keycardai-langchain

Keycard integration for LangChain agents. Every tool call gets a short-lived credential brokered by Keycard, scoped to the identity the agent is acting for, and recorded in the audit log.

Your tools never hold an API key, the model never sees a credential, and you do not write an OAuth flow.

Install

pip install keycardai-langchain

Quick start

from langchain.agents import create_agent
from langchain.tools import tool

from keycardai.langchain import (
    Access,
    KeycardGrantMiddleware,
    KeycardIdentity,
    get_access_context,
)

CALENDAR = "https://www.googleapis.com/calendar/v3"

keycard = KeycardGrantMiddleware(
    zone_url="https://your-zone.keycard.cloud",
    resources=[CALENDAR],
    client_id="your-agent",
    client_secret=...,
)


@tool
def list_events(days_ahead: int = 0) -> str:
    """List the user's calendar events."""
    token = get_access_context().access(CALENDAR).access_token
    ...


agent = create_agent(
    model,
    tools=[list_events],
    middleware=[keycard],
    context_schema=KeycardIdentity,
)

agent.invoke(
    {"messages": [...]},
    context=Access.on_behalf_of(caller_token),
)

That is the whole integration: one middleware in the agent's middleware list, and one call inside each tool to read the credential for this call.

How it works

KeycardGrantMiddleware implements LangChain's wrap_tool_call hook, so it runs at the tool-call boundary. Before each tool executes it acquires tokens for the declared resources under the identity of the run, then exposes the result to the tool as an AccessContext.

Identity travels on the agent's own context_schema, and the pause-for-authorization flow is a LangGraph interrupt.

The same middleware instance works under create_agent, a raw LangGraph graph, and create_deep_agent (deep agents are built on the same middleware system).

Access patterns

KeycardIdentity is the context schema for a run. Use an Access.* factory to select the access pattern:

A run with no identity fails with a missing_identity error, or pauses with a sign_in_required interrupt when sign_in_url is set. It never falls back to the agent's own authority: acting as itself is always an explicit choice.

On-behalf-of: a user-facing agent

The agent acts for the person in the chat. Their token is exchanged per tool call, so every resource access is attributed to agent-for-user in the audit log, and revoking the user's grant cuts the agent off immediately.

from keycardai.langchain import Access

keycard = KeycardGrantMiddleware(
    zone_url="https://your-zone.keycard.cloud",
    resources=["https://www.googleapis.com/calendar/v3"],
    client_id="your-agent",
    client_secret=os.environ["KEYCARD_CLIENT_SECRET"],
    # Optional: pause the run in-chat instead of failing.
    sign_in_url="https://your-app.example/signin",
    authorization_url="https://your-app.example/authorize",
)

agent.invoke(
    {"messages": [...]},
    context=Access.on_behalf_of(caller_token),
)

Runnable version: examples/user_facing_agent.

As itself: a background agent

No user in the loop: a scheduled digest, a queue worker, a monitor. The agent authenticates as its own application and Keycard delivers whatever credential the zone brokers for the resource, including vaulted secrets, so the worker's environment holds no API keys and revocation lives in one place.

from keycardai.langchain import Access

keycard = KeycardGrantMiddleware(
    zone_url="https://your-zone.keycard.cloud",
    resources=["https://api.github.com"],
    client_id="your-agent",
    client_secret=os.environ["KEYCARD_CLIENT_SECRET"],
)

agent.invoke(
    {"messages": [...]},
    context=Access.as_self(),
)

As-itself runs never pause on an interrupt, even when sign_in_url or authorization_url is set: there is no user to send to a consent page, so a denied grant stays on the AccessContext as an error for the tool and the operator's logs.

Runnable version: examples/background_agent.

Impersonation: acting as a specific user without their token

The agent asks for tokens as a named user, authenticated only by its own credential. This is the sharpest tool in the box and is forbidden by default; it requires an explicit impersonation policy in the zone.

from keycardai.langchain import Access

keycard = KeycardGrantMiddleware(
    zone_url="https://your-zone.keycard.cloud",
    resources=["https://www.googleapis.com/calendar/v3"],
    client_id="your-agent",
    client_secret=os.environ["KEYCARD_CLIENT_SECRET"],
)

agent.invoke(
    {"messages": [...]},
    context=Access.impersonate("user@example.com"),
)

Authenticating without a static secret

client_id / client_secret is shorthand for a ClientSecret credential. Every pattern also accepts an application_credential, so a deployed agent can authenticate with a platform-signed OIDC token instead of holding a secret:

from keycardai.oauth.server import FileTokenSource, WorkloadIdentity

keycard = KeycardGrantMiddleware(
    zone_url="https://your-zone.keycard.cloud",
    resources=["https://api.github.com"],
    application_credential=WorkloadIdentity(FileTokenSource()),
)

WorkloadIdentity fetches the platform token per call and sends it as a jwt-bearer client assertion; nothing long-lived sits in the environment.

Identity without per-run context

For a deployed agent whose surface does not thread per-run context, set fallback_identity. Pass a callable to resolve it per tool call, so a sign-in that happens mid-conversation takes effect on resume without a restart:

from keycardai.langchain import Access

keycard = KeycardGrantMiddleware(
    ...,
    fallback_identity=lambda: Access.on_behalf_of(session_token()),
)

Errors are data, not exceptions

A missing grant is normal operation in a brokered setup, so the AccessContext records failures instead of raising. Only access(resource) raises, and only when you ask for a resource that has no token:

access = get_access_context()
if access.has_errors():
    return f"Cannot reach the API yet: {access.get_errors()}"
token = access.access(CALENDAR).access_token

Returning a readable sentence beats raising here: in a chat UI a raised exception reads as an internal error, when the truthful message is "you have not granted this yet."

Pausing for sign-in and consent

With sign_in_url and authorization_url set, the middleware pauses the run with a LangGraph interrupt instead of failing, so the whole flow can live in your chat surface:

keycard = KeycardGrantMiddleware(
    zone_url=...,
    resources=[CALENDAR],
    sign_in_url="https://your-app.example/signin",
    authorization_url=lambda resources: f"https://your-app.example/authorize?r={resources[0]}",
)

Expiry is detected locally (a decode-only check of the JWT's exp; the zone stays the authority on validity), so an expired session routes to sign-in rather than to a consent page that cannot fix it. The sign_in_required payload carries a reason field (missing_identity or subject_token_expired) so a chat surface can word the prompt accordingly.

Both require a checkpointer, because pausing a run is what a checkpointer makes possible. Two details worth knowing:

• Resume needs no new token. Consent changes the grant in the zone, not the token in your session, so the existing subject token exchanges successfully afterward.
• Runtime context is not checkpointed. A resume must re-supply identity, which a server does on every run anyway.

Scope granularity falls out of this for free: if a user has granted read but not write, the read call succeeds and the write call is the one that pauses.

Without a checkpointer: interrupt_on_auth=False

Deployments that keep no graph state have nothing to resume, so the interrupt is not available to them. Set interrupt_on_auth=False and the same failure is delivered to the model as failed tool output instead of pausing the run:

keycard = KeycardGrantMiddleware(
    zone_url=...,
    resources=[CALENDAR],
    sign_in_url="https://your-app.example/signin",
    authorization_url=lambda resources: f"https://your-app.example/authorize?r={resources[0]}",
    interrupt_on_auth=False,  # no checkpointer needed
)

The tool output carries the same kind, reason and URL the interrupt payload would, worded so the model relays the URL to the user verbatim rather than paraphrasing it. The run finishes normally, with the link in the assistant's reply; the user authorizes out of band, and their next turn retries the tool, which now succeeds. Nothing resumes mid-run, so there is no bounded retry loop here.

Reach for it when your agent is stateless (a plain HTTP handler, a queue worker, a Slack or email surface with no persisted thread), or when the auth step belongs in the conversation instead of in a paused-run UI. Keep the default when you run with a checkpointer: a pause is stricter, since the model never gets a turn between the failure and the retry.

MCP tools in the same agent

Agents commonly mix two kinds of access: REST tools whose credentials this middleware brokers per tool call, and MCP servers that run their own interactive OAuth through keycardai-mcp. Both can pause the run with the same authorization_required payload, so your chat surface renders one auth UX:

from keycardai.mcp.client.integrations import langchain_agents

adapter = langchain_agents.LangChainClient(
    mcp_client,
    interrupt_on_auth=True,                          # opt in; off by default
    tool_allowlist=["list_issues", "create_issue"],  # keep the context small
)

Without interrupt_on_auth the MCP adapter keeps its own UX: it hands the model a request_authentication tool instead of interrupting.

Three things to get right when the two are combined:

MCP-backed tools exchange nothing. The MCP server's OAuth grant belongs to the user and that server, not to Keycard's exchange, so map those tools to an empty resource list or the middleware will try to broker a token they do not need:

KeycardGrantMiddleware(
    zone_url=ZONE_URL,
    resources=[CALENDAR],                 # REST tools
    tool_resources={"call_mcp_tool": []}, # MCP-backed tool
    authorization_url=lambda resources: f"{BASE_URL}/authorize?r={resources[0]}",
)

One callback route. The MCP client's coordinator owns the redirect, and a single route completes the flow for every user:

coordinator = StarletteAuthCoordinator(
    redirect_uri=f"{BASE_URL}/auth/mcp/callback",
    backend=InMemoryBackend(),
)
manager = ClientManager(servers, auth_coordinator=coordinator)


async def mcp_callback(request):
    await coordinator.handle_completion(dict(request.query_params))
    return HTMLResponse("Authorized. Return to the chat and continue.")

Per-user clients, tools bound after connect. manager.get_client(context_id=user_email) gives each user their own session; build the agent's MCP tools inside async with adapter: (entering connects and discovers the authenticated servers; get_tools() is empty without it) so the model sees the server's real tool schemas (or use adapter.get_lazy_tools() when the tool list must exist at import time). The MCP client's README covers that side in full.

Using tools outside the agent

get_access_context() normally only works inside an agent run, because the middleware sets the context at the tool-call boundary. For code that calls a tool without the agent loop, grant() enters the same access context explicitly. The motivating case is a UI panel served by the same governed tool the agent uses in chat:

from keycardai.langchain import Access

def dashboard_snapshot(session_token: str) -> str:
    with keycard.grant(Access.on_behalf_of(session_token)):
        return list_requests.invoke({})

It also serves resources that have no tool at all. Fetching a vaulted LLM key under the agent's own identity, for example:

from keycardai.langchain import Access

with keycard.grant(Access.as_self(), resources=[LLM_KEY]) as access:
    key = access.access(LLM_KEY).access_token

agrant() is the async variant. Both accept tool_name= to apply that tool's tool_resources override, or resources= to grant exactly the listed resources (one or the other, not both), and fall back to fallback_identity when no identity is passed. There is no run to pause, so nothing interrupts here: failures stay on the yielded AccessContext, exactly as tools see them.

Per-tool resources and scopes

KeycardGrantMiddleware(
    zone_url=...,
    resources=[CALENDAR],                     # default for every tool
    tool_resources={"post_message": [SLACK]}, # per-tool override
    request_scopes={CALENDAR: ["calendar.events"]},
)

request_scopes is the outbound scope requested from Keycard, for both the exchange and the as-itself grant. It is distinct from any scope enforced on the caller's inbound token.

Testing

from keycardai.langchain.testing import mock_access_context


def test_list_events():
    with mock_access_context(resource_tokens={CALENDAR: "test-token"}):
        assert list_events.invoke({"days_ahead": 0})

mock_access_context(access_token=...) serves one token for any resource, which is convenient but cannot catch a mistyped resource URL, since every lookup succeeds. Pass resource_tokens={...} when the test should assert which resource a tool reads. resource_errors= and error_message= cover the failure paths, and override_access_context takes a hand-built context for full control.

The package's own test strategy, row by row with coverage status, lives in TESTING.md.

Contract parity with TypeScript

The same integration exists for TypeScript as @keycardai/langchain. Same contract, idiomatic expression on each side: concepts, payload shapes, defaults, and behaviors are identical; the spelling follows each language.

Deliberate differences, where the language leaves no honest choice:

• TypeScript's escape hatch is a callback, not a context manager. There is no with there, so grant takes the body as a function and is always awaited, which is also why the TypeScript side has no agrant.
• The TypeScript middleware owns the context schema. LangChain 1.x middleware in JS declares contextSchema itself, so the agent does not pass one.
• MCP composition is Python-only for now. The section above pairs this middleware with keycardai-mcp; the TypeScript package deliberately has no MCP dependency in v1.

A note on tool arguments

Give tools arguments that express intent, and keep configuration and clocks out of the model's hands. A tool that accepts a resource URL will eventually be called with a resource the model invented; a tool that accepts an absolute timestamp will eventually be called with the wrong date. Prefer days_ahead: int over an ISO string, and read the resource from configuration.