Orchid vs Stele

Orchid and Stele are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Orchid

Orchid sits between your agent and any API it talks to, capturing traffic into a local SQLite file — no cloud account, no SDK changes, no telemetry leaving your machine. The built-in web UI lets you step through a completed run, inspect every prompt, response, token count, and cost. The proxy also runs a built-in MCP server, so your IDE assistant in Cursor, VS Code, or Claude Code can query recorded traffic directly. Replay is deterministic and costs nothing in API fees. The ceiling appears when your team needs cross-service aggregation or production alerting — this tool is a local debugger, not an observability platform.

Stele

Stele is a shared memory layer that sits between your agents and your codebase. Every agent reads the same knowledge graph — decisions, tasks, risks, lessons — before it acts, and writes back what it learns. The atomic task-claiming mechanism means two agents cannot pull the same work item simultaneously, which prevents duplicated effort across parallel sessions. The friction is real: the product is invite-only and cloud-hosted with no self-hosted option, so teams with strict data residency requirements hit a wall immediately.

Attribute	Orchid	Stele
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	No
Has API	No	No
Self-hosted option	Yes	No
Platforms	Docker, local	Web, local plugin (MCP)
Pros	Zero-instrumentation proxy capture, so you get full LLM and tool-call traces without touching your agent's source code — no retrofit required when a bug surfaces in a framework you do not control. Deterministic offline replay from recorded SQLite files, which means you reproduce a specific failure run as many times as you need without burning API credits on each attempt. Built-in MCP server lets IDE assistants query recorded traffic directly, so instead of manually hunting through log output you ask your coding assistant what happened on step six. Local SQLite storage with no cloud dependency, so teams under data-sensitivity constraints get full trace visibility without any traffic leaving the machine. Apache-2.0 open-source license, which means you can inspect, fork, and modify the proxy to fit your stack — no vendor lock-in on your debugging infrastructure.	Shared knowledge graph across agents, so switching from Cursor to Claude Code mid-project does not reset the session's understanding of prior decisions and open tasks. Atomic task claiming prevents two agents from starting the same work simultaneously, which means parallel sessions produce additive progress rather than duplicated or conflicting output. Risk and lesson records surface at the moment a relevant change is being made — not after it ships — so an agent flags a known production bug before the code guard that prevents it gets removed. Single CLI install with no dashboard configuration required, so the memory layer becomes active without adding a workflow step between prompts.
Cons	Orchid captures traffic on a single local machine for a single agent run; there is no aggregation across parallel runs or distributed agent instances. Teams running agents across multiple services or needing a unified view of production traffic hit this wall immediately and move to a dedicated observability platform. No alerting, no anomaly detection, no dashboards shared across a team. When the use case shifts from 'reproduce this specific failure' to 'monitor agent health in production,' Orchid has nothing to offer — teams at that stage switch to platforms built for production observability. The GitHub repository shows 3 stars and 47 commits at the time of curation, with no public issue activity. Early-stage projects at this scale carry real risk: breaking changes ship without deprecation windows, documentation gaps appear in edge cases, and community support for non-obvious integration problems is thin.	The product is invite-only during beta. Teams that need to start using a shared memory layer immediately cannot — there is no self-service onboarding path, and the waitlist timeline is not published by the vendor. The service is cloud-hosted with no self-hosted option. Teams working under data residency requirements or corporate policies that prohibit sending codebase decisions and task data to a third-party service cannot use Stele at all — and at that point the only path forward is building a local context-passing layer themselves or using a different tool that supports on-premise deployment. There is no API surface exposed by the vendor, so teams that want to pipe Stele data into existing project management or observability tooling have no programmatic integration path beyond what the CLI and agent plugins provide.

Bottom line

Orchid is free while Stele is paid; Orchid is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Orchid and Stele?

Orchid is Free and open source, while Stele is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Orchid better than Stele?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

Orchid vs Stele: which should I pick?

Pick Orchid if its pricing model, openness, or platform fit matches your constraints; pick Stele otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.