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.

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.

Related MCP server: Agent Farm

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 in one command

python -m pip install algernon-mcp

The installed MCP command is algernon.

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

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

• 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.

Apache-2.0 © 2026 ZagAIrot Technologies LLC.