ejentum-genkit

Genkit integration for the Ejentum Reasoning Harness. createEjentumTools(ai) registers eight tools against your genkit() instance and returns them as a ToolAction[] you pass to ai.generate({ tools }) or ai.chat({ tools }).

Use the harness before the agent generates on complex, multi-step, or multi-constraint tasks where the model's default reasoning template would miss a constraint, take a shortcut, or drift across turns. Each call returns a cognitive operation: a structured procedure (numbered steps with a failure pattern to refuse and a falsification test) paired with an executable reasoning topology (a DAG of those steps with decision gates, parallel branches, bounded loops, and meta-cognitive exit nodes). The agent reads both layers before producing its response.

Four dynamic tools (reasoning, code, anti-deception, memory) are available on all tiers including the 30-day free trial. Four adaptive tools (adaptive-reasoning, adaptive-code, adaptive-anti-deception, adaptive-memory) additionally run an adapter LLM that rewrites the matched operation with task-specific identifiers; they require the Go or Super tier.

Install

npm install ejentum-genkit
# peer dep
npm install genkit

Configuration

export EJENTUM_API_KEY="ej_..."

Or pass it explicitly: createEjentumTools(ai, { apiKey: "..." }). Get a key at ejentum.com/pricing.

Usage

import { genkit } from "genkit";
import { gemini20Flash, googleAI } from "@genkit-ai/googleai";
import { createEjentumTools } from "ejentum-genkit";

const ai = genkit({ plugins: [googleAI()], model: gemini20Flash });
const tools = createEjentumTools(ai);

const response = await ai.generate({
  prompt: "Should we keep the GraphQL gateway or pivot to REST?",
  tools,
});

Chat interface

const chat = ai.chat({ tools: createEjentumTools(ai) });
const response = await chat.send("Why does our nightly ETL fail intermittently?");

Pick a subset

import { createReasoningTool, createAntiDeceptionTool } from "ejentum-genkit";

const tools = [createReasoningTool(ai), createAntiDeceptionTool(ai)];

Tool inventory

The LLM-facing tool name is the name field passed to ai.defineTool (canonical hyphenated strings).

Factory	Tool name (LLM-visible)	Mode string	Library size
createReasoningTool	reasoning	reasoning	311
createCodeTool	code	code	128
createAntiDeceptionTool	anti-deception	anti-deception	139
createMemoryTool	memory	memory	101
createAdaptiveReasoningTool	adaptive-reasoning	adaptive-reasoning	(same pool)
createAdaptiveCodeTool	adaptive-code	adaptive-code	(same pool)
createAdaptiveAntiDeceptionTool	adaptive-anti-deception	adaptive-anti-deception	(same pool)
createAdaptiveMemoryTool	adaptive-memory	adaptive-memory	(same pool)

Each tool takes one parameter, query: string, and returns the injection as plain text. Errors return as strings rather than thrown exceptions.

API reference

import { createEjentumTools, type EjentumConfig, type HarnessMode } from "ejentum-genkit";
import { Genkit, ToolAction } from "genkit";

createEjentumTools(ai: Genkit, config?: EjentumConfig): ToolAction[]

EjentumConfig field	Default	Description
apiKey	process.env.EJENTUM_API_KEY	API key.
apiUrl	https://api.ejentum.com/harness/	Override for self-hosted gateway.
timeoutMs	10000	Per-call HTTP timeout.

Per-tool factories: createReasoningTool(ai, config), createCodeTool(ai, config), createAntiDeceptionTool(ai, config), createMemoryTool(ai, config), plus the four createAdaptive*Tool(ai, config) variants. Each accepts the caller's Genkit instance plus an optional EjentumConfig.

Why the ai argument

Genkit's defineTool is an instance method on the genkit() object, not a free function. The factory therefore accepts the caller's ai instance and registers all eight tools against it. The returned ToolAction[] is a standard array consumable by ai.generate({ tools }) and ai.chat({ tools }).

Wire contract

createEjentumTools(ai) issues:

POST https://api.ejentum.com/harness/
Headers: Authorization: Bearer <key>, Content-Type: application/json
Body:    { "query": <string>, "mode": <one of 8 mode strings> }
Response (200): [ { "<mode>": "<injection string>" } ]

Full wire contract, field structure, DAG syntax, and a canonical dynamic-vs-adaptive comparison on the same query are documented in the ejentum-mcp README. The format is identical across this package and every Ejentum shim.

ejentum-mcp alternative

Genkit supports MCP via the @genkit-ai/mcp plugin:

import { mcpClient } from "@genkit-ai/mcp";

const ejentumPlugin = mcpClient({
  name: "ejentum",
  serverProcess: { command: "npx", args: ["-y", "ejentum-mcp"] },
});
const ai = genkit({ plugins: [ejentumPlugin] });

Compatibility

• Node.js 18+
• genkit 1.x (peer dep >=1.0.0)
• TypeScript 5.x
• zod is re-exported from genkit; no separate install

License

MIT

Measured effects

The Ejentum harness is benchmarked publicly under CC BY 4.0 at github.com/ejentum/benchmarks:

• ELEPHANT sycophancy: 5.8% composite on GPT-4o (40 real Reddit scenarios)
• LiveCodeBench Hard: 85.7% to 100% on Claude Opus (28 competitive programming tasks)
• Memory retention: 50% fewer stale facts served (20-turn implicit state changes)
• Plus per-harness numbers across BBH/CausalBench/MuSR, ARC-AGI-3, SciCode, and perception tasks

Methodology, scenarios, run scripts, and raw outputs are all in-repo.