Kodosumi
Kodosumi is an open source, framework-agnostic runtime environment built on Ray for deploying, managing, and scaling agentic services at the enterprise level. It enables effortless deployment of AI agents with a single YAML config, offering minimal setup overhead and no vendor lock-in. Designed for handling bursty traffic and long-running workflows, it dynamically scales across Ray clusters to ensure consistent performance. Kodosumi integrates real-time logging and monitoring through the Ray dashboard, providing instant observability and streamlined debugging of complex flows. Core building blocks include autonomous agents (task performers), orchestrated flows, and deployable agentic services, all managed via a pragmatic web admin panel.
Learn more
Traccia
Traccia is an OpenTelemetry-native observability, governance, and policy enforcement platform for production AI agents. It gives engineering teams complete visibility into every LLM call, tool invocation, decision, token, and dollar spent across frameworks like LangChain, CrewAI, OpenAI Agents SDK, AutoGen, and LlamaIndex. Beyond tracing, Traccia helps organizations govern AI systems with runtime policies that can detect and block unsafe behavior, runaway costs, restricted model usage, and PII exposure before incidents reach production. Accurate cost attribution, agent health monitoring, a unified agent registry, and EU AI Act evidence generation make it suitable for enterprise deployments. With a lightweight open-source SDK and managed platform, Traccia enables teams to build, debug, monitor, and govern AI agents at scale without vendor lock-in, using standard OpenTelemetry instrumentation.
Learn more
Peta
Peta is an enterprise-grade control plane for the Model Context Protocol (MCP) that centralizes, secures, governs, and monitors how AI clients and agents access external tools, data, and APIs. It combines a zero-trust MCP gateway, secure vault, managed runtime, policy engine, human-in-the-loop approvals, and full audit logging into a single platform so organizations can enforce fine-grained access control, hide raw credentials, and track every tool call made by AI systems. Peta Core acts as a secure vault and gateway that encrypts credentials, issues short-lived service tokens, validates identity and policies on each request, orchestrates MCP server lifecycle with lazy loading and auto-recovery, and injects credentials at runtime without exposing them to agents. The Peta Console lets teams define who or which agents can access specific MCP tools in specific environments, set approval requirements, manage tokens, and analyze usage and costs.
Learn more
Jozu
Jozu is an AI supply chain security platform that verifies artifacts before execution, governs agent activity at runtime, and preserves proof of what happened afterward. Jozu Hub provides a self-hosted registry for models, agents, MCP servers, and skills, centralizing each artifact with cryptographic signatures, attestations, scanning, policy controls, and audit records. Its AI-specific security analysis covers threats such as executable code hidden in model packages, backdoored weights, data poisoning, prompt injection, compromised tools, and license violations. Policies can be authored once, distributed as signed OCI artifacts, and enforced when artifacts are pulled, promoted, admitted, or executed. Jozu Agent Guard runs alongside workloads on servers, desktops, edge devices, and air-gapped systems, applying local prompt and input-output filtering, tool-access controls, approval requirements, and runtime policy enforcement.
Learn more
