ai-infrastructure
18 servers · 1,351★ total
Open-source CMA-compatible agent runtime for any model, with MCP tools, sandboxed sessions, audit, replay, and a local console. Includes a native DeepSeek Harness bundle over stdio MCP.
🛡️ The approval and policy layer for AI agents. Intercept risky actions before they run, block them, or approve them remotely.
Plug-and-play homelab dashboard in one container — GPU, local-AI VRAM, Docker, systemd, host health. Built-in read-only MCP server so AI agents can explore it too.
Deploy production-ready AI services in minutes. One YAML file for agents, RAG pipelines, and MCP servers — run anywhere. Inspired by docker-compose.
Lightweight Linux sandbox for AI agents. Kernel-native isolation (namespaces, cgroups, seccomp, Landlock) with REST API, MCP bridge, and web dashboard. Single Rust binary.
AI Governance Infrastructure — local evaluation. The governance layer for AI agents: a single compiled Rust binary that authenticates, authorises, rate-limits, logs, and costs every AI interaction. Self-hosted, air-gap capable, provider-agnostic.
Route, manage, and analyze your LLM requests across multiple providers with a unified API interface
AI-Inference-Managed-by-AI: Go binary for managing AI inference on edge devices
Protocol for agent-to-agent communication with stateful orchestration, MCP-compatible and a public marketplace to discover and register agents.
AI governance infrastructure for agentic systems. Rust library behind systemprompt.io — MCP, A2A, OAuth2, audit trails, compile-time extensions. Evaluate with systemprompt-template.
Autonomous AI Agent Infrastructure Platform — OpenAI-compatible AI gateway with MCP support, multi-agent orchestration, tool calling, observability, memory, RAG, AI workflows, and unified infrastructure for any LLM provider.
Execution control layer for AI agents — prevents duplicate or incorrect real-world actions under retries, uncertainty, and stale context.
Model Context Protocol SDK for building structured, production-grade AI systems.
MCP server for Jungle Grid lets agents submit, monitor, and retrieve logs from AI workloads.
Python examples for AI-native messaging execution through the BridgeXAPI MCP interface.
AI Governance Infrastructure — open gateway demo. Self-hosted systemprompt.io gateway in a single Rust binary: policy, audit trails, and cost attribution on every AI request, with the Systemprompt Bridge desktop app for Windows and macOS connecting Claude Code and Claude Cowork.
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Agent memory system with retain-recall-reflect loop. Hybrid search, entity resolution, observation synthesis. TypeScript library + MCP server.