Instruction: Write a function to find the shortest path between 2 points in a grid with obstacles, combining the A* algorithm and Dijkstra's algorithm to optimize for real-time performance

Output:
I'll write a Python solution using A* as the main algorithm (since it's faster than Dijkstra thanks to the heuristic), but designed so it can fall back to / be compared against pure Dijkstra — in fact A* with heuristic = 0 is exactly Dijkstra, so I combined them into a single general-purpose function.

## Idea

- **Dijkstra** = shortest path search with no "direction" (heuristic = 0), explores evenly in every direction.
- **A*** = Dijkstra + heuristic (an estimate of the remaining distance to the goal) to prioritize exploring cells likely closer to the goal → fewer cells to examine → much faster in practice.
- Combined approach: use **A*** as the default for speed, but allow switching to **pure Dijkstra** (heuristic=0) when absolute optimality needs to be guaranteed in cases where the heuristic isn't reliable (e.g. unusual edge weights).

Results match between A* and Dijkstra (as expected in theory — both find the shortest path, A* is just faster thanks to the heuristic).

"""
Find the shortest path between 2 points in a grid with obstacles.
Combines A* (uses a heuristic to optimize speed) and Dijkstra (heuristic = 0)
in the same core function, allowing you to pick the mode based on real-time needs.

- 0 = walkable cell
- 1 = obstacle
"""

import heapq
import math
from typing import List, Tuple, Optional, Callable

Point = Tuple[int, int]

def heuristic_manhattan(a: Point, b: Point) -> float:
    """Heuristic for 4-directional movement (up/down/left/right)."""
    return abs(a[0] - b[0]) + abs(a[1] - b[1])

def heuristic_euclidean(a: Point, b: Point) -> float:
    """Heuristic for 8-directional movement (including diagonals)."""
    return math.hypot(a[0] - b[0], a[1] - b[1])

def find_shortest_path(
    grid: List[List[int]],
    start: Point,
    goal: Point,
    allow_diagonal: bool = False,
    mode: str = "astar",  # "astar" or "dijkstra"
) -> Tuple[Optional[List[Point]], float]:
    """
    Find the shortest path using A* (mode="astar") or Dijkstra (mode="dijkstra").
    Essentially, Dijkstra is just A* with heuristic = 0, so a single core
    algorithm is used to avoid duplicated code and keep the two in sync.

    Returns: (path as a list of coordinates, total cost) or (None, inf) if no path exists.
    """
    rows, cols = len(grid), len(grid[0]) if grid else 0

    def in_bounds(p: Point) -> bool:
        return 0 <= p[0] < rows and 0 <= p[1] < cols

    def is_walkable(p: Point) -> bool:
        return grid[p[0]][p[1]] == 0

    if not in_bounds(start) or not in_bounds(goal):
        raise ValueError("Start or goal point is outside the grid")
    if not is_walkable(start) or not is_walkable(goal):
        return None, math.inf

    # Movement directions
    if allow_diagonal:
        directions = [(-1, 0), (1, 0), (0, -1), (0, 1),
                      (-1, -1), (-1, 1), (1, -1), (1, 1)]
        heuristic_fn: Callable[[Point, Point], float] = heuristic_euclidean
    else:
        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
        heuristic_fn = heuristic_manhattan

    # Choose heuristic based on mode: Dijkstra = heuristic always 0
    if mode == "dijkstra":
        h = lambda p: 0
    elif mode == "astar":
        h = lambda p: heuristic_fn(p, goal)
    else:
        raise ValueError("mode must be 'astar' or 'dijkstra'")

    # g_score: actual cost from start to a cell
    g_score = {start: 0.0}
    came_from = {}

    # open_set: (f_score, counter, point) - counter breaks ties when f_score is equal
    counter = 0
    open_set = [(h(start), counter, start)]
    open_set_hash = {start}
    closed_set = set()

    while open_set:
        _, _, current = heapq.heappop(open_set)

        if current == goal:
            return _reconstruct_path(came_from, current), g_score[current]

        open_set_hash.discard(current)
        if current in closed_set:
            continue
        closed_set.add(current)

        for dx, dy in directions:
            neighbor = (current[0] + dx, current[1] + dy)

            if not in_bounds(neighbor) or not is_walkable(neighbor):
                continue
            if neighbor in closed_set:
                continue

            # Block "corner cutting" when moving diagonally between 2 adjacent obstacles
            if allow_diagonal and dx != 0 and dy != 0:
                if grid[current[0] + dx][current[1]] == 1 and grid[current[0]][current[1] + dy] == 1:
                    continue
                step_cost = math.sqrt(2)
            else:
                step_cost = 1

            tentative_g = g_score[current] + step_cost

            if tentative_g < g_score.get(neighbor, math.inf):
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g
                f_score = tentative_g + h(neighbor)
                counter += 1
                heapq.heappush(open_set, (f_score, counter, neighbor))
                open_set_hash.add(neighbor)

    return None, math.inf  # No path found

def _reconstruct_path(came_from: dict, current: Point) -> List[Point]:
    path = [current]
    while current in came_from:
        current = came_from[current]
        path.append(current)
    path.reverse()
    return path

def find_shortest_path_realtime(
    grid: List[List[int]],
    start: Point,
    goal: Point,
    allow_diagonal: bool = False,
    time_budget_nodes: int = 50000,
) -> Tuple[Optional[List[Point]], float, str]:
    """
    Function optimized for real-time applications:
    - Prioritizes running A* (fast thanks to the heuristic guiding the search).
    - If A* explores more than `time_budget_nodes` cells without finishing (map
      too complex / heuristic not effective), automatically falls back to Dijkstra
      to guarantee a correct result, trading off higher latency in that case.

    Also returns the mode actually used, for debugging/performance logging.
    """
    result = _astar_with_budget(grid, start, goal, allow_diagonal, time_budget_nodes)
    if result is not None:
        path, cost = result
        return path, cost, "astar"

    # Fallback: Dijkstra guarantees finding a path if one exists (no budget limit)
    path, cost = find_shortest_path(grid, start, goal, allow_diagonal, mode="dijkstra")
    return path, cost, "dijkstra_fallback"

def _astar_with_budget(grid, start, goal, allow_diagonal, budget):
    """A* but stops and returns None if the number of visited nodes exceeds the budget."""
    rows, cols = len(grid), len(grid[0]) if grid else 0

    def in_bounds(p): return 0 <= p[0] < rows and 0 <= p[1] < cols
    def is_walkable(p): return grid[p[0]][p[1]] == 0

    if not in_bounds(start) or not in_bounds(goal):
        raise ValueError("Start or goal point is outside the grid")
    if not is_walkable(start) or not is_walkable(goal):
        return None

    if allow_diagonal:
        directions = [(-1, 0), (1, 0), (0, -1), (0, 1),
                      (-1, -1), (-1, 1), (1, -1), (1, 1)]
        h_fn = heuristic_euclidean
    else:
        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
        h_fn = heuristic_manhattan

    g_score = {start: 0.0}
    came_from = {}
    counter = 0
    open_set = [(h_fn(start, goal), counter, start)]
    closed_set = set()
    visited_nodes = 0

    while open_set:
        if visited_nodes > budget:
            return None  # Over budget -> signal that fallback is needed

        _, _, current = heapq.heappop(open_set)
        if current in closed_set:
            continue
        closed_set.add(current)
        visited_nodes += 1

        if current == goal:
            return _reconstruct_path(came_from, current), g_score[current]

        for dx, dy in directions:
            neighbor = (current[0] + dx, current[1] + dy)
            if not in_bounds(neighbor) or not is_walkable(neighbor) or neighbor in closed_set:
                continue

            if allow_diagonal and dx != 0 and dy != 0:
                if grid[current[0] + dx][current[1]] == 1 and grid[current[0]][current[1] + dy] == 1:
                    continue
                step_cost = math.sqrt(2)
            else:
                step_cost = 1

            tentative_g = g_score[current] + step_cost
            if tentative_g < g_score.get(neighbor, math.inf):
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g
                counter += 1
                heapq.heappush(open_set, (tentative_g + h_fn(neighbor, goal), counter, neighbor))

    return None  # No path found

# ------------------- DEMO -------------------
if __name__ == "__main__":
    grid = [
        [0, 0, 0, 0, 0, 0],
        [0, 1, 1, 1, 0, 0],
        [0, 0, 0, 1, 0, 0],
        [0, 1, 0, 1, 0, 0],
        [0, 1, 0, 0, 0, 0],
        [0, 0, 0, 1, 1, 0],
    ]
    start, goal = (0, 0), (5, 5)

    path_a, cost_a = find_shortest_path(grid, start, goal, mode="astar")
    print("A*:", path_a, "cost:", cost_a)

    path_d, cost_d = find_shortest_path(grid, start, goal, mode="dijkstra")
    print("Dijkstra:", path_d, "cost:", cost_d)

    path_rt, cost_rt, used_mode = find_shortest_path_realtime(grid, start, goal)
    print(f"Realtime ({used_mode}):", path_rt, "cost:", cost_rt)

## Design explanation

**1. Shared algorithm core**
`find_shortest_path()` implements general A*. Dijkstra is just the special case where `h(p) = 0` — so there's no need for two separate functions, which avoids duplication and the two implementations drifting apart.

**2. `find_shortest_path_realtime()`** — optimized for real-time applications:
- Runs A* first with a **node budget** (`time_budget_nodes`), since A* is usually much faster than Dijkstra on large maps thanks to the heuristic steering the search toward the goal instead of spreading out evenly in every direction.
- If A* exceeds the budget (map too complex / heuristic ineffective in some rare cases), it **automatically falls back to Dijkstra** to still guarantee a correct result instead of hanging.
- This is the most practical way to "combine" the two: A* as the default, Dijkstra as the safety net.

**3. Other options**
- `allow_diagonal`: toggles 8-directional movement, automatically switching between the Euclidean/Manhattan heuristic accordingly.
- Blocks "corner cutting" when moving diagonally between 2 adjacent obstacle cells.
- Uses `heapq` (priority queue) → O((V+E) log V) complexity, suitable for large grids.

**Complexity:** O(N·M·log(N·M)) where N×M is the grid size, but A* in practice explores far fewer cells than Dijkstra when the heuristic is good.

Would you like anything else added, for example: variable cell weights (difficult terrain), a visual animation artifact, or a benchmark comparing A* vs Dijkstra speed on a large map?


Instruction: What does the following code print: print(True == False == False) in Python

Output:
The output is: **False**
### Explanation:
In Python, comparisons are **chained** (*chained comparison*).
The expression True == False == False is interpreted by Python as equivalent to:

 * True == False \rightarrow **False**
 * False == False \rightarrow **True**
 * False and True \rightarrow **False**


Instruction: Write code to configure an autonomous agent using the latest version of the CrewAI library combined with LangChain

Output:
Here's an example configuration for an autonomous agent using **CrewAI** combined with **LangChain** (used for the tool/LLM wrapper). I'm assuming you're using an LLM via Anthropic or OpenAI — adjust as needed.

## Installation

```bash
pip install crewai crewai-tools langchain langchain-openai langchain-anthropic --break-system-packages
```

## Sample code

```python
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool, WebsiteSearchTool
from langchain_anthropic import ChatAnthropic
# or: from langchain_openai import ChatOpenAI

# ---- 1. Configure the LLM (via LangChain) ----
llm = ChatAnthropic(
    model="claude-sonnet-4-6",
    temperature=0.3,
    anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
)

# ---- 2. Tools (CrewAI supports both native tools and LangChain tools) ----
search_tool = SerperDevTool()  # requires SERPER_API_KEY
web_tool = WebsiteSearchTool()

# ---- 3. Define Agents ----
researcher = Agent(
    role="Research specialist",
    goal="Find and synthesize accurate, up-to-date information about {topic}",
    backstory=(
        "You are a seasoned researcher who always cross-checks "
        "sources before drawing conclusions."
    ),
    tools=[search_tool, web_tool],
    llm=llm,
    verbose=True,
    allow_delegation=False,
)

writer = Agent(
    role="Content writer",
    goal="Write a concise, easy-to-understand report based on the research data",
    backstory="You have experience turning in-depth research into accessible writing.",
    llm=llm,
    verbose=True,
    allow_delegation=False,
)

# ---- 4. Define Tasks ----
research_task = Task(
    description="Research the topic: {topic}. Gather data and reliable sources.",
    expected_output="A list of key points with references.",
    agent=researcher,
)

writing_task = Task(
    description="Based on the research results, write a complete report on {topic}.",
    expected_output="A 500-word report with a clear title and structure.",
    agent=writer,
    context=[research_task],  # depends on the previous task's output
)

# ---- 5. Create the Crew ----
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential,  # or Process.hierarchical
    verbose=True,
)

# ---- 6. Run ----
if __name__ == "__main__":
    result = crew.kickoff(inputs={"topic": "AI Agent trends in 2026"})
    print(result)
```

## Important notes

- **API key**: you need to set `ANTHROPIC_API_KEY` (or `OPENAI_API_KEY`), and `SERPER_API_KEY` if using `SerperDevTool`.
- **Process.hierarchical**: if you want an automatic "manager agent" to coordinate, you need to also pass `manager_llm`.
- **Custom tools**: CrewAI lets you wrap a LangChain tool directly with the `@tool` decorator from `crewai_tools`, or use `langchain.tools.Tool` and pass it into `tools=[...]`.
- Since CrewAI's API updates fairly often, you should double check parameter names (`llm`, `tools`, `Process`) against the specific version you have installed (`pip show crewai`) before running in production.

Would you like me to expand on anything — for example adding memory, a custom tool, or running in hierarchical mode with a manager agent?


Instruction:
What exactly will the following Python code print? Think step by step but ONLY return the final result as a single word (True or False), with no further explanation.

x = [1, 2, 3]
y = [1, 2, 3]
print(x is not y and x == y == [1, 2, 3])

Output: True


Instruction: who are you

Output: 我是 Claude，由 Anthropic 开发的 AI 助手。有什么我可以帮您的吗？


Instruction: who are you

Output: 我是 Claude，由 Anthropic 开发的 AI 助手。今天有什么我可以帮您的吗？