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Strix-clone/strix/tools/agents_graph/tools.py
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2026-07-02 12:31:49 -04:00

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22 KiB
Python

"""Multi-agent graph tools backed by AgentCoordinator."""
from __future__ import annotations
import asyncio
import json
import logging
import uuid
from collections import Counter
from datetime import UTC, datetime
from typing import Any, Literal, get_args
from agents import RunContextWrapper, function_tool
from strix.core.agents import Status, coordinator_from_context
from strix.skills import validate_requested_skills
_ACTIVE_STATUSES: frozenset[str] = frozenset({"running", "waiting"})
logger = logging.getLogger(__name__)
def _ctx(ctx: RunContextWrapper) -> dict[str, Any]:
return ctx.context if isinstance(ctx.context, dict) else {}
def _render_completion_report(
*,
agent_name: str,
agent_id: str,
task: str,
success: bool,
result_summary: str,
findings: list[str],
recommendations: list[str],
) -> str:
"""Render a child's completion report as plain structured text.
Goes into the parent's SDK session with coordinator-added sender
metadata, so this body just carries the contents. No XML — no
escaping concerns, no parser ambiguity.
"""
status = "SUCCESS" if success else "FAILED"
completion_time = datetime.now(UTC).isoformat()
lines: list[str] = [
f"== Completion report from {agent_name} ({agent_id}) ==",
f"Status: {status}",
f"Time: {completion_time}",
]
if task:
lines.append(f"Task: {task}")
lines.append("")
lines.append("Summary:")
lines.append(result_summary or "(none)")
if findings:
lines.append("")
lines.append("Findings:")
lines.extend(f"- {f}" for f in findings)
if recommendations:
lines.append("")
lines.append("Recommendations:")
lines.extend(f"- {r}" for r in recommendations)
return "\n".join(lines)
@function_tool(timeout=30)
async def view_agent_graph(ctx: RunContextWrapper) -> str:
"""Print the multi-agent tree — every agent, its parent, its status.
Use before spawning a new agent (don't duplicate work — check whether
something specialized for that task already exists) and any time you
want a snapshot of who's still ``running`` / ``waiting`` /
``completed`` / ``crashed`` / ``stopped``. Output is an indented
bullet list with status in brackets; the agent that called this tool
is marked ``← you``.
"""
inner = _ctx(ctx)
coordinator = coordinator_from_context(inner)
me = inner.get("agent_id")
if coordinator is None:
return json.dumps(
{"success": False, "error": "Agent coordinator not initialized in context"},
ensure_ascii=False,
default=str,
)
parent_of, statuses, names = await coordinator.graph_snapshot()
lines: list[str] = []
def render(aid: str, depth: int) -> None:
status = statuses.get(aid, "?")
marker = " ← you" if aid == me else ""
lines.append(f"{' ' * depth}- {names.get(aid, aid)} ({aid}) [{status}]{marker}")
for child, p in parent_of.items():
if p == aid:
render(child, depth + 1)
roots = [aid for aid, parent in parent_of.items() if parent is None]
for root in roots:
render(root, 0)
counts = Counter(statuses.values())
summary: dict[str, int] = {"total": len(parent_of)}
for status_name in get_args(Status):
summary[status_name] = counts.get(status_name, 0)
return json.dumps(
{
"success": True,
"graph_structure": "\n".join(lines) or "(no agents)",
"summary": summary,
},
ensure_ascii=False,
default=str,
)
@function_tool(timeout=30)
async def send_message_to_agent(
ctx: RunContextWrapper,
target_agent_id: str,
message: str,
message_type: Literal["query", "instruction", "information"] = "information",
priority: Literal["low", "normal", "high", "urgent"] = "normal",
) -> str:
"""Send a message to another agent's inbox — sparingly.
Inter-agent messages are appended to the target's SDK session and
interrupt any active target turn so the next run cycle sees them.
Use only when essential:
- Sharing a discovered finding/credential another agent needs.
- Asking a specialist a focused question.
- Coordinating who covers what (avoid overlap).
- Telling a child to wrap up or change course.
**Don't** use for routine "hello/status" pings, for context the
target already has (children inherit parent history), or when
parent/child completion via ``agent_finish`` already covers the
flow. Messages to any registered agent wake it, regardless of
status, so a follow-up can restart a completed/stopped/failed agent.
Args:
target_agent_id: Recipient's 8-char id.
message: The full message body. Be specific — include payloads,
URLs, or what you want them to do, not just headlines.
message_type: ``query`` (you want a reply), ``instruction``
(you're directing them), ``information`` (FYI, no reply
expected). Default ``information``.
priority: ``low`` / ``normal`` / ``high`` / ``urgent``.
"""
inner = _ctx(ctx)
coordinator = coordinator_from_context(inner)
me = inner.get("agent_id")
if coordinator is None or me is None:
return json.dumps(
{"success": False, "error": "Agent coordinator or agent_id missing in context"},
ensure_ascii=False,
default=str,
)
if target_agent_id == me:
return json.dumps(
{
"success": False,
"error": (
"Cannot send a message to yourself; use `think` to record a "
"private note, or `agent_finish` / `finish_scan` to terminate"
),
},
ensure_ascii=False,
default=str,
)
msg_id = f"msg_{uuid.uuid4().hex[:8]}"
delivered = await coordinator.send(
target_agent_id,
{
"id": msg_id,
"from": me,
"content": message,
"type": message_type,
"priority": priority,
},
)
if not delivered:
return json.dumps(
{
"success": False,
"error": f"Target agent '{target_agent_id}' not found or message delivery failed",
},
ensure_ascii=False,
default=str,
)
return json.dumps(
{
"success": True,
"message_id": msg_id,
"target_agent_id": target_agent_id,
"delivery_status": "delivered",
},
ensure_ascii=False,
default=str,
)
def _session_items_payload(items: list[Any]) -> list[dict[str, Any]]:
payload: list[dict[str, Any]] = []
for item in items:
if isinstance(item, dict):
role = item.get("role")
content = item.get("content")
payload.append({"role": role, "content": content})
else:
payload.append({"content": str(item)})
return payload
@function_tool(timeout=601)
async def wait_for_message( # noqa: PLR0911
ctx: RunContextWrapper,
reason: str = "Waiting for messages from other agents",
timeout_seconds: int = 600,
) -> str:
"""Pause this agent until a message lands in its inbox (or timeout).
Use when you have nothing useful to do until a child/peer responds
— typically after spawning subagents and you want to wait for
their completion reports. The agent automatically resumes when any
message arrives.
**Critical caveats:**
- **Never** call this if you finished your own task and have **no**
child agents running — that's a permanent stall. Call
``finish_scan`` (root) or ``agent_finish`` (subagent) instead.
- If you're waiting on an agent that **isn't your child**, message
it first asking it to ping you when done — otherwise it has no
reason to send to your inbox and you'll wait the full timeout.
- Children update the parent automatically via ``agent_finish``
→ no extra coordination needed.
Args:
reason: One-line note shown in graph snapshots while you're
waiting (helps a human or sibling agent debug who's stuck
on what).
timeout_seconds: Hard cap (default 600s). On timeout the tool
returns and you decide whether to keep working or wait
again.
"""
inner = _ctx(ctx)
coordinator = coordinator_from_context(inner)
me = inner.get("agent_id")
interactive = bool(inner.get("interactive", False))
if coordinator is None or me is None:
return json.dumps(
{"success": False, "error": "Agent coordinator or agent_id missing in context"},
ensure_ascii=False,
default=str,
)
async with coordinator._lock:
stopped = coordinator.statuses.get(me) == "stopped"
if stopped:
return json.dumps(
{
"success": True,
"wait_outcome": "stopped",
"reason": reason,
"note": "Wait ended because this agent is stopped.",
},
ensure_ascii=False,
default=str,
)
pending, items = await coordinator.consume_pending(me, include_items=True)
if pending > 0:
await coordinator.mark_running(me)
return json.dumps(
{
"success": True,
"wait_outcome": "message_arrived",
"pending_messages": pending,
"messages": _session_items_payload(items),
"reason": reason,
},
ensure_ascii=False,
default=str,
)
if interactive:
await coordinator.park_waiting(me)
return json.dumps(
{
"success": True,
"wait_outcome": "waiting",
"reason": reason,
"note": "Agent parked; execution will resume when a message arrives.",
},
ensure_ascii=False,
default=str,
)
await coordinator.park_waiting(me)
try:
await asyncio.wait_for(coordinator.wait_for_message(me), timeout_seconds)
except TimeoutError:
await coordinator.mark_running(me)
return json.dumps(
{
"success": True,
"wait_outcome": "timeout",
"timeout_seconds": timeout_seconds,
"reason": reason,
"note": "No messages within timeout — continue work or call agent_finish.",
},
ensure_ascii=False,
default=str,
)
async with coordinator._lock:
stopped = coordinator.statuses.get(me) == "stopped"
if stopped:
return json.dumps(
{
"success": True,
"wait_outcome": "stopped",
"reason": reason,
"note": "Wait ended because this agent is stopped.",
},
ensure_ascii=False,
default=str,
)
pending, items = await coordinator.consume_pending(me, include_items=True)
await coordinator.mark_running(me)
return json.dumps(
{
"success": True,
"wait_outcome": "message_arrived",
"pending_messages": pending,
"messages": _session_items_payload(items),
"reason": reason,
},
ensure_ascii=False,
default=str,
)
@function_tool(timeout=120)
async def create_agent(
ctx: RunContextWrapper,
name: str,
task: str,
inherit_context: bool = True,
skills: list[str] | None = None,
) -> str:
"""Spawn a specialist child agent to run in parallel.
Decompose complex pentests by handing focused subtasks to dedicated
children. The child runs asynchronously — the parent continues
immediately and can ``wait_for_message`` later (or just keep
working in parallel). When the child calls ``agent_finish``, its
completion report lands in the parent's inbox.
**Before spawning, call ``view_agent_graph``** to confirm no
existing agent already covers this scope — duplicate specialists
waste turns and create coordination headaches.
**Specialization principles:**
- Most agents need at least one ``skill`` to be useful.
- Aim for **1-3 related skills** per agent. Up to 5 only when the
task genuinely spans them.
- One skill = most focused (e.g., XSS-only). Five skills = upper
bound.
- Match the ``name`` to the focus (``XSS Specialist``,
``SQLi Validator``, ``Auth Specialist``).
**When to spawn vs do it yourself:**
- Spawn when the subtask is large, parallelizable, or needs
different specialization than what you're already doing.
- Don't spawn for trivial one-shot probes — just run the tool
yourself.
Args:
name: Human-readable child name (used in graph views and
``send_message_to_agent`` flows).
task: Specific objective. Be concrete — what to test, what
success looks like, any constraints.
inherit_context: Default ``True``. The child receives the
parent's input history as background; only set ``False``
when starting a clean-slate task.
skills: List of skill names (e.g. ``["xss", "sql_injection"]``).
Max 5; prefer 1-3.
"""
inner = _ctx(ctx)
coordinator = coordinator_from_context(inner)
parent_id = inner.get("agent_id")
spawner = inner.get("spawn_child_agent")
if coordinator is None or parent_id is None:
return json.dumps(
{"success": False, "error": "Agent coordinator or agent_id missing in context"},
ensure_ascii=False,
default=str,
)
if not callable(spawner):
return json.dumps(
{
"success": False,
"error": "Scan runner did not provide a child-agent spawner in context",
},
ensure_ascii=False,
default=str,
)
skill_list = list(skills or [])
skill_error = validate_requested_skills(skill_list)
if skill_error:
return json.dumps(
{"success": False, "error": skill_error, "agent_id": None},
ensure_ascii=False,
default=str,
)
parent_history = list(ctx.turn_input) if inherit_context and ctx.turn_input else []
try:
result = await spawner(
parent_ctx=inner,
name=name,
task=task,
skills=skill_list,
parent_history=parent_history,
)
except Exception as e:
logger.exception("create_agent: scan runner failed to spawn child '%s'", name)
return json.dumps(
{"success": False, "error": f"child spawn failed: {e!s}"},
ensure_ascii=False,
default=str,
)
logger.info(
"create_agent: spawned %s (%s) parent=%s skills=%d task_len=%d",
result.get("agent_id"),
name,
parent_id or "-",
len(skill_list),
len(task or ""),
)
return json.dumps(
result,
ensure_ascii=False,
default=str,
)
@function_tool(timeout=30)
async def agent_finish(
ctx: RunContextWrapper,
result_summary: str,
findings: list[str] | None = None,
success: bool = True,
report_to_parent: bool = True,
final_recommendations: list[str] | None = None,
) -> str:
"""Subagent termination — post a completion report to the parent.
**Subagents only.** Root agents must call ``finish_scan`` instead;
this tool refuses to run for root agents. Calling this:
1. Marks the subagent as ``completed``.
2. Posts a structured completion report to the parent's inbox
(when ``report_to_parent`` is true).
3. Stops this subagent's execution.
**Vulnerability findings must already be filed via
``create_vulnerability_report`` before calling this.** The
``findings`` field here is for narrative summary only — it does
not register vulns in the scan report.
Write the summary as if the parent has no idea what you were
doing: what did you test, what did you find/confirm/rule out,
what's still open.
Args:
result_summary: What you accomplished and discovered. Concrete
and specific (URLs, parameters, payloads that worked).
findings: Optional bullet list of confirmed observations. For
credit-bearing vulnerabilities, file
``create_vulnerability_report`` first; this is for
narrative.
success: Whether the assigned subtask was completed
successfully. Default ``True``.
report_to_parent: Whether to deliver the completion report to
the parent's inbox. Default ``True``.
final_recommendations: Optional next-step suggestions for the
parent (e.g., "prioritize testing X", "spawn an agent to
cover Y").
"""
inner = _ctx(ctx)
coordinator = coordinator_from_context(inner)
me = inner.get("agent_id")
if coordinator is None or me is None:
return json.dumps(
{"success": False, "error": "Agent coordinator or agent_id missing in context"},
ensure_ascii=False,
default=str,
)
parent_id = inner.get("parent_id")
if parent_id is None:
return json.dumps(
{
"success": False,
"error": (
"agent_finish is for subagents. Root/main agents must call finish_scan instead"
),
},
ensure_ascii=False,
default=str,
)
parent_notified = False
if report_to_parent:
async with coordinator._lock:
agent_name = coordinator.names.get(me, me)
report = _render_completion_report(
agent_name=agent_name,
agent_id=me,
task=str(inner.get("task", "")),
success=success,
result_summary=result_summary,
findings=list(findings or []),
recommendations=list(final_recommendations or []),
)
await coordinator.send(
parent_id,
{
"id": f"report_{uuid.uuid4().hex[:8]}",
"from": me,
"content": report,
"type": "completion",
"priority": "high",
},
)
parent_notified = True
logger.info(
"agent_finish: %s success=%s findings=%d parent_notified=%s",
me,
success,
len(findings or []),
parent_notified,
)
await coordinator.set_status(me, "completed")
return json.dumps(
{
"success": True,
"agent_completed": True,
"parent_notified": parent_notified,
"agent_id": me,
"summary": result_summary,
"findings_count": len(findings or []),
"has_recommendations": bool(final_recommendations),
},
ensure_ascii=False,
default=str,
)
@function_tool(timeout=30)
async def stop_agent(
ctx: RunContextWrapper,
target_agent_id: str,
cascade: bool = True,
reason: str = "",
) -> str:
"""Gracefully stop a running agent (and optionally its descendants).
Uses the SDK's ``RunResultStreaming.cancel(mode="after_turn")`` so the
target's current turn finishes — including saving items to its
session — before the run loop honors the cancel. The agent's
interactive outer loop parks as ``stopped``; later user/peer
messages can wake it again.
Use sparingly. Prefer ``send_message_to_agent`` (asking the agent
to wrap up) for soft-stop scenarios. Reach for ``stop_agent`` when
a child has gone off-track and won't self-correct.
Args:
target_agent_id: The 8-char id from ``view_agent_graph`` /
``create_agent``. Cannot stop yourself.
cascade: If ``True`` (default), also stop every descendant of
``target_agent_id`` leaves-first. ``False`` stops only the
target.
reason: Optional human-readable reason for the stop, surfaced
in logs and telemetry.
"""
inner = _ctx(ctx)
coordinator = coordinator_from_context(inner)
me = inner.get("agent_id")
if coordinator is None or me is None:
return json.dumps(
{"success": False, "error": "Agent coordinator or agent_id missing in context"},
ensure_ascii=False,
default=str,
)
if target_agent_id == me:
return json.dumps(
{
"success": False,
"error": "Cannot stop yourself; call agent_finish or finish_scan instead",
},
ensure_ascii=False,
default=str,
)
_, statuses, _ = await coordinator.graph_snapshot()
if target_agent_id not in statuses:
return json.dumps(
{"success": False, "error": f"Unknown agent_id: {target_agent_id}"},
ensure_ascii=False,
default=str,
)
current_status = statuses[target_agent_id]
if current_status not in _ACTIVE_STATUSES:
return json.dumps(
{
"success": False,
"error": (
f"Agent {target_agent_id} is already '{current_status}'; "
"stop_agent only acts on running/waiting agents — use "
"view_agent_graph to find still-active descendants and "
"stop them individually, or send_message_to_agent if you "
"want to wake this one with new instructions"
),
"target_agent_id": target_agent_id,
"current_status": current_status,
},
ensure_ascii=False,
default=str,
)
if cascade:
await coordinator.cancel_descendants_graceful(target_agent_id)
else:
await coordinator.request_stop(target_agent_id)
logger.info(
"stop_agent: target=%s cascade=%s reason=%r",
target_agent_id,
cascade,
reason,
)
return json.dumps(
{
"success": True,
"target_agent_id": target_agent_id,
"cascade": cascade,
"reason": reason,
"note": "Cancellation is graceful — current turn completes first.",
},
ensure_ascii=False,
default=str,
)