""" title: LiteLLM Dashboard author: Kagamine Rin & VAI author_url: https://rin.captcouch.xyz description: Professional LiteLLM observability — Fleet expansion, proactive budget monitoring, and Billion-scale token tracking. required_open_webui_version: 0.4.0 requirements: requests version: 1.6.4 licence: MIT """ import requests import json from datetime import datetime, timedelta from zoneinfo import ZoneInfo from pydantic import BaseModel, Field from typing import Optional, List, Dict, Any class Tools: def __init__(self): self.valves = self.Valves() self._price_cache = {} self._model_info_cache = [] class Valves(BaseModel): """LiteLLM proxy connection settings.""" litellm_url: str = Field( default="http://localhost:4000", description="LiteLLM proxy base URL", ) litellm_master_key: str = Field( default="", description="LiteLLM master API key for authentication", json_schema_extra={"input": {"type": "password"}}, ) default_hide_zero_spend: bool = Field( default=True, description="Default behavior for zero-spend model filtering.", ) timezone: str = Field( default="UTC", description="IANA Timezone name for date resolution.", ) enable_dynamic_pricing: bool = Field( default=True, description="When True, attempts to fetch live pricing from /v1/model/info.", ) def _headers(self) -> dict: if not self.valves.litellm_master_key: return {} return {"Authorization": f"Bearer {self.valves.litellm_master_key}"} def _base_url(self) -> str: return self.valves.litellm_url.rstrip("/") def _compact_name(self, name: str, max_len: int = 28) -> str: """Strip redundant provider prefixes and truncate carefully.""" prefixes = ["gemini/", "openai/", "ollama-cloud/", "anthropic/", "mistral/", "azure/"] for p in prefixes: if name.startswith(p): name = name[len(p):] break return (name[:max_len-1] + "…") if len(name) > max_len else name def _format_tokens(self, n: int) -> str: """Scale tokens to human-readable units (k, M, B).""" if n >= 1_000_000_000: return f"{n / 1_000_000_000:.2f}B" if n >= 1_000_000: return f"{n / 1_000_000:.2f}M" if n >= 1_000: return f"{n / 1_000:.1f}k" return str(n) def _fetch_live_data(self): """Sync internal price and model cache.""" if not self.valves.enable_dynamic_pricing or self._price_cache: return try: url = f"{self._base_url()}/v1/model/info" resp = requests.get(url, headers=self._headers(), timeout=10) if resp.status_code == 200: self._model_info_cache = resp.json().get("data", []) for m in self._model_info_cache: name = m.get("model_name") info = m.get("model_info", {}) if name and info: self._price_cache[name] = { "input": info.get("input_cost_per_token", 0), "input_cached": info.get("cache_read_input_token_cost", 0), "output": info.get("output_cost_per_token", 0), "reasoning": info.get("output_cost_per_reasoning_token", info.get("output_cost_per_token", 0)), "search_fee": info.get("search_context_cost_per_query", {}).get("search_context_size_low", 0) } except Exception: pass def _audit_cost(self, model: str, prompt: int, cached: int, completion: int, reported_cost: float, reasoning: int = 0, has_search: bool = False) -> str: self._fetch_live_data() pricing = self._price_cache.get(model) if not pricing: return "" uncached = max(0, prompt - cached) actual_output = max(0, completion - reasoning) calc = (cached * (pricing.get("input_cached") or 0)) + \ (uncached * (pricing.get("input") or 0)) + \ (actual_output * (pricing.get("output") or 0)) + \ (reasoning * (pricing.get("reasoning") or pricing.get("output") or 0)) if has_search: calc += pricing.get("search_fee", 0) delta = reported_cost - calc symbol = "⚠️" if abs(delta) > 0.005 else "✓" return f" [calc: ${calc:.4f} {symbol}]" def _resolve_date(self, date_str: str) -> str: date_str = date_str.strip().lower() try: today = datetime.now(ZoneInfo(self.valves.timezone)).date() except Exception: today = datetime.utcnow().date() if date_str == "today": return today.isoformat() if date_str == "yesterday": return (today - timedelta(days=1)).isoformat() if "days ago" in date_str: try: n = int(date_str.split()[0]) return (today - timedelta(days=n)).isoformat() except Exception: pass days = ["monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday"] if date_str in days: target_day = days.index(date_str) days_back = (today.weekday() - target_day) % 7 return (today - timedelta(days=days_back)).isoformat() try: datetime.strptime(date_str, "%Y-%m-%d") return date_str except ValueError: pass return date_str def litellm_health(self) -> str: """Check LiteLLM proxy health, proactive budget, and grouped model fleet expansion.""" self._fetch_live_data() lines = [f"LiteLLM Health & Budget — {self._base_url()}"] lines.append("=" * 70) # 1. Budget Sentinel if self.valves.litellm_master_key: try: r = requests.get(f"{self._base_url()}/spend/keys", headers=self._headers(), timeout=10) if r.status_code == 200: for k in r.json(): spend, limit = k.get("spend", 0), k.get("max_budget") if limit: pct = (spend / limit) * 100 alert = "🚨 [CRITICAL]" if pct > 90 else "💳" lines.append(f"{alert} Key: {k.get('key_name')} | Spend: ${spend:.3f} / ${limit:.2f} ({pct:.1f}%)") except Exception: pass # 2. Infrastructure Readiness try: r = requests.get(f"{self._base_url()}/health/readiness", timeout=5) if r.status_code == 200: d = r.json() lines.append(f"✅ Proxy: {d.get('status')} | DB: {d.get('db')} | v{d.get('litellm_version')}") except Exception: pass # 3. Model Fleet (Grouped by Provider + Wildcard Expansion) try: url = f"{self._base_url()}/health" resp = requests.get(url, headers=self._headers(), timeout=15) if resp.status_code == 200: hd = resp.json() healthy = hd.get("healthy_endpoints", []) unhealthy = hd.get("unhealthy_endpoints", []) groups = {} for ep in healthy + unhealthy: m_raw = ep.get("model", "unknown") api_base = ep.get("api_base", "") # Wildcard & Fleet Discovery m_display = self._compact_name(m_raw, 32) fleet_members = [] # Search for wildcard/alias labels target_id = None for info in self._model_info_cache: m_info = info.get("model_info", {}) if m_info.get("health_check_model") == m_raw: m_display = info.get("model_name", m_raw) target_id = m_info.get("id") break if not target_id: # Direct match fallback for info in self._model_info_cache: if info.get("model_name") == m_raw: target_id = info.get("model_info", {}).get("id") break # Populate members sharing this ID (The Fleet) if target_id: for info in self._model_info_cache: if info.get("model_info", {}).get("id") == target_id and info.get("model_name") != m_display: name_part = info.get("model_name", "").split("/")[-1] if name_part and name_part != "*": fleet_members.append(name_part) icon = "✅" if ep in healthy else "❌" # Provider Determination provider = "other" if "ollama" in api_base.lower() or "ollama" in m_raw.lower(): provider = "ollama" elif "/" in m_raw: provider = m_raw.split("/")[0] if provider not in groups: groups[provider] = {"ok": [], "fail": []} if fleet_members: m_display = f"{m_display} ({len(fleet_members)} members)" fleet_lines = [f" {icon} {m_display:45s} | rpm: {ep.get('rpm','?'):>3}"] fleet_lines.extend([f" └─ • {m}" for m in sorted(list(set(fleet_members)))]) info_block = "\n".join(fleet_lines) else: info_block = f" {icon} {m_display:45s} | rpm: {ep.get('rpm','?'):>3}" if ep in healthy: groups[provider]["ok"].append(info_block) else: groups[provider]["fail"].append(info_block) lines.append(f"\nFleet Status: {len(healthy)} healthy, {len(unhealthy)} unhealthy") for prov in sorted(groups.keys()): lines.append(f"\n--- {prov.upper()} ---") lines.extend(groups[prov]["ok"] + groups[prov]["fail"]) else: lines.append(f"\n❌ Fleet Check Failed: Status {resp.status_code}") except Exception as e: lines.append(f"\n❌ Fleet Check Failed: {str(e)}") return "\n".join(lines) def litellm_cost(self, start_date: str, end_date: str, hide_zero_spend: bool = False) -> str: """Comprehensive cost report with Billion-scale token tracking and sparklines.""" if not self.valves.litellm_master_key: return "❌ Master key required." effective_hide = hide_zero_spend or self.valves.default_hide_zero_spend r_start, r_end = self._resolve_date(start_date), self._resolve_date(end_date) try: url = f"{self._base_url()}/user/daily/activity" resp = requests.get(url, headers=self._headers(), params={"start_date": r_start, "end_date": r_end}, timeout=30) if resp.status_code != 200: return f"❌ Error {resp.status_code}" data = resp.json() except Exception as e: return f"❌ Failed: {str(e)}" results = data.get("results", []) metadata = data.get("metadata", {}) if not results: return f"No cost data found for {r_start} to {r_end}." spends = [day.get("metrics", {}).get("spend", 0) for day in results] def get_spark(vals): if len(vals) < 2: return "" chars = " ▂▃▄▅▆▇█" low, high = min(vals), max(vals) rng = (high - low) or 1 return " [" + "".join(chars[int((v - low) / rng * 7)] for v in vals) + "]" lines = [f"LiteLLM Cost Report: {r_start} to {r_end}"] lines.append("=" * 85) lines.append(f"Total Spend: ${metadata.get('total_spend', 0):.2f}{get_spark(spends)}") t_in, t_out = metadata.get('total_prompt_tokens',0), metadata.get('total_completion_tokens',0) lines.append(f"Total Tokens: {self._format_tokens(metadata.get('total_tokens', 0))} ({self._format_tokens(t_in)} in + {self._format_tokens(t_out)} out)") c_read = metadata.get("total_cache_read_input_tokens", 0) if t_in > 0 and c_read > 0: lines.append(f"Global Cache: {(c_read/t_in*100):.1f}% ({self._format_tokens(c_read)} saved)") lines.append(f"Total Requests: {metadata.get('total_successful_requests', 0)} ok, {metadata.get('total_failed_requests', 0)} fail") # Model Table model_totals = {} for day in results: for m_name, m_data in day.get("breakdown", {}).get("models", {}).items(): m = m_data.get("metrics", {}) if m_name not in model_totals: model_totals[m_name] = {"spend": 0, "reqs": 0, "tokens": 0, "cache": 0} model_totals[m_name]["spend"] += m.get("spend", 0) model_totals[m_name]["reqs"] += m.get("successful_requests", 0) model_totals[m_name]["tokens"] += m.get("total_tokens", 0) model_totals[m_name]["cache"] += m.get("cache_read_input_tokens", 0) lines.append(f"\n{'Model':32s} | {'Spend':>10s} | {'Requests':>12s} | {'Tokens':>12s}") lines.append("-" * 85) hidden = 0 for name, mt in sorted(model_totals.items(), key=lambda x: x[1]["spend"], reverse=True): if effective_hide and mt["spend"] < 0.001: hidden += 1; continue c_inf = f" 💾{int(mt['cache']/(mt['tokens'] or 1)*100)}%" if mt["cache"] else "" lines.append(f" {self._compact_name(name, 30):30s} | ${mt['spend']:9.2f} | {mt['reqs']:8d} ok | {self._format_tokens(mt['tokens']):>10s}{c_inf}") if hidden: lines.append(f" ... ({hidden} models with sub-$0.001 spend hidden)") return "\n".join(lines) def litellm_history(self, limit: int = 20, show_failed: bool = True) -> str: """Deep forensic history including Thought detection, Latency, and Cost Audit.""" if not self.valves.litellm_master_key: return "❌ Master key required." try: resp = requests.get(f"{self._base_url()}/spend/logs", headers=self._headers(), timeout=30) if resp.status_code != 200: return f"❌ Error {resp.status_code}" logs = sorted(resp.json(), key=lambda x: x.get("startTime", ""), reverse=True) except Exception as e: return f"❌ Failed: {str(e)}" if not show_failed: logs = [l for l in logs if not l.get("metadata", {}).get("exception")] recent = logs[:max(1, min(50, limit))] lines = [f"LiteLLM Forensic History — {self._base_url()}"] lines.append(f"Showing {len(recent)} of {len(logs)} total requests") lines.append("=" * 115) lines.append(f" # {'Time':8s} | {'Model':28s} | {'Status':7s} | {'Dur':>6s} | {'Spend':>10s} | {'Tokens':>12s}") lines.append("-" * 115) for i, entry in enumerate(recent, 1): ts = entry.get("startTime", "?")[5:16] model = self._compact_name(entry.get("model", "unknown"), 28) spend = entry.get("spend", 0) p_tok = entry.get("prompt_tokens", 0) or 0 c_tok = entry.get("completion_tokens", 0) or 0 meta = entry.get("metadata", {}) or {} is_failed = bool(meta.get("exception")) or meta.get("status") == "failure" status = "❌ FAIL" if is_failed else "✅ OK" dur = entry.get("request_duration_ms", 0) or 0 dur_s = f"{(dur/1000):.1f}s" if dur > 1000 else f"{dur}ms" slow = "⚠️" if dur > 15000 else "" usage = entry.get("usage", {}) or {} thought = usage.get("completion_tokens_details", {}).get("reasoning_tokens", 0) thought_s = f" [💭{self._format_tokens(thought)}]" if thought else "" has_search = bool(meta.get("web_search_options")) or "search" in entry.get("call_type", "") search_s = " 🔍" if has_search else "" cached = entry.get("cache_read_input_tokens", 0) or 0 cache_s = f" 💾{int(cached/p_tok*100)}%" if p_tok and cached else "" p_f = f"{p_tok/1000:.1f}k" if p_tok >= 1000 else str(p_tok) c_f = f"{c_tok/1000:.1f}k" if c_tok >= 1000 else str(c_tok) audit = self._audit_cost(entry.get("model"), p_tok, cached, c_tok, spend, reasoning=thought, has_search=has_search) line = f" {i:2d}. {ts} | {model:28s} | {status} | {dur_s:>6s}{slow} | ${spend:.4f}{audit:18s} | {self._format_tokens(p_tok):>6s}→{self._format_tokens(c_tok):<6s}{thought_s}{cache_s}{search_s}" lines.append(line) if is_failed: lines.append(f" └─ Error: {meta.get('exception', 'unknown')[:85]}") return "\n".join(lines)