Release: 0.0.84 (#388)

README.md

@@ -1,28 +1,45 @@
 # LangSmith Client SDKs
 
-This repository contains `BaseTracer` schemas used in LangChain as well as the Python and Javascript clients for interacting with the [LangSmith platform](https://smith.langchain.com/).
+This repository contains the Python and Javascript SDK's for interacting with the [LangSmith platform](https://smith.langchain.com/).
 
-LangSmith helps you and your team debug, evaluate, and monitor your language models and intelligent agents. It works
+LangSmith helps your team debug, evaluate, and monitor your language models and intelligent agents. It works
 with any LLM Application, including a native integration with the [LangChain Python](https://github.com/hwchase17/langchain) and [LangChain JS](https://github.com/hwchase17/langchainjs) open source libraries.
 
 LangSmith is developed and maintained by [LangChain](https://langchain.com/), the company behind the LangChain framework.
 
-⚠️ The SDK and LangSmith platform is still in early beta and continuously evolving!
-
 ## Quick Start
 
 To get started with the Python SDK, [install the package](https://pypi.org/project/langsmith/), then follow the instructions in the [Python README](python/README.md).
 
 ```bash
-pip install langsmith
+pip install -U langsmith
+export LANGCHAIN_API_KEY=ls_...
+```
+
+Then start tracing your app:
+
+```
+import openai
+from langsmith import traceable
+from langsmith.wrappers import wrap_openai
+
+client = wrap_openai(openai.Client())
+
+client.chat.completions.create(
+    messages=[{"role": "user", "content": "Hello, world"}],
+    model="gpt-3.5-turbo"
+)
 ```
 
 To get started with the JavaScript / TypeScript SDK, [install the package](https://www.npmjs.com/package/langsmith), then follow the instructions in the [JS README](js/README.md).
 
 ```bash
 yarn add langsmith
+export LANGCHAIN_API_KEY=ls_...
 ```
 
+Then start tracing your app!
+
 ## Cookbook
 
 For tutorials on how to get more value out of LangSmith, check out the [Langsmith Cookbook](https://github.com/langchain-ai/langsmith-cookbook/tree/main) repo.

python/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langsmith"
-version = "0.0.83"
+version = "0.0.84"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 authors = ["LangChain <[email protected]>"]
 license = "MIT"

python/tests/integration_tests/test_client.py

@@ -345,7 +345,7 @@ def test_create_run_with_masked_inputs_outputs(
         if project.name == project_name:
             langchain_client.delete_project(project_name=project_name)
 
-    run_id = "8bac165f-470e-4bf8-baa0-15f2de4cc706"
+    run_id = uuid4()
     langchain_client.create_run(
         id=run_id,
         project_name=project_name,
@@ -359,7 +359,7 @@ def test_create_run_with_masked_inputs_outputs(
         hide_outputs=True,
     )
 
-    run_id2 = "8bac165f-490e-4bf8-baa0-15f2de4cc707"
+    run_id2 = uuid4()
     langchain_client.create_run(
         id=run_id2,
         project_name=project_name,

python/tests/integration_tests/test_runs.py

@@ -16,7 +16,7 @@
 @pytest.fixture
 def langchain_client() -> Generator[Client, None, None]:
     original = os.environ.get("LANGCHAIN_ENDPOINT")
-    os.environ["LANGCHAIN_ENDPOINT"] = "http://localhost:1984"
+    os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
     yield Client()
     if original is None:
         os.environ.pop("LANGCHAIN_ENDPOINT")
@@ -135,18 +135,15 @@ async def my_chain_run(text: str):
     assert runs_dict["my_chain_run"].execution_order == 1
     assert runs_dict["my_run"].parent_run_id == runs_dict["my_chain_run"].id
     assert runs_dict["my_run"].run_type == "chain"
-    assert runs_dict["my_run"].execution_order == 2
     assert runs_dict["my_llm_run"].parent_run_id == runs_dict["my_run"].id
     assert runs_dict["my_llm_run"].run_type == "llm"
     assert runs_dict["my_llm_run"].inputs == {"text": "foo"}
-    assert runs_dict["my_llm_run"].execution_order == 3
     assert runs_dict["my_sync_tool"].parent_run_id == runs_dict["my_run"].id
     assert runs_dict["my_sync_tool"].run_type == "tool"
     assert runs_dict["my_sync_tool"].inputs == {
         "text": "foo",
         "my_arg": 20,
     }
-    assert runs_dict["my_sync_tool"].execution_order == 4
     langchain_client.delete_project(project_name=project_name)