RelayPlane vs Agenta
Agenta is an LLM evaluation and prompt management platform. RelayPlane is an npm-native cost-intelligence proxy that routes and governs live traffic. Different tools solving different problems, often used together.
TL;DR
Choose RelayPlane if you want:
- npm install in 30 seconds, no Python or Docker required
- Per-request USD cost tracking on every live API call
- Dynamic routing: cheaper models for simple tasks, capable models for complex ones
- Per-agent cost attribution and runaway loop detection
- Drop-in proxy for Claude Code, Cursor, and any OpenAI-compatible tool
Agenta is the right fit if you need:
- Prompt versioning, A/B testing prompt variants, and collaborative prompt iteration
- Offline LLM evaluation with golden datasets, custom scorers, and annotation workflows
- Your team is Python-first and needs a full evaluation platform before shipping prompts
Feature Comparison
| Feature | RelayPlane | Agenta |
|---|---|---|
| Primary purpose RelayPlane sits in the live API call path: every request flows through it so costs and latency are tracked in real time. Agenta operates outside the call path, helping teams iterate on prompts and run offline evaluations. | LLM routing proxy with cost intelligence | Prompt management and LLM evaluation platform |
| Sits in the live API call path RelayPlane is a localhost proxy on port 4100. Every LLM request your agent makes is routed through it. Agenta integrates via a SDK for logging and evaluation but does not proxy live production traffic. | ||
| Installation method RelayPlane is a single npm command for Node.js developers. Agenta requires either a Python environment with pip or a Docker Compose setup to run the full platform locally or self-hosted. | npm install -g @relayplane/proxy | pip install agenta or Docker Compose |
| Setup time RelayPlane runs on localhost immediately after install with no additional configuration. Agenta's Docker Compose path starts multiple services and requires environment variables before the UI is reachable. | ~30 seconds | Minutes to hours (pip + config, or Docker Compose) |
| Docker required RelayPlane needs only npm. The recommended Agenta self-hosted deployment uses Docker Compose to orchestrate the backend, frontend, and database services. | Required for full self-hosted setup | |
| npm-native RelayPlane is a standard npm package that slots into any Node.js or TypeScript project. Agenta is a Python-first platform with a Python SDK. There is no npm package for Agenta. | ||
| Per-request USD cost tracking RelayPlane computes exact USD cost per request using live pricing tables and stores results locally in SQLite. Agenta captures token usage in trace logs but does not compute or display dollar costs per request. | Token counts in traces, no USD cost attribution | |
| Cost dashboard RelayPlane provides a local dashboard showing spend by model, agent, and time period. Agenta's UI focuses on prompt variants, evaluation results, and trace inspection rather than LLM spend analysis. | No cost dashboard (evaluation and trace UI only) | |
| Dynamic model routing RelayPlane routes requests dynamically: simple tasks to cheaper models, complex tasks to more capable ones. Agenta is not a router and does not intercept or redirect LLM calls at inference time. | Cost-optimized routing by task complexity | No routing (evaluation only) |
| Prompt management Agenta is purpose-built for prompt versioning, A/B testing prompt variants, and iterating on system prompts with a team. RelayPlane does not manage prompts. If prompt iteration is your primary need, Agenta is the right tool. | ||
| LLM evaluation (offline evals) Agenta provides a full evaluation framework: golden datasets, custom scorers, human annotation workflows, and automated regression testing for prompts. RelayPlane does not run evaluations. | ||
| A/B testing prompt variants Agenta supports comparing prompt variants side by side against evaluation datasets. This is core to the Agenta workflow. RelayPlane has no concept of prompt variants or A/B evaluation. | ||
| Per-agent cost attribution RelayPlane fingerprints system prompts to attribute cost and usage to individual agents. You can see which agent is responsible for which spend. Agenta traces are organized by application and prompt variant, not by cost. | ||
| Runaway loop detection RelayPlane detects agents caught in repetitive loops before they generate unexpected spend. Agenta has no spend-based anomaly detection because it does not sit in the call path. | ||
| Latency tracking RelayPlane tracks p50/p95 latency per model and provider in real time. Agenta captures request latency in its trace logs for evaluation review, but the focus is prompt quality rather than production latency analysis. | Latency in trace logs | |
| Works with Claude Code, Cursor, any OpenAI-compatible tool RelayPlane is a drop-in localhost proxy. Point any OpenAI-compatible tool at localhost:4100 and cost tracking starts with no code changes. Agenta requires instrumenting your application with its Python SDK. | Requires SDK integration in application code | |
| Open source Agenta is fully open source on GitHub with a self-hosted option. RelayPlane's proxy core is open source with paid tiers for advanced cost intelligence features. | Core proxy is open source | |
| Cloud / SaaS tier available Both tools offer a hosted cloud version. Agenta Cloud provides a managed evaluation platform. RelayPlane Cloud provides team-level cost dashboards, budget alerts, and policy enforcement. |
How RelayPlane Fits Into Your Stack
npm install in 30 seconds, no Python or Docker needed
RelayPlane is npm install -g @relayplane/proxy && relayplane start. That is the entire setup. No Python, no pip, no virtual environment, no Docker Compose, no multi-service orchestration. Agenta's local setup requires Docker Compose and spins up multiple containers before you can access the UI. For a Node.js or TypeScript developer, RelayPlane fits naturally into the existing workflow.
Sits in the call path so every request is tracked automatically
RelayPlane intercepts every LLM request your agents make and records cost, latency, model, and token counts without any code changes. Agenta integrates via a Python SDK and requires you to instrument your application code. If you want cost visibility without touching your agent logic, RelayPlane is the simpler path.
Purpose-built for cost intelligence, not added on after the fact
RelayPlane was designed from the ground up to answer one question: how much did each AI agent cost, and how can I reduce that? It includes dynamic routing to cheaper models, per-agent spend attribution, runaway loop detection, and budget enforcement. Agenta was designed for prompt evaluation and does not provide any of those cost-governance capabilities.
Use both: they solve different problems
RelayPlane and Agenta are genuinely complementary. Use Agenta to iterate on your prompts and evaluate quality before shipping. Use RelayPlane to govern, observe, and optimize cost and routing once those prompts are in production. They do not overlap and running both adds no friction.
When Agenta is the right fit
If your primary challenge is prompt quality, Agenta is purpose-built for it. Agenta gives you a full evaluation framework: version your prompts, run them against curated test datasets, compare variants, and iterate with your team before shipping. If you need to systematically improve LLM output quality and track which prompt version performs best, Agenta is a well-designed, open-source tool for that workflow.
The distinction is timing: Agenta helps before and during prompt development. RelayPlane helps after prompts are deployed, governing cost and routing in production. Many teams use both: Agenta to ship better prompts, RelayPlane to control what those prompts cost at scale.
When RelayPlane is the right fit
If your prompts are already in production and you need to understand what each agent is costing you, RelayPlane is the right tool. It installs in 30 seconds with a single npm command, proxies every LLM call through localhost, and stores cost and latency data locally with no cloud dependency. No code changes needed in your agent.
RelayPlane also handles the routing layer: if a task is simple, it can route to a cheaper model automatically. If an agent is in a runaway loop, it can be stopped before costs spiral. These are production-traffic concerns that evaluation platforms do not address.