Enterprises keep buying AI tools and wondering why output does not compound, when the real gap is governance: nobody can say which agent owns what, what it may touch, or what it cost. Ybkkasi answers those three questions in one console for every MCP server, skill, agent, and AI application you run. Register each asset and move it through a lifecycle state machine from discovery to retirement, with 85 permission points deciding who can view, call, publish, or approve at every step. Work itself is organized as goals that break into projects and tasks, assigned to digital employees who each carry a home organization, a human supervisor, and a roster status. Approvals route through a dedicated center where lifecycle changes and sensitive actions wait for a human decision instead of happening silently. Every call is metered, so usage and cost reports attribute spend by department, by agent, and by skill, making showback a query rather than an argument. The audit log records who did what to which asset and when, giving compliance a trail they can actually read. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Prismor sits between your AI coding agents and the tools they want to use, judging every shell command, file read, and API call before it runs so a helpful agent cannot quietly become a destructive one. Run it in observe mode to watch what agents actually do across every workspace, then switch chosen rules to enforce and block secret leaks, prompt-injection pivots, privilege escalation, and risky package installs before execution. When a call is sensitive rather than forbidden, Prismor can pause it for human approval, letting a person decide instead of the model. Plain-language prompt guardrails such as never push to main are added to the agent context per agent and per session, and edits are picked up on the next prompt. Every action lands in a hash-chained, Ed25519-signed audit trail you can verify later, while the web dashboard breaks down sessions, findings, friction, and token spend so you can see where runs went wrong. Agents that cannot be hooked are still governed through the LLM proxy or the MCP gateway, which evaluates each tool call and scans responses for injected instructions before the model sees them. Honeytoken canary files trip a critical finding the moment an agent reads them, catching reconnaissance early. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Audit season stops being a six week archaeology dig through shared drives when Conformiti keeps your whole compliance programme in one self-hosted system built for SOC 2, ISO/IEC 27001:2022, and PCI DSS v4.0.1. Its three complete control libraries hold 217 controls with a cross-framework crosswalk, so satisfying one requirement can answer its equivalents elsewhere instead of duplicating work. Evidence lives in a folder tree generated from those libraries, where every document declares which controls it satisfies and every control lists its documents, ending the hunt for proof at fieldwork time. Readiness is calculated rather than vibes: each control scores from implementation status, owner, evidence freshness, and testing, minus a penalty for open risks, and the dashboard tracks that score with daily snapshots. Documents carry review cadences with escalating email reminders before they expire, versions archive instead of overwriting, and uploads can pass through ClamAV scanning. A five by five risk register, vendor register with shared responsibility matrices, periodic access reviews, and a RACI matrix keep the rest of the programme beside the controls instead of in separate spreadsheets. When assessors arrive, you issue them a sealed, signed evidence package for a fixed window that verifies offline, rather than handing over drive access that outlives the engagement. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

SkillHub gives teams a private, npm-style registry for AI agent skills, so a useful skill package is published once, reviewed, versioned, and installed into a compatible agent from the command line. Upload a package from the web console, the CLI, or the REST API, pick PUBLIC, NAMESPACE_ONLY, or PRIVATE visibility, and track releases with semantic versions plus tags such as beta and stable. Team namespaces keep their own members with Owner, Admin, and Member roles and publishing policies, while full-text search filters by namespace, downloads, ratings, and recency and hides whatever a user may not see. Every submission enters a review queue: namespace admins approve or reject team skills, platform admins decide promotions to the global scope, and each governance action lands in the audit log. A built-in Skill Scanner inspects published packages and shows its report on the skill page, with optional behavioral, LLM, VirusTotal, and Cisco AI Defense engines. Members can star and rate skills, follow download counts, receive notifications, and report problem packages. Skill suites bundle several skills into one versioned collection with an entry skill, scoped API tokens serve the CLI and scripts, and the interface speaks English, Simplified Chinese, Russian, and Korean. Packages live on local disk or S3-compatible storage such as MinIO. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Every team building with coding agents has the same drawer problem: the best Skill, MCP server, or agent file lives in one person's repo, undocumented, while three colleagues rebuild it from scratch. Agentic Resource Registry is the internal catalog that ends the duplication, a self-hosted registry where your organization publishes Skills, MCP servers, hooks, prompts, agent files, and sandboxes in one searchable place. Browse by teamspace, publisher, category, or harness and see at a glance what each resource supports and how often it has been pulled. Authors build agent files in a four-step builder with a live summary and draft readiness checks, then submit them into a review queue where admins inspect the full prompt and version diffs before approving. Forking an approved release creates your own draft with pinned components, and reviewers can compare it against upstream line by line. The companion CLI installs any approved resource into Claude Code, Cursor, Codex, Kiro, Copilot, OpenCode, Pi, and more, generating the correct config for each harness automatically. Approved resources are also discoverable over the open ARD standard, so a running agent can hand part of its task to another approved agent or a remote A2A service. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

To pull a team's skills, MCP servers, hooks, prompts, and agent files out of scattered Git repositories, Observal keeps them in one reviewed registry and traces real sessions to show which ones help. Build an agent visually, with a live config preview for every harness. Submissions wait in an admin review queue, where reviewers inspect side-by-side version diffs before approving a release. Teamspaces give each team its own namespace, and approved releases can be forked into independent drafts. One pull command then writes the right config for Claude Code, Cursor, Kiro, Codex, Copilot, Pi, OpenCode, Antigravity, or Goose. Every approved resource is discoverable through the open Agentic Resource Discovery standard, and a running agent can hand part of its task to another approved agent or a remote Agent2Agent service. Hooks record each session as a trace of prompts, tool calls, MCP round trips, tokens, and errors, with turn-by-turn replay for debugging and audits. Insight reports, generated through providers such as Anthropic, OpenAI, or Ollama, list what is working, what is hindering, and quick wins. A dashboard follows adoption, cost, and department usage, a leaderboard ranks agents by downloads, and audit logs, SAML single sign-on, and SCIM provisioning are included. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

When an AI agent breaks in production, Tracely freezes that exact run into a replayable regression test and blocks the pull request that would ship the bug again. Span soup hides the failure. OTLP/HTTP traces group into conversation threads, and a waterfall marks the failing span in red with its input and output. Nobody knows if an answer was good. Evaluators sit as columns on the trace table, mixing model-free checks with LLM judges at conversation, run, or span level. Thirty-one broken runs, one issue. Clustering turns them into a single counted issue and can suggest the evaluator that would have caught it. A fix nobody can prove. One click promotes a failing trace into a hermetic case with recorded fixtures and a fail-to-pass contract, and scenarios send a red-team model after an adversarial goal. Model bills on every CI run. The suite replays offline with no API keys and no model spend, and tracely gate posts a commit status, comments on the pull request, and blocks regressions. No warning when it breaks again. Alert flows drawn on a canvas react to failed gates, broken conversations, unseen failure modes, or rising failure rates by messaging Slack, sending email, calling a webhook, or prompting an LLM. Coding agents can read traces and write evaluators through an MCP endpoint. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Every call your AI agents make becomes a searchable trace in LangWatch, and the same platform tests and scores those agents before a new prompt or model reaches users. The Trace Explorer filters by origin, user, model, cost, and evaluator, reads each run as a waterfall, flame graph, topology, or sequence diagram, and takes traces from the Python, TypeScript, and Go SDKs or any OpenTelemetry source. Agent Testing sends simulated users, in text or beta voice calls, through your agent while a judge passes or fails every criterion with its reasoning, adds multi-turn red teaming, and compares two agents side by side. The Experiments workbench scores prompts and agents on datasets with LLM judges, exact match, and RAG checks, then compares runs on cost, latency, and pass rate, while online evaluations monitor live traffic and guardrails block unsafe replies. Prompts keep version history and staging or production tags, Workflows wire prompts, Python code, and HTTP calls on a visual canvas, and annotation queues route traces to human reviewers. Alerts fire when a graph crosses a threshold, automations post to Slack, email, or webhooks, and scheduled reports go out by email or Slack. Claude Code, Codex, Copilot, Gemini CLI, and opencode report session cost, pull request cost, and terminal replays, and an MCP server exposes trace search and prompt management to coding assistants. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

A looping agent burns money quietly until someone can see where it drifted, and PandaProbe is an observability and evaluation platform for AI agents that makes that drift visible step by step. Capture complete executions as sessions, traces, and spans spanning model calls, tool calls, multi-agent handoffs, and custom logic, then follow a looping or failing run back to the decision where it drifted. Score traces and sessions with agent-specific metrics, run asynchronous and sampled evaluations so judge models do not cost more than inference, and use lightweight trajectory signals to catch stalls, bad tool use, and regressions over time. Bring agents built with LangGraph, CrewAI, the Claude Agent SDK, the OpenAI Agents SDK, and other integrations, with MCP tool calls captured automatically where supported. Organize work by user, organization, and project, send data through an SDK or HTTP API with project keys, and query filtered, paginated traces when you need the raw record. Keep sensitive workflows local when that matters: prompts, traces, and evaluation results stay inside the deployment you run, instead of leaving for a hosted console. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Your AI application is burning through API tokens faster than you can refresh the billing page, and you have no idea which prompt template is responsible. OpenLIT plugs that visibility gap with a self-hosted observability platform built specifically for LLM workloads. Add one line of code to instrument 90+ LLM providers, agent frameworks, and vector databases, then watch every request flow through a tracing dashboard that shows tokens consumed, latency measured, and dollars spent per call, per model, per environment. The requests view lists every LLM interaction with provider, model, cost, and token breakdown in a filterable table, while the trace detail panel lets you drill into individual spans to read the exact prompt sent and response received. Prompt Hub turns prompts into versioned artifacts you deploy, rollback, and A/B test without touching application code. OpenGround compares models side by side on the same input, so you can evaluate cost-versus-quality tradeoffs before committing to a provider. Automated evaluations run LLM-as-a-judge scoring on live production traces, flagging hallucinations, bias, and toxicity in real time. The Vault stores and rotates API keys centrally so secrets stay out of your codebase. Custom dashboards let you build drag-and-drop monitoring views with charts, stat cards, and tables backed by SQL queries against ClickHouse. GPU utilization, memory, temperature, and power metrics feed into the same platform for end-to-end infrastructure visibility. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Backed by Y Combinator and trusted by over 2,300 companies processing billions of observations monthly, Langfuse is the most widely adopted open-source platform for building, monitoring, evaluating, and debugging LLM applications. The hierarchical tracing engine captures every LLM call, tool invocation, retrieval step, and agent action as nested spans based on OpenTelemetry, with automatic cost calculation, latency tracking, and token usage attribution across sessions and users. Prompt Management separates prompts from code with versioned artifacts, label-based deployments, one-click rollbacks, and runtime SDK fetching with server-side caching, while linking every generation back to its exact prompt version for attribution analytics. The evaluation system supports LLM-as-a-judge scoring, heuristic code evaluators, user feedback collection, and manual annotation workflows that run automatically on production traces or against curated datasets. The Playground enables interactive prompt testing on real production inputs with side-by-side model comparison across providers. Datasets and Experiments define test cases for systematic benchmarking with comparative result visualization. Native SDKs for Python and TypeScript provide decorator-based instrumentation, while 100+ integrations cover LangChain, LlamaIndex, OpenAI SDK, LiteLLM, Vercel AI SDK, and any OpenTelemetry-instrumented framework. The analytics dashboard surfaces cost breakdowns, quality scores, latency percentiles, and usage trends across models and prompt versions. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Protecting over 10,000 organizations worldwide with 16,000+ GitHub stars, Wazuh delivers enterprise-grade security operations through a fully open-source platform with zero feature gating. The unified XDR and SIEM solution deploys lightweight agents across Linux, Windows, macOS, Solaris, AIX, and HP-UX endpoints that stream security telemetry to a centralized cluster built on OpenSearch for indexing and the Wazuh Dashboard for visualization and management. Core capabilities include real-time file integrity monitoring with inotify-based detection and who-data attribution, automated vulnerability assessment that correlates software inventories against continuously updated CVE databases, Security Configuration Assessment against CIS benchmarks, rootkit detection, and log data analysis with a rules engine supporting over 3,000 built-in detection rules mapped to MITRE ATT&CK tactics and techniques. Wazuh monitors cloud infrastructure at the API level with native modules for AWS, Azure, and Google Cloud, detects container anomalies through Docker engine integration, and ingests third-party telemetry via syslog and REST APIs from sources like VirusTotal, TheHive, YARA, Suricata, and PagerDuty. Pre-built compliance dashboards and reports cover PCI DSS, HIPAA, NIST 800-53, GDPR, and TSC frameworks. Active response capabilities automatically trigger countermeasures including firewall rule updates, account lockouts, and endpoint isolation when threats are detected. The platform scales horizontally with multi-node clustering for high availability. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPL-2.0 licensed.

Security scanners each report in their own format, and DefectDojo is the self-hosted platform that imports those reports and keeps a single, triaged list of findings. Reports in mixed formats? More than 350 built-in parsers read output from Burp Suite, ZAP, Trivy, Tenable Nessus, SonarQube, Snyk, and Checkmarx, and Generic Findings Import covers tools without a parser through CSV or JSON files. The same scan every night? Reimport adds new findings, can close the ones that vanished, and reopens the ones that return, while deduplication ties repeat detections inside an asset to their original. Decisions already made? False positive, out of scope, and risk accepted statuses survive reimport, so nobody triages the same noise twice. A deadline on every fix? Severity-based SLA countdowns flag breaches and can notify the users assigned to each asset, and full risk acceptances carry notes, files, and an expiry date that returns findings to active by default. Many teams, many applications? Organizations, assets, engagements, and tests mirror how security work is organized, with permissions that cascade downward. Pipelines that should report themselves? The REST API and CI/CD engagements import results automatically, and findings push to Jira as tickets. Someone outside security wants a summary? Dashboards track metrics, and a widget-based report builder produces HTML reports that print to PDF. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. BSD-3-Clause licensed.

One endpoint for every MCP server you run: MCPHub gathers local and remote Model Context Protocol servers behind stable URLs, so AI clients connect once and see what you allow. Claude Code, Cursor, and Cherry Studio can use four routes: everything at /mcp, a named group at /mcp/{group}, one server at /mcp/{server}, or semantic tool search at /mcp/$smart. Smart routing runs vector search on PostgreSQL with pgvector and an OpenAI-compatible key, so the model gets a shorter tool list. Servers attach over stdio, SSE, or streamable HTTP, OpenAPI services can be wrapped as MCP tools, and changes apply without downtime. In the other direction, MCP tools can be exported as an OpenAPI 3.0.3 spec for OpenWebUI and other OpenAPI clients. Bearer keys, OAuth 2.0 (client and server modes), and GitHub or Google sign-in in database mode control access, with per-user visibility for servers and groups. Encrypted per-user credentials let one shared server use each person's own key. Logs show tool-call activity, request status, and latency, health checks track each connection, and the dashboard controls which tools, prompts, and resources each server exposes. Large text tool results can be compressed, interactive MCP Apps are proxied through MCPHub routes, and a built-in market lets you browse a server catalog. Typical uses include a team-wide gateway, per-project groups, and direct single-server routes for debugging. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

With 2,600+ GitHub stars, MetaMCP solves the MCP server sprawl problem by aggregating any number of upstream servers into a single authenticated endpoint that any MCP client connects to once. Group servers into namespaces — development tools in one, data sources in another — then publish each namespace as its own SSE, Streamable HTTP, or OpenAPI endpoint with API-key authentication in headers or query parameters, or full OAuth per the MCP Spec 2025-06-18 standard. The aggregation engine discovers tools, resources, and prompts from all active servers in parallel, prefixes tool names with server identifiers to prevent collisions, and applies configurable middleware including tool filtering to reduce context-window bloat and description overrides to improve LLM comprehension. The web management UI lets you configure MCP servers with stdio, SSE, or Streamable HTTP transports, toggle servers active or inactive per namespace, create and revoke API keys per endpoint, and inspect discovered tools with their schemas. Nested MetaMCP support enables hierarchical architectures where one MetaMCP instance consumes another, creating multi-level tool organization with automatic name resolution. Compatible with Claude Desktop, Claude Code, Cursor, Open WebUI, and any MCP-compatible client through a single connection URL. The Docker container packages the TypeScript backend with PostgreSQL for configuration persistence, exposing the management UI on port 12005 and MCP endpoints on configurable ports. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Cerbos decouples authorization logic from application code entirely, evaluating human-readable YAML policies in under one millisecond through a stateless Policy Decision Point that requires no application state synchronization or cross-network fan-out. Its custom decision engine benchmarks up to 17x faster than OPA-based alternatives. Access control policies use conditions expressed in Google's Common Expression Language, supporting role-based, attribute-based, and policy-based access control patterns including derived roles, scoped policies, and permissions-aware data filtering that pushes authorization predicates directly into database queries. The PDP exposes both gRPC and HTTP APIs with SDKs for JavaScript, Python, Go, Java, .NET, Rust, PHP, and Ruby, making integration a single function call regardless of tech stack. GitOps-native workflows treat policies as code with Git versioning, CI validation through GitHub Actions, coverage reports, breaking-change detection, and audit logs of every authorization decision for ISO27001, SOC2, and HIPAA compliance. Deployment flexibility spans Kubernetes sidecars, standalone services, systemd daemons, AWS Lambda functions, and WebAssembly-embedded PDPs that run authorization logic directly in browsers, serverless architectures, and edge devices. The Admin API manages policy lifecycle operations programmatically, while the built-in Playground and REPL provide interactive testing environments for policy authoring and debugging. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.