本指南介绍使用 REST API进行追踪的两种方法：使用 POST /runs 和 PATCH /runs 端点的基础追踪，以及使用 POST /runs/multipart 实现更高吞吐量的批量导入。
For a full list of endpoints and request/response schemas, refer to the API 参考.
基础追踪
记录运行最简单的方式是通过 POST /runs 和 PATCH /runs 端点。这种方法只需最少的信息即可建立追踪层级。
以下示例追踪一个聊天补全，包含一个父链运行和一个子 LLM 运行。设置 parent_run_id 将子运行附加到其父运行：
from datetime import datetime, timezone
from langsmith import uuid7
# Send your API Key in the request headers
headers = {
"x-api-key": os.environ["LANGSMITH_API_KEY"],
"x-tenant-id": os.environ["LANGSMITH_WORKSPACE_ID"]
}
def post_run(run_id, name, run_type, inputs, parent_id=None):
"""Function to post a new run to the API."""
data = {
"id": run_id.hex,
"name": name,
"run_type": run_type,
"inputs": inputs,
"start_time": datetime.utcnow().isoformat(),
# "session_name": "project-name", # the name of the project to trace to
# "session_id": "project-id", # the ID of the project to trace to. specify one of session_name or session_id
}
if parent_id:
data["parent_run_id"] = parent_id.hex
requests.post(
"https://api.smith.langchain.com/runs", # Update for self-hosted, GCP EU (`eu.api...`), GCP APAC (`apac.api...`), or AWS US (`aws.api...`)
json=data,
headers=headers
)
def patch_run(run_id, outputs):
"""Function to patch a run with outputs."""
requests.patch(
f"https://api.smith.langchain.com/runs/{run_id}",
json={
"outputs": outputs,
"end_time": datetime.now(timezone.utc).isoformat(),
},
headers=headers,
)
# This can be a user input to your app
question = "Can you summarize this morning's meetings?"
# This can be retrieved in a retrieval step
context = "During this morning's meeting, we solved all world conflict."
messages = [
{"role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context."},
{"role": "user", "content": f"Question: {question}\nContext: {context}"}
]
# Create parent run
parent_run_id = uuid7()
post_run(parent_run_id, "Chat Pipeline", "chain", {"question": question})
# Create child run
child_run_id = uuid7()
post_run(child_run_id, "OpenAI Call", "llm", {"messages": messages}, parent_run_id)
# Generate a completion
client = openai.Client()
chat_completion = client.chat.completions.create(
model="gpt-5.4-mini",
messages=messages
)
# End runs
patch_run(child_run_id, chat_completion.dict())
patch_run(parent_run_id, {"answer": chat_completion.choices[0].message.content})更多信息，请参阅 运行（跨度）数据格式.
批量导入
为实现更快的导入和更高的速率限制，请使用 POST /runs/multipart 端点。这需要 requests-toolbelt 和 uuid-utils packages.
与基础追踪不同，此端点需要您计算并设置 dotted_order 和 trace_id yourself. dotted_order 使用点和父与子条目编码每个运行的时间戳和 UUID（例如， 20240101T000000Z<parent-uuid>.20240101T000001Z<child-uuid>），告诉 LangSmith 运行之间的关系以及它们发生的顺序。 trace_id 是根运行的 UUID。
以下示例创建一个父运行和一个子运行，在单个批量请求中发送它们，然后使用输出修补两者：
from datetime import datetime, timezone
from typing import Dict, List
from requests_toolbelt import MultipartEncoder
from uuid_utils.compat import uuid7
def create_dotted_order(
start_time: datetime | None = None,
run_id: uuid.UUID | None = None
) -> str:
"""Create a dotted order string for run ordering and hierarchy.
The dotted order is used to establish the sequence and relationships between runs.
It combines a timestamp with a unique identifier to ensure proper ordering and tracing.
"""
st = start_time or datetime.now(timezone.utc)
id_ = run_id or uuid7()
return f"{st.strftime('%Y%m%dT%H%M%S%fZ')}{id_}"
def create_run_base(
name: str,
run_type: str,
inputs: dict,
start_time: datetime
) -> dict:
"""Create the base structure for a run."""
run_id = uuid7()
return {
"id": str(run_id),
"trace_id": str(run_id),
"name": name,
"start_time": start_time.isoformat(),
"inputs": inputs,
"run_type": run_type,
}
def construct_run(
name: str,
run_type: str,
inputs: dict,
parent_dotted_order: str | None = None,
) -> dict:
"""Construct a run dictionary with the given parameters.
This function creates a run with a unique ID and dotted order, establishing its place
in the trace hierarchy if it's a child run.
"""
start_time = datetime.now(timezone.utc)
run = create_run_base(name, run_type, inputs, start_time)
current_dotted_order = create_dotted_order(start_time, uuid.UUID(run["id"]))
if parent_dotted_order:
current_dotted_order = f"{parent_dotted_order}.{current_dotted_order}"
run["trace_id"] = parent_dotted_order.split(".")[0].split("Z")[1]
run["parent_run_id"] = parent_dotted_order.split(".")[-1].split("Z")[1]
run["dotted_order"] = current_dotted_order
return run
def serialize_run(operation: str, run_data: dict) -> List[tuple]:
"""Serialize a run for the multipart request.
This function separates the run data into parts for efficient transmission and storage.
The main run data and optional fields (inputs, outputs, events) are serialized separately.
"""
run_id = run_data.get("id", str(uuid7()))
# Separate optional fields
inputs = run_data.pop("inputs", None)
outputs = run_data.pop("outputs", None)
events = run_data.pop("events", None)
parts = []
# Serialize main run data
run_data_json = json.dumps(run_data).encode("utf-8")
parts.append(
(
f"{operation}.{run_id}",
(
None,
run_data_json,
"application/json",
{"Content-Length": str(len(run_data_json))},
),
)
)
# Serialize optional fields
for key, value in [("inputs", inputs), ("outputs", outputs), ("events", events)]:
if value:
serialized_value = json.dumps(value).encode("utf-8")
parts.append(
(
f"{operation}.{run_id}.{key}",
(
None,
serialized_value,
"application/json",
{"Content-Length": str(len(serialized_value))},
),
)
)
return parts
def batch_ingest_runs(
api_url: str,
api_key: str,
posts: list[dict] | None = None,
patches: list[dict] | None = None,
) -> None:
"""Ingest multiple runs in a single batch request.
This function handles both creating new runs (posts) and updating existing runs (patches).
It's more efficient for ingesting multiple runs compared to individual API calls.
"""
boundary = uuid.uuid4().hex
all_parts = []
for operation, runs in zip(("post", "patch"), (posts, patches)):
if runs:
all_parts.extend(
[part for run in runs for part in serialize_run(operation, run)]
)
encoder = MultipartEncoder(fields=all_parts, boundary=boundary)
headers = {"Content-Type": encoder.content_type, "x-api-key": api_key}
try:
response = requests.post(
f"{api_url}/runs/multipart",
data=encoder,
headers=headers
)
response.raise_for_status()
print("Successfully ingested runs.")
except requests.RequestException as e:
print(f"Error ingesting runs: {e}")
# In a production environment, you might want to log this error or handle it more robustly
# Configure API URL and key
# For production use, consider using a configuration file or environment variables
api_url = "https://api.smith.langchain.com" # GCP EU: eu.api...; GCP APAC: apac.api...; AWS US: aws.api... for regional SaaS
api_key = os.environ.get("LANGSMITH_API_KEY")
if not api_key:
raise ValueError("LANGSMITH_API_KEY environment variable is not set")
# Create a parent run
parent_run = construct_run(
name="Parent Run",
run_type="chain",
inputs={"main_question": "Tell me about France"},
)
# Create a child run, linked to the parent
child_run = construct_run(
name="Child Run",
run_type="llm",
inputs={"question": "What is the capital of France?"},
parent_dotted_order=parent_run["dotted_order"],
)
# First, post the runs to create them
posts = [parent_run, child_run]
batch_ingest_runs(api_url, api_key, posts=posts)
# Then, update the runs with their end times and any outputs
child_run_update = {
**child_run,
"end_time": datetime.now(timezone.utc).isoformat(),
"outputs": {"answer": "Paris is the capital of France."},
}
parent_run_update = {
**parent_run,
"end_time": datetime.now(timezone.utc).isoformat(),
"outputs": {"summary": "Discussion about France, including its capital."},
}
patches = [parent_run_update, child_run_update]
batch_ingest_runs(api_url, api_key, patches=patches)
# Note: This example requires the `requests` and `requests_toolbelt` libraries.
# You can install them using pip:
# pip install requests requests_toolbelt相关
- - 运行（跨度）数据格式
- - 指定自定义运行 ID
- - 自定义插桩