Grok 4.6 — Deep Dive into SpaceXAI's Long-Range Agent Programming Model

# self_generated_sft_pipeline.py
"""
Simplified implementation of Grok 4.6's Self-Generated SFT pipeline
Demonstrating the core logic of Model-Based Filtering
"""

import json
import random
from typing import Any, Dict, List, Optional, Tuple
from dataclasses import dataclass, field


@dataclass
class SFTTrajectory:
    """Single SFT reasoning trajectory"""
    task_id: str
    domain: str                    # STEM | SWE | KnowledgeWork
    reasoning_effort: str          # low | medium | high | xhigh
    prompt: str
    steps: List[Dict[str, str]]    # Reasoning steps
    final_answer: str
    quality_score: Optional[float] = None


@dataclass
class TrajectoryFilter:
    """
    Model-Based Filtering core implementation
    Uses a judge model to evaluate trajectory quality across multiple dimensions
    """
    
    def __init__(self, quality_threshold: float = 0.7):
        self.threshold = quality_threshold
        self.dimension_weights = {
            "logical_coherence": 0.35,
            "step_completeness": 0.25,
            "answer_correctness": 0.30,
            "efficiency": 0.10,
        }
    
    def evaluate_logical_coherence(self, trajectory: SFTTrajectory) -> float:
        """Evaluate logical coherence of reasoning trajectory"""
        score = 0.0
        steps = trajectory.steps
        
        if len(steps) < 2:
            return 0.0
        
        for i in range(1, len(steps)):
            prev_step = steps[i - 1]
            curr_step = steps[i]
            
            overlap = len(
                set(prev_step.get("key_concepts", [])) & 
                set(curr_step.get("key_concepts", []))
            )
            if overlap == 0 and i > 1:
                score -= 0.2  # Logical break penalty
        
        # Check for circular reasoning
        step_texts = [s.get("reasoning", "") for s in steps]
        for i in range(len(step_texts)):
            for j in range(i + 2, len(step_texts)):
                if self._semantic_similarity(step_texts[i], step_texts[j]) > 0.85:
                    score -= 0.3  # Circular reasoning penalty
        
        score = max(0.0, min(1.0, 1.0 + score))
        return score
    
    def evaluate_step_completeness(self, trajectory: SFTTrajectory) -> float:
        """Evaluate whether reasoning steps are complete"""
        complete_steps = sum(
            1 for s in trajectory.steps 
            if s.get("has_verifiable_claim", False)
        )
        return complete_steps / max(len(trajectory.steps), 1)
    
    def evaluate_answer_correctness(self, trajectory: SFTTrajectory) -> float:
        """Evaluate final answer correctness"""
        return trajectory.quality_score if trajectory.quality_score else 0.5
    
    def evaluate_efficiency(self, trajectory: SFTTrajectory) -> float:
        """Evaluate reasoning efficiency"""
        n_steps = len(trajectory.steps)
        effort_multiplier = {
            "low": 5, "medium": 10, "high": 20, "xhigh": 40
        }
        expected_steps = effort_multiplier.get(trajectory.reasoning_effort, 10)
        
        ratio = n_steps / expected_steps
        if 0.5 <= ratio <= 1.5:
            return 1.0
        elif ratio < 0.5:
            return max(0.0, ratio * 2)
        else:
            return max(0.0, 2.0 - ratio)
    
    def _semantic_similarity(self, text_a: str, text_b: str) -> float:
        """Simplified semantic similarity (uses character-level Jaccard)"""
        set_a, set_b = set(text_a.split()), set(text_b.split())
        if not set_a or not set_b:
            return 0.0
        intersection = set_a & set_b
        union = set_a | set_b
        return len(intersection) / len(union)
    
    def filter_trajectory(self, trajectory: SFTTrajectory) -> Tuple[bool, float]:
        """Filter a single trajectory, returns (keep, composite_score)"""
        scores = {
            "logical_coherence": self.evaluate_logical_coherence(trajectory),
            "step_completeness": self.evaluate_step_completeness(trajectory),
            "answer_correctness": self.evaluate_answer_correctness(trajectory),
            "efficiency": self.evaluate_efficiency(trajectory),
        }
        
        weighted_score = sum(
            scores[dim] * self.dimension_weights[dim]
            for dim in self.dimension_weights
        )
        
        trajectory.quality_score = weighted_score
        return weighted_score >= self.threshold, weighted_score


def generate_sample_trajectories() -> List[SFTTrajectory]:
    """Generate sample trajectories for demonstration"""
    tasks = [
        {
            "task_id": "SWE-001",
            "domain": "SWE",
            "prompt": "Implement an LRU cache with O(1) get and put operations",
            "good_steps": [
                {"reasoning": "Analyze requirements: O(1) suggests hash table + doubly linked list",
                 "key_concepts": ["hash table", "doubly linked list", "LRU"],
                 "has_verifiable_claim": True},
                {"reasoning": "Design data structures: hash table maps key to node, linked list maintains order",
                 "key_concepts": ["hash table", "doubly linked list"],
                 "has_verifiable_claim": True},
                {"reasoning": "Get operation: lookup in hash table, move to head if exists",
                 "key_concepts": ["get", "hash lookup", "list move"],
                 "has_verifiable_claim": True},
            ],
            "final_answer": "class LRUCache implementation code...",
            "quality_score": 0.92,
        },
        {
            "task_id": "SWE-002",
            "domain": "SWE",
            "prompt": "Implement a simple web server",
            "bad_steps": [
                {"reasoning": "Need to use Python",
                 "key_concepts": ["Python"],
                 "has_verifiable_claim": False},
                {"reasoning": "Maybe need socket",
                 "key_concepts": ["socket"],
                 "has_verifiable_claim": False},
                {"reasoning": "Wait, I was wrong about bind, let me re-read the requirements",
                 "key_concepts": ["socket"],
                 "has_verifiable_claim": False},
            ],
            "final_answer": "import socket ...",
            "quality_score": 0.35,
        }
    ]
    
    trajectories = []
    for t in tasks:
        traj = SFTTrajectory(
            task_id=t["task_id"],
            domain=t["domain"],
            reasoning_effort="high",
            prompt=t["prompt"],
            steps=t["good_steps"] if t["quality_score"] > 0.5 else t["bad_steps"],
            final_answer=t["final_answer"],
            quality_score=t["quality_score"],
        )
        trajectories.append(traj)
    return trajectories


def main():
    """Main pipeline: demonstrates Model-Based Filtering"""
    filter_engine = TrajectoryFilter(quality_threshold=0.7)
    trajectories = generate_sample_trajectories()
    
    accepted = []
    rejected = []
    
    for traj in trajectories:
        keep, score = filter_engine.filter_trajectory(traj)
        status = "ACCEPTED" if keep else "REJECTED"
        print(f"Task {traj.task_id}: {status} (score: {score:.3f})")
        if keep:
            accepted.append(traj)
        else:
            rejected.append(traj)
    
    total = len(trajectories)
    print(f"\nTotal: {total} trajectories")
    print(f"Accepted: {len(accepted)} ({len(accepted)/total*100:.1f}%)")
    print(f"Rejected: {len(rejected)} ({len(rejected)/total*100:.1f}%)")


if __name__ == "__main__":
    main()

# grok_agent_ideation_to_app.py
"""
Demonstrating Grok 4.6's long-range agent workflow:
From product idea to runnable application
"""

import asyncio
import json
from enum import Enum
from typing import Any, Callable, Dict, List, Optional


class TaskStatus(Enum):
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    VERIFIED = "verified"
    FAILED = "failed"
    COMPLETED = "completed"


class AgentStep:
    """Single step of agent execution"""
    def __init__(self, name: str, action: Callable, verify: Optional[Callable] = None):
        self.name = name
        self.action = action
        self.verify = verify or (lambda ctx: True)
        self.status = TaskStatus.PENDING
        self.result: Any = None
        self.error: Optional[str] = None


class AgentContext:
    """Agent execution context, maintains cross-step state"""
    def __init__(self, task_description: str):
        self.task = task_description
        self.files: Dict[str, str] = {}
        self.research_notes: List[str] = []
        self.code_artifacts: List[str] = []
        self.test_results: Dict[str, bool] = {}
        self.current_step: int = 0


class GrokAgent:
    """
    Simplified implementation of Grok 4.6's long-range agent
    Demonstrating self-testing, verification, and multi-step execution
    """
    
    def __init__(self, reasoning_effort: str = "high"):
        self.reasoning_effort = reasoning_effort
        self.context: Optional[AgentContext] = None
        self.steps: List[AgentStep] = []
    
    def plan(self, task: str) -> AgentContext:
        """Planning phase: decompose task into executable steps"""
        self.context = AgentContext(task)
        self.steps = [
            AgentStep("research_domain", self._research_unfamiliar_domain),
            AgentStep("design_architecture", self._design_architecture),
            AgentStep("implement_core", self._implement_core),
            AgentStep("test_and_fix", self._test_and_fix),
            AgentStep("refine", self._refine_deliverable),
        ]
        return self.context
    
    async def execute(self) -> AgentContext:
        """Execution phase with self-testing and verification"""
        for i, step in enumerate(self.steps):
            self.context.current_step = i
            step.status = TaskStatus.IN_PROGRESS
            try:
                step.result = await step.action()
                step.status = TaskStatus.VERIFIED
            except Exception as e:
                step.error = str(e)
                step.status = TaskStatus.FAILED
                if self._can_self_heal(step):
                    step.result = await self._self_heal(step)
                    step.status = TaskStatus.VERIFIED
            
            # Post-verification
            if step.status == TaskStatus.VERIFIED and not step.verify(self.context):
                step.status = TaskStatus.FAILED
        
        return self.context
    
    async def _research_unfamiliar_domain(self) -> Dict:
        """Simulate researching unfamiliar domain"""
        self.context.research_notes = [
            "Modern web apps use React/Vue frontend frameworks",
            "Backend frameworks: FastAPI, Express",
            "Database options: PostgreSQL, MongoDB",
            "Deployment: Docker + Vercel/Cloudflare",
        ]
        return {"research_complete": True}
    
    async def _design_architecture(self) -> Dict:
        """Simulate designing application architecture"""
        architecture = {
            "frontend": "React + TypeScript",
            "backend": "FastAPI + Python",
            "database": "PostgreSQL",
            "deployment": "Docker + Vercel",
        }
        self.context.code_artifacts.append(json.dumps(architecture, indent=2))
        return architecture
    
    async def _implement_core(self) -> str:
        """Simulate implementing core interactions"""
        code = '''
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional, List

app = FastAPI(title="Grok 4.6 Demo App")

class TaskItem(BaseModel):
    id: Optional[int] = None
    title: str
    description: str
    status: str = "pending"

class TaskStore:
    def __init__(self):
        self._tasks = {}
        self._counter = 0
    
    async def create(self, task: TaskItem) -> TaskItem:
        self._counter += 1
        task.id = self._counter
        self._tasks[task.id] = task
        return task
    
    async def list(self) -> List[TaskItem]:
        return list(self._tasks.values())

store = TaskStore()

@app.post("/tasks", response_model=TaskItem)
async def create_task(task: TaskItem):
    return await store.create(task)

@app.get("/tasks", response_model=List[TaskItem])
async def list_tasks():
    return await store.list()
'''
        self.context.code_artifacts.append(code)
        return code
    
    async def _test_and_fix(self) -> Dict:
        """Simulate testing and fixing"""
        self.context.test_results = {
            "test_create_task": True,
            "test_list_tasks": True,
        }
        return self.context.test_results
    
    async def _refine_deliverable(self) -> str:
        """Simulate refining deliverable"""
        return "Application ready, code quality: 9.2/10"
    
    def _can_self_heal(self, step: AgentStep) -> bool:
        return step.error is not None
    
    async def _self_heal(self, step: AgentStep) -> Any:
        return await step.action()


async def main():
    agent = GrokAgent(reasoning_effort="high")
    task = "Build a task management web app with CRUD operations"
    context = agent.plan(task)
    result = await agent.execute()
    
    print(f"Steps completed: {sum(1 for s in agent.steps if s.status == TaskStatus.VERIFIED)}")
    print(f"Research notes: {len(result.research_notes)}")
    print(f"Code artifacts: {len(result.code_artifacts)}")
    print(f"Tests passed: {sum(result.test_results.values())}/{len(result.test_results)}")


if __name__ == "__main__":
    asyncio.run(main())

// context_manager.go
// Go implementation of Grok 4.6's context window management
// Demonstrating 500K context window state management for long-range agents

package main

import (
	"container/list"
	"crypto/sha256"
	"encoding/hex"
	"fmt"
	"strings"
	"sync"
	"time"
)

type ContextWindow struct {
	mu            sync.RWMutex
	maxTokens     int
	currentTokens int
	segments      *list.List
	cache         map[string]*ContextSegment
}

type ContextSegment struct {
	ID        string
	Content   string
	TokenCount int
	Type      string
	Timestamp time.Time
	Priority  int
}

func NewContextWindow(maxTokens int) *ContextWindow {
	return &ContextWindow{
		maxTokens: maxTokens,
		segments:  list.New(),
		cache:     make(map[string]*ContextSegment),
	}
}

func (cw *ContextWindow) AddSegment(content string, segType string, priority int) string {
	cw.mu.Lock()
	defer cw.mu.Unlock()

	tokenCount := len(content) / 4
	hash := sha256.Sum256([]byte(content + time.Now().String()))
	id := hex.EncodeToString(hash[:8])

	segment := &ContextSegment{
		ID:         id,
		Content:    content,
		TokenCount: tokenCount,
		Type:       segType,
		Timestamp:  time.Now(),
		Priority:   priority,
	}

	for cw.currentTokens+tokenCount > cw.maxTokens {
		if !cw.compressOldest() {
			cw.evictLowestPriority()
		}
	}

	cw.segments.PushBack(segment)
	cw.cache[id] = segment
	cw.currentTokens += tokenCount
	return id
}

func (cw *ContextWindow) compressOldest() bool {
	for e := cw.segments.Front(); e != nil; e = e.Next() {
		seg := e.Value.(*ContextSegment)
		if seg.Priority < 5 && seg.Type != "instruction" {
			summary := summarizeContent(seg.Content)
			savedTokens := seg.TokenCount - len(summary)/4
			seg.Content = summary
			seg.TokenCount = len(summary) / 4
			seg.Type = "summary"
			cw.currentTokens -= savedTokens
			return true
		}
	}
	return false
}

func (cw *ContextWindow) evictLowestPriority() {
	var lowest *list.Element
	for e := cw.segments.Front(); e != nil; e = e.Next() {
		seg := e.Value.(*ContextSegment)
		if seg.Type == "instruction" {
			continue
		}
		if lowest == nil || seg.Priority < lowest.Value.(*ContextSegment).Priority {
			lowest = e
		}
	}
	if lowest != nil {
		seg := lowest.Value.(*ContextSegment)
		cw.currentTokens -= seg.TokenCount
		delete(cw.cache, seg.ID)
		cw.segments.Remove(lowest)
	}
}

func summarizeContent(content string) string {
	if len(content) <= 200 {
		return content
	}
	return content[:200] + " ...[compressed]"
}

func (cw *ContextWindow) Stats() map[string]interface{} {
	cw.mu.RLock()
	defer cw.mu.RUnlock()

	stats := make(map[string]interface{})
	stats["max_tokens"] = cw.maxTokens
	stats["current_tokens"] = cw.currentTokens
	stats["usage_percent"] = float64(cw.currentTokens) / float64(cw.maxTokens) * 100
	stats["segment_count"] = cw.segments.Len()

	typeCount := make(map[string]int)
	for e := cw.segments.Front(); e != nil; e = e.Next() {
		seg := e.Value.(*ContextSegment)
		typeCount[seg.Type]++
	}
	stats["type_distribution"] = typeCount
	return stats
}

type LongRunningAgent struct {
	Name    string
	Context *ContextWindow
	History []AgentAction
}

type AgentAction struct {
	Step     int
	Action   string
	Result   string
	Duration time.Duration
}

func NewLongRunningAgent(name string) *LongRunningAgent {
	return &LongRunningAgent{
		Name:    name,
		Context: NewContextWindow(500000),
		History: make([]AgentAction, 0),
	}
}

func (agent *LongRunningAgent) ExecuteTask(task string) error {
	agent.Context.AddSegment(task, "instruction", 10)

	steps := []struct {
		name     string
		duration time.Duration
		priority int
	}{
		{"Requirements Analysis", 2 * time.Second, 8},
		{"Architecture Design", 3 * time.Second, 7},
		{"Implementation", 5 * time.Second, 6},
		{"Testing", 2 * time.Second, 5},
		{"Deployment", 1 * time.Second, 4},
	}

	for i, step := range steps {
		time.Sleep(step.duration / 10)
		result := fmt.Sprintf("Step %s completed", step.name)
		agent.History = append(agent.History, AgentAction{
			Step:     i + 1,
			Action:   step.name,
			Result:   result,
			Duration: step.duration,
		})
		agent.Context.AddSegment(result, "observation", step.priority)

		stats := agent.Context.Stats()
		fmt.Printf("[Step %d] Context usage: %.1f%% (segments: %d)\n",
			i+1, stats["usage_percent"], stats["segment_count"])
	}
	return nil
}

func main() {
	agent := NewLongRunningAgent("Grok 4.6")
	agent.ExecuteTask("Build a complete e-commerce platform")
	
	stats := agent.Context.Stats()
	fmt.Printf("\nFinal context usage: %.1f%%\n", stats["usage_percent"])
	if dist, ok := stats["type_distribution"].(map[string]int); ok {
		for t, c := range dist {
			fmt.Printf("  %s: %d segments\n", t, c)
		}
	}
}

# benchmark_analysis.py
"""
Grok 4.6 benchmark data analysis and cost efficiency calculation
"""

from dataclasses import dataclass
from typing import Dict, List


@dataclass
class BenchmarkResult:
    model: str
    scores: Dict[str, float]
    input_price: float
    output_price: float


@dataclass
class CostEfficiency:
    model: str
    cost_per_task: float
    intelligence_score: float
    efficiency_ratio: float


def load_data() -> List[BenchmarkResult]:
    return [
        BenchmarkResult("Grok 4.6 High", {
            "AA_Intelligence_Index": 61.0,
            "GDPVal_AA_v2_Elo": 1753.0,
            "CursorBench_v3_2": 69.9,
            "DeepSWE_v1_1": 65.9,
            "FrontierCode_v1_1": 61.3,
            "AA_Briefcase_Elo": 1577.0,
            "Harvey_LAB": 15.8,
        }, 2.0, 6.0),
        BenchmarkResult("GPT-5.6 Sol Max", {
            "AA_Intelligence_Index": 61.0,
            "GDPVal_AA_v2_Elo": 1728.0,
            "CursorBench_v3_2": 67.2,
            "DeepSWE_v1_1": 73.0,
            "FrontierCode_v1_1": 60.6,
            "AA_Briefcase_Elo": 1502.0,
            "Harvey_LAB": 2.5,
        }, 5.0, 30.0),
        BenchmarkResult("Claude Fable 5 Max", {
            "AA_Intelligence_Index": 62.0,
            "GDPVal_AA_v2_Elo": 1741.0,
            "CursorBench_v3_2": 70.5,
            "DeepSWE_v1_1": 70.0,
            "FrontierCode_v1_1": 63.6,
            "AA_Briefcase_Elo": 1574.0,
            "Harvey_LAB": 11.3,
        }, 15.0, 50.0),
    ]


def calculate_efficiency(results: List[BenchmarkResult]) -> List[CostEfficiency]:
    efficiencies = []
    for r in results:
        input_cost = 0.5 * r.input_price
        output_cost = 0.05 * r.output_price
        cost = input_cost + output_cost
        intelligence = r.scores.get("AA_Intelligence_Index", 0)
        efficiencies.append(CostEfficiency(
            model=r.model,
            cost_per_task=cost,
            intelligence_score=intelligence,
            efficiency_ratio=intelligence / cost if cost > 0 else 0,
        ))
    return efficiencies


def print_analysis():
    results = load_data()
    efficiencies = calculate_efficiency(results)
    
    print("=" * 60)
    print("Grok 4.6 Benchmark Deep Analysis")
    print("=" * 60)
    
    print("\n1. Cost Efficiency (500K input + 50K output per task)")
    print("-" * 50)
    for eff in sorted(efficiencies, key=lambda x: x.efficiency_ratio, reverse=True):
        ratio = eff.cost_per_task / efficiencies[-1].cost_per_task
        print(f"  {eff.model:25s} ${eff.cost_per_task:.2f} "
              f"(x{ratio:.1f}) efficiency: {eff.efficiency_ratio:.1f}")
    
    print("\n2. Agent Task Composite Score")
    print("-" * 50)
    weights = {
        "GDPVal_AA_v2_Elo": 0.30,
        "AA_Briefcase_Elo": 0.25,
        "CursorBench_v3_2": 0.20,
        "DeepSWE_v1_1": 0.15,
        "Harvey_LAB": 0.10,
    }
    for r in results:
        weighted = sum(r.scores.get(b, 0) * w for b, w in weights.items())
        print(f"  {r.model:25s}: {weighted:.1f}")


if __name__ == "__main__":
    print_analysis()

# grok_bot_architecture.py
"""
Simplified simulation of Grok Bot's persistent cloud computer architecture
"""

import asyncio
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional


class BotRole(Enum):
    SALES_OUTBOUND = "Sales Outbound"
    TALENT_SCOUT = "Talent Scout"
    BUG_REPRODUCTION = "Bug Reproduction"
    CHIEF_OF_STAFF = "Chief of Staff"


@dataclass
class CloudComputer:
    """Persistent cloud computer environment"""
    account_id: str
    browser_sessions: Dict[str, Any] = field(default_factory=dict)
    filesystem: Dict[str, str] = field(default_factory=dict)
    is_running: bool = True


@dataclass
class GrokBot:
    """Named AI agent with its own cloud computer session"""
    bot_id: str
    name: str
    role: BotRole
    cloud_pc: CloudComputer
    memory: Dict[str, Any] = field(default_factory=dict)
    learned_workflows: Dict[str, Any] = field(default_factory=dict)
    active_tasks: List[str] = field(default_factory=list)

    async def assign_task(self, task: str) -> str:
        """Assign a task to this bot"""
        workflow = self._find_matching_workflow(task)
        if workflow:
            return await self._execute_workflow(workflow, task)
        return await self._execute_autonomous(task)
    
    async def learn_workflow(self, name: str, steps: List[Dict]) -> None:
        """Learn a workflow by observation"""
        self.learned_workflows[name] = {"steps": steps, "version": 1}
    
    def _find_matching_workflow(self, task: str) -> Optional[Dict]:
        for name, workflow in self.learned_workflows.items():
            if name.lower() in task.lower():
                return workflow
        return None
    
    async def _execute_workflow(self, workflow: Dict, task: str) -> str:
        results = []
        for i, step in enumerate(workflow["steps"]):
            await asyncio.sleep(0.1)
            results.append(f"Step {i+1}: {step.get('action', 'unknown')}")
        return "\n".join(results)
    
    async def _execute_autonomous(self, task: str) -> str:
        plan = ["Analyze requirements", "Plan strategy", "Execute", "Verify", "Report"]
        for step in plan:
            await asyncio.sleep(0.1)
        return f"Task completed: {task}"


@dataclass
class GrokBotOrchestrator:
    """Bot orchestrator managing multi-bot collaboration"""
    account_id: str
    cloud_pc: CloudComputer
    bots: Dict[str, GrokBot] = field(default_factory=dict)
    
    def create_bot(self, bot_id: str, name: str, role: BotRole) -> GrokBot:
        bot = GrokBot(bot_id=bot_id, name=name, role=role, cloud_pc=self.cloud_pc)
        self.bots[bot_id] = bot
        return bot
    
    async def multi_bot_collaboration(self, tasks: Dict[str, str]) -> Dict[str, str]:
        results = {}
        async def run_bot(bot_id: str, task: str):
            results[bot_id] = await self.bots[bot_id].assign_task(task)
        await asyncio.gather(*[run_bot(bid, t) for bid, t in tasks.items()])
        return results


async def main():
    cloud_pc = CloudComputer(account_id="user-001")
    orchestrator = GrokBotOrchestrator(account_id="user-001", cloud_pc=cloud_pc)
    
    sales_bot = orchestrator.create_bot("bot-001", "Sales Agent", BotRole.SALES_OUTBOUND)
    bug_bot = orchestrator.create_bot("bot-002", "Bug Hunter", BotRole.BUG_REPRODUCTION)
    staff_bot = orchestrator.create_bot("bot-003", "Chief of Staff", BotRole.CHIEF_OF_STAFF)
    
    await sales_bot.learn_workflow("lead generation", [
        {"action": "Open LinkedIn Sales Navigator"},
        {"action": "Search target clients"},
        {"action": "Analyze lead intent"},
        {"action": "Generate personalized emails"},
    ])
    
    tasks = {
        "bot-001": "Generate this week's sales leads",
        "bot-002": "Reproduce the login crash bug",
        "bot-003": "Scan Slack and email for today's summary",
    }
    
    results = await orchestrator.multi_bot_collaboration(tasks)
    for bot_id, result in results.items():
        print(f"[{orchestrator.bots[bot_id].name}] {result[:50]}...")


if __name__ == "__main__":
    asyncio.run(main())

# grok46_api_demo.py
"""
Grok 4.6 API integration example with streaming, multi-turn, and tool calling
"""

import json
import time
from typing import AsyncGenerator, Callable, Dict, List, Optional, Any


class Grok46Client:
    """Grok 4.6 API client with streaming and tool support"""
    
    def __init__(self, api_key: str, model: str = "grok-4.6"):
        self.api_key = api_key
        self.model = model
        self.conversation_history: List[Dict] = []
    
    def set_reasoning_effort(self, effort: str):
        """Set reasoning effort: low | medium | high | xhigh"""
        valid = ["low", "medium", "high", "xhigh"]
        if effort not in valid:
            raise ValueError(f"Must be one of: {valid}")
        self.reasoning_effort = effort
    
    async def chat_completion(
        self,
        messages: List[Dict],
        temperature: float = 0.7,
        max_tokens: int = 16384,
        stream: bool = False,
        tools: Optional[List[Dict]] = None,
    ) -> Dict:
        """Chat completion with tool calling support"""
        request = {
            "model": self.model,
            "messages": messages,
            "temperature": temperature,
            "max_tokens": max_tokens,
            "stream": stream,
        }
        if hasattr(self, 'reasoning_effort'):
            request["reasoning_effort"] = self.reasoning_effort
        if tools:
            request["tools"] = tools
            request["tool_choice"] = "auto"
        
        # Simulated response
        return {
            "id": f"chatcmpl-{int(time.time())}",
            "model": self.model,
            "choices": [{
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": "Simulated Grok 4.6 response",
                },
                "finish_reason": "stop",
            }],
            "usage": {
                "prompt_tokens": 500,
                "completion_tokens": 200,
                "total_tokens": 700,
            },
        }
    
    def register_tool(self, name: str, description: str, parameters: Dict):
        """Register a tool for the model to call"""
        tool = {
            "type": "function",
            "function": {
                "name": name,
                "description": description,
                "parameters": parameters,
            },
        }
        if not hasattr(self, '_tools'):
            self._tools = []
        self._tools.append(tool)
    
    async def agentic_loop(
        self,
        task: str,
        max_iterations: int = 10,
        tool_handlers: Optional[Dict[str, Callable]] = None,
    ) -> str:
        """
        Long-range agent loop with autonomous planning, tool calling, and verification
        
        Args:
            task: Task description
            max_iterations: Maximum iterations
            tool_handlers: Mapping of tool names to handler functions
        """
        messages = [{
            "role": "system",
            "content": "You are Grok 4.6, focused on long-range agent tasks. "
                       "Plan, execute, verify, and self-correct."
        }, {"role": "user", "content": task}]
        
        for iteration in range(max_iterations):
            response = await self.chat_completion(
                messages=messages,
                tools=getattr(self, '_tools', None),
            )
            
            msg = response["choices"][0]["message"]
            messages.append(msg)
            
            if "tool_calls" in msg:
                for tc in msg["tool_calls"]:
                    name = tc["function"]["name"]
                    args = json.loads(tc["function"]["arguments"])
                    if tool_handlers and name in tool_handlers:
                        result = tool_handlers[name](**args)
                    else:
                        result = f"Tool {name} executed"
                    messages.append({
                        "role": "tool",
                        "tool_call_id": tc["id"],
                        "content": json.dumps(result),
                    })
            else:
                content = msg.get("content", "")
                if "FINAL" in content.upper() or "COMPLETE" in content.upper():
                    return content
        
        return messages[-1].get("content", "No result")


async def demo_long_running_task():
    """Demonstrate Grok 4.6 handling a long-range build task"""
    client = Grok46Client(api_key="demo-key")
    client.set_reasoning_effort("high")
    
    client.register_tool("web_search", "Search the web", {
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "Search query"},
        },
        "required": ["query"],
    })
    client.register_tool("execute_python", "Execute Python code", {
        "type": "object",
        "properties": {
            "code": {"type": "string", "description": "Python code"},
        },
        "required": ["code"],
    })
    
    handlers = {
        "web_search": lambda q: {"results": [f"Results for {q}"]},
        "execute_python": lambda code: {"output": "Code executed", "success": True},
    }
    
    task = """
    Build a data analytics dashboard application:
    1. Research current data viz frameworks
    2. Design application architecture
    3. Implement core data loading and visualization
    4. Create interactive dashboard UI
    5. Write test cases
    6. Generate deployment documentation
    """
    
    result = await client.agentic_loop(task, max_iterations=15, tool_handlers=handlers)
    print(f"Final result: {result}")
    return result


if __name__ == "__main__":
    import asyncio
    asyncio.run(demo_long_running_task())

# grok46_recommendations.py
"""
Decision framework for choosing Grok 4.6 vs alternatives
"""

from dataclasses import dataclass
from typing import Dict, List, Optional


@dataclass
class TaskProfile:
    """Profile of a task to be evaluated"""
    name: str
    estimated_steps: int
    requires_tool_use: bool
    context_sensitivity: float  # 0-1, how much history matters
    latency_sensitivity: float  # 0-1, how latency-sensitive
    cost_sensitivity: float     # 0-1, how cost-sensitive
    domain: str                 # "swe", "knowledge_work", "general"


@dataclass
class ModelRecommendation:
    model: str
    suitability_score: float
    estimated_cost: float
    reasoning: str


class ModelSelector:
    """Decision framework for model selection"""
    
    MODELS = {
        "grok-4.6": {
            "strengths": ["knowledge_work", "agentic", "long_horizon", "cost_efficient"],
            "weaknesses": ["single_turn_latency", "deep_swe"],
            "base_cost_in": 2.0,
            "base_cost_out": 6.0,
        },
        "gpt-5.6-sol": {
            "strengths": ["swe", "deep_swe", "reasoning"],
            "weaknesses": ["cost", "latency"],
            "base_cost_in": 5.0,
            "base_cost_out": 30.0,
        },
        "claude-fable-5": {
            "strengths": ["coding", "reasoning", "frontier"],
            "weaknesses": ["cost", "latency"],
            "base_cost_in": 15.0,
            "base_cost_out": 50.0,
        },
    }
    
    def evaluate(self, task: TaskProfile) -> List[ModelRecommendation]:
        """Evaluate which model is best suited for a given task"""
        recommendations = []
        
        for model_name, model_info in self.MODELS.items():
            score = 0.0
            
            # Long-horizon tasks favor Grok
            if task.estimated_steps > 20:
                if "long_horizon" in model_info["strengths"]:
                    score += 30
            
            # Tool use favors agentic models
            if task.requires_tool_use:
                if "agentic" in model_info["strengths"]:
                    score += 20
            
            # Domain matching
            if task.domain in model_info["strengths"]:
                score += 25
            
            # Cost sensitivity
            if task.cost_sensitivity > 0.7:
                cost = model_info["base_cost_out"]
                score += max(0, 20 - cost)
            
            # Latency sensitivity
            if task.latency_sensitivity > 0.7:
                if "single_turn_latency" not in model_info.get("weaknesses", []):
                    score += 15
            
            # Context sensitivity (long context tasks favor Grok)
            if task.context_sensitivity > 0.7:
                if "long_horizon" in model_info["strengths"]:
                    score += 10
            
            # Estimate cost
            est_input_tokens = task.estimated_steps * 10000
            est_output_tokens = task.estimated_steps * 2000
            cost = (est_input_tokens / 1_000_000 * model_info["base_cost_in"] +
                    est_output_tokens / 1_000_000 * model_info["base_cost_out"])
            
            reasoning = self._generate_reasoning(model_name, task, score)
            
            recommendations.append(ModelRecommendation(
                model=model_name,
                suitability_score=score,
                estimated_cost=round(cost, 2),
                reasoning=reasoning,
            ))
        
        return sorted(recommendations, key=lambda r: r.suitability_score, reverse=True)
    
    def _generate_reasoning(self, model: str, task: TaskProfile, score: float) -> str:
        reasons = []
        if score > 60:
            reasons.append("Highly suitable")
        elif score > 40:
            reasons.append("Moderately suitable")
        else:
            reasons.append("Consider alternatives")
        
        info = self.MODELS.get(model, {})
        if task.domain in info.get("strengths", []):
            reasons.append(f"Strong in {task.domain}")
        if task.domain in info.get("weaknesses", []):
            reasons.append(f"Weaker in {task.domain}")
        
        return " | ".join(reasons)


def main():
    """Demonstrate the decision framework"""
    selector = ModelSelector()
    
    # Scenario 1: Long-range agent task
    task1 = TaskProfile(
        name="Build a web app from idea",
        estimated_steps=50,
        requires_tool_use=True,
        context_sensitivity=0.9,
        latency_sensitivity=0.3,
        cost_sensitivity=0.8,
        domain="knowledge_work",
    )
    
    print("=" * 60)
    print("Scenario 1: Long-Range Agent Task")
    print(f"Task: {task1.name}")
    print(f"Estimated steps: {task1.estimated_steps}")
    print("=" * 60)
    
    for rec in selector.evaluate(task1):
        print(f"\n{rec.model:20s} Score: {rec.suitability_score:3d}  "
              f"Cost: ${rec.estimated_cost}")
        print(f"  {rec.reasoning}")
    
    # Scenario 2: Specialized SWE task
    task2 = TaskProfile(
        name="Fix deep codebase bug",
        estimated_steps=10,
        requires_tool_use=True,
        context_sensitivity=0.7,
        latency_sensitivity=0.5,
        cost_sensitivity=0.5,
        domain="swe",
    )
    
    print(f"\n{'='*60}")
    print("Scenario 2: Specialized SWE Task")
    print(f"Task: {task2.name}")
    print(f"Estimated steps: {task2.estimated_steps}")
    print("=" * 60)
    
    for rec in selector.evaluate(task2):
        print(f"\n{rec.model:20s} Score: {rec.suitability_score:3d}  "
              f"Cost: ${rec.estimated_cost}")
        print(f"  {rec.reasoning}")


if __name__ == "__main__":
    main()