How to Choose Between Assistants and Claws in LangSmith Fleet

LangSmith Fleet formalizes two agent authorization models: Assistants (on-behalf-of user credentials) and Claws (fixed service-account credentials). Picking the wrong one creates either security gaps or broken functionality. This guide helps you choose and implement correctly.

For background on why this distinction matters, see: LangChain Formalizes Two-Tier Agent Authorization in LangSmith Fleet

Decision Framework: Which Model Do You Need?

Answer these questions before you write a line of config:

1. Does the agent access data that belongs to the individual user interacting with it?

• Yes → lean toward Assistant (on-behalf-of)
• No → lean toward Claw (service account)

2. Will multiple different users interact with the same agent deployment?

• Yes, and they should only see their own data → Assistant required
• Yes, and they all share the same operational context → Claw is simpler and safer

3. Does your organization have strict data isolation requirements (HIPAA, SOC 2, GDPR)?

• Yes → Assistant with proper credential scoping per user
• Not a primary driver → Claw with a dedicated, minimally-scoped service account

4. Is the agent performing system tasks rather than user-facing tasks?

• System tasks (batch processing, pipeline automation, scheduled jobs) → Claw
• User-facing tasks (personal assistant, data lookup) → Assistant

Implementing an Assistant (On-Behalf-Of)

The core requirement: at runtime, know who is interacting and map their identity to credentials.

Step 1: Configure your identity provider

Your LangSmith Fleet deployment needs a way to receive user identity at the start of each session. If you’re using an SSO provider:

from langsmith import Client
from langsmithfleet import FleetAgent, AssistantAuth

# Initialize with your IdP config
auth = AssistantAuth(
    provider="okta",  # or "google", "azure-ad", etc.
    client_id=os.environ["OKTA_CLIENT_ID"],
    client_secret=os.environ["OKTA_CLIENT_SECRET"],
)

Step 2: Map user identities to tool credentials

For each tool the agent uses (Slack, Notion, Rippling, etc.), you need a credential store that maps user IDs to their OAuth tokens:

from langsmithfleet import CredentialStore

cred_store = CredentialStore(
    backend="dynamodb",  # or "postgres", "vault", etc.
    table="agent-user-credentials"
)

# Store a user's credentials after OAuth flow
cred_store.store(
    user_id="[email protected]",
    tool="notion",
    credentials={"access_token": alice_notion_token}
)

Step 3: Wire credentials into the agent at runtime

agent = FleetAgent(
    name="onboarding-assistant",
    auth_model="assistant",
    credential_resolver=cred_store.resolve,  # Called at runtime with user_id
    tools=[notion_tool, rippling_tool, slack_tool]
)

When Alice interacts with the agent, cred_store.resolve("[email protected]", "notion") is called automatically before any Notion tool invocation — injecting her credentials, not a shared service account.

What to watch out for

• Missing credentials: Users who haven’t completed the OAuth flow will get tool failures. Build a graceful onboarding flow that captures credentials before first use.
• Token expiry: OAuth tokens expire. Build refresh logic into your credential store.
• Scope creep: Request only the OAuth scopes the agent actually needs, not full account access.

Implementing a Claw (Service Account)

Claws are simpler to implement but require deliberate scoping to avoid over-permissioning.

Step 1: Create a dedicated service account for the agent

Do not use a human user’s credentials. Create a dedicated account in each tool the agent uses:

• Notion: create a bot integration with only the databases the agent needs
• Slack: create a bot app with only the channels and scopes required
• Google Workspace: create a service account with domain-wide delegation limited to necessary APIs

Document exactly what permissions each service account has. This is your audit trail.

Step 2: Store credentials securely

import os
from langsmithfleet import FleetAgent, ClawAuth

# Credentials come from environment variables or a secrets manager
auth = ClawAuth(
    credentials={
        "notion": {"api_key": os.environ["NOTION_BOT_API_KEY"]},
        "slack": {"bot_token": os.environ["SLACK_BOT_TOKEN"]},
        "rippling": {
            "client_id": os.environ["RIPPLING_CLIENT_ID"],
            "client_secret": os.environ["RIPPLING_CLIENT_SECRET"],
        }
    }
)

Never hardcode credentials. Use a secrets manager (AWS Secrets Manager, HashiCorp Vault, etc.) and rotate them on a schedule.

Step 3: Configure the agent

agent = FleetAgent(
    name="hr-operations-claw",
    auth_model="claw",
    auth=auth,
    tools=[rippling_tool, notion_tool, slack_tool]
)

All users who interact with this agent share the same underlying credentials — so scope those credentials to the minimum the agent actually needs.

What to watch out for

• Over-permissioned service accounts: If your Claw has admin access to Notion, every user who interacts with it effectively has admin access to Notion via the agent. Scope aggressively.
• No user isolation: By design, a Claw doesn’t isolate what different users can query. If user A asks the agent to “show me all Rippling employee records” and the service account has that access, it will comply. Build guardrails at the prompt/tool level if you need user-level access control.
• Audit trail gaps: Because all actions trace back to the service account, distinguishing which human triggered which agent action requires logging at the LangSmith layer, not just the tool layer.

Hybrid Patterns

Some deployments need both models. A common pattern:

• Claw for system operations: The agent uses a service account for actions like writing to a shared Notion database, posting to team Slack channels, or running reports
• Assistant for user-scoped lookups: The same agent uses the interacting user’s credentials for actions that must be user-scoped (reading their personal Rippling records, accessing their private calendar)

agent = FleetAgent(
    name="hybrid-ops-agent",
    auth_model="hybrid",
    claw_auth=system_auth,       # For shared operations
    assistant_auth=user_cred_store,  # For user-scoped operations
    tools=[...]
)

The agent framework determines at tool-call time whether to use the claw or assistant credentials based on which tool is being invoked and how it’s configured.

Quick Reference Checklist

Before going to production with an Assistant:

• Identity provider configured and tested
• Credential store populated for all users
• OAuth scopes minimized per tool
• Token refresh logic implemented
• Fallback behavior defined for missing credentials

Before going to production with a Claw:

• Dedicated service accounts created (not human accounts)
• Permissions scoped to minimum required
• Credentials stored in secrets manager (not env vars in source code)
• Rotation schedule set for all credentials
• Agent-level guardrails defined for actions that should be user-restricted

Sources

1. LangChain Blog — Two different types of agent authorization
2. LangSmith Fleet Documentation
3. LangSmith Fleet Launch Post

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260324-0800

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