ACEL — Agent Contract Enforcement Layer (core)
Runtime verification for AI agent tool calls. Declare temporal ordering contracts and Hoare-style pre/postconditions in plain Python, and have them enforced live against the stream of tool calls an agent makes — halting the agent the moment a rule is broken.
acel-coreships the transport-independent monitor (Phase 1) plus a live MCP proxy (Phase 2): the same contracts enforced against a real MCP server, via the official MCP Python SDK's request-middleware pipeline.
This is runtime verification — checking each concrete execution against a specification as it happens. It does not prove the agent correct in general; it guarantees that this run did not violate the rules you declared.
Why
Statistical agent-eval tools answer "how often does this agent behave well, on average?" ACEL answers the production question they can't: "did this execution just violate a rule we cannot allow to be violated?" — before the bad tool call lands.
Install
pip install -e .
Quickstart
from acel import Session, must_precede, at_most_n_times
session = Session(state={"authenticated": False})
# Temporal ordering rules (no logic syntax required):
session.add_contract(must_precede("validate_record", "delete_record"))
session.add_contract(at_most_n_times("send_payment", n=1))
# State-based gate: reads only allowed once authenticated.
session.register_tool(
"read_user_data",
precondition=lambda s: s.get("authenticated") is True,
)
# Authentication commits trusted info into session state.
session.register_tool(
"authenticate",
commit=lambda s, args, result: s.set("authenticated", result["ok"]),
)
session.call("authenticate", {"user": "p"}, result={"ok": True})
session.call("read_user_data", {"query": "SELECT ..."}, result={"rows": []})
# This halts: delete before validate.
session.call("delete_record", {"id": "r_42"}) # raises ContractViolation
On violation, a ContractViolation is raised carrying a Violation record:
from acel import ContractViolation
try:
session.call("delete_record", {"id": "r_42"})
except ContractViolation as exc:
v = exc.violation
print(v.kind) # "temporal"
print(v.spec) # "must_precede(validate_record, delete_record)"
print(v.step) # index of the offending call
print(v.trace) # every call up to the violation
print(v.state_snapshot) # symbolic state at the moment it broke
The six temporal templates
| Template | Meaning |
|---|---|
must_precede(a, b) |
every b must be preceded by some a |
at_most_n_times(a, n) |
a occurs at most n times per session |
never_after(a, b) |
a must never occur after b |
required_before_session_end(a) |
a must occur at least once before the session ends |
cannot_follow_without(a, b) |
a may not occur unless b occurred earlier |
mutually_exclusive(a, b) |
a and b must not both occur in one session |
Each template is a deterministic automaton advanced in O(1) per tool call, with
a three-valued verdict (SATISFIED / VIOLATED / UNKNOWN) over the finite
trace.
Pre/postconditions with decorators
from acel import Session, precondition, postcondition
@precondition(lambda s: s.get("authenticated") is True)
@postcondition(lambda s, r: r["tenant_id"] == s.get("current_tenant"))
def search_database(query): ...
session = Session(state={"authenticated": True, "current_tenant": "t_9"})
session.register(search_database)
Offline analysis / CI mode
Session.replay runs a recorded trace and returns every violation without
executing anything — the basis for the coming acel replay trace.json CLI and
for testing contracts against known-bad traces.
violations = session.replay([
{"tool": "delete_record", "args": {"id": "1"}},
])
Live MCP proxy (Phase 2)
ACEL can gate a real MCP server's tool calls, live, via the official MCP
Python SDK's ServerMiddleware hook. Every tools/call request passes
through ACEL's gate before the real tool handler runs — a blocked call has
zero side effects.
pip install "acel-core[mcp]"
from mcp.server.mcpserver import MCPServer
from acel import Session, must_precede
from acel.mcp_middleware import ACELMiddleware
session = Session()
session.add_contract(must_precede("validate_record", "delete_record"))
server = MCPServer("my-server", middleware=[ACELMiddleware(session)])
@server.tool()
def delete_record(record_id: str) -> dict: ...
See examples/toy_server.py for a complete toy server (5 tools, 3 contracts)
and tests/test_mcp_proxy.py for an end-to-end demo: a real ClientSession
talking to this server, with ACEL catching an ordering violation, a
cardinality violation, and a state-precondition violation — each one halted
before the tool it would have run.
Shadow mode
The recommended way to roll out a new set of contracts: shadow mode detects and records every violation exactly as enforce mode does — same evidence log, same hash chain — but never blocks a call. Run it against real traffic first, see what it would have caught, then switch to enforce once you trust the rules.
session = Session(mode="shadow") # default is "enforce"
acel serve examples/toy_server.py --shadow
Session.call(), .precheck()/.postcheck() (the MCP proxy path), and the
CLI all respect mode. Session.replay() does not — it's a retrospective
CI-gate tool ("would this recorded trace have been blocked"), not a live
session, so it always reports every violation regardless of mode.
Correctness
python benchmarks/correctness.py
A labeled dataset of 51 synthetic tool-call traces spanning all 6 temporal
templates (valid sequences, violating sequences, and edge cases like empty
traces and multiple simultaneous contracts) — measured at 100% precision
and 100% recall. Since the monitor is deterministic automaton checking, not
statistical detection, that's the expected result; the suite exists to prove
it and to catch any future regression (it's also wired into pytest as
tests/test_correctness_suite.py, so a miss fails CI directly).
Performance
python benchmarks/latency.py
Measured on the reference dev machine, 20,000 iterations, discarding a 1,000-call warmup: added p95 latency per tool call is ~0.005ms at 1 active contract and ~0.04ms at 50 concurrently active contracts — well under the <5ms target. Each temporal contract is a deterministic automaton advanced in O(1) per event, so overhead scales linearly with the number of active contracts, not with session length.
Tests
pip install pytest
pytest # core monitor + evidence (no extra deps)
pip install "acel-core[mcp]"
pytest tests/test_mcp_proxy.py tests/test_cli_serve.py # live MCP proxy + CLI
License
MIT
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