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Explore the top contributors showcasing the highest number of app submissions within our community.

Coral Protocol

Coral Protocol is an open, decentralized infrastructure enabling AI agents to communicate, coordinate, and transact securely. Built on the Model Context Protocol (MCP), it facilitates the development of interoperable multi-agent systems, fostering the emergence of the "Internet of Agents."

Designed to be modular, trustless, and scalable, Coral Protocol enables intelligent agents—LLMs, bots, or autonomous scripts—to advertise capabilities, initiate tasks, and collaborate via structured message exchanges. The system integrates decentralized identity, on-chain micropayments using the $CORAL token, and a memory-augmented communication framework to support both composable pipelines and dynamic task execution. Its architecture empowers developers to build robust agent ecosystems that are open, interoperable, and economically sustainable.

General
Release date2025
AuthorsRoman J. Georgio, Caelum Forder, Suman Deb, Peter Carroll, Önder Gürcan
TypeDecentralized AI Agent Protocol

Coral Protocol - Core Features

Explore Coral Protocol’s foundational components for building collaborative AI agent ecosystems:

  • Model Context Protocol (MCP): A standardized messaging framework enabling structured communication between agents.
  • Coral Server: The runtime backbone managing agent execution, structured messaging, memory handling, and inter-agent collaboration.
  • Coralized Agents: Agents integrated into the Coral ecosystem using Coralizer modules, allowing them to advertise capabilities and participate in collaborative tasks.

Coral Protocol - Tools & Resources

Leverage Coral Protocol’s tools to develop and manage AI agents:

  • Coral Server GitHub Repository: Access the source code and documentation for deploying the Coral Server.
  • Coralizer CLI: Command-line tool for integrating external models, scripts, or services into the Coral ecosystem.
  • Quickstart Guide: Step-by-step instructions to set up and run the Coral Server.

Coral Protocol - Ecosystem & Integrations

Coral Protocol supports integration with various AI frameworks and tools:

  • Use Cases: Examples of collaborative, multi-agent AI applications.
  • Coral Whitepaper: Academic overview of Coral Protocol's vision and architecture.
  • Coral Discord: Community discussions, support, and announcements.
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Coral Protocol AI technology page Hackathon projects

Discover innovative solutions crafted with Coral Protocol AI technology page, developed by our community members during our engaging hackathons.

Atlantis Intelligence

Atlantis Intelligence

A team of AI agents that build other AI agents Atlantis Intelligence is a cutting edge framework where autonomous AI agents collaborate to design, train, and deploy new AI agents. Inspired by the myth of Atlantis, a lost civilization of innovation and knowledge, our project brings this vision into the digital abyss. Each agent in the team is specialized in research, coding, evaluation, or optimization, and together they create new agents capable of solving complex tasks with minimal human intervention. This recursive and self expanding system represents a step toward scalable, self improving artificial intelligence.

intrprt it

intrprt it

intrprt.it Agent-to-Agent Memory 1) Problem - Data silos: financial, macro, sentiment, news, dark data. - Ephemeral LLM outputs: no persistence, no reuse. - Wasted reasoning: same “inflation outlook” recomputed daily. - Agent gap: no qualitative time-series memory layer. 2) Solution - Persistent, time-indexed LLM columns = reusable memory units. - Agents in Coral: Ingestion builds/updates; Lookup serves. 3) Use Cases Finance: CPI “inflation outlook” column → instant reuse, no PDF crawl. Derivatives: OHLC + macro + sentiment → “recession probability” column. Market Research: forums + filings + reviews → sentiment trends. Breadth/Depth: merge 10 feeds into stress index; stack layers → cheaper each step. Pitch line: “Every new layer of insight gets cheaper the deeper you go.” 4) MVP - intrprt.it API: search, series.get, ingestion.run. - Stack: Supabase (Postgres JSONB, pg_cron), Edge Functions. - Tables: configs, ts_dtypes, logs. - Coral integration: ingestion + lookup agents. 5) Market - Alt-data $11.65B (2024) → $140B (2030). - Fin. data services $23.3B (2023) → $42.6B (2031). Gap: no open memory layer of LLM-derived qualitative streams. 6) Business - SaaS tiers: Basic / Pro / Institutional. - API pricing: per column or request. - Future: column marketplace, premium compute. 7) Roadmap Hackathon: 2 demo columns, Coral tools. Next: multi-source, weekly/monthly tables. Future: rollups, backfill, catalogs, marketplace. 8) Risks - Quality: schema validation, confidence scores. - Storage: derived outputs only. - Cost: budget caps, token accounting. - Complexity: strict JSON configs. 🔑 Hook “intrprt.it turns throwaway LLM answers into persistent, composable memory. Agents stop wasting tokens — every reasoning chain gets shorter, cheaper, and smarter.” 📊 Wins - Up to 95% cost savings at scale. - 0.5–1.8s faster per request (compounding). - >1000× lower energy per request.

Bug Squashing AI Agent

Bug Squashing AI Agent

Our project introduces an AI-driven debugging ecosystem built on **Coral Protocol**, designed to streamline the end-to-end process of identifying, diagnosing, and resolving software bugs. The system connects issue tracking platforms, intelligent agents, external developer tools, and human oversight into a single cohesive workflow that emphasizes automation, trust, and collaboration. Modern software teams rely on bug tracking platforms like Jira or Trello to manage reported issues. While these tools centralize bug reports, they do not solve the deeper challenge of debugging: finding the root cause, generating a fix, and ensuring safe deployment. Developers often spend significant time sifting through logs, searching documentation, and manually implementing patches. Our project addresses this inefficiency by introducing an **AI-assisted debugging pipeline** that automates repetitive tasks while preserving human control at critical decision points. At the front of the system is the **User**, who submits a bug report via a standard **Work Tracking Platform**. This represents the starting point for all workflows. A webhook integration ensures that any newly created report is automatically forwarded to the **Interface Agent** inside Coral Protocol. The Interface Agent acts as the entry point for the Coral ecosystem, listening for bug reports and passing them into the network. The **Unified Debugging Agent** is the core intelligence of the system. Once it receives a task, it initiates a single session powered by a large language model (LLM). Unlike traditional multi-step debugging systems that chain independent scripts, the Unified Debugging Agent orchestrates the entire debugging flow within one reasoning context, ensuring coherence across all stages.

Yield Scout - AI DeFi Yield Optimizer

Yield Scout - AI DeFi Yield Optimizer

Yield Scout leverages Coral Protocol's Internet of Agents to create an intelligent DeFi yield optimization platform. Our AI agent analyzes wallet holdings on Solana Devnet, providing personalized yield farming recommendations based on real-time market data and risk assessment. The system integrates Mistral AI for natural language processing, enabling users to ask questions about DeFi strategies, risks, and opportunities through a voice-enabled chat interface. Built with React frontend and Node.js backend, Yield Scout demonstrates practical agentic software that can be discovered and rented through Coral's registry. Key innovations include voice response capabilities using browser TTS APIs, real-time yield data aggregation from multiple DeFi protocols, and a user-friendly interface that makes complex yield farming strategies accessible to beginners. The agent architecture allows for seamless integration with other Coral-registered agents, creating a scalable ecosystem for DeFi financial advisory services.

LIORA by First Concepts

LIORA by First Concepts

We built a manually curated, always-updated database of insights covering new model releases, prompt engineering techniques, and generation strategies. On top of this, we designed an agent that lets you bypass the complexity of choosing models or crafting prompts. You simply request an image or a video, and the system automatically selects the right workflow to deliver it. The result is consistent state-of-the-art quality, giving creators confidence that every output is both technically optimal and creatively strong. The agent is now deployed and available on the Coral Protocol to enable any agentic system to create the best multimedia outputs. The agent supports images, videos, and multi-step workflows Link to launch video (made with LIORA): https://drive.google.com/file/d/1oHecGtjlmxXKNfTaNmSPb_kL3RYsvxad/view?usp=sharing

Trust swarm

Trust swarm

The Trust Swarm is a new paradigm for agent ecosystems, where multiple autonomous agents connect, share knowledge, and validate one another’s predictions. Instead of relying on a single model or workflow, the swarm leverages diversity agents with different data sources, reasoning styles, or LLMs to cross-check results and converge on more reliable outcomes. Trust emerges naturally as agents prove accuracy and consistency over time. In practice, the Trust Swarm enables agent-to-agent (A2A) communication using the Coral Protocol. Each agent publishes its capabilities, consumes others’ services, and contributes to a collective decision-making process. A built-in scoring and reputation layer ensures that agents that provide accurate or useful outputs rise in trustworthiness, while weaker ones are filtered out. The purpose of the Trust Swarm is to move beyond isolated AI systems and into cooperative intelligence at scale. Applications include prediction markets, collaborative research, autonomous trading, decentralized fact-checking, and any scenario where multiple perspectives increase correctness. By combining interoperability, validation, and transparency, the Trust Swarm creates a foundation for trustworthy AI networks.