feat: Add LangGraph integration

.github/workflows/test-integrations-ai.yml

@@ -74,6 +74,10 @@ jobs:
         run: |
           set -x # print commands that are executed
           ./scripts/runtox.sh --exclude-latest "py${{ matrix.python-version }}-openai-notiktoken"
+      - name: Test langgraph pinned
+        run: |
+          set -x # print commands that are executed
+          ./scripts/runtox.sh --exclude-latest "py${{ matrix.python-version }}-langgraph"
       - name: Test openai_agents pinned
         run: |
           set -x # print commands that are executed

pyproject.toml

@@ -130,6 +130,10 @@ ignore_missing_imports = true
 module = "langchain.*"
 ignore_missing_imports = true
 
+[[tool.mypy.overrides]]
+module = "langgraph.*"
+ignore_missing_imports = true
+
 [[tool.mypy.overrides]]
 module = "executing.*"
 ignore_missing_imports = true

scripts/populate_tox/config.py

@@ -146,6 +146,9 @@
         },
         "include": "<1.0",
     },
+    "langgraph": {
+        "package": "langgraph",
+    },
     "launchdarkly": {
         "package": "launchdarkly-server-sdk",
     },

scripts/populate_tox/tox.jinja

@@ -358,6 +358,7 @@ setenv =
     huggingface_hub: TESTPATH=tests/integrations/huggingface_hub
     langchain-base: TESTPATH=tests/integrations/langchain
     langchain-notiktoken: TESTPATH=tests/integrations/langchain
+    langgraph:  TESTPATH=tests/integrations/langgraph
     launchdarkly: TESTPATH=tests/integrations/launchdarkly
     litestar: TESTPATH=tests/integrations/litestar
     loguru: TESTPATH=tests/integrations/loguru

scripts/split_tox_gh_actions/split_tox_gh_actions.py

@@ -65,6 +65,7 @@
         "langchain-notiktoken",
         "openai-base",
         "openai-notiktoken",
+        "langgraph",
         "openai_agents",
         "huggingface_hub",
     ],

sentry_sdk/consts.py

@@ -792,6 +792,7 @@ class OP:
     FUNCTION_AWS = "function.aws"
     FUNCTION_GCP = "function.gcp"
     GEN_AI_CHAT = "gen_ai.chat"
+    GEN_AI_CREATE_AGENT = "gen_ai.create_agent"
     GEN_AI_EMBEDDINGS = "gen_ai.embeddings"
     GEN_AI_EXECUTE_TOOL = "gen_ai.execute_tool"
     GEN_AI_HANDOFF = "gen_ai.handoff"

sentry_sdk/integrations/__init__.py

@@ -142,6 +142,7 @@ def iter_default_integrations(with_auto_enabling_integrations):
     "grpc": (1, 32, 0),  # grpcio
     "huggingface_hub": (0, 22),
     "langchain": (0, 1, 0),
+    "langgraph": (0, 6, 6),
     "launchdarkly": (9, 8, 0),
     "loguru": (0, 7, 0),
     "openai": (1, 0, 0),

sentry_sdk/integrations/langgraph.py

@@ -0,0 +1,301 @@
+from functools import wraps
+from typing import Any, Callable, List, Optional
+
+import sentry_sdk
+from sentry_sdk.ai.utils import set_data_normalized
+from sentry_sdk.consts import OP, SPANDATA
+from sentry_sdk.integrations import DidNotEnable, Integration
+from sentry_sdk.scope import should_send_default_pii
+from sentry_sdk.utils import safe_serialize
+
+
+try:
+    from langgraph.graph import StateGraph
+    from langgraph.pregel import Pregel
+except ImportError:
+    raise DidNotEnable("langgraph not installed")
+
+
+class LanggraphIntegration(Integration):
+    identifier = "langgraph"
+    origin = f"auto.ai.{identifier}"
+
+    def __init__(self, include_prompts=True):
+        # type: (LanggraphIntegration, bool) -> None
+        self.include_prompts = include_prompts
+
+    @staticmethod
+    def setup_once():
+        # type: () -> None
+        # LangGraph lets users create agents using a StateGraph or the Functional API.
+        # StateGraphs are then compiled to a CompiledStateGraph. Both CompiledStateGraph and
+        # the functional API execute on a Pregel instance. Pregel is the runtime for the graph
+        # and the invocation happens on Pregel, so patching the invoke methods takes care of both.
+        # The streaming methods are not patched, because due to some internal reasons, LangGraph
+        # will automatically patch the streaming methods to run through invoke, and by doing this
+        # we prevent duplicate spans for invocations.
+        StateGraph.compile = _wrap_state_graph_compile(StateGraph.compile)
+        if hasattr(Pregel, "invoke"):
+            Pregel.invoke = _wrap_pregel_invoke(Pregel.invoke)
+        if hasattr(Pregel, "ainvoke"):
+            Pregel.ainvoke = _wrap_pregel_ainvoke(Pregel.ainvoke)
+
+
+def _get_graph_name(graph_obj):
+    # type: (Any) -> Optional[str]
+    for attr in ["name", "graph_name", "__name__", "_name"]:
+        if hasattr(graph_obj, attr):
+            name = getattr(graph_obj, attr)
+            if name and isinstance(name, str):
+                return name
+    return None
+
+
+def _normalize_langgraph_message(message):
+    # type: (Any) -> Any
+    if not hasattr(message, "content"):
+        return None
+    parsed = {"role": getattr(message, "type", None), "content": message.content}
+
+    for attr in ["name", "tool_calls", "function_call", "tool_call_id"]:
+        if hasattr(message, attr):
+            value = getattr(message, attr)
+            if value is not None:
+                parsed[attr] = value
+
+    return parsed
+
+
+def _parse_langgraph_messages(state):
+    # type: (Any) -> Optional[List[Any]]
+    if not state:
+        return None
+
+    messages = None
+
+    if isinstance(state, dict):
+        messages = state.get("messages")
+    elif hasattr(state, "messages"):
+        messages = state.messages
+    elif hasattr(state, "get") and callable(state.get):
+        try:
+            messages = state.get("messages")
+        except Exception:
+            pass
+
+    if not messages or not isinstance(messages, (list, tuple)):
+        return None
+
+    normalized_messages = []
+    for message in messages:
+        try:
+            normalized = _normalize_langgraph_message(message)
+            if normalized:
+                normalized_messages.append(normalized)
+        except Exception:
+            continue
+
+    return normalized_messages if normalized_messages else None
+
+
+def _wrap_state_graph_compile(f):
+    # type: (Callable[..., Any]) -> Callable[..., Any]
+    @wraps(f)
+    def new_compile(self, *args, **kwargs):
+        # type: (Any, Any, Any) -> Any
+        integration = sentry_sdk.get_client().get_integration(LanggraphIntegration)
+
+        compiled_graph = f(self, *args, **kwargs)
+        if integration is None:
+            return compiled_graph
+
+        with sentry_sdk.start_span(
+            op=OP.GEN_AI_CREATE_AGENT,
+            name="create_agent",
+            origin=LanggraphIntegration.origin,
+        ) as span:
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "create_agent")
+            span.set_data(
+                SPANDATA.GEN_AI_AGENT_NAME, getattr(compiled_graph, "name", None)
+            )
+            span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, kwargs.get("model"))
+            tools = None
+            graph = getattr(compiled_graph, "get_graph", None)
+            if callable(graph):
+                graph_obj = graph()
+                nodes = getattr(graph_obj, "nodes", None)
+                if nodes and isinstance(nodes, dict):
+                    tools_node = nodes.get("tools")
+                    if tools_node:
+                        data = getattr(tools_node, "data", None)
+                        if data and hasattr(data, "tools_by_name"):
+                            tools = list(data.tools_by_name.keys())
+            span.set_data(SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, tools)
+            return compiled_graph
+
+    return new_compile
+
+
+def _wrap_pregel_invoke(f):
+    # type: (Callable[..., Any]) -> Callable[..., Any]
+
+    @wraps(f)
+    def new_invoke(self, *args, **kwargs):
+        # type: (Any, Any, Any) -> Any
+        integration = sentry_sdk.get_client().get_integration(LanggraphIntegration)
+        if integration is None:
+            return f(self, *args, **kwargs)
+
+        graph_name = _get_graph_name(self)
+
+        with sentry_sdk.start_span(
+            op=OP.GEN_AI_INVOKE_AGENT,
+            name="invoke_agent",
+            origin=LanggraphIntegration.origin,
+        ) as span:
+            if graph_name:
+                span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
+                span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
+
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
+            span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)
+
+            # Store input messages to later compare with output
+            input_messages = None
+            if (
+                len(args) > 0
+                and should_send_default_pii()
+                and integration.include_prompts
+            ):
+                input_messages = _parse_langgraph_messages(args[0])
+                if input_messages:
+                    set_data_normalized(
+                        span,
+                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                        safe_serialize(input_messages),
+                    )
+
+            result = f(self, *args, **kwargs)
+            _set_response_attributes(span, input_messages, result, integration)
+            return result
+
+    return new_invoke
+
+
+def _wrap_pregel_ainvoke(f):
+    # type: (Callable[..., Any]) -> Callable[..., Any]
+
+    @wraps(f)
+    async def new_ainvoke(self, *args, **kwargs):
+        # type: (Any, Any, Any) -> Any
+        integration = sentry_sdk.get_client().get_integration(LanggraphIntegration)
+        if integration is None:
+            return await f(self, *args, **kwargs)
+
+        graph_name = _get_graph_name(self)
+
+        with sentry_sdk.start_span(
+            op=OP.GEN_AI_INVOKE_AGENT,
+            name="invoke_agent",
+            origin=LanggraphIntegration.origin,
+        ) as span:
+            if graph_name:
+                span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
+                span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
+
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
+            span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)
+
+            input_messages = None
+            if (
+                len(args) > 0
+                and should_send_default_pii()
+                and integration.include_prompts
+            ):
+                input_messages = _parse_langgraph_messages(args[0])
+                if input_messages:
+                    set_data_normalized(
+                        span,
+                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                        safe_serialize(input_messages),
+                    )
+
+            result = await f(self, *args, **kwargs)
+            _set_response_attributes(span, input_messages, result, integration)
+            return result
+
+    return new_ainvoke
+
+
+def _get_new_messages(input_messages, output_messages):
+    # type: (Optional[List[Any]], Optional[List[Any]]) -> Optional[List[Any]]
+    """Extract only the new messages added during this invocation."""
+    if not output_messages:
+        return None
+
+    if not input_messages:
+        return output_messages
+
+    # only return the new messages, aka the output messages that are not in the input messages
+    input_count = len(input_messages)
+    new_messages = (
+        output_messages[input_count:] if len(output_messages) > input_count else []
+    )
+
+    return new_messages if new_messages else None
+
+
+def _extract_llm_response_text(messages):
+    # type: (Optional[List[Any]]) -> Optional[str]
+    if not messages:
+        return None
+
+    for message in reversed(messages):
+        if isinstance(message, dict):
+            role = message.get("role")
+            if role in ["assistant", "ai"]:
+                content = message.get("content")
+                if content and isinstance(content, str):
+                    return content
+
+    return None
+
+
+def _extract_tool_calls(messages):
+    # type: (Optional[List[Any]]) -> Optional[List[Any]]
+    if not messages:
+        return None
+
+    tool_calls = []
+    for message in messages:
+        if isinstance(message, dict):
+            msg_tool_calls = message.get("tool_calls")
+            if msg_tool_calls and isinstance(msg_tool_calls, list):
+                tool_calls.extend(msg_tool_calls)
+
+    return tool_calls if tool_calls else None
+
+
+def _set_response_attributes(span, input_messages, result, integration):
+    # type: (Any, Optional[List[Any]], Any, LanggraphIntegration) -> None
+    if not (should_send_default_pii() and integration.include_prompts):
+        return
+
+    parsed_response_messages = _parse_langgraph_messages(result)
+    new_messages = _get_new_messages(input_messages, parsed_response_messages)
+
+    llm_response_text = _extract_llm_response_text(new_messages)
+    if llm_response_text:
+        set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, llm_response_text)
+    elif new_messages:
+        set_data_normalized(
+            span, SPANDATA.GEN_AI_RESPONSE_TEXT, safe_serialize(new_messages)
+        )
+    else:
+        set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, result)
+
+    tool_calls = _extract_tool_calls(new_messages)
+    if tool_calls:
+        set_data_normalized(
+            span, SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS, safe_serialize(tool_calls)
+        )

setup.py

@@ -63,6 +63,7 @@ def get_file_text(file_name):
         "huey": ["huey>=2"],
         "huggingface_hub": ["huggingface_hub>=0.22"],
         "langchain": ["langchain>=0.0.210"],
+        "langgraph": ["langgraph>=0.6.6"],
         "launchdarkly": ["launchdarkly-server-sdk>=9.8.0"],
         "litestar": ["litestar>=2.0.0"],
         "loguru": ["loguru>=0.5"],

tests/integrations/langgraph/__init__.py

@@ -0,0 +1,3 @@
+import pytest
+
+pytest.importorskip("langgraph")