Headroom answers the question every multi-plan AI subscriber asks mid-task: which of my accounts still has room to work? It puts the live limits, reset times, credits, and spend of Claude, Codex, Cursor, Copilot, Grok, Antigravity, and Vercel AI Gateway accounts on one self-hosted dashboard instead of seven provider pages. Connect each account once, through its official CLI sign-in or a credential import, and a background scheduler refreshes every figure on schedule, renews tokens before they expire, and keeps 90 days of readings. Five-hour sessions, weekly pools, per-model limits, credit balances, banked resets, and on-demand spend sit side by side, and unreported figures show as unknown rather than a fake zero. Compare ranks same-provider accounts by the room they have left and names the one to use next, while Timeline lays every reset window on a two-week calendar. Analytics charts when you work, when you run dry, and whether each plan fits, and Wallet totals subscriptions, usage spend, and top-ups in one currency with renewals and budget alerts. In-app notices, Telegram messages, and signed webhooks warn about low limits, expiring resets, and broken sign-ins, and optional automations can spend a banked Codex reset or log a credit top-up the moment it lands. Personal, work, and side-project accounts stay separate under one owner login with passkeys and a Privacy Mode that blurs emails on screen. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Software developers running Claude Code, Cursor, Codex, and Aider use AgentsView to trace token expenditures, search full session transcripts, and analyze multi-agent concurrency timelines. Engineers can search through historical developer sessions to locate specific code modifications, prompt chains, or debugging rationales across extensive programming workflows. The real-time activity timeline visualizes concurrent agent operations, revealing peak execution windows, task run durations, and automated tool invocations. The usage analytics engine calculates financial costs and token volumes per model by applying live pricing tables while accounting for prompt caching read discounts. Developers inspect granular turn-by-turn session transcripts complete with raw model payloads, file difference diffs, executed shell commands, and syntax errors. The recall browser extracts key technical context from past programming sessions, organizing facts into an indexed reference knowledge corpus for reuse across future project tasks. Administrators can replicate SQLite session tables into DuckDB analytics files or PostgreSQL warehouses to query longitudinal metrics using SQL analytical tools or custom reporting scripts. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Oh My CPA gives a CLIProxyAPI gateway a proper control room, where providers, OAuth credentials, client keys, model routing, and the cost of every request are managed and watched from one self-hosted console. You add providers such as Codex, Claude, Gemini, xAI, DeepSeek, Vertex AI, and OpenAI-compatible services, each with credentials, retry and error policies, priority, weight, and an enable switch the gateway actually enforces. OAuth accounts are signed in from the console, with quota window readings, refresh schedules, and per-credential model aliases kept next to the keys they belong to. Client keys can be created, named, and revoked, and those names follow the traffic into request records and filters so you always know which key spent what. The dashboard follows request volume, token throughput, cache hit rate, and cost from the last fifteen minutes out to ninety days, with a year-long token heatmap for the long view. Insights compare keys, models, providers, and credentials against the previous period, while the live routing view shows which credential each request travelled to and whether it was rerouted. Every request stays searchable, with duration, time to first token, token breakdown, and the raw log in a detail drawer. A playground tests any routed model with text and images before your users do. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

CPA Manager Plus is a self-hosted observability dashboard and management panel that tracks every AI request flowing through your CLI Proxy API gateway, breaking down failures, costs, and account health across providers like OpenAI, Anthropic, xAI, and Codex in one interface. When a request fails, drill into the persistent history to see status codes, affected models, latency, and redacted failure evidence without exposing raw response bodies. The cost analytics engine breaks down token consumption and estimated spend by model, provider, account, API key, project, channel, and time range while tracking input, output, reasoning, cache, and service-tier pricing semantics separately. Model prices sync automatically from models.dev with LiteLLM and OpenRouter fallbacks, and you can add local overrides for aliases or internal models. For teams running Codex or xAI accounts, the health inspector reads quota windows, reset evidence, credential state, and workspace status on a configurable schedule, routing credential failures into an action queue for review rather than letting them silently degrade throughput. Deploy the Lightweight Panel to replace your existing CPA management UI without adding another service, or run Full Mode as a single Docker container that adds the Manager Server with persistent SQLite storage for request history, historical analytics, and automated account inspections. Export or import request history as JSONL for external analysis, and back up the SQLite files alongside your encrypted management keys. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Claude Code Agent Monitor puts every AI coding agent session on one live control room, so you can see what each agent is doing, what it costs, and where it got stuck. Claude Code, Cursor, and Codex sessions land together in a dashboard, a sessions list, an activity feed, and a kanban board. Open any session to read the full rendered conversation with highlighted code, tool calls, command output, and sub-agent timelines. The analytics page charts spend by model and weekday, token totals, cache hit rates, and a 52-week activity heatmap. Workflow views map parent and child agents, delegation, compactions, errors, and concurrency. An Agent Config explorer lists your skills, subagents, slash commands, hooks, plugins, MCP servers, and memory files in one place. The Run Agent page starts a conversation or one-shot job, streams output, and resumes earlier sessions without leaving the browser. Webhook alerts, push notifications, and a REST API keep the rest of your tooling informed. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

OpenClaw Dashboard is a secure real-time monitoring dashboard for OpenClaw AI agents that tracks live sessions, API costs, rate limits, and host health from one browser page. The overview refreshes every five seconds with live session activity, token totals, and lifetime spending, so you can see what every agent is doing right now. Cost views break spending down by model, session, and time period, and rate limit panels track Claude and Gemini usage against rolling windows with browser alerts before quotas run out. A live feed streams agent messages across all sessions, and a session browser adds search, status filters, timelines, and a 30 day activity heatmap. You can read agent memory files (MEMORY.md, HEARTBEAT.md, daily notes) and edit workspace files, skills, and configuration with JSON validation and automatic backups. Host panels report CPU, memory, disk, and temperature with 24 hour sparklines, plus Docker containers, cron jobs, real-time logs, and one-click service restarts. A security page shows firewall rules, open ports, fail2ban status, and SSH login history, and sign-in uses PBKDF2 hashed passwords with optional TOTP two-factor codes. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Production AI agents fail in quiet ways, and Latitude records every trace, session, and tool call so each failure can be found, grouped, and fixed. One line of telemetry from the TypeScript or Python SDK, or any OpenTelemetry exporter, sends traces from OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, and dozens of other providers and frameworks. Failing traces are clustered into signals with status, size, and trend, and flaggers and annotation queues let reviewers score outputs by hand. Evaluations are generated from those judgments and tracked with an alignment score. Semantic and exact-text search covers every trace, and saved searches, filters, and behavior groupings keep recurring questions one click away. Dashboards report cost per session, tool failure rates, and agent score across outcome, reliability, speed, and safety. Memory views show how an agent's stored memory changes over time, with diffs and the trace behind each edit. Monitors raise alerts, regression datasets replay fixes against the traces that failed, and Agent Dispatch hands the evidence to a coding agent. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT 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.

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.

When an AI agent goes wrong at step seven of twelve, Opik, an open-source LLM observability platform, shows the exact trace, tool call, and prompt behind it, then scores your fix against test datasets. Full trace trees capture every LLM call, retrieval step, and tool execution, and you can annotate traces and spans with feedback scores in the UI or through the SDK. Native integrations for OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, Google ADK, and DSPy, plus OpenTelemetry for any language with an OpenTelemetry SDK, feed traces in, or add the @track decorator to your own functions. Datasets and experiments compare prompt and model versions, scored by LLM-as-a-judge metrics for hallucination, moderation, and RAG relevance, with a PyTest integration to run evaluations in CI. A Prompt Playground tests prompts across models, and the Agent Optimizer SDK helps improve prompts and agents. Production dashboards track feedback scores, trace counts, and token usage, online evaluation rules score live traces, and optional guardrails add safety checks. An MCP server lets Claude Code, Cursor, VS Code Copilot, Codex, or opencode read traces and run evaluations from chat. Run it yourself with the ./opik.sh script on Docker Compose, or a Helm chart on Kubernetes, so trace data stays in your infrastructure. 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 (S24) and processing traces from thousands of AI agents in production, Laminar is the open-source observability platform that treats agent debugging as a first-class engineering discipline rather than an afterthought. Its OpenTelemetry-native SDK auto-instruments Vercel AI SDK, LangChain, OpenAI, Anthropic, Gemini, Browser Use, Stagehand, Mastra, Pydantic AI, and the OpenAI Agents SDK with a single line of code, capturing every LLM turn, tool call, and sub-agent delegation as nested spans with full input/output data and token costs. The Signals engine lets you describe failures in plain language — "agent is stuck in a loop" or "tool returned empty results" — then reads every trace and alerts via Slack when it detects a match. A built-in debugger records runs and replays them from cache so each iteration takes seconds, designed for Claude Code, Cursor, or Codex to drive the repair loop via the MCP server or CLI. Run code-first evaluations in Python or TypeScript locally or in CI/CD pipelines, build datasets from production traces, and query everything with raw SQL through custom dashboards, the in-app editor, or your coding agent. The Rust backend delivers 20x trace compression, a custom real-time streaming engine, ultra-fast full-text search, and gRPC ingestion, while ClickHouse powers columnar analytics and PostgreSQL stores application state. 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 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.

Instead of scattering provider credentials and vendor-specific SDK logic across dozens of microservices, engineering teams route every LLM call through GoModel, an open-source AI gateway that unifies 34 model providers behind OpenAI-compatible and Anthropic-compatible endpoints. Pointing existing OpenAI or Anthropic SDKs at the gateway unlocks chat completions, the Responses API, Anthropic Messages, embeddings, batch jobs, and realtime audio across OpenAI, Anthropic, Gemini, Bedrock, Azure, Groq, DeepSeek, Ollama, and vLLM. Platform operators publish virtual model aliases that distribute traffic via weighted round-robin or lowest-cost routing while rotating multiple API keys per provider and failing over through circuit breakers during upstream outages. Sticky session routing keeps multi-turn agent conversations on the same target to preserve warm prompt caches, while exact-match and vector semantic caching via Qdrant, pgvector, Pinecone, or Weaviate cut duplicate token costs. Versioned workflows and pre-dispatch guardrails enforce prompt injection rules, rate limits, and hard USD spend budgets across hierarchical tenant user paths before requests are dispatched. The built-in admin dashboard offers live audit logs, per-tenant cost analytics, LiteLLM key migration, an interactive request playground, Model Context Protocol tool aggregation, and OpenTelemetry and Prometheus metrics. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Point Claude Code, Codex CLI, Gemini CLI, or any OpenAI, Anthropic, or Gemini SDK at one address and Nyro routes every request to whichever model provider you choose. The gateway translates protocols on the fly, including streaming, reasoning blocks, and tool calls, so a tool built for one API can run on a model from another. Define virtual model names, bind each to one or more providers, and pick weighted load balancing or priority failover; unhealthy targets are skipped and recover automatically. Issue API keys with limits on requests and tokens per minute and per day, and bind each key only to the models it may call. Exact and semantic caching return repeated answers instantly. A request log records provider, model, token counts, latency, and streaming speed, and usage charts break traffic down by model and provider. The Connect page generates ready-to-paste samples in Python, TypeScript, and cURL, plus one-click configuration for popular coding CLIs. Choose an embedded database for a single host or an external PostgreSQL or MySQL server for production, and run the gateway, the admin UI, or both as separate processes. 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.

One endpoint, 130+ models from 90+ providers: KeiRouter is a self-hosted AI gateway, so apps, coding tools, and agents connect once and stop juggling provider keys. Your code speaks OpenAI Chat, OpenAI Responses, Anthropic Messages, or native Gemini, and KeiRouter translates to whichever upstream you want, including AWS Bedrock, Azure AI Foundry, Vertex, Snowflake Cortex, and self-hosted runtimes like Ollama and vLLM. Fallback chains keep requests moving when a provider goes down or hits a rate limit, using priority, round-robin, latency, or cost ordering, so coding sessions do not stop mid-task. Five token savers compress prompts on the way in and trim responses on the way out, with a live dashboard showing savings recovered. A response cache answers repeated prompts instantly for zero marginal cost, with optional semantic matching. Per-key budgets and rate plans set USD, token, RPM, and concurrency limits with auto-cutoff, stopping surprise bills. Guardrails scan for PII, prompt injection, toxicity, and banned topics at global, provider, model, chain, and key scopes, with a monitor showing what got blocked and what it cost. The dashboard tracks usage by provider and model, latency, a live console tracing each request through its fallbacks, and provider health. You can import from 9router or OmniRoute, white-label the portal for your team, and run replicas on Postgres and Redis 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. MIT licensed.

Routerly is the one front door for every AI model your apps use: point your OpenAI or Anthropic client at it, and it chooses the right model for each request, enforces your budget, and keeps the receipts. Nothing in your code changes except the base URL, and from that moment streaming chat completions and messages flow to OpenAI, Anthropic, Gemini, Mistral, Cohere, xAI or your own Ollama models behind a single token. Nine routing policies score every candidate in parallel: cheapest, fastest, healthiest, most capable, biggest context, least rate-limited, fairest, most budget left, and one that asks a small model to judge which model fits the task. When the winner errors or times out, the next candidate takes over automatically, and the recorded routing trace shows exactly why each model won or lost. Guardrails can block or log content that breaks your rules, and phone numbers, card numbers, SSNs, IBANs and emails can be scrubbed out before anything reaches a provider. Every call is logged with tokens, USD cost and latency, and hard limits per model, project and token reject overspending requests before a provider can charge for them. Projects keep separate tokens, model lists and budgets for each team or tool, role-based permissions decide who may change what, and alerts land in Slack, email, webhooks or PagerDuty when budgets or providers misbehave. 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.

Backed by 56,000+ GitHub stars and over 240 million Docker pulls, LiteLLM delivers the open-source AI gateway trusted by Netflix, Lemonade, Rocket Money, and thousands of engineering teams to route every LLM request through one unified API. The Rust-core gateway adds sub-millisecond overhead per request with 8ms P95 latency at 1,000 RPS, 15x throughput improvement and 11x lower memory footprint compared to Python-only proxies. A single OpenAI-compatible endpoint connects to 100+ providers and 1,800+ models spanning OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Hugging Face, vLLM, Nvidia NIM, Ollama, and Mistral with day-zero support for new model releases. The Auto Router V2 classifies request complexity across four tiers using rule-based scoring, semantic keyword matching, and adaptive Thompson sampling to route each request to the most cost-effective model without API calls or training data. Virtual API keys enable multi-tenant governance with per-team, per-user, and per-project cost tracking, budget caps with automatic fallback rerouting, and role-based access control. Built-in guardrails provide PII masking, prompt injection detection, and model-graded evaluation before requests reach providers. The Agent Gateway extends routing from model calls to agent workflows with MCP server integration. Observability integrates with Langfuse, Arize Phoenix, OpenTelemetry, and MLflow for complete request tracing. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Manifest LLM Gateway is a self-hosted gateway that puts every model you pay for behind one OpenAI- and Anthropic-compatible endpoint. Connect provider API keys, reuse subscriptions like ChatGPT Plus or Claude Max, or point it at Ollama and LM Studio on your own hardware. Send a request with the model set to auto and your routing rules choose the target by model, header, complexity, or task type, with fallbacks that take over the instant a model fails. Each agent or app gets its own harness, a named connection with a private key, spending limits, and notifications when usage climbs. The Requests view logs every call and every retry attempt, with status, cost, tokens, and the full request and response body readable as a conversation. Autofix repairs a malformed request, for example one using a renamed parameter, and resends it before your app ever sees an error. A Playground tests models from the dashboard, a command line tool and MCP server handle setup from a terminal or an AI assistant, and an n8n node plugs it into automations. Over 35 provider connections cover more than 300 models. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.