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Documentation for LangSmith, Fleet, and our open source packages.
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Documentation for LangSmith, Fleet, and our open source packages.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. This llms.txt indexes the official Ansible Community Documentation (latest stable). Source: https://docs.ansible.com/projects/ansible/latest/
A proposal that those interested in providing LLM-friendly content add a /llms.txt file to their site. This is a markdown file that provides brief background information and guidance, along with links to markdown files providing more detailed information.
An ultra-portable web-browser engine for games and desktop apps.
Documentation - Discord documentation and resources. Use this skill when working with Documentation - Discord or when the user mentions documentation - discord.
The official documentation for building on the Slack platform: apps, agents, workflows, and integrations. Covers the Slack CLI, Bolt frameworks (JavaScript, Python, Java), SDKs, the Web and Events APIs, Block Kit, authentication, and Slack Marketplace distribution.
Composio powers 1000+ toolkits, tool search, context management, authentication, and a sandboxed workbench to help you build AI agents that turn intent into action.
| name | docs-by-langchain |
| description | Documentation for LangSmith, Fleet, and our open source packages. |
| metadata | {"source":"llms.txt","source_url":"https://docs.langchain.com/llms.txt","generated":"2026-05-12T18:20:27.908Z"} |
Documentation for LangSmith, Fleet, and our open source packages.
Documentation for LangSmith, Fleet, and our open source packages.
Create Connection
List Connections
Remove Connection
Authenticate: Get OAuth token or start authentication flow if needed.
Check Oauth Token Exists: Return whether the current user has any tokens for a given provider (across agents).
Check Workspace Slack Tokens Exist: Check if the workspace has any Slack tokens.
Create Mcp Oauth Provider: Create an OAuth provider via MCP auto-discovery.
Create Oauth Provider: Create a new OAuth provider manually.
Delete Oauth Provider: Delete an OAuth provider.
Delete Oauth Tokens For User: Delete all tokens for the current user for the given provider (across agents).
Delete Single Oauth Token: Delete a specific OAuth token, revoking it at the provider first.
Get Oauth Provider: Get a specific OAuth provider.
List Oauth Providers: List OAuth providers.
List Oauth Tokens For User: List the calling user's tokens for a provider.
Oauth Callback
Oauth Callback Get: Handle OAuth callback redirect from OAuth providers.
Oauth Setup Callback: Handle OAuth setup callback redirect from GitHub Apps.
Revoke All Slack Tokens For Workspace: Revoke ALL Slack tokens for the workspace. Admin-only action that disconnects Slack entirely.
Update Oauth Provider: Update an OAuth provider.
Update Token Label: Update a token's provider_account_label. Only the token owner can update.
Wait For Auth Completion: Wait for OAuth authentication completion.
Create Deployment: Create a new deployment.
Delete Deployment: Delete a deployment by ID.
Delete Deployments: Delete multiple deployments with partial success support.
Get Deployment: Get a deployment by ID.
Get Revision: Get a revision by ID for a deployment.
List Deployments: List all deployments.
List Revisions: List all revisions for a deployment.
Patch Deployment: Patch a deployment by ID.
Redeploy Revision: Redeploy a specific revision ID.
List Forge GitHub Integrations: List available Forge GitHub integrations.
List Forge GitHub Repositories: List available GitHub repositories for a Forge integration.
List GitHub Integrations: List available GitHub integrations for LangGraph Platfom Cloud SaaS.
List GitHub Repositories: List available GitHub repositories for an integration that are available to deploy to LangSmith Deployment.
Create Listener: Create a listener.
Creating a listener is only allowed for LangSmith organizations with self-hosted enterprise plans.
Delete Listener: Delete a listener by ID.
Get Listener: Get a listener by ID.
List Listeners: List all listeners.
Patch Listener: Patch a listener by ID.
Link: [](
Attribute-based access control
Access the current run (span) within a traced function
Connect an authentication provider
Human-in-the-loop using server API
Add metadata and tags to traces
Overview
Set up Agent Auth (Beta): Enable secure access from agents to any system using OAuth 2.0 credentials with Agent Auth.
Agent Server
A2A JSON-RPC: Communicate with an assistant using the Agent-to-Agent (A2A) Protocol over JSON-RPC 2.0. This endpoint accepts a JSON-RPC envelope and dispatches based on method.
Count Assistants: Get the count of assistants matching the specified criteria.
Create Assistant: Create an assistant.
Delete Assistant: Delete an assistant by ID.
Get Assistant: Get an assistant by ID.
Get Assistant Graph: Get an assistant by ID.
Get Assistant Schemas: Get an assistant by ID.
Get Assistant Subgraphs: Get an assistant's subgraphs.
Get Assistant Subgraphs by Namespace: Get an assistant's subgraphs filtered by namespace.
Get Assistant Versions: Get all versions of an assistant.
Patch Assistant: Update an assistant.
Search Assistants: Search for assistants.
Set Latest Assistant Version: Set the latest version for an assistant.
Count Crons: Get the count of crons matching the specified criteria.
Create Cron: Create a cron to schedule runs on new threads.
Create Thread Cron: Create a cron to schedule runs on a thread.
Delete Cron: Delete a cron by ID.
Search Crons: Search all active crons
Update Cron: Update a cron job by ID.
MCP Get: Implemented according to the Streamable HTTP Transport specification.
MCP Post: Implemented according to the Streamable HTTP Transport specification. Sends a JSON-RPC 2.0 message to the server.
Terminate Session: Implemented according to the Streamable HTTP Transport specification. Terminate an MCP session. The server implementation is stateless, so this is a no-op.
Create Background Run: Create a run and return the run ID immediately. Don't wait for the final run output.
Create Run Batch: Create a batch of runs and return immediately.
Create Run, Stream Output: Create a run and stream the output.
Create Run, Wait for Output: Create a run, wait for the final output and then return it.
Delete an item.
List namespaces with optional match conditions.
Retrieve a single item.
Search or list items within a namespace prefix.: Lists items ordered by last updated time. If a query is provided, performs a natural language search instead. Supports pagination via limit and offset, and filtering via filter.
Store or update an item.
API Documentation: A local reference to the Agent Server API documentation.
Health Check: Check the health status of the server. Optionally check database connectivity.
Server Information: Get server version information, feature flags, and metadata.
System Metrics: Get system metrics in Prometheus or JSON format for monitoring and observability.
Cancel Run
Cancel Runs: Cancel one or more runs. Can cancel runs by thread ID and run IDs, or by status filter.
Create Background Run: Create a run in existing thread, return the run ID immediately. Don't wait for the final run output.
Create Run, Stream Output: Create a run in existing thread. Stream the output.
Create Run, Wait for Output: Create a run in existing thread. Wait for the final output and then return it.
Delete Run: Delete a run by ID.
Get Run: Get a run by ID.
Join Run: Wait for a run to finish.
Join Run Stream: Join a run stream. This endpoint streams output in real-time from a run similar to the /threads/THREAD_ID/runs/stream endpoint. If the run has been created with stream_resumable=true, the stream can be resumed from the last seen event ID.
List Runs: List runs for a thread.
Copy Thread: Create a new thread with a copy of the state and checkpoints from an existing thread.
Count Threads: Get the count of threads matching the specified criteria.
Create Thread: Create a thread.
Delete Thread: Delete a thread by ID.
Get Thread: Get a thread by ID.
Get Thread History: Get all past states for a thread.
Get Thread History Post: Get all past states for a thread.
Get Thread State: Get state for a thread.
Get Thread State At Checkpoint: Get state for a thread at a specific checkpoint.
Get Thread State At Checkpoint: Get state for a thread at a specific checkpoint.
Join Thread Stream: This endpoint streams output in real-time from a thread. The stream will include the output of each run executed sequentially on the thread and will remain open indefinitely. It is the responsibility of the calling client to close the connection.
Patch Thread: Update a thread.
Prune Threads: Prune threads by ID. The 'delete' strategy removes threads entirely. The 'keep_latest' strategy prunes old checkpoints but keeps threads and their latest state.
Search Threads: Search for threads.
Update Thread State: Add state to a thread.
Agent Server changelog
Distributed tracing with Agent Server: Unify traces when calling your deployed Agent Server from another service using RemoteGraph or the SDK.
How to collect user feedback for Agent Server runs
Configure LangSmith Agent Server for scale
Alerts in LangSmith
Analyze an experiment
Custom instrumentation: Instrument your code directly to control which functions are traced and how they appear in LangSmith.
Annotate traces and runs inline
Use annotation queues
Manage feedback & annotation queues programmatically
Control plane API reference for LangSmith Deployment
Application structure
Use assertions: Capture free-form acceptance criteria as dataset examples by writing assertions while reviewing runs in an annotation queue.
Assistants
Log user feedback using the SDK
How to audit evaluator scores
Audit logs: Track and review administrative actions across your LangSmith organization for security, compliance, and operational visibility.
Authentication & access control
Authentication methods
Self-hosted LangSmith on AWS
Self-hosted LangSmith on Azure
How to kick off background runs
Export trace data to BigQuery: Load LangSmith trace data into BigQuery using bulk export to GCS.
Manage billing in your account
Automatically run evaluators on experiments
Use server-side caching: Cache values server-side in your agent deployment using stale-while-revalidate and key-value cache APIs.
How to cancel a run: Cancel a single run or multiple runs via the API, and choose between interrupt and rollback actions.
Implement a CI/CD pipeline using LangSmith Deployment and Evaluation
LangGraph CLI
Cloud (SaaS)
How to define a code evaluator
How to define a code evaluator
How to compare experiment results
LangSmith Deployment components: Overview of Agent Server, LangGraph CLI, Studio, SDKs, RemoteGraph, control plane, and data plane components.
How to create a composite evaluator
How to create a composite evaluator
Conditional tracing
Use HTTP headers for runtime configuration
Include HTTP headers in server logs
Manage assistants
Configure checkpointer backend: Configure Agent Server to use PostgreSQL, MongoDB, or a custom implementation for checkpoint storage.
Configure run input and output preview: Customize what appears in the Input and Output columns of the Runs table by configuring custom preview paths for specific trace types.
How to add TTLs to your application
Context engineering concepts: Core concepts for context engineering in LangSmith, including skills, agents, versioning, and sharing.
LangSmith control plane
Core capabilities overview: Overview of Agent Server core capabilities including streaming, human-in-the-loop, MCP, A2A, distributed tracing, webhooks, and double-texting.
Cost tracking
Create a prompt
Create an account and API key
How to improve your evaluator with few-shot examples
Use cron jobs
Add custom authentication
How to use a custom checkpointer: Replace the built-in Postgres checkpointer with a custom BaseCheckpointSaver implementation in your agent deployment.
How to customize the Dockerfile
Connect to a custom model
How to add custom lifespan events
How to add custom middleware
Connect to an OpenAI compliant model provider/proxy
Custom output rendering
How to add custom routes
How to use a custom store: Replace the built-in Postgres store with a custom BaseStore implementation in your agent deployment.
Monitor projects with dashboards
Bulk export trace data: Export LangSmith trace data to an S3-compatible bucket in Parquet format.
Manage bulk export destinations: Configure and manage S3-compatible export destinations for LangSmith bulk exports.
Import exported data: Import LangSmith bulk-exported Parquet data into BigQuery, Snowflake, Redshift, Clickhouse, or DuckDB.
Monitor and troubleshoot bulk exports: Monitor bulk export status, manage running exports, and troubleshoot failures.
LangSmith data plane
Data purging for compliance
Data storage and privacy
Dataset prebuilt JSON schema types
Dataset transformations
How to define a target function to evaluate
Set up hybrid LangSmith: Connect a self-hosted data plane to the managed LangSmith control plane for hybrid agent deployment.
Deploy other frameworks: Deploy agents built with Strands, CrewAI, or other frameworks to LangSmith using the LangGraph Functional API.
Enable LangSmith Deployment, Fleet, Insights, and Polly: Enable LangSmith Deployment, Fleet, Insights, and Polly on a self-hosted LangSmith instance.
Self-host standalone servers: Deploy standalone Agent Servers using Docker, Docker Compose, or Kubernetes without the LangSmith control plane.
Deploy on Cloud: Create and manage LangSmith Cloud deployments including revisions, logs, metrics, and settings.
Deploy with control plane: Build Docker images and deploy applications to hybrid or self-hosted LangSmith instances using the control plane UI.
LangSmith Deployment: Deploy and manage agents with durable execution, real-time streaming, and horizontal scaling.
Deploy your app to cloud: Deploy your first application to LangSmith Cloud (AWS and GCP) using the LangGraph CLI.
Deploy your deep agent app to cloud
Troubleshooting for self-hosted deployments: Diagnostic steps for troubleshooting self-hosted LangSmith Deployment issues before contacting support.
Implement distributed tracing
Double texting
Add encryption at rest
Enqueue concurrent
LangSmith for Enterprise: Deployment options, access control, data privacy, cost controls, and security compliance for Enterprise users.
Environment variables
Evaluate a chatbot
Evaluate a complex agent
How to add evaluators to an existing experiment (Python only)
How to evaluate a graph
How to evaluate an LLM application
How to evaluate an application's intermediate steps
How to run a pairwise evaluation
Evaluate a RAG application
Run an evaluation with multimodal content: Learn how to create dataset examples with file attachments and use them in prompts and evaluators when running LangSmith evaluations with multimodal content.
How to evaluate with OpenTelemetry
How to retry failed runs in experiments (Python only)
LangSmith Evaluation
Application-specific evaluation approaches
How to run an evaluation asynchronously
Evaluation concepts
Evaluation quickstart
Evaluation types
Manage evaluators: View and manage evaluators at the workspace level in LangSmith.
Example data format
Experiment configuration
Export LangSmith telemetry to your observability backend
Query traces using the SDK
Frequently asked questions
Feedback data format
How to fetch performance metrics for an experiment
How to filter experiments in the UI
Filter traces
Access & oversight: Control who can access agents, how they authenticate, and audit everything they do.
Agent identity: Choose whether your Fleet agent authenticates with its own credentials or with each user's credentials.
Arcade integration: Connect your workspace to Arcade to give agents access to third-party tools like GitHub, Gmail, Slack, and more.
Auth-aware tool responses: Format tool responses to trigger OAuth flows and resume execution automatically.
Channels: Configure channels to trigger your Fleet agents automatically.
Call agents from code: Invoke Fleet agents from Python, JavaScript, or any language through the REST API.
Agent platform comparison: Compare LangSmith Fleet with Claude Cowork, Amazon Quick, Google Workspace Studio, and Microsoft Copilot to choose the right enterprise agent platform for your team
Essentials: Fleet's core features
LangSmith Fleet: Create helpful AI agents without code. Start from a template, connect your accounts, and let the agent handle routine work while you stay in control.
Manage agent settings: Manage your agents in Fleet.
LangSmith Tool Server
Pricing FAQ
Quickstart: Build an agent from a template
Remote MCP servers: Connect Fleet to popular remote MCP servers
Salesforce integration: Connect LangSmith Fleet to Salesforce so your agents can query records, navigate schemas, and read custom fields.
Schedules: Configure schedules to run your Fleet agents on a recurring basis.
Self-hosted
Skills: Use skills to give your agents access to specific capabilities.
Integrate Slack with an agent: Connect LangSmith Fleet to your Slack workspace to let your agents communicate with users in Slack.
Integrate Teams with an agent: Connect LangSmith Fleet to Microsoft Teams by bringing your own Azure Bot to let agents communicate with users in Teams.
Templates: Start faster with curated Fleet templates and customize tools, prompts, and channels.
Tool integrations: Give your agents access to a wide range of tools and services.
Fleet webhooks: Integrate agent publishing with external systems, CI/CD pipelines, or custom deployment workflows.
Manage workspace administration: Configure workspace-level settings for Fleet.
Self-hosted LangSmith on GCP
How to implement generative user interfaces with LangGraph
Integrations
Granular billable usage: Retrieve detailed trace usage data broken down by workspace, project, user, or API key.
Rebuild graph at runtime: Rebuild your graph with different configurations for each run using ServerRuntime.
LangSmith docs
Time travel using the server API
Hybrid
Improve LLM-as-judge evaluators using human feedback
Discover errors and usage patterns with the Insights Agent
Integrations
Interrupt concurrent
Manage user access in SSO organizations
Self-host LangSmith on Kubernetes
How to evaluate a runnable
LangGraph JS/TS SDK
LangGraph Python SDK
LangSmith CLI: Query and manage LangSmith projects, traces, runs, datasets, evaluators, experiments, and threads from the terminal
Configure your collector for LangSmith telemetry
LangSmith-managed ClickHouse
LangSmith MCP Server: Use the Model Context Protocol (MCP) server to let language models fetch conversation history, prompts, runs, datasets, experiments, and billing from LangSmith.
LangSmith Remote MCP: Connect MCP-compatible clients to LangSmith over OAuth—no API key, no self-hosting, no header configuration.
How to define an LLM-as-a-judge evaluator
How to define an LLM-as-a-judge evaluator
Set up the LLM auth proxy: Deploy an Envoy-based auth proxy that validates LangSmith-signed JWTs and routes LLM requests to your upstream provider or gateway.
How to run an evaluation locally (Python only)
Local development & testing: Compare langgraph dev and langgraph up for local development and production-like testing of Agent Server applications.
Log LLM calls
Log multimodal traces
Log retriever traces: Log retrieval steps in LangSmith traces for document-level visibility into your RAG pipeline.
Log traces to a specific project: Route LangSmith traces to a named project instead of the default project using environment variables or the SDK.
Metadata parameters reference
Manage contexts with the SDK: Use the LangSmith SDK to push, pull, list, and delete agent and skill repos in the Context Hub programmatically.
Manage datasets
Create and manage datasets in the UI
How to create and manage datasets programmatically
Manage your organization using the API
Manage prompts: Manage prompt versions, environments, and access controls in LangSmith.
Manage prompts programmatically
Manage a trace: Compare traces side-by-side, share traces publicly, and view server logs from the trace view in LangSmith.
Configure prompt settings
Prevent logging of sensitive data in traces
How to return categorical vs numerical metrics
Manage model configurations: Manage model configurations and control their availability across LangSmith features.
Monorepo support
How to simulate multi-turn interactions
Include multimodal content in a prompt
Test multi-turn conversations
How to return multiple scores in one evaluator
Troubleshoot trace nesting
LangSmith Observability: Instrument your LLM application, investigate traces, and monitor performance in production with LangSmith.
Observability concepts
Trace an LLM application tutorial: Add LangSmith observability to an LLM application across prototyping, beta testing, and production.
Tracing quickstart: Add LangSmith tracing to an LLM application in minutes.
Deploy an observability stack for your LangSmith deployment
Observability in Studio
Set up online code evaluators
Set up composite online evaluators
Set up LLM-as-a-judge online evaluators
Set up multi-turn online evaluators
Document API authentication in OpenAPI
Run evals with openevals package: Run evaluations using the open-source openevals and agentevals packages with LangSmith.
Optimize a classifier
Organization and workspace operations reference
Redact sensitive data with the OpenTelemetry Gateway architecture: Use an OpenTelemetry collector to redact sensitive data from traces before they land in LangSmith.
Set up LangSmith
Model providers
LangSmith Polly
LangSmith Polly
LangSmith Polly
LangSmith Polly
Collect feedback with presigned URLs: Use presigned feedback tokens to collect user feedback from client-side applications without exposing your LangSmith API key.
Frequently Asked Questions
Pricing plans
Profile configuration: Configure LangSmith SDK credentials and endpoints with a local profile file.
How to sync prompts with GitHub
Prompt engineering
Prompt engineering concepts
Prompt engineering quickstart
Prompt template format guide
How to run evaluations with pytest
Query threads using the SDK: Programmatically fetch and inspect multi-turn conversation threads from your LangSmith projects.
Get started with Studio
How to handle model rate limits
Role-based access control
How to read experiment results locally
LangSmith reference
Regions FAQ
Reject Concurrent
Release policy: Release channels, cadence, and version support for self-hosted LangSmith.
RemoteGraph
How to evaluate with repetitions
Make conversations private
Rollback Concurrent
Set up automation rules
Run backtests on a new version of an agent
Run (span) data format
How to use the REST API
Run an evaluation from the Playground
Runs: An overview of runs in Agent Server, including how to kick off background runs, stateless runs, and cancel runs.
How to run multiple agents on the same thread
Set a sampling rate for traces
Sandbox auth proxy: Inject credentials into outbound API requests from sandboxes without hardcoding secrets.
Sandbox CLI: Create, inspect, connect to, and tunnel into LangSmith sandboxes from the command line.
Sandbox access permissions: Control who in your workspace can interact with a sandbox after it has been created.
Sandbox SDK usage: Create and manage sandboxes programmatically with the Python or TypeScript SDK.
Sandbox service URLs: Access HTTP services running inside sandboxes via authenticated URLs, from a browser or programmatically.
Sandbox snapshots: Build and capture snapshots—the filesystem images every sandbox boots from.
Sandboxes overview: Use managed sandboxes to safely execute code and interact with the filesystem in isolated environments.
Scalability & resilience
Delete workspaces
Delete organizations
Delete traces
Generate ClickHouse stats
Generate query stats
Run support queries against ClickHouse
Run support queries against PostgreSQL
Basic authentication with email and password
Enable blob storage
Configure custom TLS certificates
Minimum versions for self-hosting dependencies: Minimum supported versions for the databases, tools, and infrastructure required to self-host LangSmith.
Egress for billing and operational telemetry
Connect to an external ClickHouse database
Connect to an external PostgreSQL database
Connect to an external Redis or Valkey database
FIPS-compliant images: Run self-hosted LangSmith installation on FIPS 140 compliant container images
Create an Ingress for installations (Kubernetes)
Mirror images for your LangSmith installation
View trace counts across your organization
Use environment variables for model providers
Configure LangSmith for scale
Set up SSO with OAuth2.0 and OIDC
Enable TTL and data retention
Customize the error support message: Customize support contact information in the LangSmith frontend for self-hosted deployments.
Upgrade an installation
Interact with your self-hosted instance of LangSmith
Customize user management
Use an existing secret for your installation (Kubernetes)
Self-hosted LangSmith
Self-hosted LangSmith changelog
Install Mission Control: Install Mission Control, an in-cluster console for monitoring, configuring, and operating self-hosted LangSmith on Kubernetes.
How to add semantic search to your agent deployment
A2A endpoint in Agent Server: Use the A2A protocol to enable agent-to-agent communication with distributed tracing in LangSmith.
Agent Server API reference for LangSmith Deployment
MCP endpoint in Agent Server
Trace JS functions in serverless environments
Set up custom authentication
Set up feedback criteria
Set up hierarchy
Set up resource tags: Create and manage resource tags to organize projects, datasets, prompts, and other resources within a LangSmith workspace.
How to set up an application with requirements.txt
How to set up a JavaScript application
How to set up an application with pyproject.toml
LangSmith shared responsibility model: Overview of how LangChain and customers share security responsibilities for the LangSmith platform.
LangSmith skills: Use Agent Skills to work with LangSmith traces, datasets, and evaluators from your coding agent.
LangSmith API reference
LangSmith Deployment SDK
LangSmith Go SDK
LangSmith Java SDK
LangSmith JS/TS SDK
LangSmith Python SDK
Stateless runs
LangSmith status
Streaming API
LangSmith Studio
How to define a summary evaluator
Test a ReAct agent with Pytest/Vitest and LangSmith
Configure threads
Trace Anthropic applications
Trace Amazon Bedrock applications
Trace Claude Agent SDK applications
Trace Claude Code applications
Trace Deep Agents applications
Trace DeepSeek applications
Trace generator functions
Trace LiteLLM applications
Trace OpenAI applications
Trace query syntax
Trace with API: Learn how to trace LLM applications using the LangSmith REST API directly.
Trace AutoGen applications
Trace OpenAI Codex sessions: Capture OpenAI Codex agent turns, tool calls, model metadata, and subagent threads in LangSmith.
Trace CrewAI applications
Trace Google ADK applications
Trace Google Gemini applications
Trace Instructor applications
Trace LangChain applications (Python and JS/TS)
Trace LangGraph applications
Trace LiveKit applications
Trace Mastra applications
Trace Microsoft Agent Framework applications
Trace Mistral applications
Trace n8n workflows: Learn how to trace n8n AI workflows in LangSmith.
Trace OpenAI Agents SDK applications: Trace OpenAI Agents SDK Python and JavaScript applications with LangSmith.
Trace OpenCode sessions: Capture OpenCode sessions, assistant turns, tool calls, and subagent activity in LangSmith.
Trace with OpenTelemetry: Configure OpenTelemetry tracing in LangSmith, including LANGSMITH_OTEL_ENABLED and OTEL fanout with the OpenTelemetry Collector.
Trace Pipecat applications
Trace PydanticAI applications
Trace Semantic Kernel applications
Trace Strands Agents applications: Trace Strands Agents applications with LangSmith.
Trace with Temporal: Learn how to trace Temporal workflows and activities in LangSmith using OpenTelemetry.
Trace Vercel AI SDK applications (JS/TS only)
Trace without setting environment variables
How to evaluate your agent with trajectory evaluations
Troubleshooting
Studio troubleshooting
Troubleshoot variable caching
How to upload experiments run outside of LangSmith with the REST API
Upload files with traces
How to interact with a deployment using RemoteGraph
How to use Studio
Use the Context Hub: Learn how to create, view, and promote context in the LangSmith Context Hub.
Use threads
Use tools in a prompt
Use webhooks
User management
View usage: What usage data is available in LangSmith, what each metric means, and what differs for self-hosted deployments.
How to run evaluations with Vitest/Jest
Configure webhook notifications for rules: Configure webhook notifications to receive POST requests when automation rules match new runs in LangSmith.
Workload isolation
Write your prompt with AI
Errors
Context overview
Memory overview
Frameworks, runtimes, and harnesses: Understand the differences between LangChain, LangGraph, and Deep Agents and when to use each one
Providers and models: Understand how LangChain uses providers to give you a single API for any model from any provider
Contributing to code
Co-marketing
Contributing to documentation
Implement a LangChain integration
Contributing integrations
Contributing
Publish an integration
Using standard tests
A2A server
Agent Client Protocol (ACP): Expose Deep Agents over the Agent Client Protocol (ACP) to integrate with code editors and IDEs.
Async subagents: Launch background subagents that run concurrently while the supervisor continues interacting with the user
Backends: Choose and configure filesystem backends for Deep Agents. You can specify routes to different backends, implement virtual filesystems, and enforce policies.
Changelog
Configuration: Configure the Deep Agents CLI with config.toml, hooks, and MCP servers
MCP tools: Load additional tools from MCP (Model Context Protocol) servers
Memory and Skills: Persistent memory, AGENTS.md files, and reusable skills for the Deep Agents CLI including creation, discovery, and invocation.
Deep Agents CLI: Terminal coding agent built on the Deep Agents SDK
Model providers: Configure any LangChain-compatible model provider for the Deep Agents CLI
Use remote sandboxes: Run Deep Agents CLI tool execution in LangSmith, Daytona, Modal, Runloop, or AgentCore sandboxes. Install provider extras, set credentials, and use flags and setup scripts.
Use subagents in the CLI: Define custom Deep Agents CLI subagents as AGENTS.md files with YAML frontmatter. Covers project and user paths, optional model overrides, and examples.
Comparison with Claude Agent SDK: Compare LangChain Deep Agents with the Claude Agent SDK to choose the right tool for your use case.
Build a content builder agent: Build a content writing agent with brand memory, skills, subagents, and image generation
Context engineering in Deep Agents: Control what context your deep agent has access to and how it is managed across long-running tasks
Customize Deep Agents: Learn how to customize Deep Agents with system prompts, tools, subagents, and more
Data locations: Where the Deep Agents CLI stores configuration, sessions, and customization files
Build a deep research agent: Build a multi-step web research agent with subagent delegation
Overview: Build UIs that display real-time subagent streams, task progress, and sandbox for Deep Agents
Sandbox: Build an IDE-like UI for a coding agent backed by a sandbox environment
Subagent streaming: Display specialist subagents with streaming content, progress tracking, and collapsible cards
Todo list: Track agent progress with a real-time todo list synced from agent state
Going to production: Take your deep agent to production with persistent memory, sandboxes, resilience middleware, and deployment options
Harness capabilities
Human-in-the-loop: Learn how to configure human approval for sensitive tool operations
Model Context Protocol
Memory: Add persistent memory to agents built with Deep Agents so they learn and improve across conversations
Models: Configure model providers and parameters for Deep Agents
Deep Agents overview: Build agents that can plan, use subagents, and leverage file systems for complex tasks
Permissions: Control filesystem access with declarative permission rules for Deep Agents
Profiles: Package per-provider and per-model defaults that Deep Agents applies when a model is selected
Quickstart: Build your first deep agent in minutes
Sandboxes: Execute code in isolated environments with sandbox backends
Skills: Learn how to extend your deep agent's capabilities with skills
Streaming: Stream real-time updates from deep agent runs and subagent execution
Subagents: Learn how to use subagents to delegate work and keep context clean
Callback integrations: Integrate with callbacks using LangChain JavaScript.
ChatAnthropic integration: Integrate with the ChatAnthropic chat model using LangChain JavaScript.
AzureChatOpenAI integration: Integrate with the AzureChatOpenAI chat model using LangChain JavaScript.
BedrockChat integration: Integrate with the BedrockChat chat model using LangChain JavaScript.
ChatGoogle integration: Integrate with the ChatGoogle chat model using LangChain JavaScript.
ChatGoogleGenerativeAI integration: Integrate with the ChatGoogleGenerativeAI chat model using LangChain JavaScript.
ChatVertexAI integration: Integrate with the ChatVertexAI chat model using LangChain JavaScript.
Chat model integrations: Integrate with chat models using LangChain JavaScript.
ChatOpenAI integration: Integrate with the ChatOpenAI chat model using LangChain JavaScript.
Document loader integrations: Integrate with document loaders using LangChain JavaScript.
Document transformer integrations: Integrate with document transformers using LangChain JavaScript.
AzureOpenAIEmbeddings integration: Integrate with the AzureOpenAIEmbeddings embedding model using LangChain JavaScript.
BedrockEmbeddings integration: Integrate with the BedrockEmbeddings embedding model using LangChain JavaScript.
Embedding model integrations: Integrate with embedding models using LangChain JavaScript.
OpenAIEmbeddings integration: Integrate with the OpenAIEmbeddings embedding model using LangChain JavaScript.
Cache integrations: Integrate with caches using LangChain JavaScript.
LLM integrations: Integrate with LLMs using LangChain JavaScript.
Middleware integrations: Integrate with middleware using LangChain JavaScript.
All LangChain JavaScript integration providers
Anthropic integrations: Integrate with Anthropic using LangChain JavaScript.
AWS integrations: Integrate with AWS using LangChain JavaScript.
Google integrations: Integrate with Google using LangChain JavaScript.
Microsoft integrations: Integrate with Microsoft using LangChain JavaScript.
OpenAI integrations: Integrate with OpenAI using LangChain JavaScript.
LangChain JavaScript integrations: Integrate with providers using LangChain JavaScript/TypeScript.
Retriever integrations: Integrate with retrievers using LangChain JavaScript.
Sandbox integrations: Integrate with sandbox providers using LangChain JavaScript.
Text splitter integrations: Integrate with text splitters using LangChain.
Store integrations: Integrate with stores using LangChain JavaScript.
Anthropic integration: Integrate with the Anthropic tool using LangChain JavaScript.
Google integration: Integrate with Google Gemini tools using LangChain JavaScript.
Tool integrations: Integrate with tools using LangChain JavaScript.
OpenAI integration: Integrate with the OpenAI tool using LangChain JavaScript.
Vector store integrations: Integrate with vector stores using LangChain JavaScript.
LangChain Academy
Agents
Changelog
Component architecture
Context engineering in agents
LangSmith Deployment
Branching chat: Edit messages, regenerate responses, and navigate conversation branches