forked from Rockachopa/Timmy-time-dashboard
feat: dockerize OpenFang as vendored tool runtime sidecar (#96)
This commit is contained in:
Alexander Whitestone
committed by
GitHub
GitHub
parent
d7d7a5a80a
commit
b7c89d1101
18
src/infrastructure/openfang/__init__.py
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18
src/infrastructure/openfang/__init__.py
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"""OpenFang — vendored binary sidecar for agent tool execution.
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OpenFang is a Rust-compiled Agent OS that provides real tool execution
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(browser automation, OSINT, forecasting, social management) in a
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WASM-sandboxed runtime. Timmy's coordinator dispatches to it as a
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tool vendor rather than a co-orchestrator.
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Usage:
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from infrastructure.openfang import openfang_client
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# Check if OpenFang is available
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if openfang_client.healthy:
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result = await openfang_client.execute_hand("browser", params)
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"""
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from infrastructure.openfang.client import OpenFangClient, openfang_client
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__all__ = ["OpenFangClient", "openfang_client"]
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216
src/infrastructure/openfang/client.py
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src/infrastructure/openfang/client.py
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"""OpenFang HTTP client — bridge between Timmy coordinator and OpenFang runtime.
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Follows project conventions:
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- Graceful degradation (log error, return fallback, never crash)
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- Config via ``from config import settings``
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- Singleton pattern for module-level import
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The client wraps OpenFang's REST API and exposes its Hands
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(Browser, Collector, Predictor, Lead, Twitter, Researcher, Clip)
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as callable tool endpoints.
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"""
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import logging
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import time
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from dataclasses import dataclass, field
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from typing import Any, Optional
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from config import settings
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logger = logging.getLogger(__name__)
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# Hand names that OpenFang ships out of the box
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OPENFANG_HANDS = (
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"browser",
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"collector",
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"predictor",
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"lead",
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"twitter",
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"researcher",
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"clip",
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)
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@dataclass
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class HandResult:
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"""Result from an OpenFang Hand execution."""
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hand: str
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success: bool
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output: str = ""
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error: str = ""
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latency_ms: float = 0.0
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metadata: dict = field(default_factory=dict)
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class OpenFangClient:
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"""HTTP client for the OpenFang sidecar.
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All methods degrade gracefully — if OpenFang is down the client
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returns a ``HandResult(success=False)`` rather than raising.
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"""
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def __init__(self, base_url: Optional[str] = None, timeout: int = 60) -> None:
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self._base_url = (base_url or settings.openfang_url).rstrip("/")
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self._timeout = timeout
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self._healthy = False
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self._last_health_check: float = 0.0
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self._health_cache_ttl = 30.0 # seconds
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logger.info("OpenFangClient initialised → %s", self._base_url)
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# ── Health ───────────────────────────────────────────────────────────────
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@property
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def healthy(self) -> bool:
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"""Cached health check — hits /health at most once per TTL."""
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now = time.time()
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if now - self._last_health_check > self._health_cache_ttl:
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self._healthy = self._check_health()
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self._last_health_check = now
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return self._healthy
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def _check_health(self) -> bool:
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try:
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import urllib.request
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req = urllib.request.Request(
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f"{self._base_url}/health",
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method="GET",
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headers={"Accept": "application/json"},
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)
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with urllib.request.urlopen(req, timeout=5) as resp:
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return resp.status == 200
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except Exception as exc:
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logger.debug("OpenFang health check failed: %s", exc)
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return False
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# ── Hand execution ───────────────────────────────────────────────────────
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async def execute_hand(
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self,
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hand: str,
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params: dict[str, Any],
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timeout: Optional[int] = None,
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) -> HandResult:
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"""Execute an OpenFang Hand and return the result.
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Args:
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hand: Hand name (browser, collector, predictor, etc.)
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params: Parameters for the hand (task-specific)
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timeout: Override default timeout for long-running hands
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Returns:
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HandResult with output or error details.
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"""
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if hand not in OPENFANG_HANDS:
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return HandResult(
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hand=hand,
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success=False,
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error=f"Unknown hand: {hand}. Available: {', '.join(OPENFANG_HANDS)}",
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)
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start = time.time()
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try:
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import json
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import urllib.request
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payload = json.dumps({"hand": hand, "params": params}).encode()
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req = urllib.request.Request(
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f"{self._base_url}/api/v1/hands/{hand}/execute",
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data=payload,
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method="POST",
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headers={
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"Content-Type": "application/json",
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"Accept": "application/json",
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},
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)
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effective_timeout = timeout or self._timeout
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with urllib.request.urlopen(req, timeout=effective_timeout) as resp:
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body = json.loads(resp.read().decode())
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latency = (time.time() - start) * 1000
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return HandResult(
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hand=hand,
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success=body.get("success", True),
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output=body.get("output", body.get("result", "")),
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latency_ms=latency,
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metadata=body.get("metadata", {}),
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)
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except Exception as exc:
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latency = (time.time() - start) * 1000
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logger.warning(
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"OpenFang hand '%s' failed (%.0fms): %s",
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hand,
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latency,
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exc,
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)
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return HandResult(
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hand=hand,
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success=False,
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error=str(exc),
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latency_ms=latency,
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)
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# ── Convenience wrappers for common hands ────────────────────────────────
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async def browse(self, url: str, instruction: str = "") -> HandResult:
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"""Web automation via OpenFang's Browser hand."""
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return await self.execute_hand(
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"browser", {"url": url, "instruction": instruction}
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)
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async def collect(self, target: str, depth: str = "shallow") -> HandResult:
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"""OSINT collection via OpenFang's Collector hand."""
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return await self.execute_hand(
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"collector", {"target": target, "depth": depth}
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)
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async def predict(self, question: str, horizon: str = "1w") -> HandResult:
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"""Superforecasting via OpenFang's Predictor hand."""
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return await self.execute_hand(
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"predictor", {"question": question, "horizon": horizon}
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)
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async def find_leads(self, icp: str, max_results: int = 10) -> HandResult:
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"""Prospect discovery via OpenFang's Lead hand."""
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return await self.execute_hand(
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"lead", {"icp": icp, "max_results": max_results}
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)
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async def research(self, topic: str, depth: str = "standard") -> HandResult:
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"""Deep research via OpenFang's Researcher hand."""
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return await self.execute_hand(
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"researcher", {"topic": topic, "depth": depth}
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)
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# ── Inventory ────────────────────────────────────────────────────────────
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async def list_hands(self) -> list[dict]:
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"""Query OpenFang for its available hands and their status."""
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try:
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import json
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import urllib.request
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req = urllib.request.Request(
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f"{self._base_url}/api/v1/hands",
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method="GET",
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headers={"Accept": "application/json"},
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)
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with urllib.request.urlopen(req, timeout=10) as resp:
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return json.loads(resp.read().decode())
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except Exception as exc:
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logger.debug("Failed to list OpenFang hands: %s", exc)
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return []
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def status(self) -> dict:
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"""Return a status summary for the dashboard."""
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return {
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"url": self._base_url,
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"healthy": self.healthy,
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"available_hands": list(OPENFANG_HANDS),
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}
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# ── Module-level singleton ──────────────────────────────────────────────────
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openfang_client = OpenFangClient()
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223
src/infrastructure/openfang/tools.py
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223
src/infrastructure/openfang/tools.py
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@@ -0,0 +1,223 @@
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"""Register OpenFang Hands as MCP tools in Timmy's tool registry.
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Each OpenFang Hand becomes a callable MCP tool that personas can use
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during task execution. The mapping ensures the right personas get
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access to the right hands:
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Mace (Security) → collector (OSINT), browser
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Seer (Analytics) → predictor, researcher
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Echo (Research) → researcher, browser, collector
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Helm (DevOps) → browser
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Lead hand → available to all personas via direct request
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Call ``register_openfang_tools()`` during app startup (after config
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is loaded) to populate the tool registry.
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"""
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import logging
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from typing import Any
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from infrastructure.openfang.client import OPENFANG_HANDS, openfang_client
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from mcp.schemas.base import create_tool_schema
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logger = logging.getLogger(__name__)
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# ── Tool schemas ─────────────────────────────────────────────────────────────
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_HAND_SCHEMAS: dict[str, dict] = {
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"browser": create_tool_schema(
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name="openfang_browser",
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description=(
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"Web automation via OpenFang's Browser hand. "
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"Navigates URLs, extracts content, fills forms. "
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"Includes mandatory purchase confirmation gates."
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),
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parameters={
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"url": {"type": "string", "description": "URL to navigate to"},
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"instruction": {
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"type": "string",
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"description": "What to do on the page",
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},
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},
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required=["url"],
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),
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"collector": create_tool_schema(
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name="openfang_collector",
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description=(
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"OSINT intelligence and continuous monitoring via OpenFang's "
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"Collector hand. Gathers public information on targets."
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),
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parameters={
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"target": {
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"type": "string",
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"description": "Target to investigate (domain, org, person)",
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},
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"depth": {
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"type": "string",
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"description": "Collection depth: shallow | standard | deep",
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"default": "shallow",
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},
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},
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required=["target"],
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),
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"predictor": create_tool_schema(
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name="openfang_predictor",
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description=(
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"Superforecasting with calibrated reasoning via OpenFang's "
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"Predictor hand. Produces probability estimates with reasoning."
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),
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parameters={
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"question": {
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"type": "string",
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"description": "Forecasting question to evaluate",
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},
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"horizon": {
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"type": "string",
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"description": "Time horizon: 1d | 1w | 1m | 3m | 1y",
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"default": "1w",
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},
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},
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required=["question"],
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),
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"lead": create_tool_schema(
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name="openfang_lead",
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description=(
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"Prospect discovery and ICP-based qualification via OpenFang's "
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"Lead hand. Finds and scores potential leads."
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),
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parameters={
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"icp": {
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"type": "string",
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"description": "Ideal Customer Profile description",
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},
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"max_results": {
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"type": "integer",
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"description": "Maximum leads to return",
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"default": 10,
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},
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},
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required=["icp"],
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),
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"twitter": create_tool_schema(
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name="openfang_twitter",
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description=(
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"Social account management via OpenFang's Twitter hand. "
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"Includes approval gates for sensitive actions."
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),
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parameters={
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"action": {
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"type": "string",
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"description": "Action: post | reply | search | analyze",
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},
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"content": {
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"type": "string",
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"description": "Content for the action",
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},
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},
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required=["action", "content"],
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),
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"researcher": create_tool_schema(
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name="openfang_researcher",
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description=(
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"Deep autonomous research with source verification via "
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"OpenFang's Researcher hand. Produces cited reports."
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),
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parameters={
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"topic": {
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"type": "string",
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"description": "Research topic or question",
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},
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"depth": {
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"type": "string",
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"description": "Research depth: quick | standard | deep",
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"default": "standard",
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},
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},
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required=["topic"],
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),
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"clip": create_tool_schema(
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name="openfang_clip",
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description=(
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"Video processing and social media publishing via OpenFang's "
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"Clip hand. Edits, captions, and publishes video content."
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),
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parameters={
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"source": {
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"type": "string",
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"description": "Source video path or URL",
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},
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"instruction": {
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"type": "string",
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"description": "What to do with the video",
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},
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},
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required=["source"],
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),
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}
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# Map personas to the OpenFang hands they should have access to
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PERSONA_HAND_MAP: dict[str, list[str]] = {
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"echo": ["researcher", "browser", "collector"],
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"seer": ["predictor", "researcher"],
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"mace": ["collector", "browser"],
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"helm": ["browser"],
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"forge": ["browser", "researcher"],
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"quill": ["researcher"],
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"pixel": ["clip", "browser"],
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"lyra": [],
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"reel": ["clip"],
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}
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def _make_hand_handler(hand_name: str):
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"""Create an async handler that delegates to the OpenFang client."""
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async def handler(**kwargs: Any) -> str:
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result = await openfang_client.execute_hand(hand_name, kwargs)
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if result.success:
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return result.output
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return f"[OpenFang {hand_name} error] {result.error}"
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handler.__name__ = f"openfang_{hand_name}"
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handler.__doc__ = _HAND_SCHEMAS.get(hand_name, {}).get(
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"description", f"OpenFang {hand_name} hand"
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)
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return handler
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def register_openfang_tools() -> int:
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"""Register all OpenFang Hands as MCP tools.
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Returns the number of tools registered.
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"""
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from mcp.registry import tool_registry
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count = 0
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for hand_name in OPENFANG_HANDS:
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schema = _HAND_SCHEMAS.get(hand_name)
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if not schema:
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logger.warning("No schema for OpenFang hand: %s", hand_name)
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continue
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tool_name = f"openfang_{hand_name}"
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handler = _make_hand_handler(hand_name)
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tool_registry.register(
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name=tool_name,
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schema=schema,
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handler=handler,
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category="openfang",
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tags=["openfang", hand_name, "vendor"],
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source_module="infrastructure.openfang.tools",
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requires_confirmation=(hand_name in ("twitter",)),
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)
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count += 1
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logger.info("Registered %d OpenFang tools in MCP registry", count)
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return count
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def get_hands_for_persona(persona_id: str) -> list[str]:
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"""Return the OpenFang tool names available to a persona."""
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hand_names = PERSONA_HAND_MAP.get(persona_id, [])
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return [f"openfang_{h}" for h in hand_names]
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