🛡️ PreCheck Guardian

A pre-execution approval gate for AI agents. Preview the full plan, see the risk of every step, then approve, reject, or edit — before anything runs.

🧩 Part of the Agent Loop Toolkit — three small, zero-dependency, framework-agnostic libraries you bolt onto any agent loop. Each works standalone; together they cover context → gate → steer.

Where it plugs in	Library	What it does
The context going in	context-compressor	Shrink the LLM context window 40–80% — drop noise, redundancy, long-tail detail
The plan, before a step runs ← you are here	precheck-guardian	Preview the plan, see per-step risk, approve / reject / edit
The run, while it's live	something-else	Interject, pause, or guard a live loop without restarting

Autonomous agents act fast and don't ask. One rm -rf, one DROP TABLE, one force-push, and the damage is done. PreCheck Guardian inserts a human-in-the-loop checkpoint between planning and execution: it parses what the agent is about to do, flags the dangerous steps, and waits for a human to sign off.

Framework-agnostic. No dependency on any specific agent framework. It's one function call — wire it into LangChain, a custom ReAct loop, or your own tool runner. Zero required dependencies; rich/questionary are optional niceties.

Why this exists

As agents get more autonomous, the gap between "here's what I'll do" and "...and it's already done" gets dangerous. Most "human-in-the-loop" options today force a bad trade-off:

You get the full plan up front, the risky steps highlighted, a real approve / reject / edit decision, and a compliance-ready audit log — in one call, with zero required dependencies.

See it in 10 seconds

pip install precheck-guardian
python -m approval_hook        # render a sample plan with risk levels

╭───────────────────────── Approval Required ─────────────────────────╮
│ Demo Plan        🟢 1  🟡 1  🔴 2  ⛔ 2    ⏱ 15s                     │
╰──────────────────────────────────────────────────────────────────────╯
  #  Risk      Action                                  Tool
  1  LOW       SELECT * FROM users WHERE status=...    sql_query
  3  HIGH      UPDATE accounts SET billed = 1 ...      generic_tool   ⚠ modifies rows
  4  CRITICAL  DROP TABLE users_staging                sql_query      ⚠ destroys data
  5  CRITICAL  rm -rf /tmp/build                       file_delete    ⚠ unrecoverable
  6  HIGH      git push --force origin release         git            ⚠ overwrites history

Use it in 3 lines

from approval_hook import ApprovalGuard

guard = ApprovalGuard()
if guard.is_approved(agent_plan_text):
    run(agent_plan_text)          # only runs after a human approves

is_approved parses the plan, scores each step, prints it, prompts the operator, and writes an audit-log entry — all in one call.

What it does

Install

pip install precheck-guardian            # core, zero dependencies
pip install "precheck-guardian[all]"     # + rich tables, questionary menus, Textual TUI

Or from a local clone (for development):

git clone https://github.com/uninhibited-scholar/precheck-guardian
cd precheck-guardian
pip install -e ".[dev]"

How it fits into an agent loop

agent plans  ─▶  PreCheck Guardian  ─▶  approved?  ─▶  execute tools
                  │  parse                  │  no
                  │  score risk             └────────▶  abort / replan
                  │  show to human
                  └─ record decision

Structured tool calls

from approval_hook import ApprovalGuard, ApprovalConfig

guard = ApprovalGuard(ApprovalConfig(block_critical=True, actor="ci-bot"))

decision = guard.review([
    {"tool": "read_data",  "args": {"path": "/data/in.csv"}, "description": "load input"},
    {"tool": "file_delete","args": {"path": "/data/in.csv"}, "description": "rm -rf /data/in.csv"},
])

if decision.proceed:
    execute(...)

Native OpenAI / LangChain tool-call traces

Already have a raw tool-call trace from the model? Hand it over as-is — the guard detects the shape and parses it (OpenAI arguments JSON strings included):

# an OpenAI chat-completion response, an assistant message, a message list,
# or a list of tool-call dicts — all work:
guard.review(openai_response)
guard.review([{"type": "function",
               "function": {"name": "run_sql", "arguments": '{"statement": "DROP TABLE t"}'}}])

Tuning the policy

from approval_hook import ApprovalConfig, RiskLevel

ApprovalConfig(
    require_above=RiskLevel.LOW,    # prompt for MEDIUM and up (None = always prompt)
    block_critical=False,           # True = auto-reject CRITICAL, never even ask
    audit_path="approval_audit.jsonl",
    actor="alice",
    non_interactive_default=None,   # what to do with no TTY; None = safe REJECT
)

One-line decorators

Add a checkpoint without restructuring code:

from approval_hook import gate_plan, review_result, guard_callable

@gate_plan("plan")               # review the plan passed in; skip body if rejected
def execute(plan): ...

@review_result()                 # review what the function returns; raise if rejected
def make_plan() -> str: ...

# wrap a single dangerous tool — every call is reviewed (works with LangChain Tool(func=...))
safe_delete = guard_callable(delete_file, tool_name="delete_file")

LangChain

Wrap any LangChain tool so every call is reviewed first — a drop-in replacement that keeps the tool's name, description and argument schema:

from langchain_core.tools import tool
from approval_hook.integrations.langchain import guard_langchain_tool

@tool
def delete_path(path: str) -> str:
    """Delete a file or directory."""
    ...

safe_delete = guard_langchain_tool(delete_path)   # give the agent this instead
# safe_delete.invoke({"path": "rm -rf /data"}) -> shown for approval / blocked

Install the optional extra: pip install "precheck-guardian[langchain]" (Python 3.10+).

Full-screen TUI

Prefer a richer review screen? Swap in the Textual UI — same guard, same audit log:

from approval_hook import ApprovalGuard
from approval_hook.ui.tui import TextualApprovalUI

guard = ApprovalGuard(ui=TextualApprovalUI())
guard.review(agent_plan)     # opens an interactive approval screen

Install the optional extra: pip install "precheck-guardian[tui]" (Python 3.9+).

Custom risk rules

from approval_hook import RiskAnnotator, RiskLevel
from approval_hook.core.risk_annotator import _rule

annotator = RiskAnnotator()
annotator.add_rule(_rule("no_prod", r"\bprod\b", RiskLevel.CRITICAL,
                         "Touches production.", "Use staging instead."))
guard = ApprovalGuard(annotator=annotator)

Audit log & reporting

Every decision is appended to a JSON-Lines log. Summarise it from the CLI:

precheck audit --summary --limit 5

Audit summary — approval_audit.jsonl
  total decisions : 128
  approved        : 119
  rejected        : 9
  approval rate   : 93%
  by max risk     :
    🟢 low 71   🟡 medium 33   🔴 high 18   ⛔ critical 6

Or in code: from approval_hook.audit.logger import summarize_records.

Examples

python examples/basic_approval.py        # interactive approve/reject/edit
python examples/with_diff.py             # diff an edited plan vs. the original
python examples/langchain_style_hook.py  # wire into an agent loop
python examples/decorator_gate.py        # one-line decorator + per-tool gating
python examples/langchain_real.py        # gate a real LangChain tool (needs langchain-core)
python examples/agent_workflow.py         # end-to-end multi-step agent; destructive step blocked
python examples/tui_approval.py           # full-screen Textual approval UI (needs textual)

Testing

pytest          # 67 tests, <1s

Design notes & honest scope

• Risk scoring is rule-based, not a sandbox. It's a strong heuristic safety net to surface obvious danger for a human — it is not a guarantee that an unflagged step is safe. Keep the human in the loop for anything destructive.
• The parser is best-effort. Unstructured text becomes a single step rather than being silently dropped. For exact fidelity, feed it structured tool calls.
• Zero core dependencies on purpose — easy to vendor, easy to trust.

Contributions of new risk rules, parser formats and framework adapters are welcome.

License

MIT — see LICENSE.