MAPLE – Multi-agent protocol with resource-aware scheduling

Hacker News

MAPLE (Multi Agent Protocol Language Engine) - a communication protocol for multi-agent AI systems. The gap I'm filling: MCP handles tools, A2A handles agent discovery, but neither lets agents specify resource requirements. With MAPLE, an agent can say: "I need 4GB GPU, 8GB RAM, complete within 30 seconds." Key features: - Resource-aware messaging (CPU, GPU, memory, network, deadlines) - Link Identification Mechanism (LIM) for secure agent-to-agent communication - Distributed state synchronization - Type-safe error handling - Works with any LLM/framework 900+ downloads on PyPI. pip install maple-oss GitHub: https://github.com/maheshvaikri-code/maple-oss Looking for feedback from anyone building multi-agent systems.

Visit product View on Hacker NewsDec 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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