7 @@ dev = [
   "langchain-anthropic>=0.3.15",
   "langchain-community>=0.3.25",
   "langchain-openai>=0.3.23",
-  "langgraph>=0.4.8",
+  "langgraph==0.6.5",
   "flask>=3.1.1",
   "google-genai>=1.18.0",
   "litellm>=1.72.6",
@@ -56,10 +66,14 @@ dev = [
   "groq>=0.30.0",
   "fireworks-ai>=0.17.9",
   "livekit-plugins-noise-cancellation~=0.2",
-  "crewai==0.130.0",
+  # "crewai==0.130.0",
   "llama-index-core>=0.12.49",
   "llama-index-llms-openai>=0.4.7",
   "tavily-python>=0.7.9",
   "smolagents>=1.21.2",
-  "pydantic-ai>=0.7.6"
+  "pydantic-ai>=0.7.6",
+]
+
+local-testing = [
+  "pynput>=1.8.1",
 ]
diff --git a/uv.lock b/uv.lock
index d207024..7844dcb 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,13 +1,22 @@
 version = 1
 revision = 2
-requires-python = ">=3.9.20"
+requires-python = ">=3.12"
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-    "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version < '3.10'",
+    "python_full_version < '3.12.4'",
+]
+
+[[package]]
+name = "ag-ui-protocol"
+version = "0.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic" },
+]
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+wheels = [
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 ]
 
 [[package]]
@@ -24,8 +33,7 @@ dependencies = [
     { name = "python-dotenv" },
     { name = "python-multipart" },
     { name = "pyyaml" },
-    { name = "rich", version = "13.9.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "rich", version = "14.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "rich" },
     { name = "tomli" },
     { name = "typer" },
     { name = "typing-extensions" },
@@ -60,7 +68,6 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
-    { name = "async-timeout", marker = "python_full_version < '3.11'" },
     { name = "attrs" },
     { name = "frozenlist" },
     { name = "multidict" },
@@ -69,40 +76,6 @@ dependencies = [
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 source = { editable = "." }
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+    { name = "ipykernel" },
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+    { name = "livekit-agents", extra = ["google", "openai", "silero"] },
+    { name = "livekit-api" },
+    { name = "python-dotenv" },
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+    { name = "boto3" },
+    { name = "botocore" },
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-    { name = "fireworks-ai", version = "0.17.16", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "fireworks-ai" },
     { name = "flask" },
     { name = "google-genai" },
     { name = "groq" },
@@ -2222,32 +2046,43 @@ dev = [
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     { name = "litellm" },
     { name = "livekit" },
-    { name = "livekit-agents", extra = ["google", "openai"] },
+    { name = "livekit-agents", extra = ["google", "openai", "silero"] },
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+    { name = "livekit-plugins-noise-cancellation" },
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     { name = "mistralai" },
     { name = "ollama" },
     { name = "openai-agents" },
     { name = "portkey-ai" },
+    { name = "pydantic-ai" },
     { name = "pydoc-markdown" },
     { name = "pytest" },
     { name = "python-dotenv" },
     { name = "pyyaml" },
     { name = "requests" },
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+    { name = "together" },
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     { name = "urllib3" },
     { name = "wheel" },
 ]
+local-testing = [
+    { name = "pynput" },
+]
 
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+    { name = "livekit", specifier = ">=1.0.8" },
+    { name = "livekit-agents", extras = ["google", "silero"], specifier = "~=1.0" },
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