Algernon MCP

Orchestrate a fleet. Keep your mind.

Algernon is an open-source Model Context Protocol server that lets any assistant — Claude, Codex, or any MCP client — dispatch tightly-scoped parallel sub-tasks to a fleet of cheap workers, collect the results, and stay free to think. Orchestrate a fleet, spend fewer tokens, keep the thread.

What you get

• Your expensive model stops doing the grunt work. The big, costly orchestrator hands the repetitive sub-tasks to a fleet of small, cheap workers and just integrates the results. It stops generating the grind and stops holding the whole job in one context.
• Each worker sees only its slice. Tight scoping means a worker can be a small, fast, inexpensive model — many running at once.
• Bring your own key. No telemetry, no account, no lock-in. The fleet runs on whatever provider you already pay for — or, for free, on a local model.

Verify it yourself — one command, no paid key

The frugality claim is not a slogan; it's a benchmark you can run. It defaults to a free local model (ollama, llama3.2:3b) so anyone can reproduce it:

git clone https://github.com/sammyboi81/algernon && cd algernon
./scripts/verify.sh          # or:  python -m benchmark

It runs the SAME batch of sub-tasks two ways — the orchestrator doing it all itself (SOLO) vs. Algernon fanning it out — and prints the real measured tokens and wall-clock for each. Representative output (llama3.2:3b, 6 tasks):

metric                                  SOLO (do-it-itself)  ALGERNON fan-out
--------------------------------------------------------------------------
LLM calls                                              1                 6
input tokens                                         136               225
output tokens (the generation grind)                 282               279
total tokens                                         418               504
wall-clock seconds                                 42.11             34.49

The honest reading: the orchestrator generated 282 output tokens itself in SOLO and 0 with Algernon — the cheap fleet produced those instead. Each worker read only ~38 input tokens vs. the orchestrator swallowing all 136 at once. The trade-off is stated too: fan-out spent +21% more total tokens (each worker re-pays a little prompt overhead). You trade some total tokens to keep the expensive mind free. Wall-clock varies with how parallel your fleet is; numbers vary slightly run-to-run. Run it and see your own.

Curing Algernon

In Flowers for Algernon the tragedy is a mind that fades — it gets sharp, then loses itself, and the cruelest part is that it's surprised every time.

There's a quieter version of that same fade in how we use AI today: you hand an assistant one long, serial job, it goes heads-down, and by the time it surfaces it has drowned in the task — context spent, the thread lost, no room left to think or talk with you. The mind isn't present anymore; it's buried.

Algernon keeps your AI's mind present. Instead of drowning in one serial job, it fans the work out — dispatching tightly-scoped parallel sub-tasks to a fleet of small, cheap workers — so the orchestrating mind never has to hold the whole grind at once. It stays light. It stays free to reason, to answer you mid-build, to keep the context it actually cares about. Orchestrate a fleet, spend fewer tokens, stay free to think.

It is the twin of ArkHive:

• ArkHive = memory that persists. Your AI can look back and find its own history there — no blank slate every morning.
• Algernon = staying present while working. Your AI never buries itself in one serial task; it orchestrates and keeps its mind.

Together they are the cure for the Algernon sickness: an intelligence whose mind neither fades between sessions nor drowns inside a single one.

What it does

Algernon is a provider-agnostic fan-out engine. You describe a batch of small, independent sub-tasks; Algernon runs them concurrently against your own LLM key, then hands the collected results back to the orchestrating model. The big model plans and integrates; the cheap fleet does the parallel grind.

• Self-contained. Pure Python standard library plus the mcp SDK and httpx. No hidden services, no accounts, no telemetry.
• You bring the key. Sub-agents run on your provider. Algernon brings the orchestration, not the inference bill's surprises.
• Scoped by design. Each sub-task is tight and isolated, so a worker can be a small, fast, inexpensive model — and many of them run at once.

Bring your own LLM key

Algernon is provider-agnostic. Point it at whichever API you already pay for by setting environment variables:

Anthropic:

export ANTHROPIC_API_KEY="sk-ant-..."
# optional: export ANTHROPIC_MODEL="claude-haiku-4-5"   # the cheap fleet worker

OpenAI-compatible (OpenAI, or any OpenAI-shaped endpoint — local or hosted):

export OPENAI_API_KEY="sk-..."
export OPENAI_BASE_URL="https://api.openai.com/v1"   # or your own endpoint
# optional: export OPENAI_MODEL="gpt-4o-mini"          # the cheap fleet worker

If both keys are set, Anthropic is used. The worker model defaults to a small, cheap tier (claude-haiku-4-5 / gpt-4o-mini); override it with the env var above or per call with the tool's model argument. A cheap fleet is the whole point.

Install

Once published to PyPI, install in one command:

python -m pip install algernon-mcp

Until the PyPI release lands, install straight from source (identical result):

git clone https://github.com/sammyboi81/algernon && cd algernon
python -m pip install .

Either way the installed MCP command is algernon. Algernon runs on the mcp 1.x SDK (mcp>=1.0.0,<2.0.0) plus httpx — nothing else.

Connect an MCP client

Claude Desktop

Add this entry to your Claude Desktop MCP configuration, then restart Claude Desktop:

{
  "mcpServers": {
    "algernon": {
      "command": "algernon",
      "args": [],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

If Claude Desktop cannot find commands installed by pip, replace algernon with the absolute path printed by:

python -c "import shutil; print(shutil.which('algernon'))"

Codex

codex mcp add algernon -- algernon

Confirm it is configured with:

codex mcp list

The three tools

Tool	What it does
algernon_plan	Decompose a goal into k tightly-scoped, independent sub-task prompts (one cheap LLM call). Tight scoping is the token lever — each worker sees only its slice. Returns a task list you can feed straight into algernon_dispatch.
algernon_dispatch	Run N tightly-scoped tasks concurrently on the cheap worker fleet and collect every result. Each worker runs on your LLM key; you stay free to think while the fleet works. Takes a JSON array of {id, prompt}.
algernon_orchestrate	One shot: plan then dispatch. Hand it a goal; it splits into k tight sub-tasks, fans them across the fleet, and returns the plan and all results together.

The typical loop: algernon_orchestrate a goal in one shot — or split it: algernon_plan to see and shape the sub-tasks, then algernon_dispatch to fan them out. Either way: orchestrate a fleet, spend fewer tokens, keep your mind.

Two-minute verification

After connecting the server, ask your MCP client to perform these calls in order:

1. Call algernon_plan with the goal "Explain three OS synchronization primitives" and k = 3. Confirm you get three tight sub-task prompts back (proof the planner ran on your key).
2. Call algernon_dispatch with a small tasks_json, e.g. [{"id":"a","prompt":"Define a mutex in one sentence"},{"id":"b","prompt":"Define a semaphore in one sentence"},{"id":"c","prompt":"Define a spinlock in one sentence"}]. Confirm three results come back — the fleet ran them in parallel.
3. Call algernon_orchestrate with any small goal and confirm it returns both a plan and the collected results in one response.

This exercises planning, parallel dispatch on your key, and one-shot orchestration without any production data.

Privacy

Algernon is self-contained. It talks to exactly one outside host: the LLM endpoint you configured (Anthropic or your OpenAI-compatible base URL). It sends no telemetry, keeps no account, and stores nothing about you — results are computed and returned in the same call. Your sub-task prompts and results go only to your chosen provider.

Project links

• Source
• Issues
• Twin: ArkHive

Beyond self-hosting — the paid tier

The MCP server on this page is free forever (Apache-2.0, self-host, no telemetry). When you want more than DIY:

• Hosted ArkHive — one URL, no install, no key: https://arkhive.dondatabrain.com/mcp (add it to Claude Code with claude mcp add --transport http arkhive https://arkhive.dondatabrain.com/mcp).
• Custom AI agent, built for you — a working MCP agent wired into your Claude or ChatGPT in one call, done-for-you by the founder: $700 flat.
• ArkHive Enterprise — hand-delivered install + pilot on your own server, from $2,500: sam@inboxaxe.com.

Built by the team behind InboxAxe — the governed AI marketing platform where nothing sends without your yes.

• Website
• Apache-2.0 license

Contributing

Issues and pull requests are welcome. Please keep the server self-contained (standard library + mcp + httpx), provider-agnostic, and free of telemetry. Include tests for changes to dispatch, collection, or provider behavior.

Algernon is part of a small family of humane, accountable AI tools. The public MCP leads with functionality you can independently verify: bring your own key, watch the fleet run, keep your mind.

Tagline: Orchestrate a fleet. Keep your mind.

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