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allaun
2cbba82406
Last-resort multi-model panel via OpenRouter Fusion. Only invoked when all other approaches (local model, single frontier, existing skills) have failed on the same problem. Architecture: - Problem classification (lean_proof, code_bug, architecture, math_novel, infra_debug) - Per-type model panel selection (Opus + DeepSeek + Gemini for proofs) - Context assembly (files, build logs, AGENTS.md constraints) - OpenRouter Fusion call (parallel panel + judge synthesis) - Structured output (diagnosis, solution, alternatives, risks, cost) Safety: - Explicit 'break glass:' trigger required - Cost gate: /usr/bin/bash.30-.50 per invocation, .00/session cap - Rate limit: 3 invocations per session - Audit trail via ContextStream Files: - 4-Infrastructure/shim/break_glass_mcp.py (MCP server) - 6-Documentation/docs/specs/break_glass_mcp_agent.md (design spec) - .opencode/skills/break-glass/SKILL.md (skill definition) - .mcp.json (server registration)
308 lines
9.4 KiB
Python
308 lines
9.4 KiB
Python
#!/usr/bin/env python3
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"""
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break_glass_mcp.py — Last-resort multi-model panel via OpenRouter Fusion.
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Wraps OpenRouter Fusion as an MCP tool. Only invoked when all other
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approaches have failed on a novel problem.
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Usage:
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python3 break_glass_mcp.py # starts MCP server on stdio
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Requires: OPENROUTER_API_KEY env var
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"""
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import json
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import os
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import sys
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from datetime import datetime
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try:
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import httpx
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except ImportError:
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httpx = None
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OPENROUTER_API_KEY = os.environ.get("OPENROUTER_API_KEY", "")
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OPENROUTER_BASE = "https://openrouter.ai/api/v1"
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# Problem type → model panel configuration
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PANELS = {
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"lean_proof": {
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"analysis_models": [
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"anthropic/claude-opus-4.8",
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"deepseek/deepseek-v4-pro",
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"google/gemini-3.1-pro-preview",
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],
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"judge": "anthropic/claude-opus-4.8",
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"budget": 1.00,
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},
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"code_bug": {
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"analysis_models": [
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"openai/gpt-5.5",
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"anthropic/claude-opus-4.8",
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"moonshotai/kimi-k2.6",
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],
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"judge": "anthropic/claude-opus-4.8",
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"budget": 0.75,
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},
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"architecture": {
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"analysis_models": [
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"anthropic/claude-opus-4.8",
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"openai/gpt-5.5",
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"google/gemini-3.1-pro-preview",
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],
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"judge": "anthropic/claude-opus-4.8",
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"budget": 1.50,
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},
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"math_novel": {
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"analysis_models": [
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"deepseek/deepseek-v4-pro",
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"anthropic/claude-opus-4.8",
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"google/gemini-3.1-pro-preview",
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],
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"judge": "anthropic/claude-opus-4.8",
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"budget": 1.00,
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},
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"infra_debug": {
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"analysis_models": [
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"openai/gpt-5.5",
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"anthropic/claude-opus-4.8",
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"deepseek/deepseek-v4-pro",
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],
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"judge": "anthropic/claude-opus-4.8",
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"budget": 0.75,
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},
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}
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SYSTEM_PROMPT = """You are a specialist agent assembled to attack a specific
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novel problem that has resisted all常规 approaches. You have access to:
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1. The full codebase context (provided below)
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2. The project's operating constraints
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3. The specific problem statement
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4. Previous attempts and their failure modes
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YOUR TASK:
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- Diagnose the root cause
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- Propose a concrete solution with exact file paths and code
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- If the problem is undecidable, explain why and propose what would be needed
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CONSTRAINTS:
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- Lean is the source of truth for formal claims
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- No Float in compute paths (use Q0_16 or Q16_16)
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- No sorry in committed code unless with TODO(lean-port) + human sign-off
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- All new formal work goes to SilverSight (not Research Stack)
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OUTPUT FORMAT (JSON):
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{
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"diagnosis": "...",
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"solution": {
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"approach": "...",
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"confidence": 0.0-1.0,
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"files_to_change": ["..."],
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"code_snippets": {"file": "code"}
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},
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"alternatives": ["..."],
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"risks": ["..."],
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"cost_estimate": "$X.XX"
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}"""
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def classify_problem(problem: str) -> str:
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"""Auto-classify problem type from text."""
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lower = problem.lower()
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if any(w in lower for w in ["sorry", "lean", "theorem", "proof", "lake build"]):
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return "lean_proof"
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if any(w in lower for w in ["architecture", "design", "refactor", "structure"]):
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return "architecture"
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if any(w in lower for w in ["math", "formula", "eigenvalue", "convergence"]):
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return "math_novel"
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if any(w in lower for w in ["infra", "deploy", "k3s", "docker", "ssh"]):
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return "infra_debug"
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return "code_bug"
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def call_fusion(problem: str, context: str, problem_type: str,
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budget: float = 1.00) -> dict:
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"""Call OpenRouter Fusion with the assembled context."""
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if not OPENROUTER_API_KEY:
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return {"error": "OPENROUTER_API_KEY not set"}
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if httpx is None:
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return {"error": "httpx not installed (pip install httpx)"}
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panel = PANELS.get(problem_type, PANELS["code_bug"])
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"""## Problem Statement
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{problem}
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## Codebase Context
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{context}
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## Problem Type
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{problem_type}
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Please diagnose and propose a solution."""},
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]
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payload = {
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"model": "openrouter/fusion",
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"messages": messages,
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"plugins": [{
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"id": "fusion",
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"model": panel["judge"],
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"analysis_models": panel["analysis_models"],
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}],
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"max_tokens": 8192,
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}
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headers = {
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"Authorization": f"Bearer {OPENROUTER_API_KEY}",
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"Content-Type": "application/json",
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"HTTP-Referer": "https://researchstack.info",
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"X-Title": "break-glass-agent",
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}
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try:
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with httpx.Client(timeout=120) as client:
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resp = client.post(
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f"{OPENROUTER_BASE}/chat/completions",
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json=payload,
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headers=headers,
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)
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resp.raise_for_status()
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data = resp.json()
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# Extract cost
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usage = data.get("usage", {})
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cost = usage.get("total_cost", 0)
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# Extract response
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content = data["choices"][0]["message"]["content"]
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return {
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"response": content,
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"cost": cost,
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"model": data.get("model", "openrouter/fusion"),
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"usage": usage,
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}
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except Exception as e:
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return {"error": str(e)}
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def handle_tool_call(tool_name: str, arguments: dict) -> dict:
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"""Handle an MCP tool call."""
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if tool_name != "break_glass":
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return {"error": f"Unknown tool: {tool_name}"}
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problem = arguments.get("problem", "")
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problem_type = arguments.get("problem_type") or classify_problem(problem)
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budget = arguments.get("budget", 1.00)
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context_files = arguments.get("context_files", [])
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context = arguments.get("context", "")
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# Cost gate
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panel = PANELS.get(problem_type, PANELS["code_bug"])
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if budget > panel["budget"] * 3:
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return {
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"error": f"Budget ${budget:.2f} exceeds threshold ${panel['budget'] * 3:.2f}. "
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f"Escalate to human.",
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"problem_type": problem_type,
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}
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result = call_fusion(problem, context, problem_type, budget)
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return {
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"problem_type": problem_type,
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"panel": [panel["judge"]] + panel["analysis_models"],
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"result": result,
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"timestamp": datetime.utcnow().isoformat(),
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}
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def mcp_server():
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"""Simple MCP server on stdio (JSON-RPC)."""
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for line in sys.stdin:
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try:
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req = json.loads(line.strip())
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except json.JSONDecodeError:
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continue
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method = req.get("method", "")
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req_id = req.get("id")
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params = req.get("params", {})
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if method == "initialize":
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resp = {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {
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"protocolVersion": "2024-11-05",
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"capabilities": {"tools": {}},
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"serverInfo": {
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"name": "break-glass",
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"version": "0.1.0",
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},
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},
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}
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elif method == "tools/list":
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resp = {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {
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"tools": [{
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"name": "break_glass",
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"description": (
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"Last-resort multi-model panel for novel problems. "
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"Costs 3-5x a single model call. "
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"Requires explicit 'break glass:' trigger."
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),
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"inputSchema": {
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"type": "object",
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"properties": {
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"problem": {
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"type": "string",
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"description": "The specific problem statement",
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},
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"context": {
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"type": "string",
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"description": "Relevant codebase context",
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},
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"problem_type": {
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"type": "string",
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"enum": list(PANELS.keys()),
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"description": "Problem classification (auto-detected if omitted)",
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},
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"budget": {
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"type": "number",
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"description": "Maximum cost in USD (default: 1.00)",
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},
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},
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"required": ["problem"],
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},
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}],
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},
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}
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elif method == "tools/call":
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tool_name = params.get("name", "")
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arguments = params.get("arguments", {})
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result = handle_tool_call(tool_name, arguments)
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resp = {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {
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"content": [{"type": "text", "text": json.dumps(result, indent=2)}],
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},
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}
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else:
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resp = {
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"jsonrpc": "2.0",
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"id": req_id,
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"error": {"code": -32601, "message": f"Method not found: {method}"},
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}
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print(json.dumps(resp), flush=True)
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if __name__ == "__main__":
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mcp_server()
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