Add deprecation notice for LangServe project

Added deprecation notice and migration recommendation.

.clabot

@@ -1,4 +1,4 @@
 {
-  "contributors": ["eyurtsev", "hwchase17", "nfcampos", "efriis", "jacoblee93", "dqbd", "kreneskyp", "adarsh-jha-dev", "harris", "baskaryan", "hinthornw", "bracesproul", "jakerachleff", "craigsdennis", "anhi", "169", "LarchLiu", "PaulLockett", "RCMatthias", "jwynia", "majiayu000", "mpskex", "shivachittamuru", "sinashaloudegi", "sowsan", "akira"],
-  "message": "Thank you for your pull request and welcome to our community. We require contributors to sign our Contributor License Agreement, and we don't seem to have the username {{usersWithoutCLA}} on file. In order for us to review and merge your code, please complete the Individual Contributor License Agreement here https://forms.gle/Ljhqvt9Gdi1N385W6 .\n\nThis process is done manually on our side, so after signing the form one of the maintainers will add you to the contributors list.\n\nFor more details about why we have a CLA and other contribution guidelines please see: https://github.com/langchain-ai/langserve/blob/main/CONTRIBUTING.md."
+  "contributors": ["eyurtsev", "hwchase17", "nfcampos", "efriis", "jacoblee93", "dqbd", "kreneskyp", "adarsh-jha-dev", "harris", "baskaryan", "hinthornw", "bracesproul", "jakerachleff", "craigsdennis", "anhi", "169", "LarchLiu", "PaulLockett", "RCMatthias", "jwynia", "majiayu000", "mpskex", "shivachittamuru", "sinashaloudegi", "sowsan", "akira", "lucianotonet", "JGalego", "nat-n", "dirien", "donbr", "rahilvora", "WarrenTheRabbit", "StreetLamb", "ccurme", "dennisrall", "Mingqi2", "xxsl", "joaquin-borggio-lc"],
+  "message": "Thank you for your pull request and welcome to our community. We require contributors to sign our Contributor License Agreement, and we don't seem to have the username {{usersWithoutCLA}} on file. In order for us to review and merge your code, please complete the Individual Contributor License Agreement here https://forms.gle/AQFbtkWRoHXUgipM6 .\n\nThis process is done manually on our side, so after signing the form one of the maintainers will add you to the contributors list.\n\nFor more details about why we have a CLA and other contribution guidelines please see: https://github.com/langchain-ai/langserve/blob/main/CONTRIBUTING.md."
 }

.github/dependabot.yml

@@ -0,0 +1,19 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: "pip" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"
+  - package-ecosystem: "github-actions" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"
+  - package-ecosystem: "npm" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.github/workflows/_lint.yml

@@ -1,4 +1,6 @@
 name: lint
+permissions:
+  contents: read
 
 on:
   workflow_call:
@@ -31,10 +33,10 @@ jobs:
         # Starting new jobs is also relatively slow,
         # so linting on fewer versions makes CI faster.
         python-version:
-          - "3.8"
+          - "3.10"
           - "3.11"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
         with:
           # Fetch the last FETCH_DEPTH commits, so the mtime-changing script
           # can accurately set the mtimes of files modified in the last FETCH_DEPTH commits.
@@ -115,7 +117,7 @@ jobs:
           poetry install --with dev,lint,test,typing
 
       - name: Restore black cache
-        uses: actions/cache@v3
+        uses: actions/cache@v5
         env:
           CACHE_BASE: black-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
@@ -128,7 +130,7 @@ jobs:
             ${{ env.CACHE_BASE }}-
 
       - name: Get .mypy_cache to speed up mypy
-        uses: actions/cache@v3
+        uses: actions/cache@v5
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:

.github/workflows/_pydantic_compatibility.yml

@@ -1,94 +0,0 @@
-name: pydantic v1/v2 compatibility
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-
-env:
-  POETRY_VERSION: "1.5.1"
-
-jobs:
-  build:
-    timeout-minutes: 10
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-    name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v3
-
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: pydantic-cross-compat
-
-      - name: Install dependencies
-        shell: bash
-        run: poetry install
-
-      - name: Install the opposite major version of pydantic
-        # If normal tests use pydantic v1, here we'll use v2, and vice versa.
-        shell: bash
-        run: |
-          # Determine the major part of pydantic version
-          REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
-
-          if [[ "$REGULAR_VERSION" == "1" ]]; then
-            PYDANTIC_DEP=">=2.1,<3"
-            TEST_WITH_VERSION="2"
-          elif [[ "$REGULAR_VERSION" == "2" ]]; then
-            PYDANTIC_DEP="<2"
-            TEST_WITH_VERSION="1"
-          else
-            echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test."
-            exit 1
-          fi
-
-          # Install via `pip` instead of `poetry add` to avoid changing lockfile,
-          # which would prevent caching from working: the cache would get saved
-          # to a different key than where it gets loaded from.
-          poetry run pip install "pydantic${PYDANTIC_DEP}"
-
-          # Ensure that the correct pydantic is installed now.
-          echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}"
-
-          # Determine the major part of pydantic version
-          CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
-
-          # Check that the major part of pydantic version is as expected, if not
-          # raise an error
-          if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then
-            echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}"
-            exit 1
-          fi
-          echo "Found pydantic version ${CURRENT_VERSION}, as expected"
-      - name: Run pydantic compatibility tests
-        shell: bash
-        run: make test
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_release.yml

@@ -7,6 +7,12 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        description: "From which folder this pipeline executes"
 
 env:
   POETRY_VERSION: "1.5.1"
@@ -30,7 +36,7 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
 
       - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/_test.yml

@@ -20,13 +20,11 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.8"
-          - "3.9"
           - "3.10"
           - "3.11"
     name: Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/_test_release.yml

@@ -25,7 +25,7 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
 
       - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/langserve_ci.yml

@@ -18,6 +18,10 @@ on:
       - 'Makefile'
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
+# This workflow only needs to read the repo contents.
+permissions:
+  contents: read
+
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
 #
@@ -39,13 +43,6 @@ jobs:
     with:
       working-directory: .
     secrets: inherit
-
-  pydantic-compatibility:
-    uses:
-      ./.github/workflows/_pydantic_compatibility.yml
-    with:
-      working-directory: .
-    secrets: inherit
   test:
     timeout-minutes: 10
     runs-on: ubuntu-latest
@@ -55,13 +52,11 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.8"
-          - "3.9"
           - "3.10"
           - "3.11"
     name: Python ${{ matrix.python-version }} tests
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/langserve_release.yml

@@ -4,10 +4,18 @@ name: Release
 on:
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
+permissions:
+  contents: read
+
 jobs:
   release:
     uses:
       ./.github/workflows/_release.yml
     with:
       working-directory: .
+    permissions:
+      # Trusted publishing to PyPI
+      id-token: write
+      # Creating GitHub releases
+      contents: write
     secrets: inherit

.github/workflows/langserve_test_release.yml

@@ -4,10 +4,16 @@ name: Test Release
 on:
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
+permissions:
+  contents: read
+
 jobs:
   release:
     uses:
       ./.github/workflows/_test_release.yml
     with:
       working-directory: .
+    permissions:
+      # Trusted publishing to TestPyPI
+      id-token: write
     secrets: inherit

.gitignore

@@ -160,3 +160,6 @@ cython_debug/
 #.idea/
 
 .envrc
+
+# IntelliJ IDE's
+.idea

CONTRIBUTING.md

@@ -49,3 +49,32 @@ To run linting for this project:
 ```sh
 make lint
 ```
+
+## Frontend Playground Development
+
+Here are a few tips to keep in mind when developing the LangServe playgrounds:
+
+### Setup
+
+Switch directories to `langserve/playground` or `langserve/chat_playground`, then run `yarn` to install required
+dependencies. `yarn dev` will start the playground at `http://localhost:5173/____LANGSERVE_BASE_URL/` in dev mode.
+
+You can run one of the chains in the `examples/` repo using `poetry run python path/to/file.py`.
+
+### Setting CORS
+
+You may need to add the following to an example route when developing the playground in dev mode to handle CORS:
+
+```python
+from fastapi.middleware.cors import CORSMiddleware
+
+# Set all CORS enabled origins
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+    expose_headers=["*"],
+)
+```

MIGRATION.md

@@ -0,0 +1,222 @@
+# LangGraph Platform Migration Guide
+
+We have [recently announced](https://blog.langchain.dev/langgraph-platform-announce/) LangGraph Platform, a ***significantly*** enhanced solution for deploying agentic applications at scale.
+
+LangGraph Platform incorporates [key design patterns and capabilities](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/#option-2-leveraging-langgraph-platform-for-complex-deployments) essential for production-level deployment of large language model (LLM) applications.
+
+In contrast to LangServe, LangGraph Platform provides comprehensive, out-of-the-box support for [persistence](https://langchain-ai.github.io/langgraph/concepts/application_structure/), [memory](https://langchain-ai.github.io/langgraph/concepts/assistants/), [double-texting handling](https://langchain-ai.github.io/langgraph/concepts/double_texting/), [human-in-the-loop workflows](https://langchain-ai.github.io/langgraph/concepts/assistants/), [cron job scheduling](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/#cron-jobs), [webhooks](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/#webhooks), high-load management, advanced streaming, support for long-running tasks, background task processing, and much more.
+
+The LangGraph Platform ecosystem includes the following components:
+
+- [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/): Provides an [Assistants API](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html) for LLM applications (graphs) built with [LangGraph](https://langchain-ai.github.io/langgraph/). Available in both Python and JavaScript/TypeScript.
+- [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/): A specialized IDE for real-time visualization, debugging, and interaction via a graphical interface. Available as a web application or macOS desktop app, it's a substantial improvement over LangServe's playground.
+- [SDK](https://langchain-ai.github.io/langgraph/concepts/sdk/): Enables programmatic interaction with the server, available in Python and JavaScript/TypeScript.
+- [RemoteGraph](https://langchain-ai.github.io/langgraph/how-tos/use-remote-graph/): Allows interaction with a remote graph as if it were running locally, serving as LangGraph's equivalent to LangServe's RemoteRunnable. Available in both Python and JavaScript/TypeScript.
+ 
+## Context 
+
+LangServe was built as a deployment solution for LangChain Runnables created using the [LangChain Expression Language (LCEL)](https://python.langchain.com/docs/concepts/lcel). In LangServe, the LCEL was the orchestration layer that managed the execution of the Runnable.
+
+[LangGraph](https://langchain-ai.github.io/langgraph/) is an open source library created by the LangChain team that provides a more flexible orchestration layer that's better suited for creating more complex LLM applications. LangGraph Platform
+is the deployment solution for LangGraph applications.
+
+## LangServe Support
+
+We recommend using LangGraph Platform rather than LangServe for new projects.
+
+We will continue to accept bug fixes for LangServe from the community; however, we will not be accepting new feature contributions.
+
+## Migration
+
+If you would like to migrate an existing LangServe application to LangGraph Platform, you have two options:
+
+1. You can wrap the existing `Runnable` that you expose in the LangServe application via `add_routes` in a `LangGraph` node. This is the quickest way to migrate your application to LangGraph Platform.
+2. You can do a larger refactor to break up the existing LCEL into appropriate `LangGraph` nodes. This is recommended if you want to take advantage of more advanced features in LangGraph Platform.
+
+### Option 1: Wrap Runnable in LangGraph Node
+
+This option is the quickest way to migrate your application to LangGraph Platform. You can wrap the existing `Runnable` that you expose in the LangServe application via `add_routes` in a `LangGraph` node.
+
+
+Original LangServe code:
+
+```python
+from langserve import add_routes
+
+app = FastAPI()
+
+# Some input schema
+class Input(BaseModel):
+    input: str
+    foo: Optional[str]
+
+# Some output schema
+class Output(BaseModel):
+    output: Any
+    
+    
+runnable = .... # Your existing Runnable
+runnable_with_types = runnable.with_types(input_type=Input, output_type=Output)
+
+# Adds routes to the app for using the chain under:
+add_routes(
+    app,
+    runnable_with_types,
+)
+```
+
+Migrated LangGraph Platform code:
+
+```python
+
+@dataclass
+class InputState: # Equivalent to Input in the original code
+    """Defines the input state, representing a narrower interface to the outside world.
+
+    This class is used to define the initial state and structure of incoming data.
+    See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
+    for more information.
+    """
+
+    input: str
+    foo: Optional[str] = None
+
+
+@dataclass
+class OutputState: # Equivalent to Output in the original code
+    """Defines the output state, representing a narrower interface to the outside world.
+    
+    https://langchain-ai.github.io/langgraph/concepts/low_level/#state
+    """
+    output: Any
+
+@dataclass
+class SharedState:
+    """The full graph state.
+    
+    https://langchain-ai.github.io/langgraph/concepts/low_level/#state
+    """
+    input: str
+    foo: Optional[str] = None
+    output: Any
+    
+runnable = ... # Same code as before
+
+async def my_node(state: InputState, config: RunnableConfig) -> OutputState:
+    """Each node does work."""
+    return await runnable.ainvoke({"input": state.input, "foo": state.foo})
+
+
+# Define a new graph
+builder = StateGraph(
+    SharedState, config_schema=Configuration, input=InputState, output=OutputState
+)
+
+# Add the node to the graph
+builder.add_node("my_node", my_node)
+
+# Set the entrypoint as `call_model`
+builder.add_edge("__start__", "my_node")
+
+# Compile the workflow into an executable graph
+graph = builder.compile()
+graph.name = "New Graph"  # This defines the custom name in LangSmith
+```
+
+### 2. Refactor LCEL into LangGraph Nodes
+
+This option is recommended if you want to take advantage of more advanced features in LangGraph Platform.
+
+#### Memory (alternative to `RunnableWithMessageHistory`)
+
+For example, LangGraph comes with built-in persistence that is more general than LangChain's `RunnableWithMessageHistory`.
+
+Please refer to the guide on [upgrading to LangGraph memory](https://python.langchain.com/docs/versions/migrating_memory/) for more details.
+
+#### Agents
+
+If you're relying on legacy LangChain agents, you can migrate them into the pre-built
+LangGraph agents. Please refer to the guide on [migrating agents](https://python.langchain.com/docs/how_to/migrate_agent/) for more details.
+
+#### Custom Chains
+
+If you created a custom chain and used LCEL to orchestrate it, you will usually be able to refactor it into a LangGraph without too much difficulty.
+
+There isn't a one-size-fits-all guide for this, but generally speaking, consider creating
+a separate node for any long-running step in your LCEL chain or any step that you would
+want to be able to monitor or debug separately.
+
+For example, if you have a simple Retrieval Augmented Generation (RAG) pipeline, you might have a node for the retrieval step and a node for the generation step.
+
+Original LCEL code:
+
+```python
+...
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+rag_chain.with_types(input_type=Input, output_type=Output)
+```
+
+Using LangGraph for the same pipeline:
+
+
+```python
+
+@dataclass
+class InputState: # Equivalent to Input in the original code
+    """Input question from the user."""
+    question: str
+   
+@dataclass 
+class OutputState: # Equivalent to Output in the original code
+    """The output from the graph."""
+    answer: str
+
+@dataclass 
+class SharedState:
+    question: str
+    docs: List[str]
+    response: str
+   
+async def retriever_node(state: InputState) -> SharedState:
+    """Rettrieve documents based on the user's question."""
+    documents = await retriever.ainvoke({"context": state.question})
+    return {
+        "docs": documents
+    }
+
+async def generator_node(state: SharedState) -> OutputState:
+    """Generate an answer using an LLM based on the retrieved documents and question."""
+    context = " -- DOCUMENT -- ".join(state.docs)
+    prompt = [
+        SystemMessage(
+            content=(
+                "Answer the user's question based on the list of documents "
+                "that were retrieved. Here are the documents: \n\n"
+                f"{context}"
+            )
+        ),
+        HumanMessage(content=state.question),
+    ]
+    ai_message = await llm.ainvoke(prompt)
+    return {"answer": ai_message.content}
+    
+# Define a new graph
+builder = StateGraph(
+    SharedState, config_schema=Configuration, input=InputState, output=OutputState
+)
+builder.add_node("retriever", retriever_node)
+builder.add_node("generator", generator_node)
+builder.add_edge("__start__", "retriever")
+builder.add_edge("retriever", "generator")
+graph = builder.compile()
+graph.name = "RAG Graph"
+```
+
+Please see the [LangGraph tutorials](https://langchain-ai.github.io/langgraph/tutorials/)
+for tutorials and examples that will help you get started with LangGraph
+and LangGraph Platform.

Makefile

@@ -32,12 +32,12 @@ lint format: PYTHON_FILES=.
 lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=. --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
 
 lint lint_diff:
-	poetry run ruff .
+	poetry run ruff check .
 	poetry run ruff format $(PYTHON_FILES) --check
 
 format format_diff:
 	poetry run ruff format $(PYTHON_FILES)
-	poetry run ruff --select I --fix $(PYTHON_FILES)
+	poetry run ruff check --select I --fix $(PYTHON_FILES)
 
 spell_check:
 	poetry run codespell --toml pyproject.toml

README.md

@@ -5,42 +5,69 @@
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langserve)](https://github.com/langchain-ai/langserve/issues)
 [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.com/channels/1038097195422978059/1170024642245832774)
 
-🚩 We will be releasing a hosted version of LangServe for one-click deployments of LangChain applications. [Sign up here](https://airtable.com/app0hN6sd93QcKubv/shrAjst60xXa6quV2) to get on the waitlist.
+> [!WARNING]
+> **DEPRECATED** This project has been deprecated since Nov 18, 2024 (https://github.com/langchain-ai/langserve/issues/791).
+> 
+> We recommend using LangGraph Platform rather than LangServe for new projects.
+>
+> Please see the [LangGraph Platform Migration Guide](./MIGRATION.md) for more information.
+>
+> We will continue to accept bug fixes for LangServe from the community; however, we
+> will not be accepting new feature contributions.
+
 
 ## Overview
 
-`LangServe` helps developers deploy `LangChain` [runnables and chains](https://python.langchain.com/docs/expression_language/) as a REST API.
+[LangServe](https://github.com/langchain-ai/langserve) helps developers
+deploy `LangChain` [runnables and chains](https://python.langchain.com/docs/expression_language/)
+as a REST API.
 
-This library is integrated with [FastAPI](https://fastapi.tiangolo.com/) and uses [pydantic](https://docs.pydantic.dev/latest/) for data validation.
+This library is integrated with [FastAPI](https://fastapi.tiangolo.com/) and
+uses [pydantic](https://docs.pydantic.dev/latest/) for data validation.
 
-In addition, it provides a client that can be used to call into runnables deployed on a server.
-A javascript client is available in [LangChainJS](https://js.langchain.com/docs/api/runnables_remote/classes/RemoteRunnable).
+In addition, it provides a client that can be used to call into runnables deployed on a
+server.
+A JavaScript client is available
+in [LangChain.js](https://js.langchain.com/docs/ecosystem/langserve).
 
 ## Features
 
-- Input and Output schemas automatically inferred from your LangChain object, and enforced on every API call, with rich error messages
+- Input and Output schemas automatically inferred from your LangChain object, and
+  enforced on every API call, with rich error messages
 - API docs page with JSONSchema and Swagger (insert example link)
-- Efficient `/invoke/`, `/batch/` and `/stream/` endpoints with support for many concurrent requests on a single server
-- `/stream_log/` endpoint for streaming all (or some) intermediate steps from your chain/agent
+- Efficient `/invoke`, `/batch` and `/stream` endpoints with support for many
+  concurrent requests on a single server
+- `/stream_log` endpoint for streaming all (or some) intermediate steps from your
+  chain/agent
+- **new** as of 0.0.40, supports `/stream_events` to make it easier to stream without needing to parse the output of `/stream_log`.
 - Playground page at `/playground/` with streaming output and intermediate steps
-- Built-in (optional) tracing to [LangSmith](https://www.langchain.com/langsmith), just add your API key (see [Instructions](https://docs.smith.langchain.com/)])
-- All built with battle-tested open-source Python libraries like FastAPI, Pydantic, uvloop and asyncio.
-- Use the client SDK to call a LangServe server as if it was a Runnable running locally (or call the HTTP API directly)
+- Built-in (optional) tracing to [LangSmith](https://www.langchain.com/langsmith), just
+  add your API key (see [Instructions](https://docs.smith.langchain.com/))
+- All built with battle-tested open-source Python libraries like FastAPI, Pydantic,
+  uvloop and asyncio.
+- Use the client SDK to call a LangServe server as if it was a Runnable running
+  locally (or call the HTTP API directly)
 - [LangServe Hub](https://github.com/langchain-ai/langchain/blob/master/templates/README.md)
 
-### Limitations
+## ⚠️ LangGraph Compatibility
+
+LangServe is designed to primarily deploy simple Runnables and work with well-known primitives in langchain-core.
+
+If you need a deployment option for LangGraph, you should instead be looking at [LangGraph Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/) which will
+be better suited for deploying LangGraph applications.
+
+## Limitations
 
 - Client callbacks are not yet supported for events that originate on the server
-- OpenAPI docs will not be generated when using Pydantic V2. Fast API does not support [mixing pydantic v1 and v2 namespaces](https://github.com/tiangolo/fastapi/issues/10360). See section below for more details.
-
-## Hosted LangServe
-
-We will be releasing a hosted version of LangServe for one-click deployments of LangChain applications. [Sign up here](https://airtable.com/app0hN6sd93QcKubv/shrAjst60xXa6quV2) to get on the waitlist.
+- Versions of LangServe <= 0.2.0, will not generate OpenAPI docs properly when using Pydantic V2 as Fast API does not support [mixing pydantic v1 and v2 namespaces](https://github.com/tiangolo/fastapi/issues/10360).
+  See section below for more details. Either upgrade to LangServe>=0.3.0 or downgrade Pydantic to pydantic 1.
 
 ## Security
 
-* Vulnerability in Versions 0.0.13 - 0.0.15 -- playground endpoint allows accessing arbitrary files on server. [Resolved in 0.0.16](https://github.com/langchain-ai/langserve/pull/98).
- 
+- Vulnerability in Versions 0.0.13 - 0.0.15 -- playground endpoint allows accessing
+  arbitrary files on
+  server. [Resolved in 0.0.16](https://github.com/langchain-ai/langserve/pull/98).
+
 ## Installation
 
 For both client and server:
@@ -49,32 +76,81 @@ For both client and server:
 pip install "langserve[all]"
 ```
 
-or `pip install "langserve[client]"` for client code, and `pip install "langserve[server]"` for server code.
-
+or `pip install "langserve[client]"` for client code,
+and `pip install "langserve[server]"` for server code.
 
 ## LangChain CLI 🛠️
 
 Use the `LangChain` CLI to bootstrap a `LangServe` project quickly.
 
-To use the langchain CLI make sure that you have a recent version of `langchain-cli` 
+To use the langchain CLI make sure that you have a recent version of `langchain-cli`
 installed. You can install it with `pip install -U langchain-cli`.
 
+## Setup
+
+**Note**: We use `poetry` for dependency management. Please follow poetry [doc](https://python-poetry.org/docs/) to learn more about it.
+
+### 1. Create new app using langchain cli command
+
 ```sh
-langchain app new ../path/to/directory
+langchain app new my-app
+```
+
+### 2. Define the runnable in add_routes. Go to server.py and edit
+
+```sh
+add_routes(app. NotImplemented)
+```
+
+### 3. Use `poetry` to add 3rd party packages (e.g., langchain-openai, langchain-anthropic, langchain-mistral, etc).
+
+```sh
+poetry add [package-name] // e.g `poetry add langchain-openai`
+```
+
+### 4. Set up relevant env variables. For example,
+
+```sh
+export OPENAI_API_KEY="sk-..."
+```
+
+### 5. Serve your app
+
+```sh
+poetry run langchain serve --port=8100
 ```
 
 ## Examples
 
-Get your LangServe instance started quickly with 
-[LangChain Templates](https://github.com/langchain-ai/langchain/blob/master/templates/README.md).
+Get your LangServe instances started quickly with the [examples](https://github.com/langchain-ai/langserve/tree/main/examples)
+directory.
 
-For more examples, see the templates 
-[index](https://github.com/langchain-ai/langchain/blob/master/templates/docs/INDEX.md) 
-or the [examples](https://github.com/langchain-ai/langserve/tree/main/examples) directory.
+| Description                                                                                                                                                                                                                                                        | Links                                                                                                                                                                                                                               |
+| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **LLMs** Minimal example that reserves OpenAI and Anthropic chat models. Uses async, supports batching and streaming.                                                                                                                                              | [server](https://github.com/langchain-ai/langserve/tree/main/examples/llm/server.py), [client](https://github.com/langchain-ai/langserve/blob/main/examples/llm/client.ipynb)                                                       |
+| **Retriever** Simple server that exposes a retriever as a runnable.                                                                                                                                                                                                | [server](https://github.com/langchain-ai/langserve/tree/main/examples/retrieval/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/retrieval/client.ipynb)                                           |
+| **Conversational Retriever** A [Conversational Retriever](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) exposed via LangServe                                                                           | [server](https://github.com/langchain-ai/langserve/tree/main/examples/conversational_retrieval_chain/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/conversational_retrieval_chain/client.ipynb) |
+| **Agent** without **conversation history** based on [OpenAI tools](https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent)                                                                                                            | [server](https://github.com/langchain-ai/langserve/tree/main/examples/agent/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/agent/client.ipynb)                                                   |
+| **Agent** with **conversation history** based on [OpenAI tools](https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent)                                                                                                               | [server](https://github.com/langchain-ai/langserve/blob/main/examples/agent_with_history/server.py), [client](https://github.com/langchain-ai/langserve/blob/main/examples/agent_with_history/client.ipynb)                         |
+| [RunnableWithMessageHistory](https://python.langchain.com/docs/expression_language/how_to/message_history) to implement chat persisted on backend, keyed off a `session_id` supplied by client.                                                                    | [server](https://github.com/langchain-ai/langserve/tree/main/examples/chat_with_persistence/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/chat_with_persistence/client.ipynb)                   |
+| [RunnableWithMessageHistory](https://python.langchain.com/docs/expression_language/how_to/message_history) to implement chat persisted on backend, keyed off a `conversation_id` supplied by client, and `user_id` (see Auth for implementing `user_id` properly). | [server](https://github.com/langchain-ai/langserve/tree/main/examples/chat_with_persistence_and_user/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/chat_with_persistence_and_user/client.ipynb) |
+| [Configurable Runnable](https://python.langchain.com/docs/expression_language/how_to/configure) to create a retriever that supports run time configuration of the index name.                                                                                      | [server](https://github.com/langchain-ai/langserve/tree/main/examples/configurable_retrieval/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/configurable_retrieval/client.ipynb)                 |
+| [Configurable Runnable](https://python.langchain.com/docs/expression_language/how_to/configure) that shows configurable fields and configurable alternatives.                                                                                                      | [server](https://github.com/langchain-ai/langserve/tree/main/examples/configurable_chain/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/configurable_chain/client.ipynb)                         |
+| **APIHandler** Shows how to use `APIHandler` instead of `add_routes`. This provides more flexibility for developers to define endpoints. Works well with all FastAPI patterns, but takes a bit more effort.                                                        | [server](https://github.com/langchain-ai/langserve/tree/main/examples/api_handler_examples/server.py)                                                                                                                               |
+| **LCEL Example** Example that uses LCEL to manipulate a dictionary input.                                                                                                                                                                                          | [server](https://github.com/langchain-ai/langserve/tree/main/examples/passthrough_dict/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/passthrough_dict/client.ipynb)                             |
+| **Auth** with `add_routes`: Simple authentication that can be applied across all endpoints associated with app. (Not useful on its own for implementing per user logic.)                                                                                           | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/global_deps/server.py)                                                                                                                                   |
+| **Auth** with `add_routes`: Simple authentication mechanism based on path dependencies. (No useful on its own for implementing per user logic.)                                                                                                                    | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/path_dependencies/server.py)                                                                                                                             |
+| **Auth** with `add_routes`: Implement per user logic and auth for endpoints that use per request config modifier. (**Note**: At the moment, does not integrate with OpenAPI docs.)                                                                                 | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/per_req_config_modifier/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/auth/per_req_config_modifier/client.ipynb)     |
+| **Auth** with `APIHandler`: Implement per user logic and auth that shows how to search only within user owned documents.                                                                                                                                           | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/api_handler/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/auth/api_handler/client.ipynb)                             |
+| **Widgets** Different widgets that can be used with playground (file upload and chat)                                                                                                                                                                              | [server](https://github.com/langchain-ai/langserve/tree/main/examples/widgets/chat/tuples/server.py)                                                                                                                                |
+| **Widgets** File upload widget used for LangServe playground.                                                                                                                                                                                                      | [server](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing/client.ipynb)                               |
+
+## Sample Application
 
 ### Server
 
-Here's a server that deploys an OpenAI chat model, an Anthropic chat model, and a chain that uses
+Here's a server that deploys an OpenAI chat model, an Anthropic chat model, and a chain
+that uses
 the Anthropic model to tell a joke about a topic.
 
 ```python
@@ -84,26 +160,25 @@ from langchain.prompts import ChatPromptTemplate
 from langchain.chat_models import ChatAnthropic, ChatOpenAI
 from langserve import add_routes
 
-
 app = FastAPI(
-  title="LangChain Server",
-  version="1.0",
-  description="A simple api server using Langchain's Runnable interfaces",
+    title="LangChain Server",
+    version="1.0",
+    description="A simple api server using Langchain's Runnable interfaces",
 )
 
 add_routes(
     app,
-    ChatOpenAI(),
+    ChatOpenAI(model="gpt-3.5-turbo-0125"),
     path="/openai",
 )
 
 add_routes(
     app,
-    ChatAnthropic(),
+    ChatAnthropic(model="claude-3-haiku-20240307"),
     path="/anthropic",
 )
 
-model = ChatAnthropic()
+model = ChatAnthropic(model="claude-3-haiku-20240307")
 prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
 add_routes(
     app,
@@ -117,19 +192,39 @@ if __name__ == "__main__":
     uvicorn.run(app, host="localhost", port=8000)
 ```
 
+If you intend to call your endpoint from the browser, you will also need to set CORS headers.
+You can use FastAPI's built-in middleware for that:
+
+```python
+from fastapi.middleware.cors import CORSMiddleware
+
+# Set all CORS enabled origins
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+    expose_headers=["*"],
+)
+```
+
 ### Docs
 
 If you've deployed the server above, you can view the generated OpenAPI docs using:
 
-> ⚠️ If using pydantic v2, docs will not be generated for *invoke*, *batch*, *stream*, *stream_log*. See [Pydantic](#pydantic) section below for more details.
+> ⚠️ If using LangServe <= 0.2.0 and pydantic v2, docs will not be generated for _invoke_, _batch_, _stream_,
+> _stream_log_. See [Pydantic](#pydantic) section below for more details.
+> To resolve please upgrade to LangServe 0.3.0.
 
 ```sh
 curl localhost:8000/docs
 ```
 
-make sure to **add** the `/docs` suffix. 
+make sure to **add** the `/docs` suffix.
 
-> ⚠️ Index page `/` is not defined by **design**, so `curl localhost:8000` or visiting the URL
+> ⚠️ Index page `/` is not defined by **design**, so `curl localhost:8000` or visiting
+> the URL
 > will return a 404. If you want content at `/` define an endpoint `@app.get("/")`.
 
 ### Client
@@ -171,13 +266,13 @@ chain = prompt | RunnableMap({
     "anthropic": anthropic,
 })
 
-chain.batch([{ "topic": "parrots" }, { "topic": "cats" }])
+chain.batch([{"topic": "parrots"}, {"topic": "cats"}])
 ```
 
 In TypeScript (requires LangChain.js version 0.0.166 or later):
 
 ```typescript
-import { RemoteRunnable } from "langchain/runnables/remote";
+import { RemoteRunnable } from "@langchain/core/runnables/remote";
 
 const chain = new RemoteRunnable({
   url: `http://localhost:8000/joke/`,
@@ -191,8 +286,9 @@ Python using `requests`:
 
 ```python
 import requests
+
 response = requests.post(
-    "http://localhost:8000/joke/invoke/",
+    "http://localhost:8000/joke/invoke",
     json={'input': {'topic': 'cats'}}
 )
 response.json()
@@ -201,7 +297,7 @@ response.json()
 You can also use `curl`:
 
 ```sh
-curl --location --request POST 'http://localhost:8000/joke/invoke/' \
+curl --location --request POST 'http://localhost:8000/joke/invoke' \
     --header 'Content-Type: application/json' \
     --data-raw '{
         "input": {
@@ -217,9 +313,9 @@ The following code:
 ```python
 ...
 add_routes(
-  app,
-  runnable,
-  path="/my_runnable",
+    app,
+    runnable,
+    path="/my_runnable",
 )
 ```
 
@@ -228,16 +324,24 @@ adds of these endpoints to the server:
 - `POST /my_runnable/invoke` - invoke the runnable on a single input
 - `POST /my_runnable/batch` - invoke the runnable on a batch of inputs
 - `POST /my_runnable/stream` - invoke on a single input and stream the output
-- `POST /my_runnable/stream_log` - invoke on a single input and stream the output, including output of intermediate steps as it's generated
+- `POST /my_runnable/stream_log` - invoke on a single input and stream the output,
+  including output of intermediate steps as it's generated
+- `POST /my_runnable/astream_events` - invoke on a single input and stream events as they are generated,
+  including from intermediate steps.
 - `GET /my_runnable/input_schema` - json schema for input to the runnable
 - `GET /my_runnable/output_schema` - json schema for output of the runnable
 - `GET /my_runnable/config_schema` - json schema for config of the runnable
 
-These endpoints match the [LangChain Expression Language interface](https://python.langchain.com/docs/expression_language/interface) -- please reference this documentation for more details.
+These endpoints match
+the [LangChain Expression Language interface](https://python.langchain.com/docs/expression_language/interface) --
+please reference this documentation for more details.
 
 ## Playground
 
-You can find a playground page for your runnable at `/my_runnable/playground/`. This exposes a simple UI to [configure](https://python.langchain.com/docs/expression_language/how_to/configure) and invoke your runnable with streaming output and intermediate steps.
+You can find a playground page for your runnable at `/my_runnable/playground/`. This
+exposes a simple UI
+to [configure](https://python.langchain.com/docs/expression_language/how_to/configure)
+and invoke your runnable with streaming output and intermediate steps.
 
 <p align="center">
 <img src="https://github.com/langchain-ai/langserve/assets/3205522/5ca56e29-f1bb-40f4-84b5-15916384a276" width="50%"/>
@@ -245,34 +349,116 @@ You can find a playground page for your runnable at `/my_runnable/playground/`.
 
 ### Widgets
 
-The playground supports [widgets](#playground-widgets) and can be used to test your runnable with different inputs.
-
-In addition, for configurable runnables, the playground will allow you to configure the runnable and share a link with the configuration:
+The playground supports [widgets](#playground-widgets) and can be used to test your
+runnable with different inputs. See the [widgets](#widgets) section below for more
+details.
 
 ### Sharing
 
+In addition, for configurable runnables, the playground will allow you to configure the
+runnable and share a link with the configuration:
+
 <p align="center">
 <img src="https://github.com/langchain-ai/langserve/assets/3205522/86ce9c59-f8e4-4d08-9fa3-62030e0f521d" width="50%"/>
 </p>
 
+## Chat playground
+
+LangServe also supports a chat-focused playground that opt into and use under `/my_runnable/playground/`.
+Unlike the general playground, only certain types of runnables are supported - the runnable's input schema must
+be a `dict` with either:
+
+- a single key, and that key's value must be a list of chat messages.
+- two keys, one whose value is a list of messages, and the other representing the most recent message.
+
+We recommend you use the first format.
+
+The runnable must also return either an `AIMessage` or a string.
+
+To enable it, you must set `playground_type="chat",` when adding your route. Here's an example:
+
+```python
+# Declare a chain
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful, professional assistant named Cob."),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+chain = prompt | ChatAnthropic(model="claude-2.1")
+
+
+class InputChat(BaseModel):
+    """Input for the chat endpoint."""
+
+    messages: List[Union[HumanMessage, AIMessage, SystemMessage]] = Field(
+        ...,
+        description="The chat messages representing the current conversation.",
+    )
+
+
+add_routes(
+    app,
+    chain.with_types(input_type=InputChat),
+    enable_feedback_endpoint=True,
+    enable_public_trace_link_endpoint=True,
+    playground_type="chat",
+)
+```
+
+If you are using LangSmith, you can also set `enable_feedback_endpoint=True` on your route to enable thumbs-up/thumbs-down buttons
+after each message, and `enable_public_trace_link_endpoint=True` to add a button that creates a public traces for runs.
+Note that you will also need to set the following environment variables:
+
+```bash
+export LANGCHAIN_TRACING_V2="true"
+export LANGCHAIN_PROJECT="YOUR_PROJECT_NAME"
+export LANGCHAIN_API_KEY="YOUR_API_KEY"
+```
+
+Here's an example with the above two options turned on:
+
+<p align="center">
+<img src="./.github/img/chat_playground.png" width="50%"/>
+</p>
+
+Note: If you enable public trace links, the internals of your chain will be exposed. We recommend only using this setting
+for demos or testing.
+
 ## Legacy Chains
 
-LangServe works with both Runnables (constructed via [LangChain Expression Language](https://python.langchain.com/docs/expression_language/)) and legacy chains (inheriting from `Chain`).
-However, some of the input schemas for legacy chains may be incomplete/incorrect, leading to errors.
+LangServe works with both Runnables (constructed
+via [LangChain Expression Language](https://python.langchain.com/docs/expression_language/))
+and legacy chains (inheriting from `Chain`).
+However, some of the input schemas for legacy chains may be incomplete/incorrect,
+leading to errors.
 This can be fixed by updating the `input_schema` property of those chains in LangChain.
-If you encounter any errors, please open an issue on THIS repo, and we will work to address it.
+If you encounter any errors, please open an issue on THIS repo, and we will work to
+address it.
 
 ## Deployment
 
-### Deploy to Azure 
+### Deploy to AWS
+
+You can deploy to AWS using the [AWS Copilot CLI](https://aws.github.io/copilot-cli/)
+
+```bash
+copilot init --app [application-name] --name [service-name] --type 'Load Balanced Web Service' --dockerfile './Dockerfile' --deploy
+```
+
+Click [here](https://aws.amazon.com/containers/copilot/) to learn more.
+
+### Deploy to Azure
 
 You can deploy to Azure using Azure Container Apps (Serverless):
 
 ```
-az containerapp up --name [container-app-name] --source . --resource-group [resource-group-name] --environment  [environment-name] --ingress external --target-port 8001 --env-vars=OPENAI_API_KEY=your_key  
+az containerapp up --name [container-app-name] --source . --resource-group [resource-group-name] --environment  [environment-name] --ingress external --target-port 8001 --env-vars=OPENAI_API_KEY=your_key
 ```
 
-You can find more info [here](https://learn.microsoft.com/en-us/azure/container-apps/containerapp-up)
+You can find more
+info [here](https://learn.microsoft.com/en-us/azure/container-apps/containerapp-up)
 
 ### Deploy to GCP
 
@@ -282,43 +468,100 @@ You can deploy to GCP Cloud Run using the following command:
 gcloud run deploy [your-service-name] --source . --port 8001 --allow-unauthenticated --region us-central1 --set-env-vars=OPENAI_API_KEY=your_key
 ```
 
+### Community Contributed
 
+#### Deploy to Railway
 
+[Example Railway Repo](https://github.com/PaulLockett/LangServe-Railway/tree/main)
+
+[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/pW9tXP?referralCode=c-aq4K)
 
 ## Pydantic
 
-LangServe provides support for Pydantic 2 with some limitations.
+LangServe>=0.3 fully supports Pydantic 2.
 
-1. OpenAPI docs will not be generated for invoke/batch/stream/stream_log when using Pydantic V2. Fast API does not support [mixing pydantic v1 and v2 namespaces].
-2. LangChain uses the v1 namespace in Pydantic v2. Please read the [following guidelines to ensure compatibility with LangChain](https://github.com/langchain-ai/langchain/discussions/9337)
+If you're using an earlier version of LangServe (<= 0.2), then please note that support for Pydantic 2 has the following limitations:
 
-Except for these limitations, we expect the API endpoints, the playground and any other features to work as expected. 
+1. OpenAPI docs will not be generated for invoke/batch/stream/stream_log when using
+   Pydantic V2. Fast API does not support [mixing pydantic v1 and v2 namespaces]. To fix this, use `pip install pydantic==1.10.17`.
+2. LangChain uses the v1 namespace in Pydantic v2. Please read
+   the [following guidelines to ensure compatibility with LangChain](https://github.com/langchain-ai/langchain/discussions/9337)
+
+Except for these limitations, we expect the API endpoints, the playground and any other
+features to work as expected.
 
 ## Advanced
 
-## Handling Authentication
+### Handling Authentication
 
-If you need to add authentication to your server,
-please reference FastAPI's [security documentation](https://fastapi.tiangolo.com/tutorial/security/)
-and [middleware documentation](https://fastapi.tiangolo.com/tutorial/middleware/).
+If you need to add authentication to your server, please read Fast API's documentation
+about [dependencies](https://fastapi.tiangolo.com/tutorial/dependencies/)
+and [security](https://fastapi.tiangolo.com/tutorial/security/).
 
+The below examples show how to wire up authentication logic LangServe endpoints using FastAPI primitives.
+
+You are responsible for providing the actual authentication logic, the users table etc.
+
+If you're not sure what you're doing, you could try using an existing solution [Auth0](https://auth0.com/).
+
+#### Using add_routes
+
+If you're using `add_routes`, see
+examples [here](https://github.com/langchain-ai/langserve/tree/main/examples/auth).
+
+| Description                                                                                                                                                                        | Links                                                                                                                                                                                                                           |
+| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Auth** with `add_routes`: Simple authentication that can be applied across all endpoints associated with app. (Not useful on its own for implementing per user logic.)           | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/global_deps/server.py)                                                                                                                               |
+| **Auth** with `add_routes`: Simple authentication mechanism based on path dependencies. (No useful on its own for implementing per user logic.)                                    | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/path_dependencies/server.py)                                                                                                                         |
+| **Auth** with `add_routes`: Implement per user logic and auth for endpoints that use per request config modifier. (**Note**: At the moment, does not integrate with OpenAPI docs.) | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/per_req_config_modifier/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/auth/per_req_config_modifier/client.ipynb) |
+
+Alternatively, you can use FastAPI's [middleware](https://fastapi.tiangolo.com/tutorial/middleware/).
+
+Using global dependencies and path dependencies has the advantage that auth will be properly supported in the OpenAPI docs page, but
+these are not sufficient for implement per user logic (e.g., making an application that can search only within user owned documents).
+
+If you need to implement per user logic, you can use the `per_req_config_modifier` or `APIHandler` (below) to implement this logic.
+
+**Per User**
+
+If you need authorization or logic that is user dependent,
+specify `per_req_config_modifier` when using `add_routes`. Use a callable receives the
+raw `Request` object and can extract relevant information from it for authentication and
+authorization purposes.
+
+#### Using APIHandler
+
+If you feel comfortable with FastAPI and python, you can use LangServe's [APIHandler](https://github.com/langchain-ai/langserve/blob/main/examples/api_handler_examples/server.py).
+
+| Description                                                                                                                                                                                                 | Links                                                                                                                                                                                                           |
+| :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Auth** with `APIHandler`: Implement per user logic and auth that shows how to search only within user owned documents.                                                                                    | [server](https://github.com/langchain-ai/langserve/tree/main/examples/auth/api_handler/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/auth/api_handler/client.ipynb)         |
+| **APIHandler** Shows how to use `APIHandler` instead of `add_routes`. This provides more flexibility for developers to define endpoints. Works well with all FastAPI patterns, but takes a bit more effort. | [server](https://github.com/langchain-ai/langserve/tree/main/examples/api_handler_examples/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/api_handler_examples/client.ipynb) |
+
+It's a bit more work, but gives you complete control over the endpoint definitions, so
+you can do whatever custom logic you need for auth.
 
 ### Files
 
 LLM applications often deal with files. There are different architectures
 that can be made to implement file processing; at a high level:
 
-1. The file may be uploaded to the server via a dedicated endpoint and processed using a separate endpoint
-2. The file may be uploaded by either value (bytes of file) or reference (e.g., s3 url to file content)
+1. The file may be uploaded to the server via a dedicated endpoint and processed using a
+   separate endpoint
+2. The file may be uploaded by either value (bytes of file) or reference (e.g., s3 url
+   to file content)
 3. The processing endpoint may be blocking or non-blocking
-4. If significant processing is required, the processing may be offloaded to a dedicated process pool
+4. If significant processing is required, the processing may be offloaded to a dedicated
+   process pool
 
 You should determine what is the appropriate architecture for your application.
 
-Currently, to upload files by value to a runnable, use base64 encoding for the 
-file (`multipart/form-data` is not supported yet). 
+Currently, to upload files by value to a runnable, use base64 encoding for the
+file (`multipart/form-data` is not supported yet).
 
-Here's an [example](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing) that shows
+Here's
+an [example](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing)
+that shows
 how to use base64 encoding to send a file to a remote runnable.
 
 Remember, you can always upload files by reference (e.g., s3 url) or upload them as
@@ -351,7 +594,7 @@ def func(x: Any) -> int:
 
 
 runnable = RunnableLambda(func).with_types(
-    input_schema=int,
+    input_type=int,
 )
 
 add_routes(app, runnable)
@@ -359,11 +602,11 @@ add_routes(app, runnable)
 
 ### Custom User Types
 
-Inherit from `CustomUserType` if you want the data to de-serialize into a 
+Inherit from `CustomUserType` if you want the data to de-serialize into a
 pydantic model rather than the equivalent dict representation.
 
-At the moment, this type only works *server* side and is used
-to specify desired *decoding* behavior. If inheriting from this type
+At the moment, this type only works _server_ side and is used
+to specify desired _decoding_ behavior. If inheriting from this type
 the server will keep the decoded type as a pydantic model instead
 of converting it into a dict.
 
@@ -386,12 +629,13 @@ def func(foo: Foo) -> int:
     assert isinstance(foo, Foo)
     return foo.bar
 
+
 # Note that the input and output type are automatically inferred!
 # You do not need to specify them.
 # runnable = RunnableLambda(func).with_types( # <-- Not needed in this case
-#     input_schema=Foo,
-#     output_schema=int,
-# 
+#     input_type=Foo,
+#     output_type=int,
+#
 add_routes(app, RunnableLambda(func), path="/foo")
 ```
 
@@ -399,11 +643,20 @@ add_routes(app, RunnableLambda(func), path="/foo")
 
 The playground allows you to define custom widgets for your runnable from the backend.
 
-- A widget is specified at the field level and shipped as part of the JSON schema of the input type
-- A widget must contain a key called `type` with the value being one of a well known list of widgets
-- Other widget keys will be associated with values that describe paths in a JSON object
+Here are a few examples:
 
-General schema:
+| Description                                                                           | Links                                                                                                                                                                                                 |
+| :------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Widgets** Different widgets that can be used with playground (file upload and chat) | [server](https://github.com/langchain-ai/langserve/tree/main/examples/widgets/chat/tuples/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/widgets/client.ipynb)     |
+| **Widgets** File upload widget used for LangServe playground.                         | [server](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing/server.py), [client](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing/client.ipynb) |
+
+#### Schema
+
+- A widget is specified at the field level and shipped as part of the JSON schema of the
+  input type
+- A widget must contain a key called `type` with the value being one of a well known
+  list of widgets
+- Other widget keys will be associated with values that describe paths in a JSON object
 
 ```typescript
 type JsonPath = number | string | (number | string)[];
@@ -411,16 +664,29 @@ type NameSpacedPath = { title: string; path: JsonPath }; // Using title to mimic
 type OneOfPath = { oneOf: JsonPath[] };
 
 type Widget = {
-    type: string // Some well known type (e.g., base64file, chat etc.)
-    [key: string]: JsonPath | NameSpacedPath | OneOfPath;
+  type: string; // Some well known type (e.g., base64file, chat etc.)
+  [key: string]: JsonPath | NameSpacedPath | OneOfPath;
 };
 ```
 
+### Available Widgets
+
+There are only two widgets that the user can specify manually right now:
+
+1. File Upload Widget
+2. Chat History Widget
+
+See below more information about these widgets.
+
+All other widgets on the playground UI are created and managed automatically by the UI
+based on the config schema of the Runnable. When you create Configurable Runnables,
+the playground should create appropriate widgets for you to control the behavior.
+
 #### File Upload Widget
 
 Allows creation of a file upload input in the UI playground for files
-that are uploaded as base64 encoded strings. Here's the full [example](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing).
-
+that are uploaded as base64 encoded strings. Here's the
+full [example](https://github.com/langchain-ai/langserve/tree/main/examples/file_processing).
 
 Snippet:
 
@@ -444,27 +710,113 @@ class FileProcessingRequest(CustomUserType):
 
 ```
 
-Example widget: 
+Example widget:
 
 <p align="center">
 <img src="https://github.com/langchain-ai/langserve/assets/3205522/52199e46-9464-4c2e-8be8-222250e08c3f" width="50%"/>
 </p>
 
+### Chat Widget
 
+Look
+at the [widget example](https://github.com/langchain-ai/langserve/tree/main/examples/widgets/chat/tuples/server.py).
+
+To define a chat widget, make sure that you pass "type": "chat".
+
+- "input" is JSONPath to the field in the _Request_ that has the new input message.
+- "output" is JSONPath to the field in the _Response_ that has new output message(s).
+- Don't specify these fields if the entire input or output should be used as they are (
+  e.g., if the output is a list of chat messages.)
+
+Here's a snippet:
+
+```python
+class ChatHistory(CustomUserType):
+    chat_history: List[Tuple[str, str]] = Field(
+        ...,
+        examples=[[("human input", "ai response")]],
+        extra={"widget": {"type": "chat", "input": "question", "output": "answer"}},
+    )
+    question: str
+
+
+def _format_to_messages(input: ChatHistory) -> List[BaseMessage]:
+    """Format the input to a list of messages."""
+    history = input.chat_history
+    user_input = input.question
+
+    messages = []
+
+    for human, ai in history:
+        messages.append(HumanMessage(content=human))
+        messages.append(AIMessage(content=ai))
+    messages.append(HumanMessage(content=user_input))
+    return messages
+
+
+model = ChatOpenAI()
+chat_model = RunnableParallel({"answer": (RunnableLambda(_format_to_messages) | model)})
+add_routes(
+    app,
+    chat_model.with_types(input_type=ChatHistory),
+    config_keys=["configurable"],
+    path="/chat",
+)
+```
+
+Example widget:
+
+<p align="center">
+<img src="https://github.com/langchain-ai/langserve/assets/3205522/a71ff37b-a6a9-4857-a376-cf27c41d3ca4" width="50%"/>
+</p>
+
+You can also specify a list of messages as your a parameter directly, as shown in this snippet:
+
+```python
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful assisstant named Cob."),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+chain = prompt | ChatAnthropic(model="claude-2.1")
+
+
+class MessageListInput(BaseModel):
+    """Input for the chat endpoint."""
+    messages: List[Union[HumanMessage, AIMessage]] = Field(
+        ...,
+        description="The chat messages representing the current conversation.",
+        extra={"widget": {"type": "chat", "input": "messages"}},
+    )
+
+
+add_routes(
+    app,
+    chain.with_types(input_type=MessageListInput),
+    path="/chat",
+)
+```
+
+See [this sample file](https://github.com/langchain-ai/langserve/tree/main/examples/widgets/chat/message_list/server.py) for an example.
 
 ### Enabling / Disabling Endpoints (LangServe >=0.0.33)
 
-You can enable / disable which endpoints are exposed. Use `enabled_endpoints` if you want to make sure to never get a new endpoint when upgrading langserve to a newer verison.
+You can enable / disable which endpoints are exposed when adding routes for a given chain.
 
-Enable: The code below will only enable `invoke`, `batch` and the corresponding `config_hash` endpoint variants.
+Use `enabled_endpoints` if you want to make sure to never get a new endpoint when upgrading langserve to a newer
+verison.
 
+Enable: The code below will only enable `invoke`, `batch` and the
+corresponding `config_hash` endpoint variants.
 
 ```python
-add_routes(app, chain, enabled_endpoints=["invoke", "batch", "config_hashes"])
+add_routes(app, chain, enabled_endpoints=["invoke", "batch", "config_hashes"], path="/mychain")
 ```
 
 Disable: The code below will disable the playground for the chain
 
 ```python
-add_routes(app, chain, disabled_endpoints=["playground"]) 
+add_routes(app, chain, disabled_endpoints=["playground"], path="/mychain")
 ```

SECURITY.md

@@ -1,6 +1,61 @@
 # Security Policy
 
-## Reporting a Vulnerability
+## Reporting OSS Vulnerabilities
 
-Please report security vulnerabilities by email to `security@langchain.dev`.
-This email is an alias to a subset of our maintainers, and will ensure the issue is promptly triaged and acted upon as needed.
+LangChain is partnered with [huntr by Protect AI](https://huntr.com/) to provide 
+a bounty program for our open source projects. 
+
+Please report security vulnerabilities associated with the LangChain 
+open source projects by visiting the following link:
+
+[https://huntr.com/bounties/disclose/](https://huntr.com/bounties/disclose/?target=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain&validSearch=true)
+
+Before reporting a vulnerability, please review:
+
+1) In-Scope Targets and Out-of-Scope Targets below.
+2) The [langchain-ai/langchain](https://python.langchain.com/docs/contributing/repo_structure) monorepo structure.
+3) LangChain [security guidelines](https://python.langchain.com/docs/security) to
+   understand what we consider to be a security vulnerability vs. developer
+   responsibility.
+
+### In-Scope Targets
+
+The following packages and repositories are eligible for bug bounties:
+
+- langchain-core
+- langchain (see exceptions)
+- langchain-community (see exceptions)
+- langgraph
+- langserve
+
+### Out of Scope Targets
+
+All out of scope targets defined by huntr as well as:
+
+- **langchain-experimental**: This repository is for experimental code and is not
+  eligible for bug bounties, bug reports to it will be marked as interesting or waste of
+  time and published with no bounty attached.
+- **tools**: Tools in either langchain or langchain-community are not eligible for bug
+  bounties. This includes the following directories
+  - langchain/tools
+  - langchain-community/tools
+  - Please review our [security guidelines](https://python.langchain.com/docs/security)
+    for more details, but generally tools interact with the real world. Developers are
+    expected to understand the security implications of their code and are responsible
+    for the security of their tools.
+- Code documented with security notices. This will be decided done on a case by
+  case basis, but likely will not be eligible for a bounty as the code is already
+  documented with guidelines for developers that should be followed for making their
+  application secure.
+- Any LangSmith related repositories or APIs see below.
+
+## Reporting LangSmith Vulnerabilities
+
+Please report security vulnerabilities associated with LangSmith by email to `security@langchain.dev`.
+
+- LangSmith site: https://smith.langchain.com
+- SDK client: https://github.com/langchain-ai/langsmith-sdk
+
+### Other Security Concerns
+
+For any other security concerns, please contact us at `security@langchain.dev`.

examples/agent/client.ipynb

@@ -27,7 +27,8 @@
      "data": {
       "text/plain": [
        "{'output': {'output': 'Eugene thinks that cats like fish.'},\n",
-       " 'callback_events': []}"
+       " 'callback_events': [],\n",
+       " 'metadata': {'run_id': 'f16d95e5-dd8f-48d1-8668-4b33a54023fb'}}"
       ]
      },
      "execution_count": 1,
@@ -95,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
@@ -106,7 +107,7 @@
        "{'output': 'Eugene thinks that cats like fish.'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -114,6 +115,816 @@
    "source": [
     "remote_runnable.invoke({\"input\": \"what does eugene think of cats?\"})"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream\n",
+    "\n",
+    "Please note that streaming alternates between actions and observations. It does not stream individual tokens! If you need to stream individual tokens you will need to use astream_log!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--\n",
+      "{'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])], 'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}\n",
+      "--\n",
+      "{'steps': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]), 'observation': [Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]}], 'messages': [FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')]}\n",
+      "--\n",
+      "{'output': \"Eugene thinks that cats like fish. Now let me tell you a story about that thought.\\n\\nOnce upon a time, in a small village, there lived a mischievous cat named Whiskers. Whiskers was known for his love for fish. Every day, he would venture out to the nearby river in search of his favorite food.\\n\\nOne sunny day, Whiskers set out on his usual fish-hunting expedition. As he approached the riverbank, he noticed a group of fishermen casting their nets into the water. Whiskers couldn't resist the temptation and decided to join in on the action.\\n\\nWith his agile paws, Whiskers skillfully maneuvered through the fishermen, strategically positioning himself to snatch the fish caught in their nets. The fishermen were amazed by Whiskers' quick reflexes and couldn't help but laugh at the sight of a cat fishing alongside them.\\n\\nWhiskers continued his fish-stealing escapades for several days, becoming somewhat of a local legend. People from neighboring villages would come to witness the incredible sight of a cat outsmarting seasoned fishermen.\\n\\nOne day, as Whiskers was enjoying his stolen fish by the river, he noticed a stray dog named Rover approaching him. Rover had heard about Whiskers' fishing talents and was intrigued by the cat's abilities.\\n\\nCurious, Whiskers decided to share his secret with Rover. He taught him the art of stealth and how to snatch fish from the nets without being noticed. Rover, being a quick learner, soon became Whiskers' partner in crime.\\n\\nTogether, Whiskers and Rover formed an unbeatable duo, leaving the fishermen puzzled and amazed at the disappearing fish. The villagers, entertained by their antics, started leaving fish out for Whiskers and Rover as a token of appreciation.\\n\\nAnd so, Whiskers and Rover continued their fish-stealing adventures, bringing joy and laughter to the village. Eugene's thought about cats and their love for fish certainly came to life in the mischievous and clever Whiskers, who proved that cats truly have a special affinity for fish.\", 'messages': [AIMessage(content=\"Eugene thinks that cats like fish. Now let me tell you a story about that thought.\\n\\nOnce upon a time, in a small village, there lived a mischievous cat named Whiskers. Whiskers was known for his love for fish. Every day, he would venture out to the nearby river in search of his favorite food.\\n\\nOne sunny day, Whiskers set out on his usual fish-hunting expedition. As he approached the riverbank, he noticed a group of fishermen casting their nets into the water. Whiskers couldn't resist the temptation and decided to join in on the action.\\n\\nWith his agile paws, Whiskers skillfully maneuvered through the fishermen, strategically positioning himself to snatch the fish caught in their nets. The fishermen were amazed by Whiskers' quick reflexes and couldn't help but laugh at the sight of a cat fishing alongside them.\\n\\nWhiskers continued his fish-stealing escapades for several days, becoming somewhat of a local legend. People from neighboring villages would come to witness the incredible sight of a cat outsmarting seasoned fishermen.\\n\\nOne day, as Whiskers was enjoying his stolen fish by the river, he noticed a stray dog named Rover approaching him. Rover had heard about Whiskers' fishing talents and was intrigued by the cat's abilities.\\n\\nCurious, Whiskers decided to share his secret with Rover. He taught him the art of stealth and how to snatch fish from the nets without being noticed. Rover, being a quick learner, soon became Whiskers' partner in crime.\\n\\nTogether, Whiskers and Rover formed an unbeatable duo, leaving the fishermen puzzled and amazed at the disappearing fish. The villagers, entertained by their antics, started leaving fish out for Whiskers and Rover as a token of appreciation.\\n\\nAnd so, Whiskers and Rover continued their fish-stealing adventures, bringing joy and laughter to the village. Eugene's thought about cats and their love for fish certainly came to life in the mischievous and clever Whiskers, who proved that cats truly have a special affinity for fish.\")]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream({\"input\": \"what does eugene think of cats? Then tell me a story about that thought.\"}):\n",
+    "    print('--')\n",
+    "    print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream Events\n",
+    "\n",
+    "The client is looking for a runnable name called `agent` for the chain events. This name was defined on the server side using `runnable.with_config({\"run_name\": \"agent\"}`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting agent: agent with input: {'input': 'what does eugene think of cats? Then tell me a story about that thought.'}\n",
+      "--\n",
+      "Starting tool: get_eugene_thoughts with inputs: {'query': 'cats'}\n",
+      "Done tool: get_eugene_thoughts\n",
+      "Tool output was: [Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\n",
+      "--\n",
+      "E|ug|ene| thinks| that| cats| like| fish|.| Now| let| me| tell| you| a| story| about| that| thought|:\n",
+      "\n",
+      "|Once| upon| a| time|,| in| a| small| village|,| there| lived| a| curious| cat| named| Wh|isk|ers|.| Wh|isk|ers| was| known| for| his| love| of| fish|.| Every| day|,| he| would| venture| to| the| nearby| river| in| search| of| his| favorite| meal|.\n",
+      "\n",
+      "|One| sunny| morning|,| Wh|isk|ers| set| out| on| his| daily| fish|-h|unting| expedition|.| As| he| approached| the| river|,| he| could| smell| the| fresh| scent| of| the| water| and| feel| the| excitement| building| up| inside| him|.| Wh|isk|ers| knew| that| today| might| be| his| lucky| day|.\n",
+      "\n",
+      "|He| carefully| ti|pto|ed| along| the| river|bank|,| his| eyes| fixed| on| the| water|'s| surface|.| Suddenly|,| he| spotted| a| shimmer|ing| fish| swimming| gracefully| through| the| clear| blue| water|.| Wh|isk|ers| c|rou|ched| low|,| ready| to| p|ounce|.\n",
+      "\n",
+      "|With| lightning| speed|,| he| le|aped| into| the| air|,| his| p|aws| out|st|retched| towards| the| fish|.| Splash|!| Wh|isk|ers| landed| right| in| the| middle| of| the| river|,| causing| r|ipples| to| spread| in| all| directions|.| But| he| didn|'t| care|.| All| he| wanted| was| that| delicious| fish|.\n",
+      "\n",
+      "|Wh|isk|ers| chased| the| fish| with| all| his| might|,| dart|ing| through| the| water| with| elegance| and| precision|.| The| fish| sw|am| gracefully|,| trying| to| escape| Wh|isk|ers|'| determined| pursuit|.| But| the| cat| was| relentless|.\n",
+      "\n",
+      "|After| a| few| moments| of| intense| chase|,| Wh|isk|ers| finally| managed| to| catch| the| fish| in| his| p|aws|.| He| triumph|antly| carried| it| to| the| river|bank|,| where| he| enjoyed| his| well|-des|erved| feast|.| The| taste| of| the| fresh| fish| was| heavenly|,| satisfying| his| hunger| and| bringing| a| content|ed| smile| to| his| face|.\n",
+      "\n",
+      "|From| that| day| on|,| Wh|isk|ers| became| known| as| the| legendary| fish|-catching| cat| in| the| village|.| People| would| often| gather| by| the| river| to| watch| him| in| action|,| amazed| by| his| agility| and| determination|.| Wh|isk|ers| taught| everyone| the| importance| of| perseverance| and| following| one|'s| passion|,| just| like| he| pursued| his| love| for| fish|.\n",
+      "\n",
+      "|And| so|,| Wh|isk|ers| continued| his| fish|-h|unting| adventures|,| spreading| joy| and| inspiration| to| everyone| he| encountered|.| He| proved| that| when| you| have| a| passion| for| something|,| nothing| can| stop| you| from| achieving| it| –| just| like| cats| and| their| love| for| fish|.\n",
+      "\n",
+      "|The| end|.|\n",
+      "--\n",
+      "Done agent: agent with output: Eugene thinks that cats like fish. Now let me tell you a story about that thought:\n",
+      "\n",
+      "Once upon a time, in a small village, there lived a curious cat named Whiskers. Whiskers was known for his love of fish. Every day, he would venture to the nearby river in search of his favorite meal.\n",
+      "\n",
+      "One sunny morning, Whiskers set out on his daily fish-hunting expedition. As he approached the river, he could smell the fresh scent of the water and feel the excitement building up inside him. Whiskers knew that today might be his lucky day.\n",
+      "\n",
+      "He carefully tiptoed along the riverbank, his eyes fixed on the water's surface. Suddenly, he spotted a shimmering fish swimming gracefully through the clear blue water. Whiskers crouched low, ready to pounce.\n",
+      "\n",
+      "With lightning speed, he leaped into the air, his paws outstretched towards the fish. Splash! Whiskers landed right in the middle of the river, causing ripples to spread in all directions. But he didn't care. All he wanted was that delicious fish.\n",
+      "\n",
+      "Whiskers chased the fish with all his might, darting through the water with elegance and precision. The fish swam gracefully, trying to escape Whiskers' determined pursuit. But the cat was relentless.\n",
+      "\n",
+      "After a few moments of intense chase, Whiskers finally managed to catch the fish in his paws. He triumphantly carried it to the riverbank, where he enjoyed his well-deserved feast. The taste of the fresh fish was heavenly, satisfying his hunger and bringing a contented smile to his face.\n",
+      "\n",
+      "From that day on, Whiskers became known as the legendary fish-catching cat in the village. People would often gather by the river to watch him in action, amazed by his agility and determination. Whiskers taught everyone the importance of perseverance and following one's passion, just like he pursued his love for fish.\n",
+      "\n",
+      "And so, Whiskers continued his fish-hunting adventures, spreading joy and inspiration to everyone he encountered. He proved that when you have a passion for something, nothing can stop you from achieving it – just like cats and their love for fish.\n",
+      "\n",
+      "The end.\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for event in remote_runnable.astream_events(\n",
+    "    {\"input\": \"what does eugene think of cats? Then tell me a story about that thought.\"},\n",
+    "    version=\"v1\",\n",
+    "):\n",
+    "    kind = event[\"event\"]\n",
+    "    if kind == \"on_chain_start\":\n",
+    "        if (\n",
+    "            event[\"name\"] == \"agent\"\n",
+    "        ):  # Was assigned when creating the agent with `.with_config({\"run_name\": \"Agent\"})`\n",
+    "            print(\n",
+    "                f\"Starting agent: {event['name']} with input: {event['data'].get('input')}\"\n",
+    "            )\n",
+    "    elif kind == \"on_chain_end\":\n",
+    "        if (\n",
+    "            event[\"name\"] == \"agent\"\n",
+    "        ):  # Was assigned when creating the agent with `.with_config({\"run_name\": \"Agent\"})`\n",
+    "            print()\n",
+    "            print(\"--\")\n",
+    "            print(\n",
+    "                f\"Done agent: {event['name']} with output: {event['data'].get('output')['output']}\"\n",
+    "            )\n",
+    "    if kind == \"on_chat_model_stream\":\n",
+    "        content = event[\"data\"][\"chunk\"].content\n",
+    "        if content:\n",
+    "            # Empty content in the context of OpenAI means\n",
+    "            # that the model is asking for a tool to be invoked.\n",
+    "            # So we only print non-empty content\n",
+    "            print(content, end=\"|\")\n",
+    "    elif kind == \"on_tool_start\":\n",
+    "        print(\"--\")\n",
+    "        print(\n",
+    "            f\"Starting tool: {event['name']} with inputs: {event['data'].get('input')}\"\n",
+    "        )\n",
+    "    elif kind == \"on_tool_end\":\n",
+    "        print(f\"Done tool: {event['name']}\")\n",
+    "        print(f\"Tool output was: {event['data'].get('output')}\")\n",
+    "        print(\"--\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream log\n",
+    "\n",
+    "If you need acccess the individual llm tokens from an agent use `astream_log`. Please make sure that you set **streaming=True** on your LLM (see server code). For this to work, the LLM must also support streaming!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--\n",
+      "RunLogPatch({'op': 'replace',\n",
+      "  'path': '',\n",
+      "  'value': {'final_output': None,\n",
+      "            'id': '1213bf75-c5ff-401a-b3c5-b4663ec7dfca',\n",
+      "            'logs': {},\n",
+      "            'streamed_output': []}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '7629fc73-a2fd-48fa-b9a2-c336e270180d',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableSequence',\n",
+      "            'start_time': '2024-01-05T22:24:50.737+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': [],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '72d2e143-d977-4661-b901-430cae1ee739',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableParallel<input,agent_scratchpad>',\n",
+      "            'start_time': '2024-01-05T22:24:50.738+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:1'],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '7ec78a86-596f-4904-9888-45ef67302cb8',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-05T22:24:50.739+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:input'],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '281ca194-3e0c-490c-b637-759f5d971769',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-05T22:24:50.739+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:agent_scratchpad'],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>/final_output',\n",
+      "  'value': {'output': 'what does eugene think of cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>/end_time',\n",
+      "  'value': '2024-01-05T22:24:50.740+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:2/final_output',\n",
+      "  'value': {'output': []}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>:2/end_time',\n",
+      "  'value': '2024-01-05T22:24:50.740+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>/final_output',\n",
+      "  'value': {'agent_scratchpad': [],\n",
+      "            'input': 'what does eugene think of cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>/end_time',\n",
+      "  'value': '2024-01-05T22:24:50.740+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '68c6ad9d-b9f0-47e0-9ea7-d0f170a22732',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'ChatPromptTemplate',\n",
+      "            'start_time': '2024-01-05T22:24:50.741+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:2'],\n",
+      "            'type': 'prompt'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate/final_output',\n",
+      "  'value': {'messages': [SystemMessage(content='You are a helpful assistant.'),\n",
+      "                         HumanMessage(content='what does eugene think of cats?')]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate/end_time',\n",
+      "  'value': '2024-01-05T22:24:50.741+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '883e05a9-6b29-4494-92b3-51c72973cb54',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'ChatOpenAI',\n",
+      "            'start_time': '2024-01-05T22:24:50.743+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:3'],\n",
+      "            'type': 'llm'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': ''}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '{\\n'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' '}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' \"'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': 'query'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '\":'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' \"'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': 'cats'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '\"\\n'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '}'}})})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/final_output',\n",
+      "  'value': LLMResult(generations=[[ChatGeneration(generation_info={'finish_reason': 'function_call'}, message=AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}))]], llm_output=None, run=None)},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.497+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'f8052926-8eb1-4df1-a701-309ee48fd63c',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'OpenAIFunctionsAgentOutputParser',\n",
+      "            'start_time': '2024-01-05T22:24:51.498+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:4'],\n",
+      "            'type': 'parser'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser/final_output',\n",
+      "  'value': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.499+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence/final_output',\n",
+      "  'value': {'output': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.500+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])],\n",
+      "            'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}},\n",
+      " {'op': 'replace',\n",
+      "  'path': '/final_output',\n",
+      "  'value': {'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])],\n",
+      "            'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/get_eugene_thoughts',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'e745d7ec-5992-4ab8-a3ef-191342c6470c',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'get_eugene_thoughts',\n",
+      "            'start_time': '2024-01-05T22:24:51.501+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': [],\n",
+      "            'type': 'tool'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/get_eugene_thoughts/final_output',\n",
+      "  'value': {'output': \"[Document(page_content='cats like fish'), \"\n",
+      "                      \"Document(page_content='dogs like sticks')]\"}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/get_eugene_thoughts/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.737+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'messages': [FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')],\n",
+      "            'steps': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]),\n",
+      "                       'observation': [Document(page_content='cats like fish'),\n",
+      "                                       Document(page_content='dogs like sticks')]}]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/steps',\n",
+      "  'value': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]),\n",
+      "             'observation': [Document(page_content='cats like fish'),\n",
+      "                             Document(page_content='dogs like sticks')]}]},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/messages/1',\n",
+      "  'value': FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '15d4ae13-554b-4144-9aa8-05527a15c042',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableSequence',\n",
+      "            'start_time': '2024-01-05T22:24:51.739+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': [],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '207dab98-d50c-4f2c-934d-291ba3ffd953',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableParallel<input,agent_scratchpad>',\n",
+      "            'start_time': '2024-01-05T22:24:51.740+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:1'],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:3',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '8795a642-3f8e-4e5c-aec6-825b7aac2148',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-05T22:24:51.741+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:input'],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:4',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '60be3aac-3718-4386-a941-1dd371b9a3ea',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-05T22:24:51.741+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:agent_scratchpad'],\n",
+      "            'type': 'chain'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:3/final_output',\n",
+      "  'value': {'output': 'what does eugene think of cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>:3/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.742+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:4/final_output',\n",
+      "  'value': {'output': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}),\n",
+      "                       FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>:4/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.742+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>:2/final_output',\n",
+      "  'value': {'agent_scratchpad': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}),\n",
+      "                                 FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')],\n",
+      "            'input': 'what does eugene think of cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>:2/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.742+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'f1ff89b0-ae9c-49d1-8a2e-73c7f001f1e7',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'ChatPromptTemplate',\n",
+      "            'start_time': '2024-01-05T22:24:51.743+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:2'],\n",
+      "            'type': 'prompt'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate:2/final_output',\n",
+      "  'value': {'messages': [SystemMessage(content='You are a helpful assistant.'),\n",
+      "                         HumanMessage(content='what does eugene think of cats?'),\n",
+      "                         AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}),\n",
+      "                         FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate:2/end_time',\n",
+      "  'value': '2024-01-05T22:24:51.743+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'c4aeb3d0-33c3-4f46-80e9-d1ffb0f983f2',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'ChatOpenAI',\n",
+      "            'start_time': '2024-01-05T22:24:51.744+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:3'],\n",
+      "            'type': 'llm'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI:2/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': 'E'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='E')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': 'ug'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='ug')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': 'ene'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='ene')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' has'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' has')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' two'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' two')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' thoughts'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' thoughts')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' on'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' on')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' cats'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' cats')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': '.'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='.')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' One'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' One')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' thought'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' thought')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' is'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' is')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' \"'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' \"')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': 'cats'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='cats')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' like'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' like')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' fish'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' fish')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': '\"'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='\"')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' and'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' and')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' the'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' the')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' other'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' other')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' thought'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' thought')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' is'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' is')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' \"'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' \"')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': 'dogs'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='dogs')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' like'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' like')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': ' sticks'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' sticks')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output_str/-',\n",
+      "  'value': '\".'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='\".')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI:2/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/final_output',\n",
+      "  'value': LLMResult(generations=[[ChatGeneration(text='Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".', generation_info={'finish_reason': 'stop'}, message=AIMessage(content='Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".'))]], llm_output=None, run=None)},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatOpenAI:2/end_time',\n",
+      "  'value': '2024-01-05T22:24:53.082+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '7074d8c2-df0b-4d18-9890-dd050c2f0ca9',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'OpenAIFunctionsAgentOutputParser',\n",
+      "            'start_time': '2024-01-05T22:24:53.083+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:4'],\n",
+      "            'type': 'parser'}})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser:2/final_output',\n",
+      "  'value': AgentFinish(return_values={'output': 'Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".'}, log='Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".')},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser:2/end_time',\n",
+      "  'value': '2024-01-05T22:24:53.084+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence:2/final_output',\n",
+      "  'value': {'output': AgentFinish(return_values={'output': 'Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".'}, log='Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".')}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence:2/end_time',\n",
+      "  'value': '2024-01-05T22:24:53.084+00:00'})\n",
+      "--\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'messages': [AIMessage(content='Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".')],\n",
+      "            'output': 'Eugene has two thoughts on cats. One thought is \"cats '\n",
+      "                      'like fish\" and the other thought is \"dogs like '\n",
+      "                      'sticks\".'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/output',\n",
+      "  'value': 'Eugene has two thoughts on cats. One thought is \"cats like fish\" '\n",
+      "           'and the other thought is \"dogs like sticks\".'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/messages/2',\n",
+      "  'value': AIMessage(content='Eugene has two thoughts on cats. One thought is \"cats like fish\" and the other thought is \"dogs like sticks\".')})\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream_log({\"input\": \"what does eugene think of cats?\"}):\n",
+    "    print('--')\n",
+    "    print(chunk)"
+   ]
   }
  ],
  "metadata": {
@@ -132,7 +943,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

examples/agent/server.py

@@ -1,15 +1,34 @@
 #!/usr/bin/env python
-"""Example LangChain server exposes a conversational retrieval chain."""
+"""Example LangChain server exposes a conversational retrieval agent.
+
+Relevant LangChain documentation:
+
+* Creating a custom agent: https://python.langchain.com/docs/modules/agents/how_to/custom_agent
+* Streaming with agents: https://python.langchain.com/docs/modules/agents/how_to/streaming#custom-streaming-with-events
+* General streaming documentation: https://python.langchain.com/docs/expression_language/streaming
+
+**ATTENTION**
+1. To support streaming individual tokens you will need to use the astream events
+   endpoint rather than the streaming endpoint.
+2. This example does not truncate message history, so it will crash if you
+   send too many messages (exceed token length).
+3. The playground at the moment does not render agent output well! If you want to
+   use the playground you need to customize it's output server side using astream
+   events by wrapping it within another runnable.
+4. See the client notebook it has an example of how to use stream_events client side!
+"""
+from typing import Any
+
 from fastapi import FastAPI
-from langchain.agents import AgentExecutor, tool
+from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_to_openai_functions
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.chat_models import ChatOpenAI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.pydantic_v1 import BaseModel
-from langchain.tools.render import format_tool_to_openai_function
-from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.tools import tool
+from langchain_core.utils.function_calling import format_tool_to_openai_function
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import BaseModel
 
 from langserve import add_routes
 
@@ -30,12 +49,22 @@ tools = [get_eugene_thoughts]
 prompt = ChatPromptTemplate.from_messages(
     [
         ("system", "You are a helpful assistant."),
+        # Please note that the ordering of the user input vs.
+        # the agent_scratchpad is important.
+        # The agent_scratchpad is a working space for the agent to think,
+        # invoke tools, see tools outputs in order to respond to the given
+        # user input. It has to come AFTER the user input.
         ("user", "{input}"),
         MessagesPlaceholder(variable_name="agent_scratchpad"),
     ]
 )
 
-llm = ChatOpenAI()
+# We need to set streaming=True on the LLM to support streaming individual tokens.
+# Tokens will be available when using the stream_log / stream events endpoints,
+# but not when using the stream endpoint since the stream implementation for agent
+# streams action observation pairs not individual tokens.
+# See the client notebook that shows how to use the stream events endpoint.
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, streaming=True)
 
 llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
 
@@ -56,7 +85,7 @@ agent_executor = AgentExecutor(agent=agent, tools=tools)
 app = FastAPI(
     title="LangChain Server",
     version="1.0",
-    description="Spin up a simple api server using Langchain's Runnable interfaces",
+    description="Spin up a simple api server using LangChain's Runnable interfaces",
 )
 
 
@@ -67,14 +96,20 @@ class Input(BaseModel):
 
 
 class Output(BaseModel):
-    output: str
+    output: Any
 
 
 # Adds routes to the app for using the chain under:
 # /invoke
 # /batch
 # /stream
-add_routes(app, agent_executor.with_types(input_type=Input, output_type=Output))
+# /stream_events
+add_routes(
+    app,
+    agent_executor.with_types(input_type=Input, output_type=Output).with_config(
+        {"run_name": "agent"}
+    ),
+)
 
 if __name__ == "__main__":
     import uvicorn

examples/agent_custom_streaming/client.ipynb

@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "Client code interacting with a server that implements: \n",
+    "\n",
+    "* custom streaming for an agent\n",
+    "* agent with user selected tools\n",
+    "\n",
+    "This agent does not have memory! See other examples in LangServe to see how to add memory.\n",
+    "\n",
+    "**ATTENTION** We made the agent stream strings as an output. This is almost certainly not what you would want for your application. Feel free to adapt to return more structured output; however, keep in mind that likely the client can just use `astream_events`!\n",
+    "\n",
+    "See relevant documentation about agents:\n",
+    "\n",
+    "* Creating a custom agent: https://python.langchain.com/docs/modules/agents/how_to/custom_agent\n",
+    "* Streaming with agents: https://python.langchain.com/docs/modules/agents/how_to/streaming#custom-streaming-with-events\n",
+    "* General streaming documentation: https://python.langchain.com/docs/expression_language/streaming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can interact with this via API directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word = \"audioeeeeeeeeeee\"\n",
+    "len(word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Starting agent: agent with input: {'input': 'what is the length of the word audioeeeeeeeeeee?'}\n",
+      "The length of the word \"audioeeeeeeeeeee\" is 15 characters.\n",
+      "Done agent: agent with output: The length of the word \"audioeeeeeeeeeee\" is 15 characters.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "inputs = {\"input\": {\"input\": f\"what is the length of the word {word}?\", \"chat_history\": [], \"tools\": []}}\n",
+    "response = requests.post(\"http://localhost:8000/invoke\", json=inputs)\n",
+    "\n",
+    "print(response.json()['output'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's provide it with a tool to test that tool selection works"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Starting agent: agent with input: {'input': 'what is the length of the word audioeeeeeeeeeee?'}\n",
+      "\n",
+      "Starting tool: word_length with inputs: {'word': 'audioeeeeeeeeeee'}\n",
+      "\n",
+      "Done tool: word_length with output: 16\n",
+      "The length of the word \"audioeeeeeeeeeee\" is 16.\n",
+      "Done agent: agent with output: The length of the word \"audioeeeeeeeeeee\" is 16.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "inputs = {\"input\": {\"input\": f\"what is the length of the word {word}?\", \"chat_history\": [], \"tools\": [\"word_length\", \"favorite_animal\"]}}\n",
+    "response = requests.post(\"http://localhost:8000/invoke\", json=inputs)\n",
+    "\n",
+    "print(response.json()['output'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also interact with this via the RemoteRunnable interface (to use in other chains)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remote runnable has the same interface as local runnables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream\n",
+    "\n",
+    "Streaming output from a **CUSTOM STREAMING** implementation that streams string representations of intermediate steps. Please see server side implementation for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "|Starting agent: agent with input: {'input': 'What is eugenes favorite animal?'}|\n",
+      "|I|'m| sorry|,| but| I| don|'t| have| access| to| personal| information| about| individuals| unless| it| has| been| shared| with| me| in| the| course| of| our| conversation|.|\n",
+      "|Done agent: agent with output: I'm sorry, but I don't have access to personal information about individuals unless it has been shared with me in the course of our conversation.|\n",
+      "|"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream({\"input\": \"What is eugenes favorite animal?\", \"tools\": [\"word_length\"]}):\n",
+    "    print(chunk, end='|', flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "|Starting agent: agent with input: {'input': 'What is eugenes favorite animal?'}|\n",
+      "|\n",
+      "|Starting tool: favorite_animal with inputs: {'name': 'Eugene'}|\n",
+      "|\n",
+      "|Done tool: favorite_animal with output: cat|\n",
+      "|E|ug|ene|'s| favorite| animal| is| a| cat|.|\n",
+      "|Done agent: agent with output: Eugene's favorite animal is a cat.|\n",
+      "|"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream({\"input\": \"What is eugenes favorite animal?\", \"tools\": [\"word_length\", \"favorite_animal\"]}):\n",
+    "    print(chunk, end='|', flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/agent_custom_streaming/server.py

@@ -0,0 +1,248 @@
+#!/usr/bin/env python
+"""Example LangChain server that shows how to customize streaming for an agent.
+
+Example uses a RunnableLambda that:
+
+1) Uses the agent's astream events method to create a custom streaming API endpoint.
+2) Instantiates an agent with custom tools (based on the user request).
+
+In this example, we kept things simple and are outputting strings to the client
+with all the intermediate steps of the agent. This is just for demonstration
+purposes, and usually you would want to return more structured output in the form
+of a dictionary.
+
+To add history to the agent you can use RunnableWithHistory. Please see the
+other examples in LangServe for how to use RunnableWithHistory to store history
+on the server side.
+
+Alternatively, you can keep track of history on the client side and send it to the
+server with each request. For that to work, you will definitely want to modify the
+streaming output to yield dictionaries with structured output, so it's easy
+to determine what the final agent output was on the client side.
+
+Customize the streaming output to your use case!
+
+Note that we configure the agent using the `tools` field in the input rather
+than using configurable fields. Using custom runnables and configurable fields
+is another option to customize the agent. 
+
+Please see configurable_agent_executor: https://github.com/langchain-ai/langserve/blob/main/examples/configurable_agent_executor/server.py
+for an example that uses a custom runnable with configurable fields.
+
+Relevant LangChain documentation:
+
+* Creating a custom agent: https://python.langchain.com/docs/modules/agents/how_to/custom_agent
+* Streaming with agents: https://python.langchain.com/docs/modules/agents/how_to/streaming#custom-streaming-with-events
+* General streaming documentation: https://python.langchain.com/docs/expression_language/streaming
+* Message History: https://python.langchain.com/docs/expression_language/how_to/message_history
+
+**ATTENTION**
+1. This example does not truncate message history, so it will crash if you
+   send too many messages (exceed token length).
+2. The playground at the moment does not render agent output well! If you want to
+   use the playground you need to customize it's output server side using astream
+   events by wrapping it within another runnable.
+3. See the client notebook to see how .stream() behaves!
+"""  # noqa: E501
+from typing import Any, AsyncIterator, List, Literal
+
+from fastapi import FastAPI
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad.openai_tools import (
+    format_to_openai_tool_messages,
+)
+from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import RunnableLambda
+from langchain_core.tools import tool
+from langchain_core.utils.function_calling import format_tool_to_openai_tool
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel
+
+from langserve import add_routes
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are very powerful assistant, but bad at calculating lengths of words. "
+            "Talk with the user as normal. "
+            "If they ask you to calculate the length of a word, use a tool",
+        ),
+        # Please note the ordering of the fields in the prompt!
+        # The correct ordering is:
+        # 1. user - the user's current input
+        # 2. agent_scratchpad - the agent's working space for thinking and
+        #    invoking tools to respond to the user's input.
+        # If you change the ordering, the agent will not work correctly since
+        # the messages will be shown to the underlying LLM in the wrong order.
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+
+@tool
+def word_length(word: str) -> int:
+    """Returns a counter word"""
+    return len(word)
+
+
+@tool
+def favorite_animal(name: str) -> str:
+    """Get the favorite animal of the person with the given name"""
+    if name.lower().strip() == "eugene":
+        return "cat"
+    return "dog"
+
+
+# We need to set streaming=True on the LLM to support streaming individual tokens.
+# Tokens will be available when using the stream_log / stream events endpoints,
+# but not when using the stream endpoint since the stream implementation for agent
+# streams action observation pairs not individual tokens.
+# See the client notebook that shows how to use the stream events endpoint.
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, streaming=True)
+
+TOOL_MAPPING = {
+    "word_length": word_length,
+    "favorite_animal": favorite_animal,
+}
+KnownTool = Literal["word_length", "favorite_animal"]
+
+
+def _create_agent_with_tools(requested_tools: List[KnownTool]) -> AgentExecutor:
+    """Create an agent with custom tools."""
+    tools = []
+
+    for requested_tool in requested_tools:
+        if requested_tool not in TOOL_MAPPING:
+            raise ValueError(f"Unknown tool: {requested_tool}")
+        tools.append(TOOL_MAPPING[requested_tool])
+
+    if tools:
+        llm_with_tools = llm.bind(
+            tools=[format_tool_to_openai_tool(tool) for tool in tools]
+        )
+    else:
+        llm_with_tools = llm
+
+    agent = (
+        {
+            "input": lambda x: x["input"],
+            "agent_scratchpad": lambda x: format_to_openai_tool_messages(
+                x["intermediate_steps"]
+            ),
+        }
+        | prompt
+        | llm_with_tools
+        | OpenAIToolsAgentOutputParser()
+    )
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_config(
+        {"run_name": "agent"}
+    )
+    return agent_executor
+
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using LangChain's Runnable interfaces",
+)
+
+
+# We need to add these input/output schemas because the current AgentExecutor
+# is lacking in schemas.
+class Input(BaseModel):
+    input: str
+    tools: List[KnownTool]
+
+
+async def custom_stream(input: Input) -> AsyncIterator[str]:
+    """A custom runnable that can stream content.
+
+    Args:
+        input: The input to the agent. See the Input model for more details.
+
+    Yields:
+        strings that are streamed to the client.
+
+
+    Strings were chosen for simplicity, feel free to adapt to your use case.
+
+    You will almost certainly want to return more structured output in the form
+    of a dictionary, so it's easy to determine what the agent is doing without
+    parsing the output.
+
+    Before creating a custom streaming API, you should consider if you can use
+    the existing astream events API and customize the output on the client side
+    (potentially less overall work both server and client side).
+    """
+    agent_executor = _create_agent_with_tools(input["tools"])
+    async for event in agent_executor.astream_events(
+        {
+            "input": input["input"],
+        },
+        version="v1",
+    ):
+        kind = event["event"]
+        if kind == "on_chain_start":
+            if (
+                event["name"] == "agent"
+            ):  # matches `.with_config({"run_name": "Agent"})` in agent_executor
+                yield "\n"
+                yield (
+                    f"Starting agent: {event['name']} "
+                    f"with input: {event['data'].get('input')}"
+                )
+                yield "\n"
+        elif kind == "on_chain_end":
+            if (
+                event["name"] == "agent"
+            ):  # matches `.with_config({"run_name": "Agent"})` in agent_executor
+                yield "\n"
+                yield (
+                    f"Done agent: {event['name']} "
+                    f"with output: {event['data'].get('output')['output']}"
+                )
+                yield "\n"
+        if kind == "on_chat_model_stream":
+            content = event["data"]["chunk"].content
+            if content:
+                # Empty content in the context of OpenAI means
+                # that the model is asking for a tool to be invoked.
+                # So we only print non-empty content
+                yield content
+        elif kind == "on_tool_start":
+            yield "\n"
+            yield (
+                f"Starting tool: {event['name']} "
+                f"with inputs: {event['data'].get('input')}"
+            )
+            yield "\n"
+        elif kind == "on_tool_end":
+            yield "\n"
+            yield (
+                f"Done tool: {event['name']} "
+                f"with output: {event['data'].get('output')}"
+            )
+            yield "\n"
+
+
+class Output(BaseModel):
+    output: Any
+
+
+# Adds routes to the app for using the chain under:
+# /invoke
+# /batch
+# /stream
+# /stream_events
+add_routes(
+    app,
+    RunnableLambda(custom_stream),
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/agent_with_history/client.ipynb

@@ -0,0 +1,554 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "Demo of a client interacting with a remote agent that can use history.\n",
+    "\n",
+    "See relevant documentation about agents:\n",
+    "\n",
+    "* Creating a custom agent: https://python.langchain.com/docs/modules/agents/how_to/custom_agent\n",
+    "* Streaming with agents: https://python.langchain.com/docs/modules/agents/how_to/streaming#custom-streaming-with-events\n",
+    "* General streaming documentation: https://python.langchain.com/docs/expression_language/streaming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can interact with this via API directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': {'output': 'The length of the word \"audioee\" is 7.'},\n",
+       " 'callback_events': [],\n",
+       " 'metadata': {'run_id': '1847be77-f53c-40ba-b88d-06af3a598b6e'}}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "inputs = {\"input\": {\"input\": \"what is the length of the word audioee?\", \"chat_history\": []}}\n",
+    "response = requests.post(\"http://localhost:8000/invoke\", json=inputs)\n",
+    "\n",
+    "response.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also interact with this via the RemoteRunnable interface (to use in other chains)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remote runnable has the same interface as local runnables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage, AIMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  hello\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Hello! How can I assist you today?\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  my name is eugene\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Nice to meet you, Eugene! How can I help you today?\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  what is my name\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Your name is Eugene.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  what is the length of my name\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: The length of your name, Eugene, is 6 characters.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Bye bye human\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_history = []\n",
+    "\n",
+    "while True:\n",
+    "    human = input(\"Human (Q/q to quit): \")\n",
+    "    if human in {\"q\", \"Q\"}:\n",
+    "        print('AI: Bye bye human')\n",
+    "        break\n",
+    "    ai = await remote_runnable.ainvoke({\"input\": human, \"chat_history\": chat_history})\n",
+    "    print(f\"AI: {ai['output']}\")\n",
+    "    chat_history.extend([HumanMessage(content=human), AIMessage(content=ai['output'])])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream\n",
+    "\n",
+    "Please note that streaming alternates between actions and observations. It does not stream individual tokens!\n",
+    "\n",
+    "To stream individual tokens, we need to use the astream events endpoint (see below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  hello\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Hello! How can I assist you today?\n",
+      "------\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  my name is eugene\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Nice to meet you, Eugene! How can I help you today?\n",
+      "------\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  what is my name\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Your name is Eugene.\n",
+      "------\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  what's the length of my name?\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Calling Tool ```word_length``` with input ```{'word': 'Eugene'}```\n",
+      "------\n",
+      "Got result: ```6```\n",
+      "------\n",
+      "The length of your name, Eugene, is 6 characters.\n",
+      "------\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Bye bye human\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_history = []\n",
+    "\n",
+    "while True:\n",
+    "    human = input(\"Human (Q/q to quit): \")\n",
+    "    if human in {\"q\", \"Q\"}:\n",
+    "        print('AI: Bye bye human')\n",
+    "        break\n",
+    "\n",
+    "    ai = None\n",
+    "    print(\"AI: \")\n",
+    "    async for chunk in remote_runnable.astream({\"input\": human, \"chat_history\": chat_history}):\n",
+    "        # Agent Action\n",
+    "        if \"actions\" in chunk:\n",
+    "            for action in chunk[\"actions\"]:\n",
+    "                print(\n",
+    "                    f\"Calling Tool ```{action['tool']}``` with input ```{action['tool_input']}```\"\n",
+    "                )\n",
+    "        # Observation\n",
+    "        elif \"steps\" in chunk:\n",
+    "            for step in chunk[\"steps\"]:\n",
+    "                print(f\"Got result: ```{step['observation']}```\")\n",
+    "        # Final result\n",
+    "        elif \"output\" in chunk:\n",
+    "            print(chunk['output'])\n",
+    "            ai = AIMessage(content=chunk['output'])\n",
+    "        else:\n",
+    "            raise ValueError\n",
+    "        print(\"------\")        \n",
+    "    chat_history.extend([HumanMessage(content=human), ai])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream Events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  hello! my name is eugene\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Starting agent: agent with input: {'input': 'hello! my name is eugene', 'chat_history': []}\n",
+      "Hello| Eugene|!| How| can| I| assist| you| today|?|\n",
+      "--\n",
+      "Done agent: agent with output: Hello Eugene! How can I assist you today?\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  what's the length of my name?\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Starting agent: agent with input: {'input': \"what's the length of my name?\", 'chat_history': []}\n",
+      "--\n",
+      "Starting tool: word_length with inputs: {'word': 'my name'}\n",
+      "Done tool: word_length\n",
+      "Tool output was: 7\n",
+      "--\n",
+      "The| length| of| your| name|,| \"|my| name|\",| is| |7| characters|.|\n",
+      "--\n",
+      "Done agent: agent with output: The length of your name, \"my name\", is 7 characters.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  could you tell me a long story about the length of my name?\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Starting agent: agent with input: {'input': 'could you tell me a long story about the length of my name?', 'chat_history': []}\n",
+      "Once| upon| a| time|,| there| was| a| person| named| [|Your| Name|].| Now|,| [|Your| Name|]| had| a| very| unique| and| special| name|.| It| was| a| name| that| carried| a| lot| of| meaning| and| significance|.| People| often| wondered| about| the| length| of| [|Your| Name|]'|s| name| and| how| it| compared| to| others|.\n",
+      "\n",
+      "|One| day|,| [|Your| Name|]| decided| to| embark| on| a| journey| to| discover| the| true| length| of| their| name|.| They| traveled| far| and| wide|,| seeking| the| wisdom| of| s|ages| and| scholars| who| were| known| for| their| knowledge| of| names| and| their| lengths|.\n",
+      "\n",
+      "|Along| the| way|,| [|Your| Name|]| encountered| many| interesting| characters| who| had| their| own| stories| to| tell| about| the| lengths| of| their| names|.| Some| had| short| names| that| were| easy| to| remember|,| while| others| had| long| names| that| seemed| to| go| on| forever|.\n",
+      "\n",
+      "|As| [|Your| Name|]| continued| their| quest|,| they| came| across| a| wise| old| wizard| who| claimed| to| have| a| magical| tool| that| could| calculate| the| exact| length| of| any| name|.| Intr|ig|ued|,| [|Your| Name|]| approached| the| wizard| and| asked| for| their| assistance|.\n",
+      "\n",
+      "|The| wizard| took| out| a| mystical| device| and| asked| [|Your| Name|]| to| spell| out| their| name|.| [|Your| Name|]| eagerly| complied|,| carefully| en|unci|ating| each| letter|.| The| wizard| then| waved| the| device| over| the| name| and| muttered| an| inc|ant|ation|.\n",
+      "\n",
+      "|In| an| instant|,| the| device| displayed| the| length| of| [|Your| Name|]'|s| name|.| It| was| a| number| that| represented| the| total| count| of| characters| in| their| name|,| including| spaces| and| punctuation| marks|.| [|Your| Name|]| was| amazed| to| see| the| result| and| thanked| the| wizard| for| their| help|.\n",
+      "\n",
+      "|Ar|med| with| this| newfound| knowledge|,| [|Your| Name|]| returned| home| and| shared| their| story| with| friends| and| family|.| They| realized| that| the| length| of| their| name| was| not| just| a| random| number|,| but| a| reflection| of| their| identity| and| the| unique| journey| they| had| taken| to| discover| it|.\n",
+      "\n",
+      "|And| so|,| [|Your| Name|]| lived| happily| ever| after|,| cher|ishing| their| name| and| the| story| behind| its| length|.| They| understood| that| the| length| of| a| name| is| not| just| a| matter| of| counting| letters|,| but| a| testament| to| the| individual|ity| and| significance| of| each| person|'s| identity|.|\n",
+      "--\n",
+      "Done agent: agent with output: Once upon a time, there was a person named [Your Name]. Now, [Your Name] had a very unique and special name. It was a name that carried a lot of meaning and significance. People often wondered about the length of [Your Name]'s name and how it compared to others.\n",
+      "\n",
+      "One day, [Your Name] decided to embark on a journey to discover the true length of their name. They traveled far and wide, seeking the wisdom of sages and scholars who were known for their knowledge of names and their lengths.\n",
+      "\n",
+      "Along the way, [Your Name] encountered many interesting characters who had their own stories to tell about the lengths of their names. Some had short names that were easy to remember, while others had long names that seemed to go on forever.\n",
+      "\n",
+      "As [Your Name] continued their quest, they came across a wise old wizard who claimed to have a magical tool that could calculate the exact length of any name. Intrigued, [Your Name] approached the wizard and asked for their assistance.\n",
+      "\n",
+      "The wizard took out a mystical device and asked [Your Name] to spell out their name. [Your Name] eagerly complied, carefully enunciating each letter. The wizard then waved the device over the name and muttered an incantation.\n",
+      "\n",
+      "In an instant, the device displayed the length of [Your Name]'s name. It was a number that represented the total count of characters in their name, including spaces and punctuation marks. [Your Name] was amazed to see the result and thanked the wizard for their help.\n",
+      "\n",
+      "Armed with this newfound knowledge, [Your Name] returned home and shared their story with friends and family. They realized that the length of their name was not just a random number, but a reflection of their identity and the unique journey they had taken to discover it.\n",
+      "\n",
+      "And so, [Your Name] lived happily ever after, cherishing their name and the story behind its length. They understood that the length of a name is not just a matter of counting letters, but a testament to the individuality and significance of each person's identity.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  that's not what I wanted. My name is eugene. calculate the length of my name and tell me a story about the result.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: \n",
+      "Starting agent: agent with input: {'input': \"that's not what I wanted. My name is eugene. calculate the length of my name and tell me a story about the result.\", 'chat_history': []}\n",
+      "--\n",
+      "Starting tool: word_length with inputs: {'word': 'eugene'}\n",
+      "Done tool: word_length\n",
+      "Tool output was: 6\n",
+      "--\n",
+      "The| length| of| your| name|,| Eugene|,| is| |6| characters|.| Now|,| let| me| tell| you| a| story| about| the| result|.\n",
+      "\n",
+      "|Once| upon| a| time|,| in| a| land| far| away|,| there| was| a| young| boy| named| Eugene|.| He| had| a| special| power| -| the| power| to| bring| joy| and| laughter| to| everyone| he| met|.| Eugene|'s| name|,| with| its| |6| letters|,| perfectly| reflected| his| vibrant| and| energetic| personality|.\n",
+      "\n",
+      "|One| day|,| Eugene| decided| to| embark| on| a| grand| adventure|.| He| set| off| on| a| journey| to| spread| happiness| and| positivity| throughout| the| kingdom|.| Along| the| way|,| he| encountered| people| from| all| walks| of| life| -| from| humble| farmers| to| noble| knights|.\n",
+      "\n",
+      "|With| his| infectious| smile| and| kind| heart|,| Eugene| touched| the| lives| of| everyone| he| met|.| His| name|,| with| its| |6| letters|,| became| synonymous| with| love|,| compassion|,| and| joy|.| People| would| often| say|,| \"|If| you| want| to| experience| true| happiness|,| just| spend| a| moment| with| Eugene|.\"\n",
+      "\n",
+      "|As| Eugene| continued| his| journey|,| word| of| his| incredible| ability| to| bring| happiness| spread| far| and| wide|.| People| from| distant| lands| would| travel| for| miles| just| to| catch| a| glimpse| of| him|.| His| name|,| with| its| |6| letters|,| became| a| symbol| of| hope| and| inspiration|.\n",
+      "\n",
+      "|E|ug|ene|'s| story| serves| as| a| reminder| that| sometimes|,| the| simplest| things| can| have| the| greatest| impact|.| His| name|,| with| its| |6| letters|,| became| a| beacon| of| light| in| a| world| that| often| seemed| dark| and| glo|omy|.\n",
+      "\n",
+      "|And| so|,| Eugene|'s| adventure| continues|,| as| he| spreads| joy| and| happiness| wherever| he| goes|.| His| name|,| with| its| |6| letters|,| will| forever| be| et|ched| in| the| hearts| of| those| whose| lives| he| has| touched|.\n",
+      "\n",
+      "|The| end|.|\n",
+      "--\n",
+      "Done agent: agent with output: The length of your name, Eugene, is 6 characters. Now, let me tell you a story about the result.\n",
+      "\n",
+      "Once upon a time, in a land far away, there was a young boy named Eugene. He had a special power - the power to bring joy and laughter to everyone he met. Eugene's name, with its 6 letters, perfectly reflected his vibrant and energetic personality.\n",
+      "\n",
+      "One day, Eugene decided to embark on a grand adventure. He set off on a journey to spread happiness and positivity throughout the kingdom. Along the way, he encountered people from all walks of life - from humble farmers to noble knights.\n",
+      "\n",
+      "With his infectious smile and kind heart, Eugene touched the lives of everyone he met. His name, with its 6 letters, became synonymous with love, compassion, and joy. People would often say, \"If you want to experience true happiness, just spend a moment with Eugene.\"\n",
+      "\n",
+      "As Eugene continued his journey, word of his incredible ability to bring happiness spread far and wide. People from distant lands would travel for miles just to catch a glimpse of him. His name, with its 6 letters, became a symbol of hope and inspiration.\n",
+      "\n",
+      "Eugene's story serves as a reminder that sometimes, the simplest things can have the greatest impact. His name, with its 6 letters, became a beacon of light in a world that often seemed dark and gloomy.\n",
+      "\n",
+      "And so, Eugene's adventure continues, as he spreads joy and happiness wherever he goes. His name, with its 6 letters, will forever be etched in the hearts of those whose lives he has touched.\n",
+      "\n",
+      "The end.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Human (Q/q to quit):  q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Bye bye human\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_history = []\n",
+    "\n",
+    "while True:\n",
+    "    human = input(\"Human (Q/q to quit): \")\n",
+    "    if human in {\"q\", \"Q\"}:\n",
+    "        print('AI: Bye bye human')\n",
+    "        break\n",
+    "    ai = None\n",
+    "    print(\"AI: \")\n",
+    "    async for event in remote_runnable.astream_events(\n",
+    "        {\"input\": human, \"chat_history\": chat_history},\n",
+    "        version=\"v1\",\n",
+    "    ):\n",
+    "        kind = event[\"event\"]\n",
+    "        if kind == \"on_chain_start\":\n",
+    "            if (\n",
+    "                event[\"name\"] == \"agent\"\n",
+    "            ):  # Was assigned when creating the agent with `.with_config({\"run_name\": \"Agent\"})`\n",
+    "                print(\n",
+    "                    f\"Starting agent: {event['name']} with input: {event['data'].get('input')}\"\n",
+    "                )\n",
+    "        elif kind == \"on_chain_end\":\n",
+    "            if (\n",
+    "                event[\"name\"] == \"agent\"\n",
+    "            ):  # Was assigned when creating the agent with `.with_config({\"run_name\": \"Agent\"})`\n",
+    "                print()\n",
+    "                print(\"--\")\n",
+    "                print(\n",
+    "                    f\"Done agent: {event['name']} with output: {event['data'].get('output')['output']}\"\n",
+    "                )\n",
+    "        if kind == \"on_chat_model_stream\":\n",
+    "            content = event[\"data\"][\"chunk\"].content\n",
+    "            if content:\n",
+    "                # Empty content in the context of OpenAI means\n",
+    "                # that the model is asking for a tool to be invoked.\n",
+    "                # So we only print non-empty content\n",
+    "                print(content, end=\"|\")\n",
+    "        elif kind == \"on_tool_start\":\n",
+    "            print(\"--\")\n",
+    "            print(\n",
+    "                f\"Starting tool: {event['name']} with inputs: {event['data'].get('input')}\"\n",
+    "            )\n",
+    "        elif kind == \"on_tool_end\":\n",
+    "            print(f\"Done tool: {event['name']}\")\n",
+    "            print(f\"Tool output was: {event['data'].get('output')}\")\n",
+    "            print(\"--\")    \n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/agent_with_history/server.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python
+"""Example LangChain server exposes and agent that has conversation history.
+
+In this example, the history is stored entirely on the client's side.
+
+Please see other examples in LangServe on how to use RunnableWithHistory to
+store history on the server side.
+
+Relevant LangChain documentation:
+
+* Creating a custom agent: https://python.langchain.com/docs/modules/agents/how_to/custom_agent
+* Streaming with agents: https://python.langchain.com/docs/modules/agents/how_to/streaming#custom-streaming-with-events
+* General streaming documentation: https://python.langchain.com/docs/expression_language/streaming
+* Message History: https://python.langchain.com/docs/expression_language/how_to/message_history
+
+**ATTENTION**
+1. To support streaming individual tokens you will need to use the astream events
+   endpoint rather than the streaming endpoint.
+2. This example does not truncate message history, so it will crash if you
+   send too many messages (exceed token length).
+3. The playground at the moment does not render agent output well! If you want to
+   use the playground you need to customize it's output server side using astream
+   events by wrapping it within another runnable.
+4. See the client notebook it has an example of how to use stream_events client side!
+"""  # noqa: E501
+from typing import Any, List, Union
+
+from fastapi import FastAPI
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad.openai_tools import (
+    format_to_openai_tool_messages,
+)
+from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
+from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.tools import tool
+from langchain_core.utils.function_calling import format_tool_to_openai_tool
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+from langserve import add_routes
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are very powerful assistant, but bad at calculating lengths of words. "
+            "Talk with the user as normal. "
+            "If they ask you to calculate the length of a word, use a tool",
+        ),
+        # Please note the ordering of the fields in the prompt!
+        # The correct ordering is:
+        # 1. history - the past messages between the user and the agent
+        # 2. user - the user's current input
+        # 3. agent_scratchpad - the agent's working space for thinking and
+        #    invoking tools to respond to the user's input.
+        # If you change the ordering, the agent will not work correctly since
+        # the messages will be shown to the underlying LLM in the wrong order.
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+
+@tool
+def word_length(word: str) -> int:
+    """Returns a counter word"""
+    return len(word)
+
+
+# We need to set streaming=True on the LLM to support streaming individual tokens.
+# Tokens will be available when using the stream_log / stream events endpoints,
+# but not when using the stream endpoint since the stream implementation for agent
+# streams action observation pairs not individual tokens.
+# See the client notebook that shows how to use the stream events endpoint.
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, streaming=True)
+
+tools = [word_length]
+
+
+llm_with_tools = llm.bind(tools=[format_tool_to_openai_tool(tool) for tool in tools])
+
+# ATTENTION: For production use case, it's a good idea to trim the prompt to avoid
+#            exceeding the context window length used by the model.
+#
+# To fix that simply adjust the chain to trim the prompt in whatever way
+# is appropriate for your use case.
+# For example, you may want to keep the system message and the last 10 messages.
+# Or you may want to trim based on the number of tokens.
+# Or you may want to also summarize the messages to keep information about things
+# that were learned about the user.
+#
+# def prompt_trimmer(messages: List[Union[HumanMessage, AIMessage, FunctionMessage]]):
+#     '''Trims the prompt to a reasonable length.'''
+#     # Keep in mind that when trimming you may want to keep the system message!
+#     return messages[-10:] # Keep last 10 messages.
+
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "agent_scratchpad": lambda x: format_to_openai_tool_messages(
+            x["intermediate_steps"]
+        ),
+        "chat_history": lambda x: x["chat_history"],
+    }
+    | prompt
+    # | prompt_trimmer # See comment above.
+    | llm_with_tools
+    | OpenAIToolsAgentOutputParser()
+)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using LangChain's Runnable interfaces",
+)
+
+
+# We need to add these input/output schemas because the current AgentExecutor
+# is lacking in schemas.
+class Input(BaseModel):
+    input: str
+    # The field extra defines a chat widget.
+    # Please see documentation about widgets in the main README.
+    # The widget is used in the playground.
+    # Keep in mind that playground support for agents is not great at the moment.
+    # To get a better experience, you'll need to customize the streaming output
+    # for now.
+    chat_history: List[Union[HumanMessage, AIMessage, FunctionMessage]] = Field(
+        ...,
+        extra={"widget": {"type": "chat", "input": "input", "output": "output"}},
+    )
+
+
+class Output(BaseModel):
+    output: Any
+
+
+# Adds routes to the app for using the chain under:
+# /invoke
+# /batch
+# /stream
+# /stream_events
+add_routes(
+    app,
+    agent_executor.with_types(input_type=Input, output_type=Output).with_config(
+        {"run_name": "agent"}
+    ),
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/api_handler_examples/server.py

@@ -0,0 +1,125 @@
+"""An example that shows how to use the API handler directly.
+
+For this to work with RemoteClient, the routes must match those expected
+by the client; i.e., /invoke, /batch, /stream, etc. No trailing slashes should be used.
+"""
+from importlib import metadata
+from typing import Annotated
+
+from fastapi import Depends, FastAPI, Request, Response
+from langchain_core.runnables import RunnableLambda
+from sse_starlette import EventSourceResponse
+
+from langserve import APIHandler
+
+PYDANTIC_VERSION = metadata.version("pydantic")
+_PYDANTIC_MAJOR_VERSION: int = int(PYDANTIC_VERSION.split(".")[0])
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+##
+# Example 1 -- invoke, batch together with doc-generation
+# This endpoint shows how to expose `invoke` and `batch` using the APIHandler.
+# It also shows how to generate documentation properly so it works correctly
+# depending on Fast API and pydantic versions.
+def add_one(x: int) -> int:
+    """Add one to the given number."""
+    return x + 1
+
+
+chain = RunnableLambda(add_one)
+
+api_handler = APIHandler(chain, path="/simple")
+
+
+# First register the endpoints without documentation
+@app.post("/simple/invoke", include_in_schema=False)
+async def simple_invoke(request: Request) -> Response:
+    """Handle a request."""
+    # The API Handler validates the parts of the request
+    # that are used by the runnnable (e.g., input, config fields)
+    return await api_handler.invoke(request)
+
+
+@app.post("/simple/batch", include_in_schema=False)
+async def simple_batch(request: Request) -> Response:
+    """Handle a request."""
+    # The API Handler validates the parts of the request
+    # that are used by the runnnable (e.g., input, config fields)
+    return await api_handler.batch(request)
+
+
+# Here, we show how to populate the documentation for the endpoint.
+# Please note that this is done separately from the actual endpoint.
+# This happens due to two reasons:
+# 1. FastAPI does not support using pydantic.v1 models in the docs endpoint.
+#    "https://github.com/tiangolo/fastapi/issues/10360"
+#    LangChain uses pydantic.v1 models!
+# 2. Configurable Runnables have a *dynamic* schema, which means that
+#    the shape of the input depends on the config.
+#    In this case, the openapi schema is a best effort showing the documentation
+#    that will work for the default config (and any non-conflicting configs).
+if _PYDANTIC_MAJOR_VERSION == 1:  # Do not use in your own
+    # Add documentation
+    @app.post("/simple/invoke")
+    async def simple_invoke_docs(
+        request: api_handler.InvokeRequest,
+    ) -> api_handler.InvokeResponse:
+        """API endpoint used only for documentation purposes. Populate /docs endpoint"""
+        raise NotImplementedError(
+            "This endpoint is only used for documentation purposes"
+        )
+
+    @app.post("/simple/batch")
+    async def simple_batch_docs(
+        request: api_handler.BatchRequest,
+    ) -> api_handler.BatchResponse:
+        """API endpoint used only for documentation purposes. Populate /docs endpoint"""
+        raise NotImplementedError(
+            "This endpoint is only used for documentation purposes"
+        )
+
+else:
+    print(
+        "Skipping documentation generation for pydantic v2: "
+        "https://github.com/tiangolo/fastapi/issues/10360"
+    )
+
+
+##
+# Example 2 -- Expose `invoke` and `stream` using the API Handler.
+# Uses FastAPI Depends get a ready API handler.
+async def _get_api_handler() -> APIHandler:
+    """Prepare a RunnableLambda."""
+    return APIHandler(RunnableLambda(add_one), path="/v2")
+
+
+@app.post("/v2/invoke")
+async def v2_invoke(
+    request: Request, runnable: Annotated[APIHandler, Depends(_get_api_handler)]
+) -> Response:
+    """Handle invoke request."""
+    # The API Handler validates the parts of the request
+    # that are used by the runnnable (e.g., input, config fields)
+    return await runnable.invoke(request)
+
+
+@app.post("/v2/stream")
+async def v2_stream(
+    request: Request, runnable: Annotated[APIHandler, Depends(_get_api_handler)]
+) -> EventSourceResponse:
+    """Handle stream request."""
+    # The API Handler validates the parts of the request
+    # that are used by the runnnable (e.g., input, config fields)
+    return await runnable.stream(request)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/auth/api_handler/client.ipynb

@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "This is an example client that interacts with the server that has \"auth\".\n",
+    "\n",
+    "Please reference appropriate documentation in the server code and in FastAPI to actually make this secure.\n",
+    "\n",
+    "\n",
+    "**ATTENTION** Only the invoke endpoint has been defined by the server! \n",
+    "So batch/stream won't work. If you want to add stream and batch, you can do so as well on the server side.\n",
+    "The server is implemented using the APIHandler, it's more flexible, but does require a bit more code. :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Login as Alice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "response = requests.post(\"http://localhost:8000/token\", data={\"username\": \"alice\", \"password\": \"secret1\"})\n",
+    "result = response.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "token = result['access_token']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "inputs = {\"input\": \"hello\"}\n",
+    "response = requests.post(\"http://localhost:8000/my_runnable/invoke\", \n",
+    "    json={\n",
+    "        'input': 'hello',\n",
+    "    },\n",
+    "    headers={\n",
+    "        'Authorization': f\"Bearer {token}\"\n",
+    "    }\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': [{'page_content': 'cats like mice',\n",
+       "   'metadata': {'owner_id': 'alice'},\n",
+       "   'type': 'Document'},\n",
+       "  {'page_content': 'cats like cheese',\n",
+       "   'metadata': {'owner_id': 'alice'},\n",
+       "   'type': 'Document'}],\n",
+       " 'callback_events': [],\n",
+       " 'metadata': {'run_id': '1732c9aa-c6d3-4736-b8ca-01265fa8ba06'}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "You can also interact with this via the RemoteRunnable interface (to use in other chains)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/my_runnable\", headers={\"Authorization\": f\"Bearer {token}\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='cats like mice', metadata={'owner_id': 'alice'}),\n",
+       " Document(page_content='cats like cheese', metadata={'owner_id': 'alice'})]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await remote_runnable.ainvoke(\"cat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Login as John"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "response = requests.post(\"http://localhost:8000/token\", data={\"username\": \"john\", \"password\": \"secret2\"})\n",
+    "token = response.json()['access_token']\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/my_runnable\", headers={\"Authorization\": f\"Bearer {token}\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='i like walks by the ocean', metadata={'owner_id': 'john'}),\n",
+       " Document(page_content='dogs like sticks', metadata={'owner_id': 'john'}),\n",
+       " Document(page_content='my favorite food is cheese', metadata={'owner_id': 'john'})]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await remote_runnable.ainvoke(\"water\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/auth/api_handler/server.py

@@ -0,0 +1,284 @@
+#!/usr/bin/env python
+"""Example that shows how to use the underlying APIHandler class directly with Auth.
+
+This example shows how to apply logic based on the user's identity.
+
+You can build on these concepts to implement a more complex app:
+* Add endpoints that allow users to manage their documents.
+* Make a more complex runnable that does something with the retrieved documents; e.g.,
+  a conversational agent that responds to the user's input with the retrieved documents
+  (which are user specific documents).
+
+For authentication, we use a fake token that's the same as the username, adapting
+the following example from the FastAPI docs:
+
+https://fastapi.tiangolo.com/tutorial/security/simple-oauth2/
+
+**ATTENTION**
+
+This example is not actually secure and should not be used in production.
+
+Once you understand how to use `per_req_config_modifier`, read through
+the FastAPI docs and implement proper auth:
+https://fastapi.tiangolo.com/tutorial/security/oauth2-jwt/
+
+
+**ATTENTION**
+
+This example does not integrate auth with OpenAPI, so the OpenAPI docs won't
+be able to help with authentication. This is currently a limitation
+if using `add_routes`. If you need this functionality, you can use
+the underlying `APIHandler` class directly, which affords maximal flexibility.
+"""
+from importlib import metadata
+from typing import Any, List, Optional, Union
+
+from fastapi import Depends, FastAPI, HTTPException, Request, Response, status
+from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+from langchain_core.runnables import (
+    ConfigurableField,
+    RunnableConfig,
+    RunnableSerializable,
+)
+from langchain_core.vectorstores import VectorStore
+from langchain_openai import OpenAIEmbeddings
+from pydantic import BaseModel, ConfigDict
+from typing_extensions import Annotated
+
+from langserve import APIHandler
+
+
+class User(BaseModel):
+    username: str
+    email: Union[str, None] = None
+    full_name: Union[str, None] = None
+    disabled: Union[bool, None] = None
+
+
+class UserInDB(User):
+    hashed_password: str
+
+
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+app = FastAPI()
+
+FAKE_USERS_DB = {
+    "alice": {
+        "username": "alice",
+        "full_name": "Alice Wonderson",
+        "email": "alice@example.com",
+        "hashed_password": "fakehashedsecret1",
+        "disabled": False,
+    },
+    "john": {
+        "username": "john",
+        "full_name": "John Doe",
+        "email": "johndoe@example.com",
+        "hashed_password": "fakehashedsecret2",
+        "disabled": False,
+    },
+    "bob": {
+        "username": "john",
+        "full_name": "John Doe",
+        "email": "johndoe@example.com",
+        "hashed_password": "fakehashedsecret3",
+        "disabled": True,
+    },
+}
+
+
+def _fake_hash_password(password: str) -> str:
+    """Fake a hashed password."""
+    return "fakehashed" + password
+
+
+def _get_user(db: dict, username: str) -> Union[UserInDB, None]:
+    if username in db:
+        user_dict = db[username]
+        return UserInDB(**user_dict)
+
+
+def _fake_decode_token(token: str) -> Union[User, None]:
+    # This doesn't provide any security at all
+    # Check the next version
+    user = _get_user(FAKE_USERS_DB, token)
+    return user
+
+
+@app.post("/token")
+async def login(form_data: Annotated[OAuth2PasswordRequestForm, Depends()]):
+    user_dict = FAKE_USERS_DB.get(form_data.username)
+    if not user_dict:
+        raise HTTPException(status_code=400, detail="Incorrect username or password")
+    user = UserInDB(**user_dict)
+    hashed_password = _fake_hash_password(form_data.password)
+    if not hashed_password == user.hashed_password:
+        raise HTTPException(status_code=400, detail="Incorrect username or password")
+
+    return {"access_token": user.username, "token_type": "bearer"}
+
+
+async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]):
+    user = _fake_decode_token(token)
+    if not user:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid authentication credentials",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    return user
+
+
+async def get_current_active_user(
+    current_user: Annotated[User, Depends(get_current_user)],
+):
+    if current_user.disabled:
+        raise HTTPException(status_code=400, detail="Inactive user")
+    return current_user
+
+
+class PerUserVectorstore(RunnableSerializable):
+    """A custom runnable that returns a list of documents for the given user.
+
+    The runnable is configurable by the user, and the search results are
+    filtered by the user ID.
+    """
+
+    user_id: Optional[str]
+    vectorstore: VectorStore
+
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
+
+    def _invoke(
+        self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
+    ) -> List[Document]:
+        """Invoke the retriever."""
+        # WARNING: Verify documentation of underlying vectorstore to make
+        # sure that it actually uses filters.
+        # Highly recommended to use unit-tests to verify this behavior, as
+        # implementations can be different depending on the underlying vectorstore.
+        retriever = self.vectorstore.as_retriever(
+            search_kwargs={"filter": {"owner_id": self.user_id}}
+        )
+        return retriever.invoke(input, config=config)
+
+    def invoke(
+        self, input: str, config: Optional[RunnableConfig] = None, **kwargs
+    ) -> List[Document]:
+        """Add one to an integer."""
+        return self._call_with_config(self._invoke, input, config, **kwargs)
+
+
+vectorstore = Chroma(
+    collection_name="some_collection",
+    embedding_function=OpenAIEmbeddings(),
+)
+
+vectorstore.add_documents(
+    [
+        Document(
+            page_content="cats like cheese",
+            metadata={"owner_id": "alice"},
+        ),
+        Document(
+            page_content="cats like mice",
+            metadata={"owner_id": "alice"},
+        ),
+        Document(
+            page_content="dogs like sticks",
+            metadata={"owner_id": "john"},
+        ),
+        Document(
+            page_content="my favorite food is cheese",
+            metadata={"owner_id": "john"},
+        ),
+        Document(
+            page_content="i like walks by the ocean",
+            metadata={"owner_id": "john"},
+        ),
+        Document(
+            page_content="dogs like grass",
+            metadata={"owner_id": "bob"},
+        ),
+    ]
+)
+
+per_user_retriever = PerUserVectorstore(
+    user_id=None,  # Placeholder ID that will be replaced by the per_req_config_modifier
+    vectorstore=vectorstore,
+).configurable_fields(
+    # Attention: Make sure to override the user ID for each request in the
+    # per_req_config_modifier. This should not be client configurable.
+    user_id=ConfigurableField(
+        id="user_id",
+        name="User ID",
+        description="The user ID to use for the retriever.",
+    )
+)
+
+
+# Let's define the API Handler
+api_handler = APIHandler(
+    per_user_retriever,
+    # Namespace for the runnable.
+    # Endpoints like batch / invoke should be under /my_runnable/invoke
+    # and /my_runnable/batch etc.
+    path="/my_runnable",
+)
+
+
+PYDANTIC_VERSION = metadata.version("pydantic")
+_PYDANTIC_MAJOR_VERSION: int = int(PYDANTIC_VERSION.split(".")[0])
+
+
+# **ATTENTION** Your code does not need to include both versions.
+# Use whichever version is appropriate given the pydantic version you are using.
+# Both versions are included here for demonstration purposes.
+#
+# If using pydantic <2, everything works as expected.
+# However, when using pydantic >=2 is installed, things are a bit
+# more complicated because LangChain uses the pydantic.v1 namespace
+# But the pydantic.v1 namespace is not supported by FastAPI.
+# See this issue: https://github.com/tiangolo/fastapi/issues/10360
+# So when using pydantic >=2, we need to use a vanilla starlette request
+# and response, and we will not have documentation.
+# Or we can create custom models for the request and response.
+# The underlying API Handler will still validate the request
+# correctly even if vanilla requests are used.
+if _PYDANTIC_MAJOR_VERSION == 1:
+
+    @app.post("/my_runnable/invoke")
+    async def invoke_with_auth(
+        # Included for documentation purposes
+        invoke_request: api_handler.InvokeRequest,
+        request: Request,
+        current_user: Annotated[User, Depends(get_current_active_user)],
+    ) -> Response:
+        """Handle a request."""
+        # The API Handler validates the parts of the request
+        # that are used by the runnnable (e.g., input, config fields)
+        config = {"configurable": {"user_id": current_user.username}}
+        return await api_handler.invoke(request, server_config=config)
+else:
+
+    @app.post("/my_runnable/invoke")
+    async def invoke_with_auth(
+        request: Request,
+        current_user: Annotated[User, Depends(get_current_active_user)],
+    ) -> Response:
+        """Handle a request."""
+        # The API Handler validates the parts of the request
+        # that are used by the runnnable (e.g., input, config fields)
+        config = {"configurable": {"user_id": current_user.username}}
+        return await api_handler.invoke(request, server_config=config)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/auth/global_deps/server.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+"""An example that uses Fast API global dependencies.
+
+This approach can be used if the same authentication logic can be used
+for all endpoints in the application.
+
+This may be a reasonable approach for simple applications.
+
+See:
+
+* https://fastapi.tiangolo.com/tutorial/dependencies/global-dependencies/
+* https://fastapi.tiangolo.com/tutorial/dependencies/
+* https://fastapi.tiangolo.com/tutorial/security/
+"""
+
+from fastapi import Depends, FastAPI, Header, HTTPException
+from langchain_core.runnables import RunnableLambda
+from typing_extensions import Annotated
+
+from langserve import add_routes
+
+
+async def verify_token(x_token: Annotated[str, Header()]) -> None:
+    """Verify the token is valid."""
+    # Replace this with your actual authentication logic
+    if x_token != "secret-token":
+        raise HTTPException(status_code=400, detail="X-Token header invalid")
+
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    dependencies=[Depends(verify_token)],
+)
+
+
+def add_one(x: int) -> int:
+    """Add one to an integer."""
+    return x + 1
+
+
+chain = RunnableLambda(add_one)
+
+
+add_routes(
+    app,
+    chain,
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/auth/path_dependencies/server.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+"""An example that shows how to use path dependencies for authentication.
+
+The path dependencies are applied to all the routes added by the `add_routes`.
+
+To keep this example brief, we're providing a placeholder verify_token function
+that shows how to use path dependencies.
+
+To implement proper auth, please see the FastAPI docs:
+
+* https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-in-path-operation-decorators/
+* https://fastapi.tiangolo.com/tutorial/dependencies/
+* https://fastapi.tiangolo.com/tutorial/security/
+"""  # noqa: E501
+
+from fastapi import Depends, FastAPI, Header, HTTPException
+from langchain_core.runnables import RunnableLambda
+from typing_extensions import Annotated
+
+from langserve import add_routes
+
+
+async def verify_token(x_token: Annotated[str, Header()]) -> None:
+    """Verify the token is valid."""
+    # Replace this with your actual authentication logic
+    if x_token != "secret-token":
+        raise HTTPException(status_code=400, detail="X-Token header invalid")
+
+
+app = FastAPI()
+
+
+def add_one(x: int) -> int:
+    """Add one to an integer."""
+    return x + 1
+
+
+chain = RunnableLambda(add_one)
+
+
+add_routes(
+    app,
+    chain,
+    dependencies=[Depends(verify_token)],
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/auth/per_req_config_modifier/client.ipynb

@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "This is an example client that interacts with the server that has \"auth\".\n",
+    "\n",
+    "Please reference appropriate documentation in the server code and in FastAPI to actually make this secure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Login as Alice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "response = requests.post(\"http://localhost:8000/token\", data={\"username\": \"alice\", \"password\": \"secret1\"})\n",
+    "result = response.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "token = result['access_token']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "inputs = {\"input\": \"hello\"}\n",
+    "response = requests.post(\"http://localhost:8000/invoke\", \n",
+    "    json={\n",
+    "        'input': 'hello',\n",
+    "    },\n",
+    "    headers={\n",
+    "        'Authorization': f\"Bearer {token}\"\n",
+    "    }\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': [{'page_content': 'cats like mice',\n",
+       "   'metadata': {'owner_id': 'alice'},\n",
+       "   'type': 'Document'},\n",
+       "  {'page_content': 'cats like cheese',\n",
+       "   'metadata': {'owner_id': 'alice'},\n",
+       "   'type': 'Document'}],\n",
+       " 'callback_events': [],\n",
+       " 'metadata': {'run_id': '00000000-0000-0000-0000-000000000000'}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "You can also interact with this via the RemoteRunnable interface (to use in other chains)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/\", headers={\"Authorization\": f\"Bearer {token}\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='cats like mice', metadata={'owner_id': 'alice'}),\n",
+       " Document(page_content='cats like cheese', metadata={'owner_id': 'alice'})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await remote_runnable.ainvoke(\"cat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Login as John"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "response = requests.post(\"http://localhost:8000/token\", data={\"username\": \"john\", \"password\": \"secret2\"})\n",
+    "token = response.json()['access_token']\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/\", headers={\"Authorization\": f\"Bearer {token}\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='i like walks by the ocean', metadata={'owner_id': 'john'}),\n",
+       " Document(page_content='dogs like sticks', metadata={'owner_id': 'john'}),\n",
+       " Document(page_content='my favorite food is cheese', metadata={'owner_id': 'john'})]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await remote_runnable.ainvoke(\"water\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/auth/per_req_config_modifier/server.py

@@ -0,0 +1,242 @@
+#!/usr/bin/env python
+"""Example that shows how to use `per_req_config_modifier`.
+
+This is a simple example that shows how to use configurable runnables with
+per request configuration modification to achieve behavior that's different
+depending on the user.
+
+You can build on these concepts to implement a more complex app:
+* Add endpoints that allow users to manage their documents.
+* Make a more complex runnable that does something with the retrieved documents; e.g.,
+  a conversational agent that responds to the user's input with the retrieved documents
+  (which are user specific documents).
+
+For authentication, we use a fake token that's the same as the username, adapting
+the following example from the FastAPI docs:
+
+https://fastapi.tiangolo.com/tutorial/security/simple-oauth2/
+
+**ATTENTION**
+
+This example is not actually secure and should not be used in production.
+
+Once you understand how to use `per_req_config_modifier`, read through
+the FastAPI docs and implement proper auth:
+https://fastapi.tiangolo.com/tutorial/security/oauth2-jwt/
+
+
+**ATTENTION**
+
+This example does not integrate auth with OpenAPI, so the OpenAPI docs won't
+be able to help with authentication. This is currently a limitation
+if using `add_routes`. If you need this functionality, you can use
+the underlying `APIHandler` class directly, which affords maximal flexibility.
+"""
+from typing import Any, Dict, List, Optional, Union
+
+from fastapi import Depends, FastAPI, HTTPException, Request, status
+from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+from langchain_core.runnables import (
+    ConfigurableField,
+    RunnableConfig,
+    RunnableSerializable,
+)
+from langchain_core.vectorstores import VectorStore
+from langchain_openai import OpenAIEmbeddings
+from pydantic import BaseModel, ConfigDict
+from typing_extensions import Annotated
+
+from langserve import add_routes
+
+
+class User(BaseModel):
+    username: str
+    email: Union[str, None] = None
+    full_name: Union[str, None] = None
+    disabled: Union[bool, None] = None
+
+
+class UserInDB(User):
+    hashed_password: str
+
+
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+app = FastAPI()
+
+FAKE_USERS_DB = {
+    "alice": {
+        "username": "alice",
+        "full_name": "Alice Wonderson",
+        "email": "alice@example.com",
+        "hashed_password": "fakehashedsecret1",
+        "disabled": False,
+    },
+    "john": {
+        "username": "john",
+        "full_name": "John Doe",
+        "email": "johndoe@example.com",
+        "hashed_password": "fakehashedsecret2",
+        "disabled": False,
+    },
+    "bob": {
+        "username": "john",
+        "full_name": "John Doe",
+        "email": "johndoe@example.com",
+        "hashed_password": "fakehashedsecret3",
+        "disabled": True,
+    },
+}
+
+
+def _fake_hash_password(password: str) -> str:
+    """Fake a hashed password."""
+    return "fakehashed" + password
+
+
+def _get_user(db: dict, username: str) -> Union[UserInDB, None]:
+    if username in db:
+        user_dict = db[username]
+        return UserInDB(**user_dict)
+
+
+def _fake_decode_token(token: str) -> Union[User, None]:
+    # This doesn't provide any security at all
+    # Check the next version
+    user = _get_user(FAKE_USERS_DB, token)
+    return user
+
+
+@app.post("/token")
+async def login(form_data: Annotated[OAuth2PasswordRequestForm, Depends()]):
+    user_dict = FAKE_USERS_DB.get(form_data.username)
+    if not user_dict:
+        raise HTTPException(status_code=400, detail="Incorrect username or password")
+    user = UserInDB(**user_dict)
+    hashed_password = _fake_hash_password(form_data.password)
+    if not hashed_password == user.hashed_password:
+        raise HTTPException(status_code=400, detail="Incorrect username or password")
+
+    return {"access_token": user.username, "token_type": "bearer"}
+
+
+async def get_current_active_user_from_request(request: Request) -> User:
+    """Get the current active user from the request."""
+    token = await oauth2_scheme(request)
+    user = _fake_decode_token(token)
+    if not user:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid authentication credentials",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    if user.disabled:
+        raise HTTPException(status_code=400, detail="Inactive user")
+    return user
+
+
+class PerUserVectorstore(RunnableSerializable):
+    """A custom runnable that returns a list of documents for the given user.
+
+    The runnable is configurable by the user, and the search results are
+    filtered by the user ID.
+    """
+
+    user_id: Optional[str]
+    vectorstore: VectorStore
+
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
+
+    def _invoke(
+        self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
+    ) -> List[Document]:
+        """Invoke the retriever."""
+        # WARNING: Verify documentation of underlying vectorstore to make
+        # sure that it actually uses filters.
+        # Highly recommended to use unit-tests to verify this behavior, as
+        # implementations can be different depending on the underlying vectorstore.
+        retriever = self.vectorstore.as_retriever(
+            search_kwargs={"filter": {"owner_id": self.user_id}}
+        )
+        return retriever.invoke(input, config=config)
+
+    def invoke(
+        self, input: str, config: Optional[RunnableConfig] = None, **kwargs
+    ) -> List[Document]:
+        """Add one to an integer."""
+        return self._call_with_config(self._invoke, input, config, **kwargs)
+
+
+async def per_req_config_modifier(config: Dict, request: Request) -> Dict:
+    """Modify the config for each request."""
+    user = await get_current_active_user_from_request(request)
+    config["configurable"] = {}
+    # Attention: Make sure that the user ID is over-ridden for each request.
+    # We should not be accepting a user ID from the user in this case!
+    config["configurable"]["user_id"] = user.username
+    return config
+
+
+vectorstore = Chroma(
+    collection_name="some_collection",
+    embedding_function=OpenAIEmbeddings(),
+)
+
+vectorstore.add_documents(
+    [
+        Document(
+            page_content="cats like cheese",
+            metadata={"owner_id": "alice"},
+        ),
+        Document(
+            page_content="cats like mice",
+            metadata={"owner_id": "alice"},
+        ),
+        Document(
+            page_content="dogs like sticks",
+            metadata={"owner_id": "john"},
+        ),
+        Document(
+            page_content="my favorite food is cheese",
+            metadata={"owner_id": "john"},
+        ),
+        Document(
+            page_content="i like walks by the ocean",
+            metadata={"owner_id": "john"},
+        ),
+        Document(
+            page_content="dogs like grass",
+            metadata={"owner_id": "bob"},
+        ),
+    ]
+)
+
+per_user_retriever = PerUserVectorstore(
+    user_id=None,  # Placeholder ID that will be replaced by the per_req_config_modifier
+    vectorstore=vectorstore,
+).configurable_fields(
+    # Attention: Make sure to override the user ID for each request in the
+    # per_req_config_modifier. This should not be client configurable.
+    user_id=ConfigurableField(
+        id="user_id",
+        name="User ID",
+        description="The user ID to use for the retriever.",
+    )
+)
+
+add_routes(
+    app,
+    per_user_retriever,
+    per_req_config_modifier=per_req_config_modifier,
+    enabled_endpoints=["invoke"],
+)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/chat_playground/legacy_input/server.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+"""Example of a simple chatbot that just passes current conversation
+state back and forth between server and client.
+"""
+from typing import List, Union
+
+from fastapi import FastAPI
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydantic import BaseModel, Field
+
+from langserve import add_routes
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+# Declare a chain
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful, professional assistant named Cob."),
+        MessagesPlaceholder(variable_name="messages"),
+        ("human", "{input}"),
+    ]
+)
+
+chain = prompt | ChatAnthropic(model="claude-2.1")
+
+
+class InputChat(BaseModel):
+    """Input for the chat endpoint."""
+
+    messages: List[Union[HumanMessage, AIMessage, SystemMessage]] = Field(
+        ...,
+        description="The chat messages representing the current conversation.",
+    )
+
+    input: str
+
+
+add_routes(
+    app,
+    chain.with_types(input_type=InputChat),
+    enable_feedback_endpoint=True,
+    enable_public_trace_link_endpoint=True,
+    playground_type="chat",
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/chat_playground/server.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+"""Example of a simple chatbot that just passes current conversation
+state back and forth between server and client.
+"""
+from typing import List, Union
+
+from fastapi import FastAPI
+from langchain_anthropic.chat_models import ChatAnthropic
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydantic import BaseModel, Field
+
+from langserve import add_routes
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+# Declare a chain
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful, professional assistant named Cob."),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+chain = prompt | ChatAnthropic(model_name="claude-3-sonnet-20240229")
+
+
+class InputChat(BaseModel):
+    """Input for the chat endpoint."""
+
+    messages: List[Union[HumanMessage, AIMessage, SystemMessage]] = Field(
+        ...,
+        description="The chat messages representing the current conversation.",
+    )
+
+
+add_routes(
+    app,
+    chain.with_types(input_type=InputChat),
+    enable_feedback_endpoint=True,
+    enable_public_trace_link_endpoint=True,
+    playground_type="chat",
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/chat_with_persistence/client.ipynb

@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chat History\n",
+    "\n",
+    "An example of a client interacting with a chatbot where message history is persisted on the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "chat = RemoteRunnable(\"http://localhost:8000/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a prompt composed of a system message and a human message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "session_id = str(uuid.uuid4())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" Hello Eugene! My name is Claude. It's nice to meet another cat lover.\")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke({\"human_input\": \"my name is eugene. i like cats. what is your name?\"}, {'configurable': { 'session_id': session_id } })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' You told me your name is Eugene.')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke({\"human_input\": \"what was my name?\"}, {'configurable': { 'session_id': session_id } })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' You said you like cats.')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke({\"human_input\": \"What animal do i like?\"}, {'configurable': { 'session_id': session_id } })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Sure\n",
+      ",\n",
+      " I\n",
+      "'d\n",
+      " be\n",
+      " happy\n",
+      " to\n",
+      " count\n",
+      " to\n",
+      " 10\n",
+      ":\n",
+      "\n",
+      "\n",
+      "1\n",
+      ",\n",
+      " 2\n",
+      ",\n",
+      " 3\n",
+      ",\n",
+      " 4\n",
+      ",\n",
+      " 5\n",
+      ",\n",
+      " 6\n",
+      ",\n",
+      " 7\n",
+      ",\n",
+      " 8\n",
+      ",\n",
+      " 9\n",
+      ",\n",
+      " 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in chat.stream({'human_input': \"Can you count till 10?\"},  {'configurable': { 'session_id': session_id } }):\n",
+    "    print()\n",
+    "    print(chunk.content, end='', flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m[\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"my name is eugene. i like cats. what is your name?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\" Hello Eugene! My name is Claude. It's nice to meet another cat lover.\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"what was my name?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\" You told me your name is Eugene.\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"What animal do i like?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\" You said you like cats.\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"Can you count till 10?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"AIMessageChunk\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\" Sure, I'd be happy to count to 10:\\n\\n1, 2, 3, 4, 5, 6, 7, 8, 9, 10\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"AIMessageChunk\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "\u001b[1;39m]\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat chat_histories/c7a327f3-5578-4fb7-a8f2-3082d7cb58cc.json | jq ."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/chat_with_persistence/server.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+"""Example of a chat server with persistence handled on the backend.
+
+For simplicity, we're using file storage here -- to avoid the need to set up
+a database. This is obviously not a good idea for a production environment,
+but will help us to demonstrate the RunnableWithMessageHistory interface.
+
+We'll use cookies to identify the user and/or session. This will help illustrate how to
+fetch configuration from the request.
+"""
+import re
+from pathlib import Path
+from typing import Callable, Union
+
+from fastapi import FastAPI, HTTPException
+from langchain_anthropic import ChatAnthropic
+from langchain_community.chat_message_histories import FileChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from pydantic import BaseModel, Field
+
+from langserve import add_routes
+
+
+def _is_valid_identifier(value: str) -> bool:
+    """Check if the session ID is in a valid format."""
+    # Use a regular expression to match the allowed characters
+    valid_characters = re.compile(r"^[a-zA-Z0-9-_]+$")
+    return bool(valid_characters.match(value))
+
+
+def create_session_factory(
+    base_dir: Union[str, Path],
+) -> Callable[[str], BaseChatMessageHistory]:
+    """Create a session ID factory that creates session IDs from a base dir.
+
+    Args:
+        base_dir: Base directory to use for storing the chat histories.
+
+    Returns:
+        A session ID factory that creates session IDs from a base path.
+    """
+    base_dir_ = Path(base_dir) if isinstance(base_dir, str) else base_dir
+    if not base_dir_.exists():
+        base_dir_.mkdir(parents=True)
+
+    def get_chat_history(session_id: str) -> FileChatMessageHistory:
+        """Get a chat history from a session ID."""
+        if not _is_valid_identifier(session_id):
+            raise HTTPException(
+                status_code=400,
+                detail=f"Session ID `{session_id}` is not in a valid format. "
+                "Session ID must only contain alphanumeric characters, "
+                "hyphens, and underscores.",
+            )
+        file_path = base_dir_ / f"{session_id}.json"
+        return FileChatMessageHistory(str(file_path))
+
+    return get_chat_history
+
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+# Declare a chain
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You're an assistant by the name of Bob."),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", "{human_input}"),
+    ]
+)
+
+chain = prompt | ChatAnthropic(model="claude-2.1")
+
+
+class InputChat(BaseModel):
+    """Input for the chat endpoint."""
+
+    # The field extra defines a chat widget.
+    # As of 2024-02-05, this chat widget is not fully supported.
+    # It's included in documentation to show how it should be specified, but
+    # will not work until the widget is fully supported for history persistence
+    # on the backend.
+    human_input: str = Field(
+        ...,
+        description="The human input to the chat system.",
+        extra={"widget": {"type": "chat", "input": "human_input"}},
+    )
+
+
+chain_with_history = RunnableWithMessageHistory(
+    chain,
+    create_session_factory("chat_histories"),
+    input_messages_key="human_input",
+    history_messages_key="history",
+).with_types(input_type=InputChat)
+
+
+add_routes(
+    app,
+    chain_with_history,
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/chat_with_persistence_and_user/client.ipynb

@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chat History\n",
+    "\n",
+    "Here we'll be interacting with a server that's exposing a chat bot with message history being persisted on the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "conversation_id = str(uuid.uuid4())\n",
+    "chat = RemoteRunnable(\"http://localhost:8000/\", cookies={\"user_id\": \"eugene\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a prompt composed of a system message and a human message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Hello Eugene! I'm Bob, your virtual assistant. How can I assist you today?\")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke({\"human_input\": \"my name is eugene. what is your name?\"}, {'configurable': { 'conversation_id': conversation_id } })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Your name is Eugene. Is there something specific you would like assistance with, Eugene?')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke({\"human_input\": \"what was my name?\"}, {'configurable': { 'conversation_id': conversation_id } })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use different user but same conversation id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = RemoteRunnable(\"http://localhost:8000/\", cookies={\"user_id\": \"nuno\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"I apologize, but I don't have access to personal information about users. As an AI assistant, I prioritize user privacy and data protection.\")"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.invoke({\"human_input\": \"what was my name?\"}, {'configurable': { 'conversation_id': conversation_id }})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Of\n",
+      " course\n",
+      "!\n",
+      " Here\n",
+      " you\n",
+      " go\n",
+      ":\n",
+      "\n",
+      "\n",
+      "1\n",
+      ",\n",
+      " \n",
+      "2\n",
+      ",\n",
+      " \n",
+      "3\n",
+      ",\n",
+      " \n",
+      "4\n",
+      ",\n",
+      " \n",
+      "5\n",
+      ",\n",
+      " \n",
+      "6\n",
+      ",\n",
+      " \n",
+      "7\n",
+      ",\n",
+      " \n",
+      "8\n",
+      ",\n",
+      " \n",
+      "9\n",
+      ",\n",
+      " \n",
+      "10\n",
+      ".\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in chat.stream({'human_input': \"Can you count till 10?\"},  {'configurable': { 'conversation_id': conversation_id } }):\n",
+    "    print()\n",
+    "    print(chunk.content, end='', flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cd8e5a55-0295-41cd-a885-775e0403fd25'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[01;34mchat_histories/\u001b[0m\n",
+      "├── \u001b[01;34meugene\u001b[0m\n",
+      "│   └── cd8e5a55-0295-41cd-a885-775e0403fd25.json\n",
+      "└── \u001b[01;34mnuno\u001b[0m\n",
+      "    └── cd8e5a55-0295-41cd-a885-775e0403fd25.json\n",
+      "\n",
+      "2 directories, 2 files\n"
+     ]
+    }
+   ],
+   "source": [
+    "!tree chat_histories/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m[\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"my name is eugene. what is your name?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"Hello Eugene! I'm Bob, your virtual assistant. How can I assist you today?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"what was my name?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"Your name is Eugene. Is there something specific you would like assistance with, Eugene?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "\u001b[1;39m]\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat chat_histories/eugene/cd8e5a55-0295-41cd-a885-775e0403fd25.json | jq ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m[\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"what was my name?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"I apologize, but I don't have access to personal information about users. As an AI assistant, I prioritize user privacy and data protection.\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ai\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"Can you count till 10?\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"human\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[1;39m{\n",
+      "    \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"AIMessageChunk\"\u001b[0m\u001b[1;39m,\n",
+      "    \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
+      "      \u001b[0m\u001b[34;1m\"content\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"Of course! Here you go:\\n\\n1, 2, 3, 4, 5, 6, 7, 8, 9, 10.\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"additional_kwargs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"AIMessageChunk\"\u001b[0m\u001b[1;39m,\n",
+      "      \u001b[0m\u001b[34;1m\"example\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mfalse\u001b[0m\u001b[1;39m\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
+      "\u001b[1;39m]\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat chat_histories/nuno/cd8e5a55-0295-41cd-a885-775e0403fd25.json | jq ."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/chat_with_persistence_and_user/server.py

@@ -0,0 +1,182 @@
+#!/usr/bin/env python
+"""Example of a chat server with persistence handled on the backend.
+
+For simplicity, we're using file storage here -- to avoid the need to set up
+a database. This is obviously not a good idea for a production environment,
+but will help us to demonstrate the RunnableWithMessageHistory interface.
+
+We'll use cookies to identify the user. This will help illustrate how to
+fetch configuration from the request.
+"""
+import re
+from pathlib import Path
+from typing import Any, Callable, Dict, Union
+
+from fastapi import FastAPI, HTTPException, Request
+from langchain_community.chat_message_histories import FileChatMessageHistory
+from langchain_core import __version__
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import ConfigurableFieldSpec
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_openai import ChatOpenAI
+from typing_extensions import TypedDict
+
+from langserve import add_routes
+
+# Define the minimum required version as (0, 1, 0)
+# Earlier versions did not allow specifying custom config fields in
+# RunnableWithMessageHistory.
+MIN_VERSION_LANGCHAIN_CORE = (0, 1, 0)
+
+# Split the version string by "." and convert to integers
+LANGCHAIN_CORE_VERSION = tuple(map(int, __version__.split(".")))
+
+if LANGCHAIN_CORE_VERSION < MIN_VERSION_LANGCHAIN_CORE:
+    raise RuntimeError(
+        f"Minimum required version of langchain-core is {MIN_VERSION_LANGCHAIN_CORE}, "
+        f"but found {LANGCHAIN_CORE_VERSION}"
+    )
+
+
+def _is_valid_identifier(value: str) -> bool:
+    """Check if the value is a valid identifier."""
+    # Use a regular expression to match the allowed characters
+    valid_characters = re.compile(r"^[a-zA-Z0-9-_]+$")
+    return bool(valid_characters.match(value))
+
+
+def create_session_factory(
+    base_dir: Union[str, Path],
+) -> Callable[[str], BaseChatMessageHistory]:
+    """Create a factory that can retrieve chat histories.
+
+    The chat histories are keyed by user ID and conversation ID.
+
+    Args:
+        base_dir: Base directory to use for storing the chat histories.
+
+    Returns:
+        A factory that can retrieve chat histories keyed by user ID and conversation ID.
+    """
+    base_dir_ = Path(base_dir) if isinstance(base_dir, str) else base_dir
+    if not base_dir_.exists():
+        base_dir_.mkdir(parents=True)
+
+    def get_chat_history(user_id: str, conversation_id: str) -> FileChatMessageHistory:
+        """Get a chat history from a user id and conversation id."""
+        if not _is_valid_identifier(user_id):
+            raise ValueError(
+                f"User ID {user_id} is not in a valid format. "
+                "User ID must only contain alphanumeric characters, "
+                "hyphens, and underscores."
+                "Please include a valid cookie in the request headers called 'user-id'."
+            )
+        if not _is_valid_identifier(conversation_id):
+            raise ValueError(
+                f"Conversation ID {conversation_id} is not in a valid format. "
+                "Conversation ID must only contain alphanumeric characters, "
+                "hyphens, and underscores. Please provide a valid conversation id "
+                "via config. For example, "
+                "chain.invoke(.., {'configurable': {'conversation_id': '123'}})"
+            )
+
+        user_dir = base_dir_ / user_id
+        if not user_dir.exists():
+            user_dir.mkdir(parents=True)
+        file_path = user_dir / f"{conversation_id}.json"
+        return FileChatMessageHistory(str(file_path))
+
+    return get_chat_history
+
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+def _per_request_config_modifier(
+    config: Dict[str, Any], request: Request
+) -> Dict[str, Any]:
+    """Update the config"""
+    config = config.copy()
+    configurable = config.get("configurable", {})
+    # Look for a cookie named "user_id"
+    user_id = request.cookies.get("user_id", None)
+
+    if user_id is None:
+        raise HTTPException(
+            status_code=400,
+            detail="No user id found. Please set a cookie named 'user_id'.",
+        )
+
+    configurable["user_id"] = user_id
+    config["configurable"] = configurable
+    return config
+
+
+# Declare a chain
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You're an assistant by the name of Bob."),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", "{human_input}"),
+    ]
+)
+
+chain = prompt | ChatOpenAI()
+
+
+class InputChat(TypedDict):
+    """Input for the chat endpoint."""
+
+    human_input: str
+    """Human input"""
+
+
+chain_with_history = RunnableWithMessageHistory(
+    chain,
+    create_session_factory("chat_histories"),
+    input_messages_key="human_input",
+    history_messages_key="history",
+    history_factory_config=[
+        ConfigurableFieldSpec(
+            id="user_id",
+            annotation=str,
+            name="User ID",
+            description="Unique identifier for the user.",
+            default="",
+            is_shared=True,
+        ),
+        ConfigurableFieldSpec(
+            id="conversation_id",
+            annotation=str,
+            name="Conversation ID",
+            description="Unique identifier for the conversation.",
+            default="",
+            is_shared=True,
+        ),
+    ],
+).with_types(input_type=InputChat)
+
+
+add_routes(
+    app,
+    chain_with_history,
+    per_req_config_modifier=_per_request_config_modifier,
+    # Disable playground and batch
+    # 1) Playground we're passing information via headers, which is not supported via
+    #    the playground right now.
+    # 2) Disable batch to avoid users being confused. Batch will work fine
+    #    as long as users invoke it with multiple configs appropriately, but
+    #    without validation users are likely going to forget to do that.
+    #    In addition, there's likely little sense in support batch for a chatbot.
+    disabled_endpoints=["playground", "batch"],
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/configurable_agent_executor/client.ipynb

@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "Demo of a client interacting with a custom runnable executor that supports configuration.\n",
+    "\n",
+    "This server does not support invoke or batch! only stream and astream log! (see backend code.)\n",
+    "\n",
+    "The underlying backend code is just a demo in this case -- it's working around an existing bug, but uses \n",
+    "the opportunity to show how to create custom runnables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can interact with this via API directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "event: metadata\n",
+      "data: {\"run_id\": \"5e6ce60a-95c4-4fe5-8c4a-ec1d347afd83\"}\n",
+      "\n",
+      "event: data\n",
+      "data: {\"actions\":[{\"tool\":\"get_eugene_thoughts\",\"tool_input\":{\"query\":\"cats\"},\"log\":\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\",\"type\":\"AgentActionMessageLog\",\"message_log\":[{\"content\":\"\",\"additional_kwargs\":{\"function_call\":{\"name\":\"get_eugene_thoughts\",\"arguments\":\"{\\n  \\\"query\\\": \\\"cats\\\"\\n}\"}},\"type\":\"ai\",\"example\":false}]}],\"messages\":[{\"content\":\"\",\"additional_kwargs\":{\"function_call\":{\"name\":\"get_eugene_thoughts\",\"arguments\":\"{\\n  \\\"query\\\": \\\"cats\\\"\\n}\"}},\"type\":\"ai\",\"example\":false}]}\n",
+      "\n",
+      "event: data\n",
+      "data: {\"steps\":[{\"action\":{\"tool\":\"get_eugene_thoughts\",\"tool_input\":{\"query\":\"cats\"},\"log\":\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\",\"type\":\"AgentActionMessageLog\",\"message_log\":[{\"content\":\"\",\"additional_kwargs\":{\"function_call\":{\"name\":\"get_eugene_thoughts\",\"arguments\":\"{\\n  \\\"query\\\": \\\"cats\\\"\\n}\"}},\"type\":\"ai\",\"example\":false}]},\"observation\":[{\"page_content\":\"cats like fish\",\"metadata\":{},\"type\":\"Document\"},{\"page_content\":\"dogs like sticks\",\"metadata\":{},\"type\":\"Document\"}]}],\"messages\":[{\"content\":\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\",\"additional_kwargs\":{},\"type\":\"function\",\"name\":\"get_eugene_thoughts\"}]}\n",
+      "\n",
+      "event: data\n",
+      "data: {\"output\":\"Eugene thinks that cats like fish.\",\"messages\":[{\"content\":\"Eugene thinks that cats like fish.\",\"additional_kwargs\":{},\"type\":\"ai\",\"example\":false}]}\n",
+      "\n",
+      "event: end\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "inputs = {\"input\": {\"input\": \"what does eugene think of cats?\"}}\n",
+    "response = requests.post(\"http://localhost:8000/stream\", json=inputs)\n",
+    "\n",
+    "print(response.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also interact with this via the RemoteRunnable interface (to use in other chains)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remote runnable has the same interface as local runnables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'output': 'Hello! How can I assist you today?', 'messages': [AIMessage(content='Hello! How can I assist you today?')]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream({\"input\": \"hi!\"}):\n",
+    "    print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RunLogPatch({'op': 'replace',\n",
+      "  'path': '',\n",
+      "  'value': {'final_output': None,\n",
+      "            'id': '9f415b49-ba69-4fdf-9b9c-5ccc1805487f',\n",
+      "            'logs': {},\n",
+      "            'streamed_output': []}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '95a0ba72-9511-4fff-8b8c-410e7108ee60',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableSequence',\n",
+      "            'start_time': '2024-01-06T03:12:42.213+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': [],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'a0afa5a6-a408-4a2d-83f8-8e4c8193f963',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableParallel<input,agent_scratchpad>',\n",
+      "            'start_time': '2024-01-06T03:12:42.214+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:1'],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '2aed7d13-2d0b-4c37-a4b3-d3f266bbf7e6',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-06T03:12:42.215+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:input'],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '9328b5cf-fd7b-48fe-affb-2973643189e9',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-06T03:12:42.215+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:agent_scratchpad'],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>/final_output',\n",
+      "  'value': {'output': 'what does eugene think about cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.217+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:2/final_output',\n",
+      "  'value': {'output': []}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.217+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>/final_output',\n",
+      "  'value': {'agent_scratchpad': [],\n",
+      "            'input': 'what does eugene think about cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.218+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'a08bec71-bf7f-46c5-b082-14f7bff421cd',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'ChatPromptTemplate',\n",
+      "            'start_time': '2024-01-06T03:12:42.219+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:2'],\n",
+      "            'type': 'prompt'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate/final_output',\n",
+      "  'value': {'messages': [SystemMessage(content='You are a helpful assistant.'),\n",
+      "                         HumanMessage(content='what does eugene think about cats?')]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.219+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'bf859261-467b-44b3-84fb-80d8c9e9dff2',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'LLM',\n",
+      "            'start_time': '2024-01-06T03:12:42.221+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:3'],\n",
+      "            'type': 'llm'}})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': ''}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '{\\n'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' '}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' \"'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': 'query'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '\":'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' \"'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': 'cats'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '\"\\n'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '}'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM/final_output',\n",
+      "  'value': LLMResult(generations=[[ChatGeneration(generation_info={'finish_reason': 'function_call'}, message=AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}))]], llm_output=None, run=None)},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.806+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'd635ccbb-1db3-4515-b1ca-0c14752d361a',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'OpenAIFunctionsAgentOutputParser',\n",
+      "            'start_time': '2024-01-06T03:12:42.807+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:4'],\n",
+      "            'type': 'parser'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser/final_output',\n",
+      "  'value': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.809+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence/final_output',\n",
+      "  'value': {'output': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence/end_time',\n",
+      "  'value': '2024-01-06T03:12:42.809+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])],\n",
+      "            'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}},\n",
+      " {'op': 'replace',\n",
+      "  'path': '/final_output',\n",
+      "  'value': {'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])],\n",
+      "            'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/get_eugene_thoughts',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '5eecdbe8-3374-4f0e-8ae8-d8a7e2394ae1',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'get_eugene_thoughts',\n",
+      "            'start_time': '2024-01-06T03:12:42.810+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': [],\n",
+      "            'type': 'tool'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/get_eugene_thoughts/final_output',\n",
+      "  'value': {'output': \"[Document(page_content='cats like fish'), \"\n",
+      "                      \"Document(page_content='dogs like sticks')]\"}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/get_eugene_thoughts/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.095+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'messages': [FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')],\n",
+      "            'steps': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]),\n",
+      "                       'observation': [Document(page_content='cats like fish'),\n",
+      "                                       Document(page_content='dogs like sticks')]}]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/steps',\n",
+      "  'value': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]),\n",
+      "             'observation': [Document(page_content='cats like fish'),\n",
+      "                             Document(page_content='dogs like sticks')]}]},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/messages/1',\n",
+      "  'value': FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '89bf8195-df48-4fde-81b0-9e1982051caa',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableSequence',\n",
+      "            'start_time': '2024-01-06T03:12:43.097+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': [],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '567c178a-4897-4865-af27-7122432df7bd',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'RunnableParallel<input,agent_scratchpad>',\n",
+      "            'start_time': '2024-01-06T03:12:43.098+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:1'],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:3',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '03530072-e5b2-4d7e-b875-f77e3d47481b',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-06T03:12:43.099+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:input'],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:4',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': 'c554e615-1b70-4dc1-a4aa-aa09ccdacb09',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': '<lambda>',\n",
+      "            'start_time': '2024-01-06T03:12:43.099+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['map:key:agent_scratchpad'],\n",
+      "            'type': 'chain'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:3/final_output',\n",
+      "  'value': {'output': 'what does eugene think about cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>:3/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.100+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/<lambda>:4/final_output',\n",
+      "  'value': {'output': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}),\n",
+      "                       FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/<lambda>:4/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.100+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>:2/final_output',\n",
+      "  'value': {'agent_scratchpad': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}),\n",
+      "                                 FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')],\n",
+      "            'input': 'what does eugene think about cats?'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableParallel<input,agent_scratchpad>:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.101+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '724c5bf8-d475-45ec-a69a-a4f188dc405f',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'ChatPromptTemplate',\n",
+      "            'start_time': '2024-01-06T03:12:43.101+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:2'],\n",
+      "            'type': 'prompt'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate:2/final_output',\n",
+      "  'value': {'messages': [SystemMessage(content='You are a helpful assistant.'),\n",
+      "                         HumanMessage(content='what does eugene think about cats?'),\n",
+      "                         AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}),\n",
+      "                         FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/ChatPromptTemplate:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.102+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '4542ae99-f854-41be-9669-41d8cd7dfb24',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'LLM',\n",
+      "            'start_time': '2024-01-06T03:12:43.103+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:3'],\n",
+      "            'type': 'llm'}})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': 'E'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='E')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': 'ug'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='ug')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': 'ene'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='ene')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' thinks'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' thinks')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' that'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' that')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' cats'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' cats')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' like'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' like')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' fish'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' fish')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': '.'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='.')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM:2/final_output',\n",
+      "  'value': LLMResult(generations=[[ChatGeneration(text='Eugene thinks that cats like fish.', generation_info={'finish_reason': 'stop'}, message=AIMessage(content='Eugene thinks that cats like fish.'))]], llm_output=None, run=None)},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.776+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '281c8ae8-dbc6-433f-86c8-20b86b3c8eee',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'OpenAIFunctionsAgentOutputParser',\n",
+      "            'start_time': '2024-01-06T03:12:43.776+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:4'],\n",
+      "            'type': 'parser'}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser:2/final_output',\n",
+      "  'value': AgentFinish(return_values={'output': 'Eugene thinks that cats like fish.'}, log='Eugene thinks that cats like fish.')},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/OpenAIFunctionsAgentOutputParser:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.777+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence:2/final_output',\n",
+      "  'value': {'output': AgentFinish(return_values={'output': 'Eugene thinks that cats like fish.'}, log='Eugene thinks that cats like fish.')}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/RunnableSequence:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:43.778+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'messages': [AIMessage(content='Eugene thinks that cats like fish.')],\n",
+      "            'output': 'Eugene thinks that cats like fish.'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/output',\n",
+      "  'value': 'Eugene thinks that cats like fish.'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/messages/2',\n",
+      "  'value': AIMessage(content='Eugene thinks that cats like fish.')})\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream_log({\"input\": \"what does eugene think about cats?\"}):\n",
+    "    print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RunLogPatch({'op': 'replace',\n",
+      "  'path': '',\n",
+      "  'value': {'final_output': None,\n",
+      "            'id': '51c65021-1b40-4a45-81b3-95fc5c5b545a',\n",
+      "            'logs': {},\n",
+      "            'streamed_output': []}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '7a34b2a3-d6c0-4a0b-9939-2bde70a98958',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'LLM',\n",
+      "            'start_time': '2024-01-06T03:12:43.812+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:3'],\n",
+      "            'type': 'llm'}})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': ''}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '{\\n'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' '}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' \"'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': 'query'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '\":'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': ' \"'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': 'cats'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '\"\\n'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='', additional_kwargs={'function_call': {'arguments': '}'}})})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM/final_output',\n",
+      "  'value': LLMResult(generations=[[ChatGeneration(generation_info={'finish_reason': 'function_call'}, message=AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}}))]], llm_output=None, run=None)},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM/end_time',\n",
+      "  'value': '2024-01-06T03:12:44.588+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])],\n",
+      "            'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}},\n",
+      " {'op': 'replace',\n",
+      "  'path': '/final_output',\n",
+      "  'value': {'actions': [AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})])],\n",
+      "            'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]}})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'messages': [FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')],\n",
+      "            'steps': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]),\n",
+      "                       'observation': [Document(page_content='cats like fish'),\n",
+      "                                       Document(page_content='dogs like sticks')]}]}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/steps',\n",
+      "  'value': [{'action': AgentActionMessageLog(tool='get_eugene_thoughts', tool_input={'query': 'cats'}, log=\"\\nInvoking: `get_eugene_thoughts` with `{'query': 'cats'}`\\n\\n\\n\", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_eugene_thoughts', 'arguments': '{\\n  \"query\": \"cats\"\\n}'}})]),\n",
+      "             'observation': [Document(page_content='cats like fish'),\n",
+      "                             Document(page_content='dogs like sticks')]}]},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/messages/1',\n",
+      "  'value': FunctionMessage(content=\"[Document(page_content='cats like fish'), Document(page_content='dogs like sticks')]\", name='get_eugene_thoughts')})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM:2',\n",
+      "  'value': {'end_time': None,\n",
+      "            'final_output': None,\n",
+      "            'id': '8ff3b248-fb7e-4305-bd57-54e21a85bacb',\n",
+      "            'metadata': {'__langserve_endpoint': 'stream_log',\n",
+      "                         '__langserve_version': '0.0.37',\n",
+      "                         '__useragent': 'python-httpx/0.25.2'},\n",
+      "            'name': 'LLM',\n",
+      "            'start_time': '2024-01-06T03:12:44.756+00:00',\n",
+      "            'streamed_output': [],\n",
+      "            'streamed_output_str': [],\n",
+      "            'tags': ['seq:step:3'],\n",
+      "            'type': 'llm'}})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': 'E'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='E')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': 'ug'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='ug')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': 'ene'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='ene')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' thinks'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' thinks')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' that'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' that')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' cats'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' cats')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' like'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' like')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ' fish'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content=' fish')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': '.'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='.')})\n",
+      "RunLogPatch({'op': 'add', 'path': '/logs/LLM:2/streamed_output_str/-', 'value': ''},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/streamed_output/-',\n",
+      "  'value': AIMessageChunk(content='')})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/logs/LLM:2/final_output',\n",
+      "  'value': LLMResult(generations=[[ChatGeneration(text='Eugene thinks that cats like fish.', generation_info={'finish_reason': 'stop'}, message=AIMessage(content='Eugene thinks that cats like fish.'))]], llm_output=None, run=None)},\n",
+      " {'op': 'add',\n",
+      "  'path': '/logs/LLM:2/end_time',\n",
+      "  'value': '2024-01-06T03:12:45.171+00:00'})\n",
+      "RunLogPatch({'op': 'add',\n",
+      "  'path': '/streamed_output/-',\n",
+      "  'value': {'messages': [AIMessage(content='Eugene thinks that cats like fish.')],\n",
+      "            'output': 'Eugene thinks that cats like fish.'}},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/output',\n",
+      "  'value': 'Eugene thinks that cats like fish.'},\n",
+      " {'op': 'add',\n",
+      "  'path': '/final_output/messages/2',\n",
+      "  'value': AIMessage(content='Eugene thinks that cats like fish.')})\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in remote_runnable.astream_log({\"input\": \"what does eugene think about cats?\"}, include_names=[\"LLM\"]):\n",
+    "    print(chunk)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/configurable_agent_executor/server.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python
+"""An example that shows how to create a custom agent executor like Runnable.
+
+At the time of writing, there is a bug in the current AgentExecutor that
+prevents it from correctly propagating configuration of the underlying
+runnable. While that bug should be fixed, this is an example shows
+how to create a more complex custom runnable.
+
+Please see documentation for custom agent streaming here:
+
+https://python.langchain.com/docs/modules/agents/how_to/streaming#stream-tokens
+
+**ATTENTION**
+To support streaming individual tokens you will need to manually set the streaming=True
+on the LLM and use the stream_log endpoint rather than stream endpoint.
+"""
+from typing import Any, AsyncIterator, Dict, List, Optional, cast
+
+from fastapi import FastAPI
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import (
+    ConfigurableField,
+    ConfigurableFieldSpec,
+    Runnable,
+    RunnableConfig,
+)
+from langchain_core.runnables.utils import Input, Output
+from langchain_core.tools import tool
+from langchain_core.utils.function_calling import format_tool_to_openai_function
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import BaseModel
+
+from langserve import add_routes
+
+vectorstore = FAISS.from_texts(
+    ["cats like fish", "dogs like sticks"], embedding=OpenAIEmbeddings()
+)
+retriever = vectorstore.as_retriever()
+
+
+@tool
+def get_eugene_thoughts(query: str) -> list:
+    """Returns Eugene's thoughts on a topic."""
+    return retriever.get_relevant_documents(query)
+
+
+tools = [get_eugene_thoughts]
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful assistant."),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+# We need to set streaming=True on the LLM to support streaming individual tokens.
+# when using the stream_log endpoint.
+# .stream for agents streams action observation pairs not individual tokens.
+llm = ChatOpenAI(temperature=0, streaming=True).configurable_fields(
+    temperature=ConfigurableField(
+        id="llm_temperature",
+        name="LLM Temperature",
+        description="The temperature of the LLM",
+    )
+)
+
+llm_with_tools = llm.bind(
+    functions=[format_tool_to_openai_function(t) for t in tools]
+).with_config({"run_name": "LLM"})
+
+
+class CustomAgentExecutor(Runnable):
+    """A custom runnable that will be used by the agent executor."""
+
+    def __init__(self, **kwargs):
+        """Initialize the runnable."""
+        super().__init__(**kwargs)
+        self.agent = (
+            {
+                "input": lambda x: x["input"],
+                "agent_scratchpad": lambda x: format_to_openai_functions(
+                    x["intermediate_steps"]
+                ),
+            }
+            | prompt
+            | llm_with_tools
+            | OpenAIFunctionsAgentOutputParser()
+        )
+
+    def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output:
+        """Will not be used."""
+        raise NotImplementedError()
+
+    @property
+    def config_specs(self) -> List[ConfigurableFieldSpec]:
+        return self.agent.config_specs
+
+    async def astream(
+        self,
+        input: Input,
+        config: Optional[RunnableConfig] = None,
+        **kwargs: Optional[Any],
+    ) -> AsyncIterator[Output]:
+        """Stream the agent's output."""
+        configurable = cast(Dict[str, Any], config.pop("configurable", {}))
+
+        if configurable:
+            configured_agent = self.agent.with_config(
+                {
+                    "configurable": configurable,
+                }
+            )
+        else:
+            configured_agent = self.agent
+
+        executor = AgentExecutor(
+            agent=configured_agent,
+            tools=tools,
+        ).with_config({"run_name": "executor"})
+
+        async for output in executor.astream(input, config=config, **kwargs):
+            yield output
+
+
+app = FastAPI()
+
+
+# We need to add these input/output schemas because the current AgentExecutor
+# is lacking in schemas.
+class Input(BaseModel):
+    input: str
+
+
+class Output(BaseModel):
+    output: Any
+
+
+runnable = CustomAgentExecutor()
+
+# Add routes to the app for using the custom agent executor.
+add_routes(
+    app,
+    runnable.with_types(input_type=Input, output_type=Output),
+    disabled_endpoints=["invoke", "batch"],  # not implemented
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/configurable_chain/client.ipynb

@@ -23,14 +23,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "inputs = {\"input\": {\"topic\": \"sports\"}}\n",
-    "response = requests.post(\"http://localhost:8000/configurable_temp/invoke\", json=inputs)\n",
-    "\n",
-    "response.json()"
-   ]
+   "source": ["import requests\n\ninputs = {\"input\": {\"topic\": \"sports\"}}\nresponse = requests.post(\"http://localhost:8000/configurable_temp/invoke\", json=inputs)\n\nresponse.json()"]
   },
   {
    "cell_type": "markdown",
@@ -46,11 +39,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "from langserve import RemoteRunnable\n",
-    "\n",
-    "remote_runnable = RemoteRunnable(\"http://localhost:8000/configurable_temp\")"
-   ]
+   "source": ["from langserve import RemoteRunnable\n\nremote_runnable = RemoteRunnable(\"http://localhost:8000/configurable_temp\")"]
   },
   {
    "cell_type": "markdown",
@@ -66,9 +55,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "response = await remote_runnable.ainvoke({\"topic\": \"sports\"})"
-   ]
+   "source": ["response = await remote_runnable.ainvoke({\"topic\": \"sports\"})"]
   },
   {
    "cell_type": "markdown",
@@ -84,11 +71,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "from langchain.schema.runnable.config import RunnableConfig\n",
-    "\n",
-    "remote_runnable.batch([{\"topic\": \"sports\"}, {\"topic\": \"cars\"}])"
-   ]
+   "source": ["from langchain_core.runnables import RunnableConfig\n\nremote_runnable.batch([{\"topic\": \"sports\"}, {\"topic\": \"cars\"}])"]
   },
   {
    "cell_type": "markdown",
@@ -104,10 +87,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "async for chunk in remote_runnable.astream({\"topic\": \"bears, but a bit verbose\"}):\n",
-    "    print(chunk, end=\"\", flush=True)"
-   ]
+   "source": ["async for chunk in remote_runnable.astream({\"topic\": \"bears, but a bit verbose\"}):\n    print(chunk, end=\"\", flush=True)"]
   },
   {
    "cell_type": "markdown",
@@ -157,14 +137,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "await remote_runnable.ainvoke(\n",
-    "    {\"topic\": \"sports\"},\n",
-    "    config={\n",
-    "        \"configurable\": {\"prompt\": \"how to say {topic} in french\", \"llm\": \"low_temp\"}\n",
-    "    },\n",
-    ")"
-   ]
+   "source": ["await remote_runnable.ainvoke(\n    {\"topic\": \"sports\"},\n    config={\n        \"configurable\": {\"prompt\": \"how to say {topic} in french\", \"llm\": \"low_temp\"}\n    },\n)"]
   },
   {
    "cell_type": "markdown",
@@ -221,13 +194,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# The model will fail with an auth error\n",
-    "unauthenticated_response = requests.post(\n",
-    "    \"http://localhost:8000/auth_from_header/invoke\", json={\"input\": \"hello\"}\n",
-    ")\n",
-    "unauthenticated_response.json()"
-   ]
+   "source": ["# The model will fail with an auth error\nunauthenticated_response = requests.post(\n    \"http://localhost:8000/auth_from_header/invoke\", json={\"input\": \"hello\"}\n)\nunauthenticated_response.json()"]
   },
   {
    "cell_type": "markdown",
@@ -244,25 +211,14 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# The model will succeed as long as the above shell script is run previously\n",
-    "import os\n",
-    "\n",
-    "test_key = os.environ[\"TEST_API_KEY\"]\n",
-    "authenticated_response = requests.post(\n",
-    "    \"http://localhost:8000/auth_from_header/invoke\",\n",
-    "    json={\"input\": \"hello\"},\n",
-    "    headers={\"x-api-key\": test_key},\n",
-    ")\n",
-    "authenticated_response.json()"
-   ]
+   "source": ["# The model will succeed as long as the above shell script is run previously\nimport os\n\ntest_key = os.environ[\"TEST_API_KEY\"]\nauthenticated_response = requests.post(\n    \"http://localhost:8000/auth_from_header/invoke\",\n    json={\"input\": \"hello\"},\n    headers={\"x-api-key\": test_key},\n)\nauthenticated_response.json()"]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [""]
   }
  ],
  "metadata": {

examples/configurable_chain/server.py

@@ -10,10 +10,10 @@ from typing import Any, Dict
 
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import PromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import ConfigurableField
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import ConfigurableField
+from langchain_openai import ChatOpenAI
 
 from langserve import add_routes
 

examples/configurable_retrieval/server.py

@@ -3,20 +3,21 @@
 from typing import Any, Iterable, List, Optional, Type
 
 from fastapi import FastAPI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.schema import Document
-from langchain.schema.embeddings import Embeddings
-from langchain.schema.retriever import BaseRetriever
-from langchain.schema.runnable import (
+from langchain.schema.vectorstore import VST
+from langchain_community.vectorstores import FAISS
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_core.retrievers import BaseRetriever
+from langchain_core.runnables import (
     ConfigurableFieldSingleOption,
     RunnableConfig,
     RunnableSerializable,
 )
-from langchain.schema.vectorstore import VST
-from langchain.vectorstores import FAISS, VectorStore
+from langchain_core.vectorstores import VectorStore
+from langchain_openai import OpenAIEmbeddings
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 vectorstore1 = FAISS.from_texts(
     ["cats like fish", "dogs like sticks"], embedding=OpenAIEmbeddings()

examples/conversational_retrieval_chain/server.py

@@ -13,17 +13,14 @@ from operator import itemgetter
 from typing import List, Tuple
 
 from fastapi import FastAPI
-from langchain.chat_models import ChatOpenAI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.prompts import ChatPromptTemplate
-from langchain.prompts.prompt import PromptTemplate
-from langchain.schema import format_document
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnableMap, RunnablePassthrough
-from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, format_document
+from langchain_core.runnables import RunnableMap, RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 _TEMPLATE = """Given the following conversation and a follow up question, rephrase the 
 follow up question to be a standalone question, in its original language.

examples/file_processing/server.py

@@ -15,10 +15,10 @@ allowing one to upload a binary file using the langserve playground UI.
 import base64
 
 from fastapi import FastAPI
-from langchain.document_loaders.blob_loaders import Blob
-from langchain.document_loaders.parsers.pdf import PDFMinerParser
-from langchain.pydantic_v1 import Field
-from langchain.schema.runnable import RunnableLambda
+from langchain_community.document_loaders.parsers.pdf import PDFMinerParser
+from langchain_core.document_loaders import Blob
+from langchain_core.runnables import RunnableLambda
+from pydantic import Field
 
 from langserve import CustomUserType, add_routes
 

examples/llm/client.ipynb

@@ -16,9 +16,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "from langchain.prompts.chat import ChatPromptTemplate"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate"]
   },
   {
    "cell_type": "code",
@@ -27,12 +25,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "from langserve import RemoteRunnable\n",
-    "\n",
-    "openai_llm = RemoteRunnable(\"http://localhost:8000/openai/\")\n",
-    "anthropic = RemoteRunnable(\"http://localhost:8000/anthropic/\")"
-   ]
+   "source": ["from langserve import RemoteRunnable\n\nopenai_llm = RemoteRunnable(\"http://localhost:8000/openai/\")\nanthropic = RemoteRunnable(\"http://localhost:8000/anthropic/\")"]
   },
   {
    "cell_type": "markdown",
@@ -48,18 +41,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a highly educated person who loves to use big words. \"\n",
-    "            + \"You are also concise. Never answer in more than three sentences.\",\n",
-    "        ),\n",
-    "        (\"human\", \"Tell me about your favorite novel\"),\n",
-    "    ]\n",
-    ").format_messages()"
-   ]
+   "source": ["prompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a highly educated person who loves to use big words. \"\n            + \"You are also concise. Never answer in more than three sentences.\",\n        ),\n        (\"human\", \"Tell me about your favorite novel\"),\n    ]\n).format_messages()"]
   },
   {
    "cell_type": "markdown",
@@ -86,9 +68,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "anthropic.invoke(prompt)"
-   ]
+   "source": ["anthropic.invoke(prompt)"]
   },
   {
    "cell_type": "code",
@@ -97,9 +77,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "openai_llm.invoke(prompt)"
-   ]
+   "source": ["openai_llm.invoke(prompt)"]
   },
   {
    "cell_type": "markdown",
@@ -126,9 +104,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "await openai_llm.ainvoke(prompt)"
-   ]
+   "source": ["await openai_llm.ainvoke(prompt)"]
   },
   {
    "cell_type": "code",
@@ -149,9 +125,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "anthropic.batch([prompt, prompt])"
-   ]
+   "source": ["anthropic.batch([prompt, prompt])"]
   },
   {
    "cell_type": "code",
@@ -172,9 +146,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "await anthropic.abatch([prompt, prompt])"
-   ]
+   "source": ["await anthropic.abatch([prompt, prompt])"]
   },
   {
    "cell_type": "markdown",
@@ -198,10 +170,7 @@
      ]
     }
    ],
-   "source": [
-    "for chunk in anthropic.stream(prompt):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
+   "source": ["for chunk in anthropic.stream(prompt):\n    print(chunk.content, end=\"\", flush=True)"]
   },
   {
    "cell_type": "code",
@@ -218,19 +187,14 @@
      ]
     }
    ],
-   "source": [
-    "async for chunk in anthropic.astream(prompt):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
+   "source": ["async for chunk in anthropic.astream(prompt):\n    print(chunk.content, end=\"\", flush=True)"]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain.schema.runnable import RunnablePassthrough"
-   ]
+   "source": ["from langchain_core.runnables import RunnablePassthrough"]
   },
   {
    "cell_type": "code",
@@ -239,37 +203,7 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "comedian_chain = (\n",
-    "    ChatPromptTemplate.from_messages(\n",
-    "        [\n",
-    "            (\n",
-    "                \"system\",\n",
-    "                \"You are a comedian that sometimes tells funny jokes and other times you just state facts that are not funny. Please either tell a joke or state fact now but only output one.\",\n",
-    "            ),\n",
-    "        ]\n",
-    "    )\n",
-    "    | openai_llm\n",
-    ")\n",
-    "\n",
-    "joke_classifier_chain = (\n",
-    "    ChatPromptTemplate.from_messages(\n",
-    "        [\n",
-    "            (\n",
-    "                \"system\",\n",
-    "                \"Please determine if the joke is funny. Say `funny` if it's funny and `not funny` if not funny. Then repeat the first five words of the joke for reference...\",\n",
-    "            ),\n",
-    "            (\"human\", \"{joke}\"),\n",
-    "        ]\n",
-    "    )\n",
-    "    | anthropic\n",
-    ")\n",
-    "\n",
-    "\n",
-    "chain = {\"joke\": comedian_chain} | RunnablePassthrough.assign(\n",
-    "    classification=joke_classifier_chain\n",
-    ")"
-   ]
+   "source": ["comedian_chain = (\n    ChatPromptTemplate.from_messages(\n        [\n            (\n                \"system\",\n                \"You are a comedian that sometimes tells funny jokes and other times you just state facts that are not funny. Please either tell a joke or state fact now but only output one.\",\n            ),\n        ]\n    )\n    | openai_llm\n)\n\njoke_classifier_chain = (\n    ChatPromptTemplate.from_messages(\n        [\n            (\n                \"system\",\n                \"Please determine if the joke is funny. Say `funny` if it's funny and `not funny` if not funny. Then repeat the first five words of the joke for reference...\",\n            ),\n            (\"human\", \"{joke}\"),\n        ]\n    )\n    | anthropic\n)\n\n\nchain = {\"joke\": comedian_chain} | RunnablePassthrough.assign(\n    classification=joke_classifier_chain\n)"]
   },
   {
    "cell_type": "code",
@@ -290,9 +224,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "chain.invoke({})"
-   ]
+   "source": ["chain.invoke({})"]
   }
  ],
  "metadata": {

examples/llm/server.py

@@ -2,7 +2,8 @@
 """Example LangChain server exposes multiple runnables (LLMs in this case)."""
 
 from fastapi import FastAPI
-from langchain.chat_models import ChatAnthropic, ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_openai import ChatOpenAI
 
 from langserve import add_routes
 
@@ -14,12 +15,12 @@ app = FastAPI(
 
 add_routes(
     app,
-    ChatOpenAI(),
+    ChatOpenAI(model="gpt-3.5-turbo-0125"),
     path="/openai",
 )
 add_routes(
     app,
-    ChatAnthropic(),
+    ChatAnthropic(model="claude-3-haiku-20240307"),
     path="/anthropic",
 )
 

examples/local_llm/client.ipynb

@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Local LLM\n",
+    "\n",
+    "Here, we'll use a server that's serving a local LLM.\n",
+    "\n",
+    "**Attention** This is OK for prototyping / dev usage, but should not be used for production cases when there might be concurrent requests from different users. As of the time of writing, Ollama is designed for single user and cannot handle concurrent requests see this issue: https://github.com/ollama/ollama/issues/358"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": ["from langchain_core.prompts import ChatPromptTemplate"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": ["from langserve import RemoteRunnable\n\nmodel = RemoteRunnable(\"http://localhost:8000/ollama/\")"]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's test out the standard interface of a chat model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": ["prompt = \"Tell me a 3 sentence story about a cat.\""]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"\\nSure! Here is a three sentence story about a cat:\\n\\nMittens the cat purred contentedly on the windowsill, basking in the warm sunlight. Suddenly, a bird perched nearby and Mittens' ears perked up, ready to pounce. With lightning quick reflexes, Mittens leapt into the air, but the bird had flown away, leaving Mittens to settle for just lounging in the sun once again.\")"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": ["model.invoke(prompt)"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"\\nSure! Here is a three sentence story about a cat:\\n\\nMittens the cat purred contentedly on the windowsill, basking in the warm sunlight. Suddenly, a bird flew by, catching Mittens' attention and causing her to leap into action. With lightning quick reflexes, Mittens pounced on the bird, but it flew away just in time, leaving Mittens frustrated but still purring happily.\")"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": ["await model.ainvoke(prompt)"]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Batched API works, but b/c ollama does not support parallelism, it's no faster than using .invoke twice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.65 ms, sys: 6.57 ms, total: 14.2 ms\n",
+      "Wall time: 5.51 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content='\\nSure! Here is a three sentence story about a cat:\\n\\nMr. Whiskers was a sleek black cat with bright green eyes. He spent his days lounging in the sunbeams that streamed through the living room window, chasing the occasional fly, and purring contentedly. Despite his lazy demeanor, Mr. Whiskers was always on the lookout for a warm lap to curl up in.'),\n",
+       " AIMessage(content='\\nSure! Here is a three sentence story about a cat:\\n\\nMittens the cat purred contentedly on the windowsill, basking in the warm sunlight that streamed through the glass. Suddenly, a tiny bird perched on the ledge outside, tweeting nervously as it eyed the cat with suspicion. Without hesitation, Mittens pounced, snatching the bird in mid-air and devouring it in one quick motion.')]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": ["%%time\nmodel.batch([prompt, prompt])"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.72 ms, sys: 7.59 ms, total: 17.3 ms\n",
+      "Wall time: 5.56 s\n"
+     ]
+    }
+   ],
+   "source": ["%%time\nfor _ in range(2):\n    model.invoke(prompt)"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\"\\nSure, here's a three sentence story about a cat:\\n\\nMittens the cat purred contentedly on the windowsill, basking in the warm sunlight that streamed through the glass. Her bright green eyes sparkled as she watched a bird flit and flutter outside, wishing she could join it in its flight. Just then, her owner entered the room with a bowl of creamy milk, causing Mittens to jump down from the windowsill and rub against their legs in excitement.\"),\n",
+       " AIMessage(content='\\nSure! Here is a three sentence story about a cat:\\n\\nMittens the cat purred contentedly on my lap, her soft fur a soothing balm for my frazzled nerves. As I stroked her back, she gazed up at me with big, round eyes, purring even louder. It was hard to resist the charm of this little ball of fluff, and I found myself smiling and scratching behind her ears.')]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": ["await model.abatch([prompt, prompt])"]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Streaming is available by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "|S|ure|,| here| is| a| |3| sentence| story| about| a| cat|:|\n",
+      "|\n",
+      "|M|itt|ens| the| cat| pur|red| content|edly| on| the| windows|ill|,| her| tail| tw|itch|ing| as| she| watched| the| birds| outside|.| Sud|den|ly|,| a| squ|ir|rel| sc|am|per|ed| by| and| Mitt|ens| was| on| high| alert|,| her| ears| per|ked| up| and| ready| to| p|ounce|.| With| light|ning| quick| ref|lex|es|,| Mitt|ens| jump|ed| from| the| windows|ill| and| ch|ased| after| the| squ|ir|rel|,| her| tail| streaming| behind| her|.||"
+     ]
+    }
+   ],
+   "source": ["for chunk in model.stream(prompt):\n    print(chunk.content, end=\"|\", flush=True)"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "|The| cat| pur|red| content|edly| on| my| lap|,| its| soft| fur| a| so|othing| bal|m| for| my| fra|zz|led| n|erves|.| As| I| st|rok|ed| its| back|,| it| gaz|ed| up| at| me| with| soul|ful| eyes|,| p|urr|ing| loud|ly| in| appro|val|.| In| that| moment|,| all| was| right| with| the| world|.||"
+     ]
+    }
+   ],
+   "source": ["async for chunk in model.astream(prompt):\n    print(chunk.content, end=\"|\", flush=True)"]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And so is the event stream API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'event': 'on_chat_model_start', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'name': '/ollama', 'tags': [], 'metadata': {}, 'data': {'input': 'Tell me a 3 sentence story about a cat.'}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content='\\n')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content='S')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content='ure')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=',')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=' here')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=' is')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=' a')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=' ')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content='3')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=' sentence')}}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '6c2bdfc1-d482-4861-886c-c737a50771c3', 'tags': [], 'metadata': {}, 'name': '/ollama', 'data': {'chunk': AIMessageChunk(content=' story')}}\n",
+      "...\n"
+     ]
+    }
+   ],
+   "source": ["i = 0\nasync for event in model.astream_events(prompt, version='v1'):\n    print(event)\n    if i > 10:\n        print('...')\n        break\n    i += 1"]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/local_llm/server.py

@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+"""Example LangChain Server that runs a local llm.
+
+**Attention** This is OK for prototyping / dev usage, but should not be used
+for production cases when there might be concurrent requests from different
+users. As of the time of writing, Ollama is designed for single user and cannot
+handle concurrent requests see this issue: 
+https://github.com/ollama/ollama/issues/358
+
+When deploying local models, make sure you understand whether the model is able
+to handle concurrent requests or not. If concurrent requests are not handled
+properly, the server will either crash or will just not be able to handle more
+than one user at a time.
+"""
+from fastapi import FastAPI
+from langchain_community.chat_models import ChatOllama
+
+from langserve import add_routes
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+llm = ChatOllama(model="llama2")
+
+add_routes(
+    app,
+    llm,
+    path="/ollama",
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/multiple_servers/client_server.py

@@ -0,0 +1,35 @@
+"""Client server that interacts with the main server via a remote runnable.
+
+This server sets up a simple proxy to the main server. It uses the RemoteRunnable
+to interact with the main server. The main server is expected to be running at
+http://localhost:8123.
+
+A client server will likely end up doing something more clever rather than
+just being a proxy.
+"""
+from fastapi import FastAPI
+
+from langserve import RemoteRunnable, add_routes
+
+app = FastAPI()
+
+MAIN_SERVER_URL = (
+    "http://localhost:8123/chat_model/"  # <-- URL of the RUNNABLE on the main server
+)
+# Type inference is not automatic for remote runnables at the moment,
+# so you must specify which types are used for the playground to work.
+remote_runnable = RemoteRunnable(MAIN_SERVER_URL).with_types(input_type=str)
+
+
+# Let's add an example chain
+add_routes(
+    app,
+    remote_runnable,
+    path="/proxied",
+)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app)

examples/multiple_servers/main_server.py

@@ -0,0 +1,20 @@
+"""Main server that exposes one or more chains as HTTP endpoints."""
+from fastapi import FastAPI
+from langchain_openai import ChatOpenAI
+
+from langserve import add_routes
+
+app = FastAPI()
+
+# Let's add an example chain
+add_routes(
+    app,
+    ChatOpenAI(),
+    path="/chat_model",
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    # Running on port 8123
+    uvicorn.run(app, port=8123)

examples/passthrough_dict/client.ipynb

@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Passthrough information\n",
+    "\n",
+    "An example that shows how to pass through additional info with the request, and get it back with the response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": ["from langchain_core.prompts import ChatPromptTemplate"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": ["from langserve import RemoteRunnable\n\nchain = RemoteRunnable(\"http://localhost:8000/v1/\")"]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a prompt composed of a system message and a human message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': AIMessage(content='`apple` translates to `mela` in Italian.'),\n",
+       " 'info': {'info': {'user_id': 42, 'user_info': {'address': 42}}}}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": ["chain.invoke({'thing': 'apple', 'language': 'italian', 'info': {\"user_id\": 42, \"user_info\": {\"address\": 42}}})"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'info': {'info': {'user_id': 42, 'user_info': {'address': 42}}}}\n",
+      "{'output': AIMessageChunk(content='')}\n",
+      "{'output': AIMessageChunk(content='m')}\n",
+      "{'output': AIMessageChunk(content='ela')}\n",
+      "{'output': AIMessageChunk(content='')}\n"
+     ]
+    }
+   ],
+   "source": ["for chunk in chain.stream({'thing': 'apple', 'language': 'italian', 'info': {\"user_id\": 42, \"user_info\": {\"address\": 42}}}):\n    print(chunk)"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": ["from langserve import RemoteRunnable\n\nchain = RemoteRunnable(\"http://localhost:8000/v2/\")"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': AIMessage(content='`apple` translates to `mela` in Italian.'),\n",
+       " 'info': {'user_id': 42, 'user_info': {'address': 42}}}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": ["chain.invoke({'thing': 'apple', 'language': 'italian', 'info': {\"user_id\": 42, \"user_info\": {\"address\": 42}}})"]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/passthrough_dict/server.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+"""Example LangChain server passes through some of the inputs in the response."""
+
+from typing import Any, Callable, Dict, List, Optional, TypedDict
+
+from fastapi import FastAPI
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+from langchain_openai import ChatOpenAI
+
+from langserve import add_routes
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+def _create_projection(
+    *, include_keys: Optional[List] = None, exclude_keys: Optional[List[str]] = None
+) -> Callable[[dict], dict]:
+    """Create a projection function."""
+
+    def _project_dict(
+        d: dict,
+    ) -> dict:
+        """Project dictionary."""
+        keys = d.keys()
+        if include_keys is not None:
+            keys = set(keys) & set(include_keys)
+        if exclude_keys is not None:
+            keys = set(keys) - set(exclude_keys)
+        return {k: d[k] for k in keys}
+
+    return _project_dict
+
+
+prompt = ChatPromptTemplate.from_messages(
+    [("human", "translate `{thing}` to {language}")]
+)
+model = ChatOpenAI()
+
+underlying_chain = prompt | model
+
+wrapped_chain = RunnableParallel(
+    {
+        "output": _create_projection(exclude_keys=["info"]) | underlying_chain,
+        "info": _create_projection(include_keys=["info"]),
+    }
+)
+
+
+class Input(TypedDict):
+    thing: str
+    language: str
+    info: Dict[str, Any]
+
+
+class Output(TypedDict):
+    output: underlying_chain.output_schema
+    info: Dict[str, Any]
+
+
+add_routes(
+    app, wrapped_chain.with_types(input_type=Input, output_type=Output), path="/v1"
+)
+
+
+# Version 2
+# Uses RunnablePassthrough.assign
+wrapped_chain_2 = RunnablePassthrough.assign(output=underlying_chain) | {
+    "output": lambda x: x["output"],
+    "info": lambda x: x["info"],
+}
+
+add_routes(
+    app,
+    wrapped_chain_2.with_types(input_type=Input, output_type=Output),
+    path="/v2",
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/retrieval/server.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 """Example LangChain server exposes a retriever."""
 from fastapi import FastAPI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
 
 from langserve import add_routes
 

examples/router/server.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+"""Example LangChain Server that uses a Fast API Router.
+
+When applications grow, it becomes useful to use FastAPI's Router to organize
+the routes.
+
+See more documentation at:
+
+https://fastapi.tiangolo.com/tutorial/bigger-applications/
+"""
+from fastapi import APIRouter, FastAPI
+from langchain_anthropic import ChatAnthropic
+from langchain_openai import ChatOpenAI
+
+from langserve import add_routes
+
+app = FastAPI()
+
+router = APIRouter(prefix="/models")
+
+# Invocations to this router will appear in trace logs as /models/openai
+add_routes(
+    router,
+    ChatOpenAI(model="gpt-3.5-turbo-0125"),
+    path="/openai",
+)
+# Invocations to this router will appear in trace logs as /models/anthropic
+add_routes(
+    router,
+    ChatAnthropic(model="claude-3-haiku-20240307"),
+    path="/anthropic",
+)
+
+app.include_router(router)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/widgets/chat/message_list/server.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+"""Example of a simple chatbot that just passes current conversation
+state back and forth between server and client.
+"""
+from typing import List, Union
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydantic import BaseModel, Field
+
+from langserve import add_routes
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+# Set all CORS enabled origins
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+    expose_headers=["*"],
+)
+
+
+# Declare a chain
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful assisstant named Cob."),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+chain = prompt | ChatAnthropic(model="claude-2.1") | StrOutputParser()
+
+
+class InputChat(BaseModel):
+    """Input for the chat endpoint."""
+
+    messages: List[Union[HumanMessage, AIMessage, SystemMessage]] = Field(
+        ...,
+        description="The chat messages representing the current conversation.",
+        extra={"widget": {"type": "chat", "input": "messages"}},
+    )
+
+
+add_routes(
+    app,
+    chain.with_types(input_type=InputChat),
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/widgets/chat/tuples/server.py

@@ -6,16 +6,19 @@ from typing import Any, Dict, List, Tuple
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from langchain.document_loaders.blob_loaders import Blob
-from langchain.document_loaders.parsers.pdf import PDFMinerParser
-from langchain.pydantic_v1 import BaseModel, Field
-from langchain.schema.messages import (
+from langchain_community.document_loaders.parsers.pdf import PDFMinerParser
+from langchain_core.document_loaders import Blob
+from langchain_core.messages import (
     AIMessage,
     BaseMessage,
     FunctionMessage,
+    HumanMessage,
 )
-from langchain.schema.runnable import RunnableLambda
+from langchain_core.runnables import RunnableLambda, RunnableParallel
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
 
+from langserve import CustomUserType
 from langserve.server import add_routes
 
 app = FastAPI(
@@ -35,8 +38,11 @@ app.add_middleware(
     expose_headers=["*"],
 )
 
+# Example 1: Chat Widget
+# This shows how to create a chat widget.
 
-class ChatHistory(BaseModel):
+
+class ChatHistory(CustomUserType):
     chat_history: List[Tuple[str, str]] = Field(
         ...,
         examples=[[("a", "aa")]],
@@ -45,6 +51,35 @@ class ChatHistory(BaseModel):
     question: str
 
 
+def _format_to_messages(input: ChatHistory) -> List[BaseMessage]:
+    """Format the input to a list of messages."""
+    history = input.chat_history
+    user_input = input.question
+
+    messages = []
+
+    for human, ai in history:
+        messages.append(HumanMessage(content=human))
+        messages.append(AIMessage(content=ai))
+    messages.append(HumanMessage(content=user_input))
+    return messages
+
+
+model = ChatOpenAI()
+chat_model = RunnableParallel({"answer": (RunnableLambda(_format_to_messages) | model)})
+add_routes(
+    app,
+    chat_model.with_types(input_type=ChatHistory),
+    config_keys=["configurable"],
+    path="/chat",
+)
+
+
+# Example 2: Chat Widget with History
+# This one isn't hooked up toa model. It just shows that FunctionMessages can be used
+# surfaced as well in the playground.
+
+
 class ChatHistoryMessage(BaseModel):
     chat_history: List[BaseMessage] = Field(
         ...,
@@ -53,19 +88,6 @@ class ChatHistoryMessage(BaseModel):
     location: str
 
 
-class FileProcessingRequest(BaseModel):
-    file: bytes = Field(..., extra={"widget": {"type": "base64file"}})
-    num_chars: int = 100
-
-
-def chat_with_bot(input: Dict[str, Any]) -> Dict[str, Any]:
-    """Bot that repeats the question twice."""
-    return {
-        "answer": input["question"] * 2,
-        "woof": "its so bad to woof, meow is better",
-    }
-
-
 def chat_message_bot(input: Dict[str, Any]) -> List[BaseMessage]:
     """Bot that repeats the question twice."""
     return [
@@ -83,6 +105,21 @@ def chat_message_bot(input: Dict[str, Any]) -> List[BaseMessage]:
     ]
 
 
+add_routes(
+    app,
+    RunnableLambda(chat_message_bot).with_types(input_type=ChatHistoryMessage),
+    config_keys=["configurable"],
+    path="/chat_message",
+)
+
+# Example 3: File Processing Widget
+
+
+class FileProcessingRequest(BaseModel):
+    file: bytes = Field(..., extra={"widget": {"type": "base64file"}})
+    num_chars: int = 100
+
+
 def process_file(input: Dict[str, Any]) -> str:
     """Extract the text from the first page of the PDF."""
     content = base64.decodebytes(input["file"])
@@ -92,13 +129,6 @@ def process_file(input: Dict[str, Any]) -> str:
     return content[: input["num_chars"]]
 
 
-add_routes(
-    app,
-    RunnableLambda(chat_with_bot).with_types(input_type=ChatHistory),
-    config_keys=["configurable"],
-    path="/chat",
-)
-
 add_routes(
     app,
     RunnableLambda(process_file).with_types(input_type=FileProcessingRequest),
@@ -106,14 +136,6 @@ add_routes(
     path="/pdf",
 )
 
-add_routes(
-    app,
-    RunnableLambda(chat_message_bot).with_types(input_type=ChatHistoryMessage),
-    config_keys=["configurable"],
-    path="/chat_message",
-)
-
-
 if __name__ == "__main__":
     import uvicorn
 

langserve/__init__.py

@@ -4,9 +4,16 @@ This is the ONLY public interface into the package. All other modules are
 to be considered private and subject to change without notice.
 """
 
+from langserve.api_handler import APIHandler
 from langserve.client import RemoteRunnable
 from langserve.schema import CustomUserType
 from langserve.server import add_routes
 from langserve.version import __version__
 
-__all__ = ["RemoteRunnable", "add_routes", "__version__", "CustomUserType"]
+__all__ = [
+    "RemoteRunnable",
+    "APIHandler",
+    "add_routes",
+    "__version__",
+    "CustomUserType",
+]

langserve/_pydantic.py

@@ -0,0 +1,53 @@
+from typing import Any, Dict, Type, cast
+
+from pydantic import BaseModel, ConfigDict, RootModel
+from pydantic.json_schema import (
+    DEFAULT_REF_TEMPLATE,
+    GenerateJsonSchema,
+    JsonSchemaMode,
+)
+
+
+def _create_root_model(name: str, type_: Any) -> Type[RootModel]:
+    """Create a base class."""
+
+    def schema(
+        cls: Type[BaseModel],
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+    ) -> Dict[str, Any]:
+        # Complains about schema not being defined in superclass
+        schema_ = super(cls, cls).schema(  # type: ignore[misc]
+            by_alias=by_alias, ref_template=ref_template
+        )
+        schema_["title"] = name
+        return schema_
+
+    def model_json_schema(
+        cls: Type[BaseModel],
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+        mode: JsonSchemaMode = "validation",
+    ) -> Dict[str, Any]:
+        # Complains about model_json_schema not being defined in superclass
+        schema_ = super(cls, cls).model_json_schema(  # type: ignore[misc]
+            by_alias=by_alias,
+            ref_template=ref_template,
+            schema_generator=schema_generator,
+            mode=mode,
+        )
+        schema_["title"] = name
+        return schema_
+
+    base_class_attributes = {
+        "__annotations__": {"root": type_},
+        "model_config": ConfigDict(arbitrary_types_allowed=True),
+        "schema": classmethod(schema),
+        "model_json_schema": classmethod(model_json_schema),
+        # Should replace __module__ with caller based on stack frame.
+        "__module__": "langserve._pydantic",
+    }
+
+    custom_root_type = type(name, (RootModel,), base_class_attributes)
+    return cast(Type[RootModel], custom_root_type)

langserve/callbacks.py

@@ -4,13 +4,16 @@ import uuid
 from typing import Any, Dict, List, Optional, Sequence
 from uuid import UUID
 
-from langchain.callbacks.base import AsyncCallbackHandler
-from langchain.callbacks.manager import (
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.callbacks import AsyncCallbackHandler
+from langchain_core.callbacks.manager import (
     BaseRunManager,
     ahandle_event,
     handle_event,
 )
-from langchain.schema import AgentAction, AgentFinish, BaseMessage, Document, LLMResult
+from langchain_core.documents import Document
+from langchain_core.messages import BaseMessage
+from langchain_core.outputs import LLMResult
 from typing_extensions import TypedDict
 
 
@@ -45,7 +48,7 @@ class AsyncEventAggregatorCallback(AsyncCallbackHandler):
 
     async def on_chat_model_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: Optional[Dict[str, Any]],
         messages: List[List[BaseMessage]],
         *,
         run_id: UUID,
@@ -70,7 +73,7 @@ class AsyncEventAggregatorCallback(AsyncCallbackHandler):
 
     async def on_chain_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: Optional[Dict[str, Any]],
         inputs: Dict[str, Any],
         *,
         run_id: UUID,
@@ -135,7 +138,7 @@ class AsyncEventAggregatorCallback(AsyncCallbackHandler):
 
     async def on_retriever_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: Optional[Dict[str, Any]],
         query: str,
         *,
         run_id: UUID,
@@ -199,7 +202,7 @@ class AsyncEventAggregatorCallback(AsyncCallbackHandler):
 
     async def on_tool_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: Optional[Dict[str, Any]],
         input_str: str,
         *,
         run_id: UUID,
@@ -303,7 +306,7 @@ class AsyncEventAggregatorCallback(AsyncCallbackHandler):
 
     async def on_llm_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: Optional[Dict[str, Any]],
         prompts: List[str],
         *,
         run_id: UUID,
@@ -442,7 +445,14 @@ async def ahandle_callbacks(
         if event["parent_run_id"] is None:  # How do we make sure it's None!?
             event["parent_run_id"] = callback_manager.run_id
 
-        event_data = {key: value for key, value in event.items() if key != "type"}
+        event_data = {
+            key: value
+            for key, value in event.items()
+            if key != "type" and key != "kwargs"
+        }
+
+        if "kwargs" in event:
+            event_data.update(event["kwargs"])
 
         await ahandle_event(
             # Unpacking like this may not work
@@ -464,7 +474,14 @@ def handle_callbacks(
         if event["parent_run_id"] is None:  # How do we make sure it's None!?
             event["parent_run_id"] = callback_manager.run_id
 
-        event_data = {key: value for key, value in event.items() if key != "type"}
+        event_data = {
+            key: value
+            for key, value in event.items()
+            if key != "type" and key != "kwargs"
+        }
+
+        if "kwargs" in event:
+            event_data.update(event["kwargs"])
 
         handle_event(
             # Unpacking like this may not work

langserve/chat_playground/.eslintrc.cjs

@@ -0,0 +1,18 @@
+module.exports = {
+  root: true,
+  env: { browser: true, es2020: true },
+  extends: [
+    'eslint:recommended',
+    'plugin:@typescript-eslint/recommended',
+    'plugin:react-hooks/recommended',
+  ],
+  ignorePatterns: ['dist', '.eslintrc.cjs'],
+  parser: '@typescript-eslint/parser',
+  plugins: ['react-refresh'],
+  rules: {
+    'react-refresh/only-export-components': [
+      'warn',
+      { allowConstantExport: true },
+    ],
+  },
+}

langserve/chat_playground/.gitignore

@@ -0,0 +1,28 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+.yarn
+
+!dist

langserve/chat_playground/README.md

@@ -0,0 +1,27 @@
+# React + TypeScript + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend updating the configuration to enable type aware lint rules:
+
+- Configure the top-level `parserOptions` property like this:
+
+```js
+   parserOptions: {
+    ecmaVersion: 'latest',
+    sourceType: 'module',
+    project: ['./tsconfig.json', './tsconfig.node.json'],
+    tsconfigRootDir: __dirname,
+   },
+```
+
+- Replace `plugin:@typescript-eslint/recommended` to `plugin:@typescript-eslint/recommended-type-checked` or `plugin:@typescript-eslint/strict-type-checked`
+- Optionally add `plugin:@typescript-eslint/stylistic-type-checked`
+- Install [eslint-plugin-react](https://github.com/jsx-eslint/eslint-plugin-react) and add `plugin:react/recommended` & `plugin:react/jsx-runtime` to the `extends` list

langserve/chat_playground/dist/index.html

@@ -0,0 +1,42 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" href="/____LANGSERVE_BASE_URL/favicon.ico" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Chat Playground</title>
+    <script type="module" crossorigin src="/____LANGSERVE_BASE_URL/assets/index-53ad47d4.js"></script>
+    <link rel="stylesheet" href="/____LANGSERVE_BASE_URL/assets/index-434ff580.css">
+  </head>
+  <body>
+    <div id="root"></div>
+    <script>
+      try {
+        window.CONFIG_SCHEMA = ____LANGSERVE_CONFIG_SCHEMA;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.INPUT_SCHEMA = ____LANGSERVE_INPUT_SCHEMA;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.OUTPUT_SCHEMA = ____LANGSERVE_OUTPUT_SCHEMA;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.FEEDBACK_ENABLED = ____LANGSERVE_FEEDBACK_ENABLED;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.PUBLIC_TRACE_LINK_ENABLED = ____LANGSERVE_PUBLIC_TRACE_LINK_ENABLED;
+      } catch (e) {
+        // pass
+      }
+    </script>
+    
+  </body>
+</html>

langserve/chat_playground/index.html

@@ -0,0 +1,40 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" href="/favicon.ico" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Chat Playground</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script>
+      try {
+        window.CONFIG_SCHEMA = ____LANGSERVE_CONFIG_SCHEMA;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.INPUT_SCHEMA = ____LANGSERVE_INPUT_SCHEMA;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.OUTPUT_SCHEMA = ____LANGSERVE_OUTPUT_SCHEMA;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.FEEDBACK_ENABLED = ____LANGSERVE_FEEDBACK_ENABLED;
+      } catch (e) {
+        // pass
+      }
+      try {
+        window.PUBLIC_TRACE_LINK_ENABLED = ____LANGSERVE_PUBLIC_TRACE_LINK_ENABLED;
+      } catch (e) {
+        // pass
+      }
+    </script>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>

langserve/chat_playground/package.json

@@ -0,0 +1,59 @@
+{
+  "name": "langserve-chat-playground",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "packageManager": "yarn@1.22.19",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc && vite build",
+    "lint": "eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@emotion/react": "^11.11.1",
+    "@emotion/styled": "^11.11.0",
+    "@jsonforms/core": "^3.2.1",
+    "@microsoft/fetch-event-source": "^2.0.1",
+    "@mui/icons-material": "^5.14.11",
+    "@mui/material": "^5.14.11",
+    "@mui/x-date-pickers": "^6.16.0",
+    "@radix-ui/react-toggle-group": "^1.0.4",
+    "clsx": "^2.0.0",
+    "dayjs": "^1.11.10",
+    "fast-json-patch": "^3.1.1",
+    "lodash": "^4.18.1",
+    "lz-string": "^1.5.0",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-toastify": "^9.1.3",
+    "swr": "^2.2.4",
+    "tailwind-merge": "^1.14.0",
+    "use-debounce": "^9.0.4",
+    "vaul": "^0.7.3"
+  },
+  "devDependencies": {
+    "@types/lodash": "^4.14.200",
+    "@types/react": "^18.2.15",
+    "@types/react-dom": "^18.2.7",
+    "@typescript-eslint/eslint-plugin": "^6.0.0",
+    "@typescript-eslint/parser": "^6.0.0",
+    "@vitejs/plugin-react": "^4.0.3",
+    "autoprefixer": "^10.4.16",
+    "eslint": "^8.45.0",
+    "eslint-plugin-react-hooks": "^4.6.0",
+    "eslint-plugin-react-refresh": "^0.4.3",
+    "postcss": "^8.4.31",
+    "tailwindcss": "^3.3.3",
+    "typescript": "^5.0.2",
+    "vite": "^6.4.2",
+    "vite-plugin-svgr": "^4.1.0"
+  },
+  "resolutions": {
+    "braces": "^3.0.3",
+    "cross-spawn": "^7.0.5",
+    "rollup": "^3.30.0",
+    "ajv": "^8.18.0",
+    "esbuild": "0.25.0"
+  }
+}

langserve/chat_playground/postcss.config.js

@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+};

langserve/chat_playground/src/App.css

@@ -0,0 +1,116 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+@layer base {
+  * {
+    color: #043D5C;
+    font-weight: 300;
+    @font-face {
+      font-family: 'Manrope';
+      src: url('/dist/Manrope-VariableFont_wght.ttf') format('truetype');
+    }
+    border-color: #043D5C;
+  }
+  
+  input,
+  textarea,
+  select {
+    background: transparent;
+  }
+
+  /* clash between MUI and Tailwind */
+  input:focus,
+  textarea:focus,
+  select:focus {
+    box-shadow: none;
+    outline: none;
+  }
+
+  :root {
+    --popover: 0 0% 100%;
+    --background: #F8F7FF;
+
+    --divider-500: 210 40% 96.1%; /* slate-100 */
+    --divider-700: 214.3 31.8% 91.4%; /* slate-200 */
+
+    --ls-blue: 211.5 91.8% 61.8%;
+    --ls-black: 222.2 47.4% 11.2%; /* slate-900 */
+    --ls-gray-100: 215.4 16.3% 46.9%; /* slate-500 */
+    --ls-gray-200: 212.7 26.8% 83.9%; /* slate-300 */
+    --ls-gray-300: 214.3 31.8% 91.4%; /* slate-200 */
+    --ls-gray-400: 210 40% 96.1%; /* slate-100 */
+
+    --button-green: #162E2E;
+    --button-green-disabled: rgba(4, 61, 92, 0.20);
+    --button-inline: #006BA41A;
+  }
+
+  @media (prefers-color-scheme: dark) {
+    :root {
+      --popover: 240 11.6% 8.4%;
+
+      --divider-500: 217.2 32.6% 17.5%; /* slate-800 */
+      --divider-700: 215.3 25% 26.7%; /* slate-700 */
+
+      --ls-blue: 211.5 91.8% 61.8%;
+      --ls-black: 0 0% 100%; /* white */
+      --ls-gray-100: 215 20.2% 65.1%; /* slate-400 */
+      --ls-gray-200: 215.4 16.3% 46.9%; /* slate-500 */
+      --ls-gray-300: 215.3 25% 26.7%; /* slate-700 */
+      --ls-gray-400: 217.2 32.6% 17.5%; /* slate-800 */
+    }
+  }
+}
+
+.control {
+  @apply flex flex-col border border-divider-700 rounded-lg p-3 gap-1 relative bg-background transition-all outline-ls-blue/20;
+  @apply focus-within:border-ls-blue focus-within:outline focus-within:outline-4 focus-within:outline-ls-blue/20;
+}
+
+.control > label,
+.control h6 {
+  @apply text-xs uppercase font-semibold text-ls-gray-100;
+}
+
+.control div .MuiGrid-item {
+  @apply pt-0;
+}
+
+.control > select {
+  @apply -ml-1;
+}
+
+.control > .input-description,
+.control > .validation {
+  @apply absolute right-3 top-3 text-xs;
+}
+
+.group-layout {
+  @apply flex flex-col gap-4 bg-background p-4 border border-divider-700 rounded-lg;
+}
+
+.no-scrollbar {
+  scrollbar-width: none;
+}
+
+.no-scrollbar::-webkit-scrollbar {
+  display: none;
+}
+
+.vertical-layout {
+  @apply flex flex-col gap-4;
+}
+
+.share-button:hover {
+  background: linear-gradient(270deg, #BCB2FD 0.29%, #D65622 92%);
+}
+
+.share-button:hover > * {
+  color: white;
+}
+
+a {
+  color: blue;
+  text-decoration: underline;
+}

langserve/chat_playground/src/App.tsx

@@ -0,0 +1,73 @@
+import "./App.css";
+
+import { ChatWindow } from "./components/ChatWindow";
+import { AppCallbackContext, useAppStreamCallbacks } from "./useStreamCallback";
+import { useInputSchema, useOutputSchema } from "./useSchemas";
+import { useStreamLog } from "./useStreamLog";
+
+export function App() {
+  const { context, callbacks } = useAppStreamCallbacks();
+  const { startStream, stopStream } = useStreamLog(callbacks);
+  const inputSchema = useInputSchema({});
+  const outputSchema = useOutputSchema({});
+  const inputProps = inputSchema?.data?.schema?.properties;
+  const outputDataSchema = outputSchema?.data?.schema;
+  const isLoading = inputProps === undefined || outputDataSchema === undefined;
+  const inputKeys = Object.keys(inputProps ?? {});
+  const inputSchemaSupported = (
+    inputKeys.length === 1 &&
+    inputProps?.[inputKeys[0]].type === "array"
+  ) || (
+    inputKeys.length === 2 && (
+      (
+        inputProps?.[inputKeys[0]].type === "array" ||
+        inputProps?.[inputKeys[1]].type === "string"
+      ) || (
+        inputProps?.[inputKeys[0]].type === "string" ||
+        inputProps?.[inputKeys[1]].type === "array" 
+      )
+    )
+  );
+  const outputSchemaSupported = (
+    outputDataSchema?.anyOf?.find((option) => option.properties?.type?.enum?.includes("ai")) ||
+    outputDataSchema?.oneOf?.find((option) => option.properties?.type?.enum?.includes("ai")) ||
+    outputDataSchema?.type === "string"
+  );
+  const isSupported = isLoading || (inputSchemaSupported && outputSchemaSupported);
+  return (
+    <div className="flex items-center flex-col text-ls-black bg-background">
+      <AppCallbackContext.Provider value={context}>
+        {isSupported
+          ? <ChatWindow
+              startStream={startStream}
+              stopStream={stopStream}
+              messagesInputKey={inputProps?.[inputKeys[0]].type === "array" ? inputKeys[0] : inputKeys[1]}
+              inputKey={inputProps?.[inputKeys[0]].type === "string" ? inputKeys[0] : inputKeys[1]}
+            ></ChatWindow>
+          : <div className="h-[100vh] w-[100vw] flex justify-center items-center text-xl p-16">
+              <span>
+                The chat playground is only supported for chains that take one of the following as input:
+                <ul className="mt-8 list-disc ml-6">
+                  <li>
+                    a dict with a single key containing a list of messages
+                  </li>
+                  <li>
+                    a dict with two keys: one a string input, one an list of messages
+                  </li>
+                </ul>
+                <br />
+                and which return either an <code>AIMessage</code> or a string.
+                <br />
+                <br />
+                You can test this chain in the default LangServe playground instead.
+                <br />
+                <br />
+                To use the default playground, set <code>playground_type="default"</code> when adding the route in your backend.
+              </span>
+            </div>}
+      </AppCallbackContext.Provider>
+    </div>
+  );
+}
+
+export default App;

langserve/chat_playground/src/assets/ArrowUp.svg

@@ -0,0 +1 @@
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langserve/chat_playground/src/assets/ChatIcon.svg

@@ -0,0 +1,6 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path fill-rule="evenodd" clip-rule="evenodd"
+    d="M7.7588 2H16.2414C17.0464 1.99999 17.7107 1.99998 18.2519 2.04419C18.814 2.09012 19.3307 2.18868 19.8161 2.43597C20.5687 2.81947 21.1806 3.43139 21.5641 4.18404C21.8114 4.66937 21.91 5.18608 21.9559 5.74817C22.0001 6.28936 22.0001 6.95372 22.0001 7.75868V13.2413C22.0001 14.0463 22.0001 14.7106 21.9559 15.2518C21.91 15.8139 21.8114 16.3306 21.5641 16.816C21.1806 17.5686 20.5687 18.1805 19.8161 18.564C19.3307 18.8113 18.814 18.9099 18.2519 18.9558C17.7107 19 17.0464 19 16.2414 19H13.6838C13.0197 19 12.8263 19.0047 12.6504 19.0408C12.4738 19.0771 12.303 19.137 12.1425 19.219C11.9826 19.3007 11.8286 19.4178 11.31 19.8327L8.89688 21.7632C8.7132 21.9102 8.52597 22.06 8.36137 22.1689C8.20394 22.273 7.8987 22.4593 7.50172 22.4597C7.0449 22.4602 6.61276 22.2525 6.32778 21.8955C6.08012 21.5852 6.03492 21.2305 6.01785 21.0425C6 20.846 6.00005 20.6062 6.00009 20.371L6.0001 18.9918C5.60829 18.9789 5.27229 18.9461 4.96482 18.8637C3.58445 18.4938 2.50626 17.4156 2.13639 16.0353C1.9993 15.5236 1.99962 14.933 2.00005 14.1376C2.00007 14.0924 2.0001 14.0465 2.0001 14L2.0001 7.7587C2.00008 6.95373 2.00007 6.28937 2.04429 5.74817C2.09022 5.18608 2.18878 4.66937 2.43607 4.18404C2.81956 3.43139 3.43149 2.81947 4.18413 2.43597C4.66947 2.18868 5.18617 2.09012 5.74827 2.04419C6.28947 1.99998 6.95383 1.99999 7.7588 2ZM5.91113 4.03755C5.47272 4.07337 5.24852 4.1383 5.09212 4.21799C4.71579 4.40973 4.40983 4.7157 4.21808 5.09202C4.13839 5.24842 4.07347 5.47262 4.03765 5.91104C4.00087 6.36113 4.0001 6.94342 4.0001 7.8V14C4.0001 14.9944 4.00869 15.2954 4.06824 15.5176C4.25318 16.2078 4.79227 16.7469 5.48246 16.9319C5.70474 16.9914 6.00574 17 7.0001 17C7.55238 17 8.0001 17.4477 8.0001 18V19.9194L10.0606 18.271C10.0834 18.2528 10.1058 18.2348 10.1279 18.2171C10.55 17.8791 10.8691 17.6237 11.2326 17.4379C11.5536 17.274 11.8952 17.1541 12.2483 17.0817C12.6482 16.9996 13.0569 16.9998 13.5976 17C13.626 17 13.6547 17 13.6838 17H16.2001C17.0567 17 17.639 16.9992 18.0891 16.9624C18.5275 16.9266 18.7517 16.8617 18.9081 16.782C19.2844 16.5903 19.5904 16.2843 19.7821 15.908C19.8618 15.7516 19.9267 15.5274 19.9625 15.089C19.9993 14.6389 20.0001 14.0566 20.0001 13.2V7.8C20.0001 6.94342 19.9993 6.36113 19.9625 5.91104C19.9267 5.47262 19.8618 5.24842 19.7821 5.09202C19.5904 4.7157 19.2844 4.40973 18.9081 4.21799C18.7517 4.1383 18.5275 4.07337 18.0891 4.03755C17.639 4.00078 17.0567 4 16.2001 4H7.8001C6.94352 4 6.36122 4.00078 5.91113 4.03755Z"
+    fill="currentColor" />
+</svg>
+  

langserve/chat_playground/src/assets/CheckCircleIcon.svg

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+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path fill-rule="evenodd" clip-rule="evenodd"
+    d="M12 5.45455C8.38505 5.45455 5.45455 8.38505 5.45455 12C5.45455 15.615 8.38505 18.5455 12 18.5455C15.615 18.5455 18.5455 15.615 18.5455 12C18.5455 8.38505 15.615 5.45455 12 5.45455ZM4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20C7.58172 20 4 16.4183 4 12ZM15.787 9.30392C16.071 9.58794 16.071 10.0484 15.787 10.3324L11.4233 14.6961C11.1393 14.9801 10.6788 14.9801 10.3948 14.6961L8.21301 12.5143C7.929 12.2303 7.929 11.7697 8.21301 11.4857C8.49703 11.2017 8.95751 11.2017 9.24153 11.4857L10.9091 13.1533L14.7585 9.30392C15.0425 9.01991 15.503 9.01991 15.787 9.30392Z"
+    fill="currentColor" />
+</svg>

langserve/chat_playground/src/assets/CheckCircleIcon2.svg

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+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-check-circle"><path d="M22 11.08V12a10 10 0 1 1-5.93-9.14"></path><polyline points="22 4 12 14.01 9 11.01"></polyline></svg>

langserve/chat_playground/src/assets/ChevronRight.svg

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+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path fill-rule="evenodd" clip-rule="evenodd"
+    d="M8.29289 5.29289C8.68342 4.90237 9.31658 4.90237 9.70711 5.29289L15.7071 11.2929C16.0976 11.6834 16.0976 12.3166 15.7071 12.7071L9.70711 18.7071C9.31658 19.0976 8.68342 19.0976 8.29289 18.7071C7.90237 18.3166 7.90237 17.6834 8.29289 17.2929L13.5858 12L8.29289 6.70711C7.90237 6.31658 7.90237 5.68342 8.29289 5.29289Z"
+    fill="currentColor" />
+</svg>
+  

langserve/chat_playground/src/assets/CircleSpinIcon.svg

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+<svg
+  aria-hidden="true"
+  viewBox="0 0 100 101"
+  fill="none"
+  xmlns="http://www.w3.org/2000/svg"
+>
+  <path
+    d="M100 50.5908C100 78.2051 77.6142 100.591 50 100.591C22.3858 100.591 0 78.2051 0 50.5908C0 22.9766 22.3858 0.59082 50 0.59082C77.6142 0.59082 100 22.9766 100 50.5908ZM9.08144 50.5908C9.08144 73.1895 27.4013 91.5094 50 91.5094C72.5987 91.5094 90.9186 73.1895 90.9186 50.5908C90.9186 27.9921 72.5987 9.67226 50 9.67226C27.4013 9.67226 9.08144 27.9921 9.08144 50.5908Z"
+    fill="currentColor"
+  />
+  <path
+    d="M93.9676 39.0409C96.393 38.4038 97.8624 35.9116 97.0079 33.5539C95.2932 28.8227 92.871 24.3692 89.8167 20.348C85.8452 15.1192 80.8826 10.7238 75.2124 7.41289C69.5422 4.10194 63.2754 1.94025 56.7698 1.05124C51.7666 0.367541 46.6976 0.446843 41.7345 1.27873C39.2613 1.69328 37.813 4.19778 38.4501 6.62326C39.0873 9.04874 41.5694 10.4717 44.0505 10.1071C47.8511 9.54855 51.7191 9.52689 55.5402 10.0491C60.8642 10.7766 65.9928 12.5457 70.6331 15.2552C75.2735 17.9648 79.3347 21.5619 82.5849 25.841C84.9175 28.9121 86.7997 32.2913 88.1811 35.8758C89.083 38.2158 91.5421 39.6781 93.9676 39.0409Z"
+    fill="currentFill"
+  />
+</svg>

langserve/chat_playground/src/assets/CodeIcon.svg

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+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path fill-rule="evenodd" clip-rule="evenodd"
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+    fill="currentColor" />
+</svg>
+  

langserve/chat_playground/src/assets/CopyIcon.svg

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+<svg width="25" height="25" viewBox="0 0 25 25" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path
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+    fill="currentColor" />
+</svg>
+  

langserve/chat_playground/src/assets/EmptyState.svg

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+<g id="Group 145">
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+<path id="Vector_2" d="M52.3165 46.0721C62.1536 36.2829 63.7681 22.0183 55.9226 14.211C48.0771 6.40383 33.7426 8.01049 23.9054 17.7996C14.0683 27.5888 12.4538 41.8534 20.2993 49.6606C28.1448 57.4679 42.4794 55.8612 52.3165 46.0721Z" stroke="url(#paint1_linear_10720_23418)" stroke-width="1.5" stroke-miterlimit="10"/>
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+</defs>
+</svg>

langserve/chat_playground/src/assets/LangServeLogo.svg

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+<svg width="20" height="21" viewBox="0 0 20 21" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path fill-rule="evenodd" clip-rule="evenodd"
+    d="M9.41009 2.41009C9.73553 2.08466 10.2632 2.08466 10.5886 2.41009L13.9219 5.74343C14.2474 6.06886 14.2474 6.5965 13.9219 6.92194C13.5965 7.24738 13.0689 7.24738 12.7434 6.92194L10.8327 5.01119V12.9993C10.8327 13.4596 10.4596 13.8327 9.99935 13.8327C9.53911 13.8327 9.16602 13.4596 9.16602 12.9993V5.01119L7.25527 6.92194C6.92984 7.24738 6.4022 7.24738 6.07676 6.92194C5.75132 6.5965 5.75132 6.06886 6.07676 5.74343L9.41009 2.41009ZM2.49935 9.66602C2.95959 9.66602 3.33268 10.0391 3.33268 10.4993V13.9993C3.33268 14.7132 3.33333 15.1984 3.36398 15.5735C3.39383 15.9388 3.44793 16.1257 3.51434 16.256C3.67413 16.5696 3.9291 16.8246 4.2427 16.9844C4.37303 17.0508 4.55987 17.1049 4.92521 17.1347C5.30029 17.1654 5.78553 17.166 6.49935 17.166H13.4993C14.2132 17.166 14.6984 17.1654 15.0735 17.1347C15.4388 17.1049 15.6257 17.0508 15.756 16.9844C16.0696 16.8246 16.3246 16.5696 16.4844 16.256C16.5508 16.1257 16.6049 15.9388 16.6347 15.5735C16.6654 15.1984 16.666 14.7132 16.666 13.9993V10.4993C16.666 10.0391 17.0391 9.66602 17.4993 9.66602C17.9596 9.66602 18.3327 10.0391 18.3327 10.4993V14.0338C18.3327 14.7046 18.3327 15.2582 18.2959 15.7092C18.2576 16.1776 18.1754 16.6082 17.9694 17.0127C17.6498 17.6399 17.1399 18.1498 16.5126 18.4694C16.1082 18.6754 15.6776 18.7576 15.2092 18.7959C14.7582 18.8327 14.2046 18.8327 13.5338 18.8327H6.46491C5.79411 18.8327 5.24049 18.8327 4.78949 18.7959C4.32108 18.7576 3.89049 18.6754 3.48605 18.4694C2.85884 18.1498 2.34891 17.6399 2.02933 17.0127C1.82325 16.6082 1.74112 16.1776 1.70284 15.7092C1.666 15.2582 1.66601 14.7046 1.66602 14.0338L1.66602 10.4993C1.66602 10.0391 2.03911 9.66602 2.49935 9.66602Z"
+    fill="currentColor" />
+</svg>
+  

langserve/chat_playground/src/assets/ThumbsDownIcon.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-thumbs-down"><path d="M10 15v4a3 3 0 0 0 3 3l4-9V2H5.72a2 2 0 0 0-2 1.7l-1.38 9a2 2 0 0 0 2 2.3zm7-13h2.67A2.31 2.31 0 0 1 22 4v7a2.31 2.31 0 0 1-2.33 2H17"></path></svg>

langserve/chat_playground/src/assets/ThumbsUpIcon.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-thumbs-up"><path d="M14 9V5a3 3 0 0 0-3-3l-4 9v11h11.28a2 2 0 0 0 2-1.7l1.38-9a2 2 0 0 0-2-2.3zM7 22H4a2 2 0 0 1-2-2v-7a2 2 0 0 1 2-2h3"></path></svg>

langserve/chat_playground/src/assets/TrashIcon.svg

@@ -0,0 +1,6 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+  <path fill-rule="evenodd" clip-rule="evenodd"
+    d="M8 3C8 2.44772 8.44772 2 9 2H15C15.5523 2 16 2.44772 16 3C16 3.55228 15.5523 4 15 4H9C8.44772 4 8 3.55228 8 3ZM4.99224 5H3C2.44772 5 2 5.44772 2 6C2 6.55228 2.44772 7 3 7H4.06445L4.70614 16.6254C4.75649 17.3809 4.79816 18.006 4.87287 18.5149C4.95066 19.0447 5.07405 19.5288 5.33109 19.98C5.73123 20.6824 6.33479 21.247 7.06223 21.5996C7.52952 21.826 8.0208 21.917 8.55459 21.9593C9.06728 22 9.69383 22 10.4509 22H13.5491C14.3062 22 14.9327 22 15.4454 21.9593C15.9792 21.917 16.4705 21.826 16.9378 21.5996C17.6652 21.247 18.2688 20.6824 18.6689 19.98C18.926 19.5288 19.0493 19.0447 19.1271 18.5149C19.2018 18.006 19.2435 17.3808 19.2939 16.6253L19.9356 7H21C21.5523 7 22 6.55228 22 6C22 5.44772 21.5523 5 21 5H19.0078C19.0019 4.99995 18.9961 4.99995 18.9903 5H5.00974C5.00392 4.99995 4.99809 4.99995 4.99224 5ZM17.9311 7H6.06889L6.69907 16.4528C6.75274 17.2578 6.78984 17.8034 6.85166 18.2243C6.9117 18.6333 6.98505 18.8429 7.06888 18.99C7.26895 19.3412 7.57072 19.6235 7.93444 19.7998C8.08684 19.8736 8.30086 19.9329 8.71286 19.9656C9.13703 19.9993 9.68385 20 10.4907 20H13.5093C14.3161 20 14.863 19.9993 15.2871 19.9656C15.6991 19.9329 15.9132 19.8736 16.0656 19.7998C16.4293 19.6235 16.7311 19.3412 16.9311 18.99C17.015 18.8429 17.0883 18.6333 17.1483 18.2243C17.2102 17.8034 17.2473 17.2578 17.3009 16.4528L17.9311 7Z"
+    fill="currentColor" />
+</svg>
+  

langserve/chat_playground/src/assets/XCircle.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-x-circle"><circle cx="12" cy="12" r="10"></circle><line x1="15" y1="9" x2="9" y2="15"></line><line x1="9" y1="9" x2="15" y2="15"></line></svg>

langserve/chat_playground/src/components/AutosizeTextarea.tsx

@@ -0,0 +1,55 @@
+import { Ref } from "react";
+import { cn } from "../utils/cn";
+
+const COMMON_CLS = cn(
+  "text-lg col-[1] row-[1] m-0 resize-none overflow-hidden whitespace-pre-wrap break-words border-none bg-transparent p-0"
+);
+
+export function AutosizeTextarea(props: {
+  id?: string;
+  inputRef?: Ref<HTMLTextAreaElement>;
+  value?: string | null | undefined;
+  placeholder?: string;
+  className?: string;
+  onChange?: (e: string) => void;
+  onFocus?: () => void;
+  onBlur?: () => void;
+  onKeyDown?: (e: React.KeyboardEvent<HTMLTextAreaElement>) => void;
+  autoFocus?: boolean;
+  readOnly?: boolean;
+  cursorPointer?: boolean;
+  disabled?: boolean;
+  fullHeight?: boolean;
+}) {
+  return (
+    <div className={cn("grid w-full", props.className) + (props.fullHeight ? "" : " max-h-80 overflow-auto")}>
+      <textarea
+        ref={props.inputRef}
+        id={props.id}
+        className={cn(
+          COMMON_CLS,
+          "text-transparent caret-black"
+        )}
+        disabled={props.disabled}
+        value={props.value ?? ""}
+        rows={1}
+        onChange={(e) => {
+          const target = e.target as HTMLTextAreaElement;
+          props.onChange?.(target.value);
+        }}
+        onFocus={props.onFocus}
+        onBlur={props.onBlur}
+        placeholder={props.placeholder}
+        readOnly={props.readOnly}
+        autoFocus={props.autoFocus && !props.readOnly}
+        onKeyDown={props.onKeyDown}
+      />
+      <div
+        aria-hidden
+        className={cn(COMMON_CLS, "pointer-events-none select-none")}
+      >
+        {props.value}{" "}
+      </div>
+    </div>
+  );
+}

langserve/chat_playground/src/components/ChatMessage.tsx

@@ -0,0 +1,118 @@
+import { useState } from "react";
+import { CorrectnessFeedback } from "./feedback/CorrectnessFeedback";
+import { resolveApiUrl } from "../utils/url";
+import { AutosizeTextarea } from "./AutosizeTextarea";
+import TrashIcon from "../assets/TrashIcon.svg?react";
+import RefreshCW from "../assets/RefreshCW.svg?react";
+
+export type ChatMessageType = "human" | "ai" | "function" | "tool" | "system";
+
+export type ChatMessageBody = {
+  type: ChatMessageType;
+  content: string;
+  runId?: string;
+}
+
+export function ChatMessage(props: {
+  message: ChatMessageBody;
+  isLoading?: boolean;
+  onError?: (e: any) => void;
+  onTypeChange?: (newValue: string) => void;
+  onChange?: (newValue: string) => void;
+  onRemove?: (e: any) => void;
+  onRegenerate?: (e?: any) => void;
+  isFinalMessage?: boolean;
+  feedbackEnabled?: boolean;
+  publicTraceLinksEnabled?: boolean;
+}) {
+  const { message, feedbackEnabled, publicTraceLinksEnabled, onError, isLoading } = props;
+  const { content, type, runId } = message;
+
+  const [publicTraceLink, setPublicTraceLink] = useState<string | null>(null);
+  const [messageActionIsLoading, setMessageActionIsLoading] = useState(false);
+  const openPublicTrace = async () => {
+    if (messageActionIsLoading) {
+      return;
+    }
+    if (publicTraceLink) {
+      window.open(publicTraceLink, '_blank');
+      return;
+    }
+    setMessageActionIsLoading(true);
+    const payload = { run_id: runId };
+    const response = await fetch(resolveApiUrl("/public_trace_link"), {
+      method: "PUT",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payload),
+    });
+
+    if (!response.ok) {
+      if (response.status === 404) {
+        onError?.(new Error(`Feedback endpoint not found. Please enable it in your LangServe endpoint.`));
+      } else {
+        try {
+          const errorResponse = await response.json();
+          onError?.(new Error(`${errorResponse.detail}`));
+        } catch (e) {
+          onError?.(new Error(`Request failed with status: ${response.status}`));
+        }
+      }
+      setMessageActionIsLoading(false);
+      throw new Error(`Failed request ${response.status}`)
+    }
+    const parsedResponse = await response.json();
+    setMessageActionIsLoading(false);
+    setPublicTraceLink(parsedResponse.public_url);
+    window.open(parsedResponse.public_url, '_blank');
+  };
+
+  return (
+    <div className="mb-8 group">
+      <div className="flex justify-between">
+        <select
+          className="font-medium text-transform uppercase mb-2 appearance-none"
+          defaultValue={type}
+          onChange={(e) => props.onTypeChange?.(e.target.value)}
+        >
+          <option value="human">HUMAN</option>
+          <option value="ai">AI</option>
+          <option value="system">SYSTEM</option>
+        </select>
+        <span className="flex">
+          {props.isFinalMessage &&
+            type === "human" && 
+            <RefreshCW className="opacity-0 group-hover:opacity-50 transition-opacity duration-200 cursor-pointer h-4 w-4 mr-2" onMouseUp={props.onRegenerate}></RefreshCW>}
+          <TrashIcon
+            className="opacity-0 group-hover:opacity-50 transition-opacity duration-200 cursor-pointer h-4 w-4"
+            onMouseUp={props.onRemove}
+          ></TrashIcon>
+        </span>
+      </div>
+      <AutosizeTextarea value={content} fullHeight={true} onChange={props.onChange} onKeyDown={(e) => {
+        if (
+          e.key === 'Enter' &&
+          !e.shiftKey &&
+          props.isFinalMessage &&
+          type === "human"
+        ) {
+          e.preventDefault();
+          props.onRegenerate?.();
+        }
+      }}></AutosizeTextarea>
+      {type === "ai" && !isLoading && runId != null && (
+        <div className="mt-2 flex items-center">
+          {feedbackEnabled && <span className="mr-2"><CorrectnessFeedback runId={runId} onError={props.onError}></CorrectnessFeedback></span>}
+          {publicTraceLinksEnabled && <>
+            <button
+              className="bg-button-inline p-2 rounded-lg text-xs font-medium hover:opacity-80"
+              disabled={messageActionIsLoading || isLoading}
+              onMouseUp={openPublicTrace}
+            >
+              🛠️ View LangSmith trace
+            </button>
+          </>}
+        </div>
+      )}
+    </div> 
+  )
+};

langserve/chat_playground/src/components/ChatWindow.tsx

@@ -0,0 +1,217 @@
+import { useState, useRef } from "react";
+import { ToastContainer, toast } from 'react-toastify';
+import 'react-toastify/dist/ReactToastify.css';
+
+import { AutosizeTextarea } from "./AutosizeTextarea";
+import {
+  ChatMessage,
+  type ChatMessageType,
+  type ChatMessageBody,
+} from "./ChatMessage";
+import { ShareDialog } from "./ShareDialog";
+import { useStreamCallback } from "../useStreamCallback";
+
+import ArrowUp from "../assets/ArrowUp.svg?react";
+import CircleSpinIcon from "../assets/CircleSpinIcon.svg?react";
+import EmptyState from "../assets/EmptyState.svg?react";
+import LangServeLogo from "../assets/LangServeLogo.svg?react";
+import { useFeedback, usePublicTraceLink } from "../useSchemas";
+
+export type AIMessage = {
+  content: string;
+  type: "AIMessage" | "AIMessageChunk";
+  name?: string;
+  additional_kwargs?: { [key: string]: unknown };
+}
+
+export function isAIMessage(x: unknown): x is AIMessage {
+  return x != null &&
+    typeof (x as AIMessage).content === "string" &&
+    ["AIMessageChunk", "AIMessage"].includes((x as AIMessage).type);
+}
+
+export function ChatWindow(props: {
+  startStream: (input: unknown, config: unknown) => Promise<void>;
+  stopStream: (() => void) | undefined;
+  messagesInputKey: string;
+  inputKey?: string;
+}) {
+  const { startStream, messagesInputKey, inputKey } = props;
+
+  const [currentInputValue, setCurrentInputValue] = useState("");
+  const [isLoading, setIsLoading] = useState(false);
+  const [messages, setMessages] = useState<ChatMessageBody[]>([]);
+  const messageInputRef = useRef<HTMLTextAreaElement>(null);
+
+  const feedbackEnabled = useFeedback()
+  const publicTraceLinksEnabled = usePublicTraceLink();
+
+  const submitMessage = () => {
+    const submittedValue = currentInputValue;
+    if (submittedValue.length === 0 || isLoading) {
+      return;
+    }
+    setIsLoading(true);
+    const newMessages = [
+      ...messages,
+      { type: "human", content: submittedValue } as const
+    ];
+    setMessages(newMessages);
+    setCurrentInputValue("");
+    // TODO: Add config schema support
+    if (inputKey === undefined) {
+      startStream({ [messagesInputKey]: newMessages }, {});
+    } else {
+      startStream({
+        [messagesInputKey]: newMessages.slice(0, -1),
+        [inputKey]: newMessages[newMessages.length - 1].content
+      }, {});
+    }
+  };
+
+  const regenerateMessages = () => {
+    if (isLoading) {
+      return;
+    }
+    setIsLoading(true);
+    // TODO: Add config schema support
+    if (inputKey === undefined) {
+      startStream({ [messagesInputKey]: messages }, {});
+    } else {
+      startStream({
+        [messagesInputKey]: messages.slice(0, -1),
+        [inputKey]: messages[messages.length - 1].content
+      }, {});
+    }
+  };
+  
+  useStreamCallback("onStart", () => {
+    setMessages((prevMessages) => [
+      ...prevMessages,
+      { type: "ai", content: "" },
+    ]);
+  });
+  useStreamCallback("onChunk", (_chunk, aggregatedState) => {
+    const finalOutput = aggregatedState?.final_output;
+    if (typeof finalOutput === "string") {
+      setMessages((prevMessages) => [
+        ...prevMessages.slice(0, -1),
+        { type: "ai", content: finalOutput, runId: aggregatedState?.id }
+      ]); 
+    } else if (isAIMessage(finalOutput)) {
+      setMessages((prevMessages) => [
+        ...prevMessages.slice(0, -1),
+        { type: "ai", content: finalOutput.content, runId: aggregatedState?.id }
+      ]);
+    }
+  });
+  useStreamCallback("onSuccess", () => {
+    setIsLoading(false);
+  });
+  useStreamCallback("onError", (e) => {
+    setIsLoading(false);
+    toast(e.message + "\nCheck your backend logs for errors.", { hideProgressBar: true });
+    setCurrentInputValue(messages[messages.length - 2]?.content);
+    setMessages((prevMessages) => [
+      ...prevMessages.slice(0, -2),
+    ]);
+  });
+
+  return (
+    <div className="flex flex-col h-screen w-screen">
+      <nav className="flex items-center justify-between p-8">
+        <div className="flex items-center">
+          <LangServeLogo />
+          <span className="ml-1">Playground</span>
+        </div>
+        <div className="flex items-center space-x-4">
+          <ShareDialog config={{}}>
+            <button
+              type="button"
+              className="px-3 py-1 border rounded-full px-8 py-2 share-button"
+            >
+              <span>Share</span>
+            </button>
+          </ShareDialog>
+        </div>
+      </nav>
+      <div className="flex-grow flex flex-col items-center justify-center mt-8">
+        {messages.length > 0 ? (
+          <div className="flex flex-col-reverse basis-0 overflow-auto flex-re grow max-w-[640px] w-[640px]">
+            {messages.map((message, i) => {
+              return (
+                <ChatMessage
+                  message={message}
+                  key={i}
+                  isLoading={isLoading}
+                  onError={(e: any) => toast(e.message, { hideProgressBar: true })}
+                  feedbackEnabled={feedbackEnabled.data}
+                  publicTraceLinksEnabled={publicTraceLinksEnabled.data}
+                  isFinalMessage={i === messages.length - 1}
+                  onRemove={() => setMessages(
+                    (previousMessages) => [...previousMessages.slice(0, i), ...previousMessages.slice(i + 1)]
+                  )}
+                  onTypeChange={(newValue) => {
+                    setMessages(
+                      (previousMessages) => [
+                        ...previousMessages.slice(0, i),
+                        {...message, type: newValue as ChatMessageType},
+                        ...previousMessages.slice(i + 1)
+                      ]
+                    )
+                  }}
+                  onChange={(newValue) => {
+                    setMessages(
+                      (previousMessages) => [
+                        ...previousMessages.slice(0, i),
+                        {...message, content: newValue},
+                        ...previousMessages.slice(i + 1)
+                      ]
+                    );
+                  }}
+                  onRegenerate={() => regenerateMessages()}
+                ></ChatMessage>
+              );
+            }).reverse()}
+          </div>
+        ) : (
+          <div className="flex flex-col items-center justify-center">
+            <EmptyState />
+            <h1 className="text-lg">Start testing your application</h1>
+          </div>
+        )}
+      </div>
+      <div className="m-16 mt-4 flex justify-center">
+        <div className="flex items-center p-3 rounded-[48px] border shadow-sm max-w-[768px] grow" onClick={() => messageInputRef.current?.focus()}>
+          <AutosizeTextarea
+            inputRef={messageInputRef}
+            className="flex-grow mr-4 ml-8 border-none focus:ring-0 py-2 cursor-text"
+            placeholder="Send a message..."
+            value={currentInputValue}
+            onChange={(newValue) => {
+              setCurrentInputValue(newValue);
+            }}
+            onKeyDown={(e) => {
+              if (e.key === 'Enter' && !e.shiftKey) {
+                e.preventDefault();
+                submitMessage();
+              }
+            }}
+          />
+          <button
+            className={"flex items-center justify-center px-3 py-1 rounded-[40px] " + (isLoading ? "" : currentInputValue.length > 0 ? "bg-button-green" : "bg-button-green-disabled")}
+            onClick={(e) => {
+              e.preventDefault();
+              submitMessage();
+            }}
+          >
+            {isLoading 
+              ? <CircleSpinIcon className="animate-spin w-5 h-5 text-background fill-background" />
+              : <ArrowUp className="mx-2 my-2 h-5 w-5 stroke-white" />}
+          </button>
+        </div>
+      </div>
+      <ToastContainer />
+    </div>
+  )
+}

langserve/chat_playground/src/components/ShareDialog.tsx

@@ -0,0 +1,141 @@
+import { Drawer } from "vaul";
+import { ReactNode, useEffect, useMemo, useRef, useState } from "react";
+import CheckCircleIcon from "../assets/CheckCircleIcon.svg?react";
+import CodeIcon from "../assets/CodeIcon.svg?react";
+import CopyIcon from "../assets/CopyIcon.svg?react";
+import PadlockIcon from "../assets/PadlockIcon.svg?react";
+import ShareIcon from "../assets/ShareIcon.svg?react";
+import { compressToEncodedURIComponent } from "lz-string";
+import { getStateFromUrl } from "../utils/url";
+
+const URL_LENGTH_LIMIT = 2000;
+
+function CopyButton(props: { value: string }) {
+  const [copied, setCopied] = useState(false);
+  const cbRef = useRef<number | null>(null);
+
+  function toggleCopied() {
+    setCopied(true);
+
+    if (cbRef.current != null) window.clearTimeout(cbRef.current);
+    cbRef.current = window.setTimeout(() => setCopied(false), 1500);
+  }
+
+  useEffect(() => {
+    return () => {
+      if (cbRef.current != null) {
+        window.clearTimeout(cbRef.current);
+      }
+    };
+  }, []);
+
+  return (
+    <button
+      className="px-3 py-1"
+      onClick={() => {
+        navigator.clipboard.writeText(props.value).then(toggleCopied);
+      }}
+    >
+      {copied ? <CheckCircleIcon /> : <CopyIcon />}
+    </button>
+  );
+}
+
+export function ShareDialog(props: { config: unknown; children: ReactNode }) {
+  const hash = useMemo(() => {
+    return compressToEncodedURIComponent(JSON.stringify(props.config));
+  }, [props.config]);
+
+  const state = getStateFromUrl(window.location.href);
+
+  // get base URL
+  const targetUrl = `${state.basePath}/c/${hash}`;
+
+  // .../c/[hash]/playground
+  const playgroundUrl = `${targetUrl}/playground`;
+
+  // cURL, JS: .../c/[hash]/invoke
+  // Python: .../c/[hash]
+  const invokeUrl = `${targetUrl}/invoke`;
+
+  const pythonSnippet = `
+from langserve import RemoteRunnable
+
+chain = RemoteRunnable("${targetUrl}")
+chain.invoke({ ... })
+`;
+
+  const typescriptSnippet = `
+import { RemoteRunnable } from "langchain/runnables/remote";
+
+const chain = new RemoteRunnable({ url: \`${invokeUrl}\` });
+const result = await chain.invoke({ ... });
+`;
+
+  return (
+    <Drawer.Root>
+      <Drawer.Trigger asChild>{props.children}</Drawer.Trigger>
+      <Drawer.Portal>
+        <Drawer.Overlay className="fixed inset-0 bg-black/40" />
+        <Drawer.Content className="flex justify-center items-center mt-24 fixed bottom-0 left-0 right-0 !pointer-events-none after:!bg-background">
+          <div className="p-4 bg-background max-w-[calc(800px-2rem)] rounded-t-2xl border border-divider-500 border-b-background pointer-events-auto">
+            <h3 className="flex items-center text-lg font-light">
+              <ShareIcon className="flex-shrink-0 mr-2" />
+              <span>Share</span>
+            </h3>
+
+            <hr className="border-divider-500 my-4 -mx-4" />
+
+            <div className="flex flex-col gap-3">
+              {playgroundUrl.length < URL_LENGTH_LIMIT && (
+                <div className="flex flex-col gap-2 p-3 rounded-2xl">
+                  <div className="flex items-center">
+                    <div className="w-10 h-10 flex items-center justify-center text-center text-sm bg-background rounded-xl">
+                      🦜
+                    </div>
+                    <span>Chat interface</span>
+                  </div>
+                  <div className="grid grid-cols-[auto,1fr,auto] rounded-xl text-sm items-center border">
+                    <PadlockIcon className="mx-3" />
+                    <div className="overflow-auto whitespace-nowrap py-3 no-scrollbar">
+                      {playgroundUrl.split("://")[1]}
+                    </div>
+                    <CopyButton value={playgroundUrl} />
+                  </div>
+                </div>
+              )}
+
+              <div className="flex flex-col gap-2 p-3 rounded-2xl">
+                <div className="flex items-center">
+                  <div className="w-10 h-10 flex items-center justify-center text-center text-sm bg-background rounded-xl">
+                    <CodeIcon className="w-4 h-4" />
+                  </div>
+                  <span>Get the code</span>
+                </div>
+
+                {targetUrl.length < URL_LENGTH_LIMIT && (
+                  <div className="grid grid-cols-[1fr,auto] rounded-xl text-sm items-center border">
+                    <div className="overflow-auto whitespace-nowrap px-3 py-3 no-scrollbar">
+                      Python SDK
+                    </div>
+                    <CopyButton value={pythonSnippet.trim()} />
+                  </div>
+                )}
+
+                {invokeUrl.length < URL_LENGTH_LIMIT && (
+                  <div className="grid grid-cols-[1fr,auto] rounded-xl text-sm items-center border">
+                    <div className="overflow-auto whitespace-nowrap px-3 py-3 no-scrollbar">
+                      TypeScript SDK
+                    </div>
+
+                    <CopyButton value={typescriptSnippet.trim()} />
+                  </div>
+                )}
+              </div>
+            </div>
+          </div>
+        </Drawer.Content>
+      </Drawer.Portal>
+    </Drawer.Root>
+  );
+}

langserve/chat_playground/src/components/feedback/CorrectnessFeedback.tsx

@@ -0,0 +1,102 @@
+import { toast } from "react-toastify";
+import ThumbsUpIcon from "../../assets/ThumbsUpIcon.svg?react";
+import ThumbsDownIcon from "../../assets/ThumbsDownIcon.svg?react";
+import CircleSpinIcon from "../../assets/CircleSpinIcon.svg?react";
+import CheckCircleIcon2 from "../../assets/CheckCircleIcon2.svg?react";
+import XCircle from "../../assets/XCircle.svg?react";
+import { resolveApiUrl } from "../../utils/url";
+import { useState } from "react";
+import useSWRMutation from "swr/mutation";
+
+const useFeedbackMutation = (runId: string, onError?: (e: any) => void) => {
+  interface FeedbackArguments {
+    key: string;
+    score: number;
+  }
+
+  const [lastArg, setLastArg] = useState<FeedbackArguments | null>(null);
+
+  const mutation = useSWRMutation(
+    ["feedback", runId],
+    async ([, runId], { arg }: { arg: FeedbackArguments }) => {
+      const payload = { run_id: runId, key: arg.key, score: arg.score };
+      setLastArg(arg);
+
+      const request = await fetch(resolveApiUrl("/feedback"), {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify(payload),
+      });
+
+      if (!request.ok) {
+        if (request.status === 404) {
+          onError?.(new Error(`Feedback endpoint not found. Please enable it in your LangServe endpoint.`));
+        } else {
+          try {
+            const errorResponse = await request.json();
+            onError?.(new Error(`${errorResponse.detail}`));
+          } catch (e) {
+            onError?.(new Error(`Request failed with status: ${request.status}`));
+          }
+        }
+        throw new Error(`Failed request ${request.status}`)
+      }
+      const json: {
+        id: string;
+        score: number;
+      } = await request.json();
+
+      toast("Feedback sent successfully!", { hideProgressBar: true });
+      return json;
+    }
+  );
+
+  return { lastArg: mutation.isMutating ? lastArg : null, mutation };
+};
+
+export function CorrectnessFeedback(props: { runId: string, onError?: (e: any) => void }) {
+  const score = useFeedbackMutation(props.runId, props.onError);
+
+  if (props.runId == null) return null;
+  return (
+    <>
+      <button
+        type="button"
+        className={"bg-background rounded p-1 hover:opacity-80"}
+        disabled={score.mutation.isMutating}
+        onClick={() => {
+          if (score.mutation.data?.score !== 1) {
+            score.mutation.trigger({ key: "correctness", score: 1 });
+          }
+        }}
+      >
+        {score.lastArg?.score === 1 ? (
+          <CircleSpinIcon className="animate-spin w-4 h-4 text-white/50 fill-white" />
+        ) : (
+          (score.mutation.data?.score !== 1
+            ? <ThumbsUpIcon className="w-4 h-4" />
+            : <CheckCircleIcon2 className="w-4 h-4 stroke-teal-500" />)
+        )}
+      </button>
+
+      <button
+        type="button"
+        className={"bg-background rounded p-1 hover:opacity-80"}
+        disabled={score.mutation.isMutating}
+        onClick={() => {
+          if (score.mutation.data?.score !== 0) {
+            score.mutation.trigger({ key: "correctness", score: 0 });
+          }
+        }}
+      >
+        {score.lastArg?.score === 0 ? (
+          <CircleSpinIcon className="animate-spin w-4 h-4 text-white/50 fill-white" />
+        ) : (
+          (score.mutation.data?.score !== 0
+            ? <ThumbsDownIcon className="w-4 h-4" />
+            : <XCircle className="w-4 h-4 stroke-red-500" />)
+        )}
+      </button>
+    </>
+  );
+}

langserve/chat_playground/src/main.tsx

@@ -0,0 +1,11 @@
+import ReactDOM from "react-dom/client";
+import App from "./App.tsx";
+
+import dayjs from "dayjs";
+import utc from "dayjs/plugin/utc";
+import relativeDate from "dayjs/plugin/relativeTime";
+
+dayjs.extend(relativeDate);
+dayjs.extend(utc);
+
+ReactDOM.createRoot(document.getElementById("root")!).render(<App />);

langserve/chat_playground/src/types.tsx

@@ -0,0 +1,9 @@
+import type { Operation } from "fast-json-patch";
+import type { RunState } from "./useStreamLog";
+
+export interface StreamCallback {
+  onSuccess?: (ctx: { input: unknown; output: unknown }) => void;
+  onChunk?: (chunk: { ops?: Operation[] }, aggregatedState: RunState | null) => void;
+  onError?: (error: any) => void;
+  onStart?: (ctx: { input: unknown }) => void;
+}

langserve/chat_playground/src/useSchemas.tsx

@@ -0,0 +1,131 @@
+import { JsonSchema } from "@jsonforms/core";
+import { compressToEncodedURIComponent } from "lz-string";
+import { resolveApiUrl } from "./utils/url";
+import { simplifySchema } from "./utils/simplifySchema";
+
+import useSWR from "swr";
+import defaults from "./utils/defaults";
+
+declare global {
+  interface Window {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    CONFIG_SCHEMA?: any;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    INPUT_SCHEMA?: any;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    OUTPUT_SCHEMA?: any;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    FEEDBACK_ENABLED?: any;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    PUBLIC_TRACE_LINK_ENABLED?: any;
+  }
+}
+
+export function useFeedback() {
+  return useSWR(["/feedback"], async () => {
+    if (!import.meta.env.DEV && window.FEEDBACK_ENABLED) {
+      return window.FEEDBACK_ENABLED === "true";
+    }
+
+    const response = await fetch(resolveApiUrl("/feedback"), {
+      method: "HEAD",
+    });
+    return response.ok;
+  });
+}
+
+export function usePublicTraceLink() {
+  return useSWR(["/public_trace_link"], async () => {
+    if (!import.meta.env.DEV && window.PUBLIC_TRACE_LINK_ENABLED) {
+      return window.PUBLIC_TRACE_LINK_ENABLED === "true";
+    }
+
+    const response = await fetch(resolveApiUrl("/public_trace_link"), {
+      method: "HEAD",
+    });
+    return response.ok;
+  });
+}
+
+export function useConfigSchema() {
+  return useSWR(["/config_schema"], async () => {
+    let schema: JsonSchema | null = null;
+    if (!import.meta.env.DEV && window.CONFIG_SCHEMA) {
+      schema = await simplifySchema(window.CONFIG_SCHEMA);
+    } else {
+      const response = await fetch(resolveApiUrl(`/config_schema`));
+      if (!response.ok) throw new Error(await response.text());
+
+      const json = await response.json();
+      schema = await simplifySchema(json);
+    }
+
+    if (schema == null) return null;
+    return {
+      schema,
+      defaults: defaults(schema),
+    };
+  });
+}
+
+export function useInputSchema(configData?: unknown) {
+  return useSWR(
+    ["/input_schema", configData],
+    async ([, configData]) => {
+      // TODO: this won't work if we're already seeing a prefixed URL
+      const prefix = configData
+        ? `/c/${compressToEncodedURIComponent(JSON.stringify(configData))}`
+        : "";
+
+      let schema: JsonSchema | null = null;
+
+      if (!prefix && !import.meta.env.DEV && window.INPUT_SCHEMA) {
+        schema = await simplifySchema(window.INPUT_SCHEMA);
+      } else {
+        const response = await fetch(resolveApiUrl(`${prefix}/input_schema`));
+        if (!response.ok) throw new Error(await response.text());
+
+        const json = await response.json();
+        schema = await simplifySchema(json);
+      }
+
+      if (schema == null) return null;
+      return {
+        schema,
+        defaults: defaults(schema),
+      };
+    },
+    { keepPreviousData: true }
+  );
+}
+
+export function useOutputSchema(configData?: unknown) {
+  return useSWR(
+    ["/output_schema", configData],
+    async ([, configData]) => {
+      // TODO: this won't work if we're already seeing a prefixed URL
+      const prefix = configData
+        ? `/c/${compressToEncodedURIComponent(JSON.stringify(configData))}`
+        : "";
+
+      let schema: JsonSchema | null = null;
+
+      if (!prefix && !import.meta.env.DEV && window.OUTPUT_SCHEMA) {
+        schema = await simplifySchema(window.OUTPUT_SCHEMA);
+      } else {
+        const response = await fetch(resolveApiUrl(`${prefix}/output_schema`));
+        if (!response.ok) throw new Error(await response.text());
+
+        const json = await response.json();
+        schema = await simplifySchema(json);
+      }
+
+      if (schema == null) return null;
+      return {
+        schema,
+        defaults: defaults(schema),
+      };
+    },
+    { keepPreviousData: true }
+  );
+}

langserve/chat_playground/src/useStreamCallback.tsx

@@ -0,0 +1,78 @@
+import {
+  MutableRefObject,
+  createContext,
+  useContext,
+  useEffect,
+  useRef,
+} from "react";
+
+import { StreamCallback } from "./types";
+
+export const AppCallbackContext = createContext<MutableRefObject<{
+  onStart: Exclude<StreamCallback["onStart"], undefined>[];
+  onChunk: Exclude<StreamCallback["onChunk"], undefined>[];
+  onSuccess: Exclude<StreamCallback["onSuccess"], undefined>[];
+  onError: Exclude<StreamCallback["onError"], undefined>[];
+}> | null>(null);
+
+export function useAppStreamCallbacks() {
+  // callbacks handling
+  const context = useRef<{
+    onStart: Exclude<StreamCallback["onStart"], undefined>[];
+    onChunk: Exclude<StreamCallback["onChunk"], undefined>[];
+    onSuccess: Exclude<StreamCallback["onSuccess"], undefined>[];
+    onError: Exclude<StreamCallback["onError"], undefined>[];
+  }>({ onStart: [], onChunk: [], onSuccess: [], onError: [] });
+
+  const callbacks: StreamCallback = {
+    onStart(...args) {
+      for (const callback of context.current.onStart) {
+        callback(...args);
+      }
+    },
+    onChunk(...args) {
+      for (const callback of context.current.onChunk) {
+        callback(...args);
+      }
+    },
+    onSuccess(...args) {
+      for (const callback of context.current.onSuccess) {
+        callback(...args);
+      }
+    },
+    onError(...args) {
+      for (const callback of context.current.onError) {
+        callback(...args);
+      }
+    },
+  };
+
+  return { context, callbacks };
+}
+
+export function useStreamCallback<
+  Type extends "onStart" | "onChunk" | "onSuccess" | "onError"
+>(type: Type, callback: Exclude<StreamCallback[Type], undefined>) {
+  type CallbackType = Exclude<StreamCallback[Type], undefined>;
+
+  const appCbRef = useContext(AppCallbackContext);
+
+  const callbackRef = useRef<CallbackType>(callback);
+  callbackRef.current = callback;
+
+  useEffect(() => {
+    // @ts-expect-error Not sure why I can't expand the tuple
+    const current = (...args) => callbackRef.current?.(...args);
+    appCbRef?.current[type].push(current);
+
+    return () => {
+      if (!appCbRef) return;
+
+      // @ts-expect-error Assingability issues due to the tuple object
+      // eslint-disable-next-line react-hooks/exhaustive-deps
+      appCbRef.current[type] = appCbRef.current[type].filter(
+        (callbacks) => callbacks !== current
+      );
+    };
+  }, [type, appCbRef]);
+}