Implement distributed tracing

Sometimes, you need to trace a request across multiple services. LangSmith supports distributed tracing out of the box, linking runs within a trace across services using context propagation headers (langsmith-trace and optional baggage for metadata/tags). Example client-server setup:

Trace starts on client
Continues on server

Only accept distributed-tracing headers from trusted services. The langsmith-trace and baggage headers are consumed as trusted tracing context. Do not add TracingMiddleware (or pass inbound request headers as the tracing parent) on a service that receives requests directly from untrusted third parties or the public internet. Keep distributed tracing to internal, service-to-service calls, and strip these headers from untrusted inbound requests at your gateway or proxy. Trusting baggage from an external caller lets them influence how your runs are recorded.

Distributed tracing in Python

# client.py
from langsmith.run_helpers import get_current_run_tree, traceable
import httpx

@traceable
async def my_client_function():
    headers = {}
    async with httpx.AsyncClient(base_url="...") as client:
        if run_tree := get_current_run_tree():
            # add langsmith-id to headers
            headers.update(run_tree.to_headers())
        return await client.post("/my-route", headers=headers)

Then the server (or other service) can continue the trace by handling the headers appropriately. If you are using an asgi app Starlette or FastAPI, you can connect the distributed trace using LangSmith’s TracingMiddleware.

The TracingMiddleware class was added in langsmith==0.1.133.

Example using FastAPI:

from langsmith import traceable
from langsmith.middleware import TracingMiddleware
from fastapi import FastAPI, Request

app = FastAPI()  # Or Flask, Django, or any other framework
app.add_middleware(TracingMiddleware)

@traceable
async def some_function():
    ...

@app.post("/my-route")
async def fake_route(request: Request):
    return await some_function()

Or in Starlette:

from starlette.applications import Starlette
from starlette.middleware import Middleware
from langsmith.middleware import TracingMiddleware

routes = ...
middleware = [
    Middleware(TracingMiddleware),
]
app = Starlette(..., middleware=middleware)

If you are using other server frameworks, you can always “receive” the distributed trace by passing the headers in through langsmith_extra:

# server.py
import langsmith as ls
from fastapi import FastAPI, Request

@ls.traceable
async def my_application():
    ...

app = FastAPI()  # Or Flask, Django, or any other framework

@app.post("/my-route")
async def fake_route(request: Request):
    # request.headers:  {"langsmith-trace": "..."}
    # as well as optional metadata/tags in `baggage`
    with ls.tracing_context(parent=request.headers):
        return await my_application()

The example above uses the tracing_context context manager. You can also directly specify the parent run context in the langsmith_extra parameter of a method wrapped with @traceable.

# ... same as above

@app.post("/my-route")
async def fake_route(request: Request):
    # request.headers:  {"langsmith-trace": "..."}
    my_application(langsmith_extra={"parent": request.headers})

Distributed tracing in TypeScript

Distributed tracing in TypeScript requires langsmith>=0.1.31

First, we obtain the current run tree from the client and convert it to langsmith-trace and baggage header values, which we can pass to the server:

// client.mts
import { getCurrentRunTree, traceable } from "langsmith/traceable";

const client = traceable(
    async () => {
        const runTree = getCurrentRunTree();
        return await fetch("...", {
            method: "POST",
            headers: runTree.toHeaders(),
        }).then((a) => a.text());
    },
    { name: "client" }
);

await client();

Then, the server converts the headers back to a run tree, which it uses to further continue the tracing. To pass the newly created run tree to a traceable function, we can use the withRunTree helper, which will ensure the run tree is propagated within traceable invocations.

// server.mts
import { RunTree } from "langsmith";
import { traceable, withRunTree } from "langsmith/traceable";
import express from "express";
import bodyParser from "body-parser";

    const server = traceable(
        (text: string) => `Hello from the server! Received "${text}"`,
        { name: "server" }
    );

    const app = express();
    app.use(bodyParser.text());

app.post("/", async (req, res) => {
    const runTree = RunTree.fromHeaders(req.headers);
    const result = await withRunTree(runTree, () => server(req.body));
    res.send(result);
});

// server.mts
import { RunTree } from "langsmith";
import { traceable, withRunTree } from "langsmith/traceable";
import { Hono } from "hono";

    const server = traceable(
        (text: string) => `Hello from the server! Received "${text}"`,
        { name: "server" }
    );

    const app = new Hono();

app.post("/", async (c) => {
    const body = await c.req.text();
    const runTree = RunTree.fromHeaders(c.req.raw.headers);
    const result = await withRunTree(runTree, () => server(body));
    return c.body(result);
});

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Tracing setup

Configuration & troubleshooting

Distributed tracing in Python

Distributed tracing in TypeScript

​Distributed tracing in Python

​Distributed tracing in TypeScript

Distributed tracing in Python

Distributed tracing in TypeScript