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.

Preloop is a self-hostable control plane that governs every AI agent tool call and model call, and can run ticket-triggered agent work that returns as reviewable pull requests. An MCP firewall can allow, deny, or hold any tool call for approval, with require-justification rules written as YAML and CEL policy. Human approvals arrive on mobile, watch, Slack, Mattermost, email, webhook, or the command line, and agents can ask a person a question before continuing. The model gateway speaks OpenAI, Anthropic, and Gemini formats, applies budgets and allowed-model lists, and attributes token spend by model, agent, session, API key, flow, and user. Flows start agents from GitHub, GitLab, Bitbucket Cloud, Jira, webhooks, or CI jobs, in hosted containers or on private runners that connect over an outbound WebSocket. Twenty-one presets review pull requests, implement issues, triage tickets, scan for vulnerabilities, and collect evidence packs. Operators can steer a running agent from the console, phone, or watch with notes, then review the resulting pull request. One session timeline records tool calls, model calls, policy decisions, approvals, spend, outcomes, and synchronized recordings. Fleet Map shows people, devices, agents, models, and observed work in one view. An account kill switch blocks gateway and tool traffic and stops managed runs, with audited recovery. 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.

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.

Your AI coding agent has your API keys in its environment and unrestricted network access, which means one prompt injection away from sending those secrets anywhere. Pipelock closes that gap by sitting as a proxy between your agents and every outbound connection, scanning the actual content of HTTP, WebSocket, MCP, and Agent-to-Agent traffic before it leaves your server. An 11-layer scanner pipeline checks every request against 62 credential patterns covering AWS, GCP, Azure, GitHub, OpenAI, Anthropic, SSH keys, and database URLs, then inspects every response for prompt injection using 29 detection patterns with six-pass normalization that catches base64-encoded, leetspeak, and whitespace-obfuscated payloads. The MCP proxy wraps any Model Context Protocol server (stdio, HTTP, or WebSocket) with bidirectional scanning that detects tool description poisoning and mid-session rug-pull changes via SHA-256 fingerprinting. Every scanning decision produces a cryptographically signed action receipt that third parties can verify offline without trusting the agent or the vendor. The Operator Console provides a web dashboard for reviewing evidence scorecards, receipt timelines, agent sessions, enforcement decisions, and fleet posture at a glance. Cross-request taint tracking catches slow-drip exfiltration attempts that spread a secret across multiple calls. Canary tokens plant synthetic secrets that trip alerts the moment an agent tries to exfiltrate them. Pre-built Prometheus metrics and a Grafana dashboard provide real-time visibility into traffic volumes and block rates. Deploy 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.

One OpenAI-compatible endpoint, 1,600+ models behind it: Portkey Gateway sits between your app and every major LLM provider, deciding in real time where each request should go. When a provider times out or returns errors, automatic retries with exponential backoff kick in, and fallback rules quietly move traffic to your second-choice model before users notice anything. Load balancing spreads calls across multiple API keys and providers by weight, conditional routing sends each request down a different path based on its content or metadata, and per-request timeouts cut off providers that hang. Guardrails inspect inputs and outputs as traffic passes through: block banned words, screen for sensitive content, and enforce the checks your compliance team asked for, with dozens of pre-built rules ready to attach. Text is only the start: the same API shape reaches vision models, speech-to-text, text-to-speech, image generation, and realtime websocket APIs, so multimodal features stop multiplying your integration work. Routing policies live in JSON configs you attach per request or per client, which means experiments, cost controls, and provider migrations ship as configuration changes, not redeploys. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT 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.

Every employee gets one AI key, and every dollar of model spending gets a name next to it. AIHelms is an admin console and user portal that sits in front of your model providers, so a team can use Claude, DeepSeek, GPT, and local models through one entry point, with IT keeping control. Administrators onboard providers, publish models, and balance traffic across several deployments of the same model, with both OpenAI and Anthropic request formats accepted. Each AI identity carries a model allow-list, quota, rate limits, and an expiry date. Budgets can be set per person, department, project, or model, with soft alerts and hard cut-offs. Internal settlement prices sit beside real provider costs, so departments can be billed with or without a markup. Dashboards show adoption, daily calls, token use, and top users, and weekly or monthly efficiency reports drill down by department. A built-in marketplace registers, approves, and distributes MCP servers and agent Skills, and an AI policy scan flags risky Skills before anyone installs them. Audit logs record every administrator action. Employees see their key, endpoint, models, and monthly spend in the portal. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPL-3.0 licensed.

With over 330 GitHub stars and described as "OpenRouter, but for agent tools instead of models," Treg is the unified tool registry that gives your AI agent one base URL, one token, and instant access to approximately 2,800 cataloged endpoints across 56 providers — SEO and backlink analysis via Semrush and Moz, social media intelligence from TikTok and Instagram, people and company enrichment through Crunchbase and Hunter, media buying, scraping, and SERP data — all priced per call from fractions of a cent with no provider signup required. The FastAPI backend with PostgreSQL stores credential bindings and injects them server-side into every proxied request, ensuring API keys and OAuth tokens never touch the agent or local machine while every call is audit-logged. Teams register their own paid API accounts, OAuth connections, vendor CLIs like Stripe, GitHub CLI, Vercel, and gcloud, plus custom SKILL.md definitions — all callable by every teammate's agent through a single treg token with automatic credential injection. The evidence-based tool selector shows success rate, response speed, and last-seen timestamp for each provider, so agents pick tools on measured performance rather than guesswork. Bring-your-own-key routes bypass metering entirely, and the one-dollar free credit on every new team lets agents start calling immediately. The Vue.js web dashboard provides team management, audit logs, catalog browsing, and guided onboarding. Self-host with pip install tools-registry[server] 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.

Agenta delivers a comprehensive open-source LLMOps workspace that covers the full lifecycle of AI application development — from prompt engineering through production monitoring. The platform supports 15+ model providers including OpenAI, Anthropic, Google Gemini, Mistral, Groq, Together AI, Azure, AWS Bedrock, and self-hosted models via Ollama, enabling teams to switch between providers without code changes. The prompt playground allows side-by-side comparison of different configurations, while the evaluation system offers LLM-as-a-Judge assessment, 20+ pre-built evaluators covering semantic similarity, regex matching, and factual accuracy, plus custom Python evaluators for domain-specific requirements. Teams run evaluations through both the web UI for subject matter experts and the Evaluation SDK for programmatic CI/CD integration. The observability layer captures full trace visibility across complex agentic workflows, flagging quality issues like hallucinations and off-topic responses in real time. Human annotation workflows let domain experts review and annotate LLM outputs, feeding corrections back into the evaluation loop. The architecture supports Chain of Prompts, RAG pipelines, and multi-step agent workflows, integrating with frameworks like LangChain and LlamaIndex. Self-hosting deploys via Docker Compose with Traefik for routing, requiring only a clone, environment configuration, and a single docker compose command. On RepoCloud, deploy Agenta on a dedicated VPS with root SSH access, persistent storage for evaluation datasets and traces, and complete control over model provider credentials, all under the MIT license with no usage restrictions.

Point it at your cloud accounts and it returns a ranked list of what is misconfigured, mapped to the standards auditors ask about. Connect providers like AWS, Azure, Google Cloud, Kubernetes, Microsoft 365, and GitHub, launch a scan or schedule recurring ones, and read the results on an overview with a ThreatScore gauge, severity bars, and a resource inventory by category. The Findings page groups failed checks, expands each group into the affected resources, and lets teams mute noise, triage status, send groups to Jira, and create alerts that email or message people after each scan. Compliance pages score every framework from CIS and NIST to PCI DSS, SOC 2, HIPAA, GDPR, and ISO 27001, with a watchlist for the ones you care about and downloadable reports. Attack Paths draws a graph from IAM roles and policies to outcomes like code execution, using a Neo4j graph database filled by AWS scans. A built-in AI assistant answers questions about critical findings and compliance gaps with the model provider you configure. Organizations, roles, and API keys keep access tidy. 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.

With over 14,000 GitHub stars and a growing global network of security deployments, CrowdSec turns every attack on any participating server into protection for the entire community. The security engine operates as a combined IDS/IPS and WAF, analyzing log sources from Nginx, Apache, SSH, WordPress, and over 50 other services to detect brute force attacks, port scans, web vulnerability exploitation, and credential stuffing in real time. When one server detects a new threat, the attacker's IP is shared through the community blocklist, proactively protecting thousands of other installations before the attacker can reach them. The built-in WAF powered by Coraza v3 inspects HTTP requests at the application layer, validates against OpenAPI schemas, and applies custom rules with flexible AND/OR condition mixing for precise threat detection. Bot detection serves challenge pages with client fingerprinting to distinguish legitimate traffic from automated scrapers and scanners. Remediation components block malicious IPs at multiple infrastructure layers including iptables, nftables, Nginx, HAProxy, Cloudflare, and AWS Security Groups through the detect-here-remedy-there architecture. The scenario-based detection system ships with default rules for common attack patterns and supports custom scenarios written in YAML with an expressive filter language. A centralized console provides real-time visualization of alerts, threat intelligence analysis, and management of multiple distributed security engines. GDPR compliant by design, all log analysis happens locally and raw logs never leave your infrastructure. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.