RelayPlane vs AgentOps
AgentOps is a Python-first AI agent observability platform for session replay, cost tracking, and error monitoring. RelayPlane is an MIT-licensed npm proxy that intercepts LLM requests for real-time cost control and model routing. Here is how they compare for teams building and governing AI agents.
TL;DR
Choose RelayPlane when you want:
- npm install and running in 30 seconds with no account or SDK setup
- Real-time cost control and model routing in the request path
- Local SQLite cost tracking with no data leaving your machine
- Node.js and TypeScript native tooling, not a Python-first platform
- OpenAI-compatible drop-in with one baseURL swap, zero code changes
AgentOps may work for you if you need:
- Full agent session replay with a timeline of every LLM call and tool use
- Error tracking and alerting for Python agent failures and exceptions
- Deep native integrations with LangChain, CrewAI, and AutoGen
- Session-level debugging for multi-step Python agent workflows
Feature Comparison
| Feature | RelayPlane | AgentOps |
|---|---|---|
| Product type RelayPlane sits in the critical path of every LLM request: it intercepts, routes, and logs each call transparently. AgentOps is an asynchronous observability layer that collects agent session data after requests are made via SDK instrumentation. AgentOps does not proxy or route requests. | npm-native LLM proxy and gateway (local-first) | Agent observability platform (session replay, cost tracking, error monitoring) |
| Install method RelayPlane ships as a standalone npm binary: one command and you are proxying requests. AgentOps is a Python package that must be imported and initialized in your application code. There is no Node.js native SDK. If your stack is TypeScript or Node.js, AgentOps integration requires additional work. | npm install -g @relayplane/proxy | pip install agentops (Python SDK), account required |
| No account required RelayPlane starts with zero signup, zero credit card, and zero cloud dependency. AgentOps requires you to create an account and obtain an API key before you can record a single session. Data is sent to AgentOps cloud by default. | ||
| Free tier RelayPlane has no request cap or free tier limit. AgentOps offers a free tier with up to 10,000 events per month. Each LLM call, tool call, or action event counts toward this limit. Production agent workloads can exhaust the free tier quickly. | MIT open source, no usage limits, fully free to self-host | Free tier: up to 10,000 events per month |
| Primary language support AgentOps is built around its Python SDK. The package is pip-installed and the documentation, integrations, and examples are Python-centric. RelayPlane is an HTTP proxy so any language that can make HTTP requests benefits automatically with no dedicated SDK needed. | Node.js proxy (any language via HTTP) | Python-first (agentops PyPI package) |
| No code changes required RelayPlane requires zero application code changes. Set OPENAI_BASE_URL=http://localhost:4100 and your existing code is automatically proxied. AgentOps requires you to add agentops.init() at startup and wrap or instrument your agent code to capture session events. | ||
| Request interception (proxy mode) RelayPlane intercepts every LLM request transparently via a baseURL swap. No code changes needed beyond pointing your client at localhost:4100. AgentOps requires SDK integration in your application and does not operate as an HTTP proxy. | ||
| Model routing and fallback RelayPlane routes requests to different models based on complexity and cost, with automatic fallback on provider failures. AgentOps does not route requests. It observes and records what your agents did, but cannot influence which model is called or switch providers on failure. | ||
| Local SQLite cost tracking RelayPlane logs every request's exact dollar cost in local SQLite with no data leaving your machine. AgentOps tracks LLM costs as part of session observability on their cloud platform. Cost data is sent off-machine and visible in the AgentOps dashboard. | ||
| No data leaves your machine RelayPlane runs entirely on localhost by default with zero external telemetry. AgentOps sends all session data including LLM inputs and outputs to AgentOps cloud. There is no self-hosted deployment option for keeping data on-premises. | ||
| Spend governance and budget limits RelayPlane can enforce spend limits and route away from expensive models when budgets are exceeded. AgentOps tracks cost as an observability metric but has no mechanism to block, reroute, or cap spending on live requests. | ||
| OpenAI-compatible drop-in RelayPlane exposes an OpenAI-compatible endpoint: set OPENAI_BASE_URL=http://localhost:4100 and your existing code works unchanged. AgentOps requires SDK calls woven into your application logic and does not expose a compatible LLM API endpoint. | ||
| Works with Claude Code and Cursor RelayPlane is designed for Claude Code, Cursor, Windsurf, and Aider with direct integration docs. AgentOps is an application-level SDK that cannot intercept traffic from AI coding assistants you do not control the source code of. | Not applicable (observability layer, not a proxy) | |
| Agent session replay AgentOps provides full agent session replay: a timeline view of every LLM call, tool invocation, error, and action taken during an agent run. You can replay sessions to debug unexpected behavior. RelayPlane logs per-request metadata including model, tokens, cost, and latency, but does not reconstruct agent session timelines. | Basic request logging | |
| Error tracking and alerts AgentOps captures agent errors, exceptions, and unexpected exits and surfaces them in a dedicated error tracking view. You can set up alerts for error spikes. RelayPlane focuses on cost control and routing rather than agent error monitoring. | ||
| Framework integrations (LangChain, CrewAI, AutoGen) AgentOps has deep native integrations for LangChain, CrewAI, AutoGen, and other Python agent frameworks. These integrations automatically capture multi-step agent traces. RelayPlane works with any framework by intercepting HTTP calls, but does not produce nested agent trace trees with named steps. | Works transparently via HTTP proxy | |
| Open source RelayPlane is MIT licensed end to end. AgentOps open-sources its client SDK but the backend platform and dashboard are closed-source SaaS. | MIT licensed | Partial (client SDK open source, backend closed) |
Why Teams Choose RelayPlane When They Need Cost Control, Not Just Observability
A proxy intercepts requests. An observability SDK instruments code. These solve different problems.
AgentOps and RelayPlane are not direct substitutes. AgentOps is an asynchronous observability layer: you add agentops.init() to your Python agent, session data is collected after requests complete, and you get a dashboard for debugging and error tracking. RelayPlane is a synchronous proxy that sits in the request path: you change one baseURL and every LLM call flows through it, enabling real-time routing, cost tracking, and spend governance. If you need to see what your Python agent did in a session replay, AgentOps helps. If you need to control what your agent spends and which models it calls, you need a proxy.
npm install in 30 seconds vs pip install plus SDK instrumentation
npm install -g @relayplane/proxy and you are proxying requests. RelayPlane requires zero changes to your application code. AgentOps requires pip install, account creation, API key setup, adding agentops.init() to your code, and potentially wrapping LLM calls with the SDK. For existing agents or tools you do not control the source of, AgentOps instrumentation is not possible. RelayPlane's HTTP proxy approach works regardless of whether you own the code making the LLM calls.
Node.js and TypeScript native, not an afterthought.
AgentOps is built around its Python SDK. The documentation, quickstarts, framework integrations, and examples are all Python-first. If your agent stack is Node.js, TypeScript, or a polyglot environment, you are working against the grain of the platform. RelayPlane is an npm package that ships as a Node.js binary and works natively with any HTTP client in any language via its OpenAI-compatible endpoint.
Built-in cost control. Not just cost observation.
AgentOps is useful for seeing what your agents spent. It captures token counts and estimates per-session LLM costs in its dashboard. But it cannot stop you from spending. RelayPlane tracks cost and can enforce it: budget limits, routing away from expensive models when thresholds are hit, automatic fallback on cost overruns. For agentic workloads that can run for minutes and burn unexpected tokens, having a proxy that can cap spending in real time is meaningfully different from a dashboard that shows you what happened after the fact.
AgentOps Solves Agent Observability Problems. RelayPlane Solves Cost Control Problems.
AgentOps is a focused product for Python agent developers who need visibility into what their agents actually did during a run. Its session replay, error tracking, and per-session cost summaries are genuinely useful for debugging complex multi-step agent workflows built with LangChain, CrewAI, or AutoGen. If your primary challenge is understanding why your agent failed at step 7 or what tools it called during a session, AgentOps is worth evaluating.
But AgentOps cannot route requests, enforce budget limits, or intercept traffic from tools you do not control. It requires adding SDK initialization to your Python application. It does not work with Claude Code, Cursor, or other AI coding assistants because those tools do not expose Python instrumentation hooks. If you want to start tracking and controlling LLM costs today across any framework, any language, and any tool you use, RelayPlane installs in one npm command and runs on localhost with zero account or code changes required.
AgentOps Pricing at a Glance
| Plan | Price | Events/mo | Account Required |
|---|---|---|---|
| Free | Free | 10,000 | Yes |
| Pro | Contact AgentOps | Higher limits | Yes |
| RelayPlane | Free (MIT) | Unlimited | No |
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No account. No SDK changes. No code instrumentation: