Bounty #4 - Performance Optimization - $1500 #111
Description
Summary
JuliaOS was designed for performance-first AI coordination — but as the codebase grows, so does the opportunity to fine-tune and optimize key workflows. This bounty focuses on profiling and improving performance across JuliaOS' core execution paths, including agent execution, swarm behaviors, and devtool responsiveness.
🔍 Scope Detail
Your task is to identify and improve performance bottlenecks across the JuliaOS system. This includes:
🔬 Core Execution Optimization
- Profile agent lifecycle functions (e.g.,
agent.step(),agent.useLLM()) and identify latency sources. - Optimize memory usage and runtime execution without breaking compatibility.
🧠 Swarm Performance
- Profile coordination across large agent swarms.
- Improve task distribution, message handling, or scoring throughput.
⚙️ Tooling & CLI Responsiveness
- Speed up CLI commands (e.g., agent init, deploy).
- Optimize any devtool actions that feel slow (especially in low-resource environments).
🧪 Optional Enhancements
- Add benchmarking tools or profiling utilities for future contributors.
- Suggest settings or modes for lightweight or high-performance deployments.
✅ Submission Requirements
How to Participate:
- Clone the [JuliaOS repository](https://github.com/Juliaoscode).
- Create a new branch and follow the installation instructions in the README.
- Submit your pull request referencing the GitHub issue .
- Bonus: complete this feedback form for eligibility to our upcoming Ambassador program (exclusive perks, events, rewards, and social badges).
Development Requirements:
- Code Quality: Ensure clear, modular optimizations that don’t compromise readability.
- Testing: Add or extend tests to confirm performance changes do not break functionality.
- Documentation: Include before/after benchmarks or logs demonstrating impact.
- License: All contributions must comply with the MIT license.
🧪 Judging Criteria
- Performance Gains: Clear improvement in speed, memory, or efficiency.
- Stability: No regression in correctness, compatibility, or deployment behavior.
- Clarity: Documented changes, benchmark results, and justification.
- Utility: Do changes help the entire ecosystem (devs, dApp builders, users)?
- Composability: Are the changes easy to extend in future performance work?
💸 Reward Structure
- Reward: $800 – $1,500 USD
- Paid in: $JOS or USDT
- Deadline: August 6th; early submissions may receive spotlight features on our main X account.