
Claude Octopus
io.github.xiaolaiPublished by GitHub account xiaolaiv1.1.10Updated Oct 24, 2026
Spawn multiple specialized Claude Code agents as MCP servers, each independently configured.
The publisher's own server: Octopus.do
Overview
Wraps the Claude Agent SDK so an assistant can run multiple separately configured Claude Code agents as MCP tools, with timelines, transcripts and reports.
- What you can do
- Send tasks to a configured Claude Code agent and continue conversations by session ID, with per-call model, working directory, tool restrictions, effort and budget overrides. Run several instances with different models, prompts and tool sets, each appearing as its own tool. Group related agent calls into runs and query a cross-agent timeline. Read full session transcripts and list Claude Code session history for this project or all projects. Generate self-contained HTML reports of runs, and optionally expose only a factory wizard that writes new agent configs.
- When to use it
- Useful when you want several specialized Claude Code agents (for example a read-only reviewer, a test writer and a cheap quick helper) inside one MCP client, or when you need to trace and report on multi-agent workflows.
- Requirements
- Runs locally over stdio as an npm package; needs Node.js 18 or newer and a working Claude Code CLI installation. Authentication comes from an Anthropic API key in ANTHROPIC_API_KEY or a Claude Code OAuth session, inherited from the parent process if unset. Configuration is through CLAUDE_* environment variables.
Installation
In SourceWeft
- Open Claude Octopus in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
Claude Octopus
One brain, many arms.
An MCP server that wraps the Claude Agent SDK, letting you run multiple specialized Claude Code agents — each with its own model, tools, system prompt, and personality — from any MCP client.
Why
Claude Code is powerful. But one instance does everything the same way. Sometimes you want a strict code reviewer that only reads files. A test writer that defaults to TDD. A cheap quick helper on Haiku. A deep thinker on Opus.
Claude Octopus lets you spin up as many of these as you need. Same binary, different configurations. Each one shows up as a separate tool in your MCP client.
Prerequisites
- Node.js >= 18
- Claude Code — the Claude Agent SDK is bundled as a dependency, but it spawns Claude Code under the hood, so you need a working
claudeCLI installation - Anthropic API key (
ANTHROPIC_API_KEYenv var) or an active Claude Code OAuth session
Install
Three paths, pick whichever matches your MCP client.
npm (most direct)
npx (no install needed)
Skip the install entirely — reference claude-octopus@latest in your .mcp.json and the client will fetch on demand (see Quick Start below).
MCP Registry
The server is published to the MCP Registry under the name io.github.xiaolai/claude-octopus. Registry-aware MCP clients can resolve and install it by that name without touching npm directly.
Quick Start
The fastest way to get started:
This interactive wizard lets you pick a template, detects your MCP client, and writes the config for you.
Or add to your .mcp.json manually:
This gives you six tools:
That's it — you have Claude Code as a tool, with full workflow observability built in.
Multiple Agents
The real power is running several instances with different configurations:
Your MCP client now sees distinct tools for each agent — code_reviewer, test_writer, quick_qa — each purpose-built.
Multi-Agent Orchestration
Agents can coordinate through a coordinator pattern: one agent has the others as inner MCP tools via CLAUDE_MCP_SERVERS, and its system prompt drives the pipeline.
The coordinator agent autonomously calls researcher, architect, etc. as MCP tools — fully autonomous, no human in the loop until it finishes. Every invocation is tracked in the shared timeline.
Agent Factory
Don't want to write configs by hand? Add a factory instance:
This exposes a single create_claude_code_mcp tool — an interactive wizard. Tell it what you want ("a strict code reviewer that only reads files") and it generates the .mcp.json entry for you, listing all available options you can customize.
In factory-only mode, no query tools are registered — just the wizard. This keeps routing clean: the factory creates agents, the agents do work.
Init Wizard
Don't want to edit JSON by hand? The init wizard gets you from zero to working in 30 seconds:
It auto-detects installed MCP clients (Claude Desktop, Claude Code, Cursor, Windsurf), merges with existing config, and warns before overwriting.
Skip the menu
Templates
Five built-in templates, battle-tested and ready to use:
Each agent comes pre-tuned with appropriate model, tools, effort level, and system prompt.
Dashboard
Monitor your agents in real time:
Opens a local web dashboard at http://localhost:3456 with:
- Live stats — total runs, invocations, cost, SDK turns, responses, errors
- Recent activity — agent cards for the latest run
- Run table — all runs with cost, duration, and status
- Auto-refresh — SSE connection pushes updates as agents run
The dashboard reads the same timeline index used by the _timeline and _report tools. No additional configuration needed.
Tools
Each non-factory instance exposes:
Query and reply parameters
Timeline
Every agent invocation is recorded in a lightweight JSONL index at ~/.claude-octopus/timelines/timeline.jsonl. This solves the multi-agent correlation problem: when several agents participate in a workflow, the timeline tracks which sessions belong to the same run, in what order they executed, and what role each played.
Full session transcripts stay in Claude Code's own storage (~/.claude/projects/). The timeline is just the table of contents — ~200 bytes per entry — that cross-references via session_id.
How it works
- Every
<name>and<name>_replycall appends one line to the timeline - If you pass
run_id, all agents sharing the samerun_idare grouped into one run - If you omit
run_id, one is auto-generated and returned in the response — pass it to subsequent agents to keep them grouped
Querying the timeline
Multi-agent workflow example
HTML Reports
Generate self-contained HTML reports with agent sequence visualization, cost breakdown, and collapsible transcripts. Dark theme, no external dependencies — one file, open in any browser.
Via MCP tool
Via CLI
What's in the report
- Run summary — agent count, total cost, duration, SDK turns, responses, tool calls
- Timeline bar — numbered dots for each agent (green = success, red = error)
- Agent cards — timing, cost, effort counters, session ID, prompt excerpt
- Collapsible transcripts — full tool calls, reasoning, and results per agent
Reading the effort counters
Three numbers describe how much work an invocation took. They are not interchangeable, and the first one is the one that surprises people:
When an agent calls several tools in parallel, num_turns climbs faster than
the number of visible responses. A run with 3 assistant responses issuing 4
tool calls reports num_turns: 5, response_groups: 3, tool_calls: 4.
maxTurns, meanwhile, is enforced against round trips: that same run
completes under maxTurns: 3 and aborts under maxTurns: 2. So size
maxTurns against responses, not against SDK turns.
Both new counters cover the main agent loop only — work inside a sub-agent
(Task) belongs to its own loop and is excluded. Timeline entries written by
older versions have neither, and render as — rather than as a false zero.
Configuration
All configuration is via environment variables in .mcp.json. Every env var is optional.
Identity
Agent
Prompts
Advanced
Timeline
Authentication
Leave both unset to inherit auth from the parent process. Set one per agent to use a different account or billing source.
Lists accept JSON arrays when values contain commas: ["path,with,comma", "/normal"]
Security
- Permission mode defaults to
default— tool executions prompt for approval unless you explicitly setbypassPermissions. cwdoverrides preserve agent knowledge — when the host overridescwd, the agent's configured base directory is automatically added toadditionalDirectoriesso it retains access to its own context.- Tool restrictions narrow, never widen — per-invocation
toolsintersects with the server restriction (can only remove tools, not add).disallowedToolsunions (can only block more). _replyand_transcripttools respect persistence — not registered whenCLAUDE_PERSIST_SESSION=false.- Timeline writes are best-effort — a failed timeline append never blocks or fails the primary query.
Architecture
How It Compares
Development
License
ISC - Xiaolai Li
Source: README.md at commit af84244
Tools
0Version history
1- v1.1.10LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
Other Octopus integrations
Octopus.do
do.octopusOfficial publisher
A remote MCP server that lets an assistant read and edit Octopus.do visual sitemaps — pages, blocks and review notes — as the signed-in user.

Octopus Energy (octopus-mcp)
Published by GitHub account mattoddie
Unofficial MCP server for UK Octopus Energy customers: lets an assistant read smart meter usage, costs, tariffs and bills, and optionally manage EV charging…

Codex Octopus
Published by GitHub account xiaolai
Wraps the OpenAI Codex SDK so an assistant can run one or more separately configured Codex agents as tools, each with its own model, sandbox and instructions.

Octopus Energy
Published by GitHub account pipeworx-io
Octopus Energy's hosted MCP server; no sign-in required; Streamable HTTP; 48 tools.

Octopus Blackboard
Published by GitHub account octoryn
A local-first shared blackboard that lets multiple AI coding agents record claims, tasks, decisions, messages and commit attribution on one repo.