LangGraph callback handlers for Sigil Python SDK
Project description
Sigil Python Framework Module: LangGraph
sigil-sdk-langgraph provides callback handlers that map LangGraph lifecycle events into Sigil generation recorder lifecycles.
Installation
pip install sigil-sdk sigil-sdk-langgraph
pip install langgraph langchain-openai
Usage
from sigil_sdk import Client
from sigil_sdk_langgraph import with_sigil_langgraph_callbacks
client = Client()
config = with_sigil_langgraph_callbacks(None, client=client, provider_resolver="auto")
End-to-end example (graph invoke + stream)
from typing import TypedDict
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph
from sigil_sdk import Client
from sigil_sdk_langgraph import SigilLangGraphHandler, with_sigil_langgraph_callbacks
class GraphState(TypedDict):
prompt: str
answer: str
client = Client()
handler = SigilLangGraphHandler(
client=client,
provider_resolver="auto",
agent_name="langgraph-example",
agent_version="1.0.0",
)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
def run_model(state: GraphState) -> GraphState:
response = llm.invoke(
state["prompt"],
config=with_sigil_langgraph_callbacks(None, client=client, provider_resolver="auto"),
)
return {"prompt": state["prompt"], "answer": response.content}
workflow = StateGraph(GraphState)
workflow.add_node("model", run_model)
workflow.set_entry_point("model")
workflow.add_edge("model", END)
graph = workflow.compile()
# Non-stream graph invocation.
out = graph.invoke({"prompt": "Explain SLO burn rate in one paragraph.", "answer": ""})
print(out["answer"])
# Streamed graph events.
for _event in graph.stream(
{"prompt": "List three practical alerting tips.", "answer": ""},
):
pass
client.shutdown()
Persistent thread example (LangGraph checkpointer)
from langgraph.checkpoint.memory import MemorySaver
checkpointer = MemorySaver()
graph = workflow.compile(checkpointer=checkpointer)
thread_config = {
**with_sigil_langgraph_callbacks(None, client=client, provider_resolver="auto"),
"configurable": {"thread_id": "customer-42"},
}
graph.invoke({"prompt": "Remember that my timezone is UTC+1.", "answer": ""}, config=thread_config)
graph.invoke({"prompt": "What timezone did I just give you?", "answer": ""}, config=thread_config)
# Advanced usage: explicit handler wiring remains supported.
_ = graph.invoke(
{"prompt": "manual handler wiring", "answer": ""},
config={"callbacks": [handler]},
)
When thread_id is present, the handler records:
conversation_id=<thread_id>metadata["sigil.framework.run_id"]=<run id>metadata["sigil.framework.thread_id"]=<thread id>- generation span attributes
sigil.framework.run_idandsigil.framework.thread_id
Behavior
- Lifecycle mapping:
on_llm_start/on_chat_model_start-> generation recorderon_tool_start/on_tool_end/on_tool_error->start_tool_executionon_chain_start/on_chain_end/on_chain_error-> framework chain spanson_retriever_start/on_retriever_end/on_retriever_error-> framework retriever spanson_llm_new_token-> first-token timestamp for stream mode
- Mode mapping: non-stream ->
SYNC, stream ->STREAM. - Provider resolver parity:
- explicit provider metadata when available
- model-name inference (
gpt-/o1/o3/o4->openai,claude-->anthropic,gemini-->gemini) - fallback ->
custom
- Framework tags/metadata are always set:
sigil.framework.name=langgraphsigil.framework.source=handlersigil.framework.language=pythonmetadata["sigil.framework.run_id"]=<run id>metadata["sigil.framework.thread_id"]=<thread id>(when present in callback metadata/config)metadata["sigil.framework.parent_run_id"](when available)metadata["sigil.framework.component_name"](serialized component identity)metadata["sigil.framework.run_type"](llm,chat,tool,chain,retriever)metadata["sigil.framework.tags"](normalized callback tags)metadata["sigil.framework.retry_attempt"](when available)metadata["sigil.framework.langgraph.node"](when callback context exposes node identity)- generation span attributes mirror low-cardinality framework metadata keys
Call client.shutdown() during teardown to flush buffered telemetry.
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