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Orcareplay

de Continuum-AI-Corpversión 0.5.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.Continuum-AI-Corp/orcareplay

Instalar

Resumen

Descripción del creador (original en inglés)

Read, replay and fork recorded coding-agent runs.

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Herramientas para desarrolladores

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • npmNode.js instalado (para el comando npx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

{
  "mcpServers": {
    "orcareplay": {
      "command": "npx",
      "args": [
        "-y",
        "orcareplay"
      ]
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add orcareplay -- npx -y orcareplay

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "orcareplay": {
      "command": "npx",
      "args": [
        "-y",
        "orcareplay"
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "orcareplay": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "orcareplay"
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub Continuum-AI-Corp, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Herramientas para desarrolladoresi

  • Más estrellas que el 0% de los otros 5549 servidores de esta categoría
  • Publicado en el registro más recientemente que el 46% de los otros 5549 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 5549 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Orcareplay?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.5.0) llegó al registro hace 40 días.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: GitHub, Desktop Commander, Cloudflare MCP

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

GitHub

Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.

de githubMuy usado★ 34,2 milActualizado hace 5 días

Computadora o nube

Desktop Commander

MCP server for terminal commands, file operations, and process management

de wonderwhy-erMuy usado↓ 709,6 mil/sem★ 10,2 milActualizado hace 17 días

Se ejecuta en tu computadora

Cloudflare MCP

Cloudflare MCP servers

de cloudflareMuy usado★ 4,4 milActualizado hace 14 meses

Se ejecuta en la nube✓ mcp.cloudflare.com

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

OrcaReplay

English · 简体中文 · 日本語 · 한국어 · Deutsch · Français · Español · العربية

Your agent broke something at 2am. Replay it at 9am — exactly, offline, as many times as you like.

Record any coding agent. Reproduce the run byte-for-byte with no model called. Fork it from any step onto a different model and see who gets it right.

Built by the team behind [OrcaRouter](https://www.orcarouter.ai) — one API key and one endpoint for Claude, GPT, Gemini, Grok, DeepSeek, Qwen and the rest. It is what orca setup points at by default, and what makes orca compare a single command instead of four provider accounts.

Find us: OrcaRouter All model APIs

Github Repos: OrcaCode Review · OrcaRouter Lite

Connect: X · Discord · Hugging Face · Ollama

Real output from one session — a Claude Code run recorded, replayed against the recording, then forked at checkpoint 4 onto two models and graded by npx tsc --noEmit. Nothing here is mocked up.

Try it in three commands

One npm package named orcareplay, which puts one command named orca on your PATH. There is no separate tool called "orca", and nothing is installed into your agent.

npm i -g orcareplay             # the package is orcareplay; the command it installs is orca

orca record claude # your agent, unmodified, doing whatever it does orca replay last # the same run again — no network, no tokens, no charge orca replay last --from 4 --model claude-haiku-4-5 --ui

The third line is the one people stay for: same files, same conversation prefix, different model from step 4 onward. The model is the only variable, which is what makes the answer mean anything.

The three commands at the top need an agent installed, a key, a network and real tokens. If you have none of those yet, one command brings its own:

orca quickstart

It writes a small project with a genuine bug in it and a recording of an agent fixing that bug, then replays the recording against the project with no model called: two failing tests before, four passing after, three turns served from the trace and nothing spent. --full prints the whole timeline and the replay as it happened.

Read your agent's own system prompt

A proxy that sees the whole loop also sees the prompt the harness assembled before it sent anything. One command captures it, scrubs the machine out of it, and files it by model:

node capture/capture.mjs claude --model claude-opus-5

Interactive prompts and -p prompts are not the same prompt, and neither is the same across models. See capture/README.md for the measured differences, the pitfalls, and the sanitising rules.

Why this exists

Agent debugging today is archaeology. You scroll a terminal, you re-run and get a different failure, you add print statements to someone else's harness. The tools that exist are observability tools: they tell you a run cost $4.12 and used 61k tokens, which is not the question you have. The question you have is *why did it delete my migration file.*

OrcaReplay answers that by giving you the run back.

Observability toolsOrcaReplay
Tells you what a run cost✅✅
Tells you which tool call deleted the filesometimes✅
Runs the agent again and gets the same answer❌✅ from the recording, byte-for-byte
Lets you change the model and re-run from step 4❌✅
Needs you to modify your agentusually an SDK wrapper❌ two env vars
Works after you close the terminal❌✅ it is a file
Sees past the model API — shell exit codes, file writes❌✅ every turn
Records an agent with no API endpoint to redirect❌✅ opt-in --tls-intercept

The last two rows are the ones an SDK wrapper structurally cannot reach. Capture happens *below* the agent — at the process and socket boundary — so it does not matter whether the agent is yours, whether you can edit it, or whether it even holds an API key: a Codex CLI signed in with a ChatGPT subscription talks to its own backend over TLS, with a credential that only that backend accepts, so it cannot be pointed elsewhere and stay the same session — and orca can still record it. See when the harness will not be redirected.

How it works

Model APIs are stateless, so on every turn an agent resends the entire conversation — including the previous turn's tool results. A proxy in front of the model therefore sees the whole loop: each request, each streamed response, every tool call the model emitted, and every tool result the harness produced. That one property is what the tool is built on, and it is why OrcaReplay does not patch your agent — it stands up a local proxy, sets two environment variables, and gets out of the way.

What "egress blocked" means, exactly. On replay the proxy refuses to forward anything it cannot serve from the trace, so no model is called and no tokens are spent — that run prints egress=blocked. --loose lifts it deliberately: an unmatched request is then answered by the provider and recorded as a major divergence, and the same line reads egress=live-on-unmatched. A replay still *executes the recorded tool calls for real*, and a tool that opens its own socket — a shell command running curl, an MCP server fetching something — is outside the guarantee either way. By default it never reaches the proxy and goes to the network as usual. Under --tls-intercept it does reach the proxy, because that sets HTTPS_PROXY for the whole child: a host off the intercept list is tunnelled through untouched, though its hostname, port and byte counts still land in the trace, and a host on the list is refused there like any other unmatched call. Replay is not a sandbox; if you need one, run it inside one.

Three more layers catch what the protocol cannot see: an exit code, a real duration, which stream a byte came out of, a file written without telling anyone. A fifth exists for the agents that read no base-URL variable at all — see which agents. A sixth reads the agent's own account of its structure, for a harness that has one: which sub-agent ran, which handed off to which, whether a guardrail tripped, which graph node produced what and which of them called no model at all — none of which reaches the wire.

%%{init: {'theme':'neutral'}}%%

flowchart LR A[" your agent unmodified "]

subgraph orca["orca · six capture layers"] direction TB P[" proxy base-URL env var"] SH[" PATH shim exit code · timing · streams"] MC[" JSON-RPC tee MCP config rewrite"] FS[" shadow git index workspace per turn"] FH[" fetch hook for a hardcoded origin"] end

A --> P & SH & MC & FS & FH P -->|"forwarded, auth intact"| U[" the model API or OrcaRouter · any gateway "] orca ==> T[(" one trace .orca/runs/run_a1b2c3")]

They all land in the same timeline, ordered by when they actually happened rather than when orca got around to reading them.

Exact, fork and compare are one thing

They are not three subsystems. They are the same proxy with a cursor — the position in the recorded stream where it stops answering from disk and starts answering from the network.

%%{init: {'theme':'neutral'}}%%

flowchart LR subgraph disk["from disk · byte-for-byte · network blocked"] direction LR T1["turn 1"] --> T2["turn 2"] --> T3["turn 3"] --> T4["turn 4"] end T4 ==> CUR{{" cursor "}} CUR ==> T5 subgraph net["from the network · any model you name"] direction LR T5["turn 5"] --> T6["turn 6"] --> T7["…"] end

commandwhere the cursor sitswhat you get
orca replay lastat the endthe whole run again, network blocked — no tokens, no charge, no variance
orca replay last --from 4 --model Xat checkpoint 4turns up to 4 identical, then a different model takes over
orca compare last --from 4 --models a,bat checkpoint 4, several timesone table, one variable — the model

A checkpoint is not recorded; it is *derived* — any point where the conversation prefix is complete and the workspace was snapshotted. Every fork therefore starts from a state that provably existed.

What a bug hunt actually looks like

Your agent was supposed to fix a failing auth test. It exited 0 and the test still fails. Start with what it actually did:

$ orca show last

run_6473f858b59e generic-openai@0.1.0 14 events exit 0

SEQ KIND WHAT DETAIL 0 RUN run started generic-openai 1 SNAP tree 919d32ba037537b43814c83779963b2cc3023db7 0 changed 2 MODEL claude-opus-5 1 messages 3 MODEL claude-opus-5 stop: tool_use · 100 in · 20 out 4 TOOL edit_file {"path":"auth.ts",…} 5 SNAP tree c6af62b75c0c8b8938bd6087328b5148f3dcd534 1 changed 6 FILE auth.ts modified +1 −3 7 TOOL edit_file ok 8 MODEL claude-opus-5 3 messages 9 MODEL claude-opus-5 stop: end_turn · 101 in · 5 out 10 SNAP tree c6af62b75c0c8b8938bd6087328b5148f3dcd534 0 changed 11 SHELL ["sh","-c","node --check nonexistent-file.ts"] /tmp/hunt 12 SHELL shell result exit 1 · 43ms 13 RUN run ended exit 0

info usage input=201 output=25 cost=$0.004890

Three facts the model's own transcript could not have told you, and the run's exit code hid: the file really changed (seq 6, +1 −3), the check the agent ran failed (seq 12, exit 1), and it finished anyway. The run exited 0 because the *agent* exited 0.

That last fact is the one worth a command of its own. orca show gives you the order things happened in; orca graph gives you what produced what:

$ orca graph last

FROM TO KIND WHY 3 model.response 4 tool.call recorded tool_use block in the response 4 tool.call 6 fs.change inferred changed path appears in tool input, same or previous turn 4 tool.call 7 tool.result recorded tool result answers its call 7 tool.resu

Solo se muestra el principio; lee el resto en el repo

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