What problem does this solve?
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AI agents today are often isolated because they speak different protocols (MCP, ACP, native A2A) and use differing data schemas. This middleware acts as a universal translator, allowing agents to seamlessly collaborate without changing their underlying code.
How is this different from normal API gateways?
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Traditional API gateways just route raw requests between services. This system is agent-native. It translates protocol envelopes (MCP, ACP, A2A) and actively resolves semantic differences in data payloads using ontologies and rule engines.
Do I need to use all features?
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No. Each feature is modular. You can integrate protocol translation only, semantic mapping only, or the full orchestration and discovery stack depending on your needs.
Does this replace my existing agents?
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No. It acts as a translation and communication layer between your agents. Your existing agents and underlying backends remain intact.
How are messages translated before delivery?
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Every message flows through protocol conversion, semantic mapping rules, and validation. If default mapping fails, an ML fallback model suggests field alignments before forwarding to ensure data integrity.
Is this only for LLM-based agents?
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No. It works with any autonomous process capable of communicating over network protocols — LLM agents, automated workflows, robotic systems, or traditional software services.
How are data schemas aligned?
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Through OWL ontologies, JSON Schema validation, and PyDatalog rules that dynamically map fields (e.g., `user_info.name` to `profile.fullname`) between different agent schemas.
Can agents dynamically discover collaborators?
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Yes. Agents can use the Registry & Discovery service to find collaborators. They publish their capabilities, and a compatibility score is evaluated before handoff.
Is it cloud-based?
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No. It can run locally via Docker Compose, be self-hosted, or integrated into your existing cloud infrastructure like Render or Cloud Run.
Is this production-ready?
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It is designed as a robust middleware foundation. Production readiness depends on your specific scaling, high availability (e.g., Redis deployment), and authentication configuration.
Who is this built for?
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Developers and teams building multi-agent AI systems that require seamless interoperability, protocol translation, and semantic data coordination across isolated stacks.
Why not just force all agents to use one standard?
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Standardization across diverse ecosystems is difficult and slow. This middleware embraces protocol diversity, enforcing translation outside the agent so you don't have to rewrite existing integrations.