llmsessioncontract 0.3.1

Runtime monitor for LLM agent interaction protocols based on session type theory

llmcontract

A runtime monitor for LLM agent interaction protocols based on session type theory.

llmcontract lets you define communication protocols using a concise DSL inspired by session types, then monitor agent interactions at runtime to catch protocol violations the moment they happen.

Installation

pip install -e .

Protocol DSL

Protocols are written as strings using this syntax:

Syntax	Meaning
!label	Send action
?label	Receive action
!{a, b}	Internal choice (sender chooses)
?{a, b}	External choice (receiver chooses)
.	Sequence
rec X. ...X...	Recursion
end	Terminal state

Examples

A flight booking protocol — a strict linear sequence:

!SearchFlights.?FlightResults.!PresentOptions.?UserApproval.!BookFlight.?BookingConfirmation.end

A card payment protocol — with branching and recursion:

!CreateCard.?{CardCreated.rec X.!Transaction.?{TransactionOK.X, SessionEnd}, CardError}.end

Usage

from llmcontract import Monitor, Ok, Violation, Blocked

protocol = "!SearchFlights.?FlightResults.!BookFlight.?BookingConfirmation.end"
m = Monitor(protocol)

m.send("SearchFlights")    # Ok()
m.receive("FlightResults") # Ok()
m.send("BookFlight")       # Ok()
m.receive("BookingConfirmation") # Ok()
assert m.is_terminal

Catching violations

m = Monitor("!Ping.?Pong.end")
m.send("Ping")       # Ok()
m.send("Pong")       # Violation(expected=['?Pong'], got='!Pong')
m.send("Anything")   # Blocked('monitor halted after a previous violation')

Working with choices

protocol = "!CreateCard.?{CardCreated.!Done.end, CardError.end}"
m = Monitor(protocol)
m.send("CreateCard")       # Ok()
m.receive("CardError")     # Ok() — the receiver chose this branch
assert m.is_terminal

Recursion

protocol = "rec X.!Ping.?Pong.X"
m = Monitor(protocol)
for _ in range(100):
    m.send("Ping")     # Ok()
    m.receive("Pong")  # Ok()

Handling natural-language input: Unrecognized

When the projection layer (typically over user chat) can't classify an event into a known label, it can emit the sentinel UNRECOGNIZED instead. The monitor treats this as a soft signal — distinct from Violation — without halting or advancing state, so the outer loop can drive a clarification turn:

from llmcontract import Monitor, Ok, Unrecognized, UNRECOGNIZED

m = Monitor("?{Yes.end, No.end}")
result = m.receive(UNRECOGNIZED)         # projection couldn't decide
assert isinstance(result, Unrecognized)  # not a Violation
# state preserved; ask the agent to ask the user to clarify, then:
m.receive("Yes")                         # Ok()

A protocol can also handle Unrecognized explicitly as a first-class branch — useful for "ask again" loops:

protocol = "rec Loop.!Ask.?{Yes.end, No.end, Unrecognized.Loop}"
m = Monitor(protocol)
m.send("Ask")
m.receive(UNRECOGNIZED)  # Ok — protocol routes back to Loop
m.send("Ask")
m.receive("Yes")         # Ok — terminal

The distinction matters at the system boundary: Violation means the agent broke the rules; Unrecognized means we don't have enough information to decide yet. Different responses (halt vs. clarify) come naturally from the typed result.

Integration Layer

For real agent loops, llmcontract provides a client wrapper and tool middleware that share a single monitor — so the full interaction is tracked automatically.

Client Wrapper

Wraps any LLM client call. Checks !Send before calling the LLM and ?Receive after getting the response. SDK-agnostic — you provide a small adapter function.

from llmcontract import Monitor, MonitoredClient, LLMResponse, ToolCall

monitor = Monitor(
    "rec Loop.!Request.?{ToolCall.!ToolResult.Loop, FinalAnswer.end}"
)

# Adapt your SDK's response to LLMResponse
def adapt(raw):
    if raw.tool_calls:
        return LLMResponse(tool_calls=[
            ToolCall(name=tc.function.name, arguments=tc.arguments, id=tc.id)
            for tc in raw.tool_calls
        ])
    return LLMResponse(content=raw.content)

client = MonitoredClient(
    llm_call=openai.chat.completions.create,
    response_adapter=adapt,
    monitor=monitor,
    send_label="Request",
    receive_label=lambda r: "ToolCall" if r.has_tool_calls else "FinalAnswer",
)

response = client.call(model="gpt-4", messages=[...])
# Automatically fires !Request then ?ToolCall or ?FinalAnswer

Tool Middleware

Wraps tool execution. When the LLM requests a tool, the middleware checks ?Receive (tool requested) and !Send (result returned) against the protocol.

from llmcontract import ToolMiddleware

middleware = ToolMiddleware(
    monitor=monitor,  # same monitor as the client
    tools={
        "search": search_fn,
        "book": book_fn,
    },
)

# Process all tool calls from a response
results = middleware.process(response)
# Each tool call checks ?receive and !send against the protocol

Combined Agent Loop

from llmcontract import (
    Monitor, MonitoredClient, ToolMiddleware,
    LLMResponse, ToolCall, ProtocolViolationError,
)

protocol = "rec Loop.!Request.?{ToolCall.!ToolResult.Loop, FinalAnswer.end}"
monitor = Monitor(protocol)

client = MonitoredClient(
    llm_call=my_llm_fn,
    response_adapter=my_adapter,
    monitor=monitor,
    send_label="Request",
    receive_label=lambda r: "ToolCall" if r.has_tool_calls else "FinalAnswer",
)

while True:
    try:
        response = client.call(messages=messages)
    except ProtocolViolationError as e:
        print(f"Protocol violated: {e}")
        break

    if not response.has_tool_calls:
        break  # FinalAnswer — protocol complete

    # Execute tools, send results back
    for tc in response.tool_calls:
        result = tools[tc.name](**tc.arguments)
        monitor.send("ToolResult")  # record the send
        messages.append(tool_result_msg(tc.id, result))

LangChain Integration (llmcontract.langchain, 0.3.0+)

A focused FSM-as-data API for users who want to wire protocol monitoring into LangChain agents without touching the DSL parser. Tool references are real Python callables, transitions are explicit objects with optional guards and actions, and violation handling is fully user-controlled.

pip install llmsessioncontract[langchain]

from langchain_core.tools import tool
from langchain.agents import create_agent
from llmcontract.langchain import (
    ProtocolFSM, Transition, ProtocolMonitor,
    ProtocolEnforcerMiddleware, ViolationEvent,
    ProtocolViolationError, ref,
)

@tool
def search(query: str) -> str:
    """Search for available flights."""
    return f"Results for: {query}"

@tool
def book(result: str) -> str:
    """Book a selected flight."""
    return f"Booked: {result}"

search_ref = ref(search)
book_ref = ref(book)

fsm = (
    ProtocolFSM(initial="idle")
    .add_transition(Transition(source="idle", tool=search_ref, phase="send", target="searching"))
    .add_transition(Transition(source="searching", tool=search_ref, phase="recv", target="results"))
    .add_transition(Transition(source="results", tool=book_ref, phase="send", target="booking",
                               guard=lambda ctx: bool(ctx.metadata.get("args", {}))))
    .add_transition(Transition(source="booking", tool=book_ref, phase="recv", target="done"))
    .mark_terminal("done")
)

def on_violation(v: ViolationEvent) -> None:
    raise ProtocolViolationError(f"Illegal {v.phase}:{v.tool_ref.label} from {v.current_state!r}", violation=v)

monitor = ProtocolMonitor(fsm=fsm, on_violation=on_violation)
middleware = ProtocolEnforcerMiddleware(monitor=monitor, tool_refs=[search_ref, book_ref]).middleware

agent = create_agent(model=..., tools=[search, book], middleware=[middleware])
agent.invoke({"messages": [("user", "Book me a flight to Rome.")]})

print(monitor.state)        # → "done"
print(monitor.is_complete()) # → True
print(monitor.trace)        # → ["send:search", "recv:search", "send:book", "recv:book"]

Mixing tool calls and non-tool events (0.3.1+)

Transition accepts either a tool: ToolRef (fired automatically by the middleware) or an event_label: str (fired explicitly by the orchestrator). Use event_label for agent text replies, projected user replies, timeouts, or any other non-tool signal you want the FSM to model:

fsm.add_transition(Transition(source="results", phase="send",
                              target="presented", event_label="PresentOptions"))
fsm.add_transition(Transition(source="presented", phase="recv",
                              target="approved", event_label="UserApproval"))

# In the orchestrator, after the agent emits a text reply:
monitor.transition_event("PresentOptions", "send")

# After the user reply is projected:
monitor.transition_event("UserApproval", "recv")

Same FSM, two firing surfaces — the middleware for tool events, the orchestrator for everything else. The library never interprets natural language; the orchestrator owns the projection.

When to pick this over the DSL Monitor:

• You're already in a LangChain stack and want a drop-in AgentMiddleware
• You need per-transition guards and actions (e.g., audit logs, business rules)
• You want enforcement (block tool calls), not just observation
• You don't need recursion / choice / Unrecognized from the DSL

When to stick with the DSL Monitor:

• You want to write protocols as concise strings (!Search.?Result.end)
• You need recursion or compositional choice
• You're outside LangChain (Anthropic SDK, OpenAI SDK, custom loop)
• You want first-class natural-language ambiguity via Unrecognized

Worked example: examples/langchain_booking/booking_agent_submodule.py.

Langfuse Integration

Track protocol compliance in Langfuse — every send/receive is recorded as a guardrail observation with a pass/fail score.

pip install llmsessioncontract[langfuse]

from langfuse import get_client
from llmcontract.integration.langfuse import LangfuseMonitor

langfuse = get_client()

with langfuse.start_as_current_observation(name="agent-run") as trace:
    monitor = LangfuseMonitor(
        protocol="!Request.?{ToolCall.!ToolResult.end, FinalAnswer.end}",
        langfuse=langfuse,
    )

    monitor.send("Request")       # guardrail: ok ✓
    monitor.receive("ToolCall")   # guardrail: ok ✓
    monitor.send("ToolResult")    # guardrail: ok ✓
    monitor.send("ExtraCall")     # guardrail: VIOLATION ✗

langfuse.flush()

Each step appears as a guardrail observation in your Langfuse trace with:

• Input: the action attempted, direction, label, protocol
• Output: passed: true/false, violation details if applicable
• Score: protocol_compliance (boolean) for filtering and analytics

Claude Code Plugin

A Claude Code plugin ships with this repo: protocol-builder walks you through designing a session-type protocol conversationally, validates it as you go, and emits a ready-to-paste Python integration snippet.

# Install in Claude Code
/plugin marketplace add chrisbartoloburlo/llmcontract
/plugin install protocol-builder@llmcontract

# Then in any conversation
/protocol-builder

The skill validates each draft DSL against llmcontract's parser, so anything it produces is guaranteed to load with Monitor(...). Source lives under skills/protocol-builder/.

Case Studies

• llmcontract-tau2 — user ↔ agent layer. Standalone replay of tau2-bench's shipped trajectories through Monitor. Headline: 11/1755 (0.6%) of trajectories that tau2 scored as passing violate the documented "obtain user confirmation before mutating the database" policy. Discussion upstream: tau2-bench#298.
• llmcontract-playwright-mcp — agent ↔ tool layer. 90-trajectory sweep across Claude Haiku 4.5 / Sonnet 4.6 / Opus 4.7 driving @playwright/mcp, checked against two invariants from the server's documented usage. Headline: 9% violate snap-before-interact, 29% violate stay-on-snapshot-refs — and the failure modes scale opposite directions with model capability (Haiku snapshots religiously but ignores the snapshot 57% of the time; Opus skips the snapshot 13% of the time but follows through cleanly when it commits).

Research

This work is based on the theory developed in:

Christian Bartolo Burlò, Adrian Francalanza, Alceste Scalas. "On the Monitorability of Session Types, in Theory and in Practice". 35th European Conference on Object-Oriented Programming (ECOOP 2021), pp. 20:1–20:30, Schloss Dagstuhl, 2021. [PDF] [Google Scholar]

Architecture

DSL string ──▶ Parser ──▶ AST ──▶ FSM Compiler ──▶ Automaton ──▶ Monitor

• Parser (llmcontract.dsl.parser) — hand-written recursive descent parser that produces an AST
• AST (llmcontract.dsl.ast) — frozen dataclasses: Send, Receive, InternalChoice, ExternalChoice, Sequence, Recursion, RecVar, End
• FSM Compiler (llmcontract.monitor.automaton) — compiles the AST into a finite state automaton with transitions keyed by (direction, label)
• Monitor (llmcontract.monitor.monitor) — steps through the automaton on each send/receive call, returning Ok, Violation, or Blocked
• MonitoredClient (llmcontract.integration.client) — wraps any LLM client call with automatic protocol checks
• ToolMiddleware (llmcontract.integration.middleware) — intercepts tool execution with protocol checks
• LangfuseMonitor (llmcontract.integration.langfuse) — records protocol events as Langfuse guardrail observations

Tests

pip install -e ".[dev]"
pytest llmcontract/tests/ -v

License

MIT