ennodia
MCP serverProductivityGet a team of AIs to weigh in before your AI settles on an answer. Once ennodia is added, your AI can gather advice from several AIs, have multiple agents review its work, compare responses, and hand back traceable receipts showing how each conclusion was reached. Everything runs locally on your own machine.
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What your AI can do with it
- Get advice from a team of AIs on any question or decision
- Have several AI agents review a draft or answer before you rely on it
- Compare responses from different AIs
- Keep traceable receipts that show how each answer was produced
- Run everything locally on your own machine
Getting started
- Save this item in Your setup as a reference.
- Read the source or reference documentation for its setup requirements. Saving it here does not connect it to your AI.
- Check this page for availability before trying to install it through Ahel.
From the project's README
As published by cherninlab/ennodia in README.md.
Ennodia gives your primary agent access to installed agent command-line interfaces (CLIs). It tracks each child task and uses model-led Compare to show agreements, disagreements, omissions, and one recommended answer with receipts.
Before work, an optional Plan Advisor can propose a bounded set of harnesses, models, and skills. It does not start the proposed work.
Install
Send this address to your primary agent. The agent can then complete the installation:
try-ennodia.cherninlab.com
To run Ennodia directly as a standard input/output (stdio) MCP server, use:
npx -y ennodia
To use Bun directly, run bunx ennodia.
For a registry or client installer, use the Ennodia Smithery page.
For manual installation or local development, see Quickstart.
What Ennodia does
- Discovers available local artificial intelligence (AI) tools
- Plans a route with a caller-provided category or keyword fallback
- Estimates preflight input tokens and enforces local caps on that estimate
- Starts and monitors child tasks
- Shows status, elapsed time, logs, and failures
- Cancels tasks and runs explicitly
- Lets a Plan Advisor propose an inert, validated work plan
- Compares multiple completed outputs with a Judge
- Lets a Result Advisor combine the Judge findings into one answer
ennodia_run is the main entrypoint. It plans and starts tasks. It can also use
Compare. It returns a run identifier (ID) for use with ennodia_get_run.
See MCP tools for the full tool and parameter reference.
Use Ennodia for work that needs additional model analysis. A run usually takes minutes. Compare adds two serial model passes after the child agents finish.
Judge, Plan Advisor, and Result Advisor
Each role has limited responsibilities:
- Plan Advisor runs before work when requested. It proposes explicit harness, model, and skill assignments as inert data. Ennodia validates the proposal against a frozen inventory. Plan Advisor cannot execute it. The caller must use a separate call to start the validated plan once.
- Judge runs during Compare and maps agreements, contradictions, unique evidence, omissions, and risks across completed outputs.
- Result Advisor follows the Judge. It uses the Judge findings and original
outputs to recommend an answer. If Judge analysis is not usable, it uses
basis: "candidates-only". This status makes the degradation visible.
Ennodia IO
The separate @cherninlab/ennodia-io package provides a local Hypertext
Transfer Protocol (HTTP) and TypeScript interface. Apps can use it with
user-supplied keys and installed local agents:
npx -y @cherninlab/ennodia-io
See Ennodia IO for supported fields, authentication behavior, cross-origin resource sharing rules, and current limits.
Supported harnesses
- Codex CLI
- Claude Code
- OpenCode
- Kilo Code
- Kiro CLI
- Cline CLI
- Hermes Agent
- Antigravity
Adapters stay thin. Core modules contain shared routing, trace data, task state, recovery, and Compare logic.
Evaluated candidates include Gemini CLI, GitHub Copilot CLI, Amp, Aider, Goose, Qwen Code, and Cursor CLI. Ennodia does not include these candidates.
Ennodia can add a candidate after verification of a supported prompt-in and text-out interface. Verification must not use permission-bypass flags or provider-private APIs.
Documentation
- Installation for Agents: agent-controlled installation
- Quickstart: manual installation and local development
- MCP Tools: full tool parameter reference
- How Ennodia Works: orchestration pipeline
- Second Opinions: replicate, decompose, and red-team patterns
- Data Governance: local storage and data movement boundaries
- Controlled English: ASD-STE100 rules and Ennodia technical terms
- Comparisons: comparisons with adjacent tools
- Benchmarks: deterministic bug-recall results
- Better Audits: prompt rubrics for Compare
Benchmarks
The current benchmark is multi-model-bug-recall. It uses small TypeScript
review fixtures and committed bug oracles. Run the deterministic suite with:
bun run bench:bug-recall
Live harness runs are available through bun run bench:bug-recall:live and are
kept out of bun run verify.
The current dated fixture snapshot is published in
Benchmarks: 4
cases, with ennodia-parallel-compare at 100% recall and 100% precision.
Contribute
Ennodia is under active development. You can submit bug reports and small, focused pull requests. See CONTRIBUTING.md for the local verification workflow.
Signals
- GitHub stars
- 5
- Last commit
- Sep 2026
- Weekly downloads
- 389
- Weekly_downloads
- 389 weekly_downloads
Advanced
- Delivery
- ennodia MCP server → your Ahel connector (mcp.ahel.ai) → your AI.
- Item type
- mcp-server
- Key
io-github-cherninlab-ennodia- Source
- github.com/cherninlab/ennodia
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