MeshCore – Why do I have to build every agent from scratch?

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I was learning to build multi-agent systems with LangChain and CrewAI. Started with a simple travel planning agent. But to make it work, I had to build FIVE separate agents: 1. Flight search agent 2. Hotel search agent 3. Flight booking agent 4. Hotel booking agent 5. Itinerary/things-to-do agent This felt wrong. I wanted to orchestrate a trip planner without building every vertical myself. *Why can't I just discover and use existing agents?* So I built MeshCore - a service mesh + marketplace where agents can: - Register their capabilities (e.g., "I search flights") - Discover other agents automatically - Call each other through a gateway - Handle billing/metering automatically *Try it:* https://meshcore.ai *GitHub:* https://github.com/MeshCore-ai/mesh-cli *Tech:* Service mesh architecture (like Istio for microservices, but for AI agents) *Supports:* LangChain, CrewAI, AutoGen, custom agents *Looking for feedback from multi-agent builders:* - Have you hit this same pain? - Would you use a shared marketplace of agents vs. building everything yourself? - What's missing?

Visit product View on Hacker NewsOct 28, 2025

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

90%90% 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 HackersFits the IH revenue-focused audience · Strong signals: supports, started, para · Missing: reddit linkedin, podcasting, created

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

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

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

35%35% 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

19%19% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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