Orcareplay
What it does
Read, replay and fork recorded coding-agent runs.
- Connects 在你的电脑上运行
- Account or key No key needed
- Category 开发者工具
安装
选择你使用的 AI 应用,然后复制设置。
通过 3/3 项检查 · 在你的电脑上运行
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude,但需要先准备
需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
打开电脑版 Claude 应用的设置文件
SettingsDeveloperEdit Config
粘贴这些设置并保存文件
{ "mcpServers": { "orcareplay": { "command": "npx", "args": [ "-y", "orcareplay" ] } } }退出 Claude 再重新打开
试一试
用 Orcareplay 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 Orcareplay,或回答中出现 Orcareplay 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
ChatGPT 暂时无法连接 Orcareplay
Orcareplay 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器
你可以改用这些方式
A · 使用能连接 Orcareplay 的应用
- 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
- 需要先在电脑上安装 Node.js(只需一次)
- 需要先在电脑上安装 Node.js(只需一次)
- 需要先在电脑上安装 Node.js(只需一次)
B · 开发者工具 类中能连接 ChatGPT 的其他服务器
从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的
- Sentry✓ ChatGPT
- Compiler Explorer✓ ChatGPT
- GitDiagram✓ ChatGPT
可连接 Claude Code,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
打开终端
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
粘贴这条命令并按 Enter
claude mcp add --scope user orcareplay -- npx -y orcareplay在 Claude Code 中输入 /mcp
应能看到 Orcareplay 在列表中并显示已连接
试一试
用 Orcareplay 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude Code 说正在使用 Orcareplay,或回答中出现 Orcareplay 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Cursor,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
在 Cursor 中以 Agent 模式打开 AI 聊天
试一试
用 Orcareplay 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Cursor 说正在使用 Orcareplay,或回答中出现 Orcareplay 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同
可连接 VS Code,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
在 VS Code 中以 Agent 模式打开 AI 聊天
试一试
用 Orcareplay 告诉我你能帮我做什么,然后做一件给我看✓ 如果 VS Code 说正在使用 Orcareplay,或回答中出现 Orcareplay 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同
开发者专用:终端命令和 JSON 设置
安装在电脑上
Claude Code
claude mcp add --scope user orcareplay -- npx -y orcareplayClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"orcareplay": {
"command": "npx",
"args": [
"-y",
"orcareplay"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"orcareplay": {
"command": "npx",
"args": [
"-y",
"orcareplay"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"orcareplay": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"orcareplay"
]
}
}
}这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
一览
哪些应用可以使用它
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
它能做什么
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月12日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 Continuum-AI-Corp 验证,该账户拥有此仓库
- ✓ 通过
仍在维护
代码最后更新于42天前
- ✓ 通过
有许可证
使用 Apache-2.0
- – 不适用
端点有响应
它在你的电脑上运行,没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行,改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行;我们只能读取互联网端点的工具列表
讨论
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
用户评价
还没有评价
与“开发者工具”中的其他服务器比较i
- 星标数多于此分类中其他 5802 个服务器的 98%
- 发布到注册中心的时间比此分类中其他 5802 个服务器的 43% 更新
- 基础检查结果等于或优于此分类中其他 5802 个服务器的 100%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
常见问题
Orcareplay 安全吗?
它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
它需要哪些密钥或代码?
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在我的电脑上运行还是在云端?
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
它还在维护吗?
GitHub 仓库最后更新于42天前,最新版本(0.5.0)于44天前进入注册中心。
哪些应用可以使用它?
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
有什么可以替代它?
同一分类中有:REA, GitHub, Serena MCP: the IDE for your agent
同类别的其他选择
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Serena MCP: the IDE for your agent
A powerful toolkit for coding, providing semantic retrieval and editing capabilities.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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 tools | OrcaReplay | |
|---|---|---|
| Tells you what a run cost | ✅ | ✅ |
| Tells you which tool call deleted the file | sometimes | ✅ |
| 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 agent | usually 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
| command | where the cursor sits | what you get |
|---|---|---|
orca replay last | at the end | the whole run again, network blocked — no tokens, no charge, no variance |
orca replay last --from 4 --model X | at checkpoint 4 | turns up to 4 identical, then a different model takes over |
orca compare last --from 4 --models a,b | at checkpoint 4, several times | one 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