mirror of
https://github.com/MervinPraison/PraisonAI.git
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Dhivya-BharathyandCursor
bc8a39dd29
- Fix AgentTeam.start() result handling across 22+ workflow/usecase examples - Fix missing AgentFlow/AgentTeam/AutoAgents imports and registry override demo - Fix scope_identifiers, state_redis db import, and ToolsMCPServer.run_sse API - Mark STT and stale tool-import examples as skip until refreshed - Remove stale db mapping that shadowed praisonaiagents.db submodule Co-authored-by: Cursor <[email protected]>
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
from praisonaiagents import Agent, Task, AgentTeam
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# Create generator and evaluator agents
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generator = Agent(
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name="Generator",
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role="Solution generator",
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goal="Generate initial solutions and incorporate feedback",
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instructions=(
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"1. Look at the context from previous tasks.\n"
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"2. If you see that you have already produced 2 points, then add another 2 new points "
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" so that the total becomes 10.\n"
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"3. Otherwise, just produce the first 2 points.\n"
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"4. Return only the final list of points, with no extra explanation."
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)
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)
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evaluator = Agent(
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name="Evaluator",
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role="Solution evaluator",
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goal="Evaluate solutions and provide improvement feedback",
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instructions=(
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"1. Count how many lines in the response start with a number and a period (like '1. ' or '2. ').\n"
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"2. If there are 10 or more, respond with 'done'.\n"
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"3. Otherwise, respond with 'more'.\n"
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"4. Return only the single word: 'done' or 'more'."
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)
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)
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# Create tasks for the feedback loop
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generate_task = Task(
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name="generate",
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description="Write 2 points about AI incuding if anything exiting from previous points",
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expected_output="2 points",
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agent=generator,
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is_start=True,
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task_type="decision",
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next_tasks=["evaluate"]
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)
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evaluate_task = Task(
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name="evaluate",
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description="Check if there are 10 points about AI",
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expected_output="more or done",
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agent=evaluator,
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next_tasks=["generate"],
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context=[generate_task],
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task_type="decision",
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condition={
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"more": ["generate"], # Continue to generate
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"done": [""] # Exit when optimization complete
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}
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)
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# Create workflow manager
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workflow = AgentTeam(
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agents=[generator, evaluator],
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tasks=[generate_task, evaluate_task],
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process="workflow", output="verbose"
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)
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# Run optimization workflow
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results = workflow.start()
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# Print results
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print("\nEvaluator-Optimizer Results:")
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task_results = (
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results.get("task_results", {})
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if isinstance(results, dict)
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else {}
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)
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if not task_results and isinstance(results, str):
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print(f"\nWorkflow output:\n{results}")
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for task_id, result in task_results.items():
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if result:
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print(f"Task {task_id}: {result.raw}")
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