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chapter 6 updates and adding new trees
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‎agentic_btree_coding_challenge.py

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import textwrap
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import time
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import py_trees
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from playground.behavior_trees import (
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create_assistant_action_on_thread,
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create_assistant_condition,
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)
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from playground.assistants_api import api
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# Create the root node (sequence)
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root = py_trees.composites.Sequence("RootSequence", memory=True)
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thread = api.create_thread()
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challenge = textwrap.dedent("""
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Plant the Grass
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You will be given a matrix representing a field g and two numbers x, y coordinate.
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There are three types of possible characters in the matrix:
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x representing a rock.
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o representing a dirt space.
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+ representing a grassed space.
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You have to simulate grass growing from the position (x, y).
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Grass can grow in all four directions (up, left, right, down).
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Grass can only grow on dirt spaces and can't go past rocks.
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Return the simulated matrix.
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Examples
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simulate_grass([
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"xxxxxxx",
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"xooooox",
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"xxxxoox"
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"xoooxxx"
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"xxxxxxx"
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], 1, 1) ➞ [
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"xxxxxxx",
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"x+++++x",
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"xxxx++x"
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"xoooxxx"
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"xxxxxxx"
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]
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Notes
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There will always be rocks on the perimeter
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""")
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judge_test_cases = textwrap.dedent("""
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Test.assert_equals(simulate_grass(["xxxxxxx","xooooox","xxxxoox","xoooxxx","xxxxxxx"], 1, 1), ["xxxxxxx","x+++++x","xxxx++x","xoooxxx","xxxxxxx"])
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Test.assert_equals(simulate_grass(["xxxxxxx","xoxooox","xxoooox","xooxxxx","xoxooox","xoxooox","xxxxxxx"], 2, 3), ["xxxxxxx","xox+++x","xx++++x","x++xxxx","x+xooox","x+xooox","xxxxxxx"])
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Test.assert_equals(simulate_grass(["xxxxxx","xoxoox","xxooox","xoooox","xoooox","xxxxxx"], 1, 1), ["xxxxxx","x+xoox","xxooox","xoooox","xoooox","xxxxxx"])
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Test.assert_equals(simulate_grass(["xxxxx","xooox","xooox","xooox","xxxxx"], 1, 1),["xxxxx","x+++x","x+++x","x+++x","xxxxx"])
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Test.assert_equals(simulate_grass(["xxxxxx","xxxxox","xxooox","xoooxx","xooxxx","xooxxx","xxooox","xxxoxx","xxxxxx"], 4, 1),["xxxxxx","xxxx+x","xx+++x","x+++xx","x++xxx","x++xxx","xx+++x","xxx+xx","xxxxxx"])
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Test.assert_equals(simulate_grass(["xxxxxxxxxxx", "xoxooooooox", "xoxoxxxxxox", "xoxoxoooxox", "xoxoxoxoxox", "xoxoxoxoxox", "xoxoxxxoxox", "xoxoooooxox", "xoxxxxxxxox", "xooooooooox", "xxxxxxxxxxx"], 1, 1), ["xxxxxxxxxxx", "x+x+++++++x", "x+x+xxxxx+x", "x+x+x+++x+x", "x+x+x+x+x+x", "x+x+x+x+x+x", "x+x+xxx+x+x", "x+x+++++x+x", "x+xxxxxxx+x", "x+++++++++x", "xxxxxxxxxxx"])
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""")
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hacker = create_assistant_action_on_thread(
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thread=thread,
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action_name="Hacker",
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assistant_name="Python Coding Assistant",
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assistant_instructions=textwrap.dedent(f"""
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Challenge goal:
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{challenge}
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Solve the challenge and output the final solution to a file called solution.py
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"""),
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)
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root.add_child(hacker)
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judge = create_assistant_action_on_thread(
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thread=thread,
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action_name="Judge solution",
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assistant_name="Coding Challenge Judge",
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assistant_instructions=textwrap.dedent(
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f"""
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Challenge goal:
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{challenge}
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Load the solution from the file solution.py.
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Then confirm is a solution to the challenge and test it with the following test cases:
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{judge_test_cases}
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Run the code for the solution and confirm it passes all the test cases.
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If the solution passes all tests save the solution to a file called judged_solution.py
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""",
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),
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)
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root.add_child(judge)
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# verifier operates on a different thread, essentially in closed room
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verifier = create_assistant_condition(
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condition_name="Verify solution",
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assistant_name="Python Coding Assistant",
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assistant_instructions=textwrap.dedent(
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f"""
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Challenge goal:
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{challenge}
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Load the file called judged_solution.py and verify that the solution is correct by running the code and confirm it passes all the test cases:
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{judge_test_cases}
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If the solution is correct, return only the single word SUCCESS, otherwise return the single word FAILURE.
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""",
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),
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)
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root.add_child(verifier)
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# Create the behavior tree
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tree = py_trees.trees.BehaviourTree(root)
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# Tick the tree to run it
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# for i in range(1000):
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# print(f"Tick {i + 1}")
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# tree.tick()
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# time.sleep(30) # Simulate time between ticks
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while True:
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tree.tick()
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time.sleep(20) # Simulate time between ticks
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if root.status == py_trees.common.Status.SUCCESS:
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break

‎agentic_btree_on_thread.py

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# Create the root node (sequence)
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root = py_trees.composites.Sequence("RootSequence", memory=True)
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# selector = py_trees.composites.Selector("Hacker Team Selector", memory=True)
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# root.add_child(selector)
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thread = api.create_thread()
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challenge = textwrap.dedent("""

‎agentic_btree_video_poster_v1.py

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import time
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import py_trees
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from playground.behavior_trees import (
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create_assistant_action,
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)
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# Create the root node (sequence)
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root = py_trees.composites.Sequence("RootSequence", memory=True)
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search_term = "GPT Agents"
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search_youtube_action = create_assistant_action(
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action_name=f"Search YouTube({search_term})",
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assistant_name="YouTube Researcher v2",
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assistant_instructions=f"""
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Search Term: {search_term}
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Use the query "{search_term}" to search for videos on YouTube.
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then for each video download the transcript and summarize it for relevance to {search_term}
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be sure to include a link to each of the videos,
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and then save all summarizations to a file called youtube_transcripts.txt
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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root.add_child(search_youtube_action)
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write_post_action = create_assistant_action(
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action_name="Write Post",
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assistant_name="Twitter Post Writer",
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assistant_instructions="""
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Load the file called youtube_transcripts.txt,
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analyze the contents for references to search term at the top and then select
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the most exciting and relevant video related to:
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eductional, entertaining, or informative, to post on Twitter.
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Then write a Twitter post that is relevant to the video,
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and include a link to the video, along
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with exciting highlights or mentions,
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and save it to a file called youtube_twitter_post.txt.
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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root.add_child(write_post_action)
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post_action = create_assistant_action(
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action_name="Post",
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assistant_name="Social Media Assistant",
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assistant_instructions="""
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Load the file called youtube_twitter_post.txt and post the content to Twitter.
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If the content is empty please do not post anything.
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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root.add_child(post_action)
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# Create the behavior tree
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tree = py_trees.trees.BehaviourTree(root)
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# Tick the tree to run it
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while True:
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tree.tick()
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time.sleep(30) # Simulate time between ticks

‎agentic_btree_video_poster_v2.py

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import time
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import py_trees
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from playground.behavior_trees import (
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create_assistant_action,
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)
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search_term = "GPT Agents"
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# Create the root node (sequence)
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root = py_trees.composites.Sequence("RootSequence", memory=True)
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selector = py_trees.composites.Selector("Search Selector", memory=True)
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root.add_child(selector)
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search_term = "GPT Agents"
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search_youtube_action = create_assistant_action(
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action_name=f"Search YouTube({search_term})",
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assistant_name="YouTube Researcher v2",
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assistant_instructions=f"""
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Search Term: {search_term}
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Use the query "{search_term}" to search for videos on YouTube.
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then for each video download the transcript and summarize it for relevance to {search_term}
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be sure to include a link to each of the videos,
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and then save all summarizations to a file called youtube_transcripts.txt
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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selector.add_child(search_youtube_action)
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search_term = "OpenAI"
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search_youtube_action = create_assistant_action(
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action_name=f"Search YouTube({search_term})",
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assistant_name="YouTube Researcher v2",
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assistant_instructions=f"""
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Search Term: {search_term}
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Use the query "{search_term}" to search for videos on YouTube.
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then for each video download the transcript and summarize it for relevance to {search_term}
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be sure to include a link to each of the videos,
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and then save all summarizations to a file called youtube_transcripts.txt
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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selector.add_child(search_youtube_action)
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search_term = "GPT-4o"
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search_youtube_action = create_assistant_action(
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action_name=f"Search YouTube({search_term})",
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assistant_name="YouTube Researcher v2",
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assistant_instructions=f"""
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Search Term: {search_term}
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Use the query "{search_term}" to search for videos on YouTube.
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then for each video download the transcript and summarize it for relevance to {search_term}
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be sure to include a link to each of the videos,
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and then save all summarizations to a file called youtube_transcripts.txt
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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selector.add_child(search_youtube_action)
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write_post_action = create_assistant_action(
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action_name="Write Post",
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assistant_name="Twitter Post Writer",
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assistant_instructions="""
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Load the file called youtube_transcripts.txt,
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analyze the contents for references to search term at the top and then select
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the most exciting and relevant video related to:
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eductional, entertaining, or informative, to post on Twitter.
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Then write a Twitter post that is relevant to the video,
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and include a link to the video, along
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with exciting highlights or mentions,
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and save it to a file called youtube_twitter_post.txt.
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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root.add_child(write_post_action)
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post_action = create_assistant_action(
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action_name="Post",
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assistant_name="Social Media Assistant",
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assistant_instructions="""
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Load the file called youtube_twitter_post.txt and post the content to Twitter.
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If the content is empty please do not post anything.
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If you encounter any errors, please return just the word FAILURE.
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""",
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)
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root.add_child(post_action)
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# Create the behavior tree
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tree = py_trees.trees.BehaviourTree(root)
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# Tick the tree to run it
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while True:
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tree.tick()
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time.sleep(30) # Simulate time between ticks

‎agentic_conversation_btree.py

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import textwrap
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import time
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import py_trees
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from playground.behavior_trees import (
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create_assistant_action_on_thread,
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create_assistant_condition_on_thread,
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)
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from playground.assistants_api import api
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# Create the root node (sequence)
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root = py_trees.composites.Sequence("RootSequence", memory=True)
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bug_file = """
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def add_numbers(num1, num2):
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# Intentional error: '+' should be used instead of '-'
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return num1 - num2
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def subtract_numbers(num1, num2):
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return num1 - num2
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def multiply_numbers(num1, num2):
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return num3 * num2
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def divide_numbers(num1, num2):
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return num1 / num2
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def main():
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print("Performing arithmetic operations with predefined values:")
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add_result = add_numbers()
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print(f"Addition Result: {add_result}")
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subtract_result = subtract_numbers()
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print(f"Subtraction Result: {subtract_result}")
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multiply_result = multiply_numbers()
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print(f"Multiplication Result: {multiply_result}")
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divide_result = divide_numbers()
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print(f"Division Result: {divide_result}")
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if __name__ == "__main__":
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main()
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"""
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thread = api.create_thread()
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debug_code = create_assistant_action_on_thread(
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thread=thread,
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action_name="Debug code",
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assistant_name="Python Debugger",
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assistant_instructions=textwrap.dedent(f"""
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Here is the code with bugs in it:
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{bug_file}
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Run the code to identify the bugs and fix them.
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Be sure to test the code to ensure it runs without errors or throws any exceptions.
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"""),
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)
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root.add_child(debug_code)
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verify = create_assistant_condition_on_thread(
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thread=thread,
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condition_name="Verify",
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assistant_name="Python Coding Assistant",
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assistant_instructions=textwrap.dedent(
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"""
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Verify the solution fixes the bug and there are no more issues.
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Verify that no exceptions are thrown when the code is run.
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Reply with SUCCESS if the solution is correct, otherwise return FAILURE.
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If you are happy with the solution, save the code to a file called fixed_bug.py.
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""",
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),
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)
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root.add_child(verify)
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# Create the behavior tree
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tree = py_trees.trees.BehaviourTree(root)
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while True:
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tree.tick()
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if root.status == py_trees.common.Status.SUCCESS:
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break
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time.sleep(20) # Simulate time between ticks

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