refactor: params to plan prompt

[jmatejcz]

Purpose

• Parametrize Megamind plan prompt

Proposed Changes

PlanPrompts dataclass

Issues

• Links to relevant issues

Testing

Test it out with some random prompts in file rai_bench/examples/tool_calling_custom_agent.py

prompts = PlanPrompts(
        objective_template="xddddd\n",
        first_step_prompt="\ndo first step!!!\n",
        steps_done_header="Bly bly bly:\n",
        next_step_prompt="do somth lol: \n",
        completion_prompt="return when finished",
    )

  agent = create_megamind(
      megamind_llm=supervisor_llm,
      megamind_system_prompt=task.get_system_prompt(),
      executors=executors,
      anylyzer_prompt=task.get_planning_prompt(),
      plan_prompts=prompts,
  )

You should see difference in user prompt

[codecov]

Codecov Report

❌ Patch coverage is 45.83333% with 13 lines in your changes missing coverage. Please review.
✅ Project coverage is 65.32%. Comparing base (148bf74) to head (d13face).
✅ All tests successful. No failed tests found.

Files with missing lines	Patch %	Lines
src/rai_core/rai/agents/langchain/core/megamind.py	45.83%	13 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #709      +/-   ##
==========================================
- Coverage   65.32%   65.32%   -0.01%     
==========================================
  Files          78       78              
  Lines        3386     3403      +17     
==========================================
+ Hits         2212     2223      +11     
- Misses       1174     1180       +6     

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[Juliaj]

Hello @deepkarkada, thanks for your interest in contributing! Based on your interest for agentic programming, this PR relates to the long-horizon planning agent and could use your feedback. A good starting point for understanding this PR is to look into #679 which describes some initial design decisions of the agent.

One way to get hands-on experience is to set up your dev environment to run the script from PR #679. To do that, you'll need to set up RAI. Instructions can be found at https://robotecai.github.io/rai/setup/install/. Please feel free to ping technical-support or reach out to me if you run into any issues setting up your machine. Any of us will be happy to walk you through the steps.

As for timeline, since this is your first PR, I wouldn't stress about it. As long as you get started, the overall goal is achieved.