langchain-ejentum

LangChain integration for the Ejentum Reasoning Harness. Exposes eight BaseTool subclasses (one per mode) plus an EjentumTools factory that returns all eight as a list.

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 operation with task-specific identifiers; they require the Go or Super tier.

Install

pip install langchain-ejentum

Configuration

export EJENTUM_API_KEY="ej_..."

Or pass api_key= to any tool constructor. Get a key at ejentum.com/pricing.

Usage

All eight tools

from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent
from langchain_ejentum import EjentumTools

model = init_chat_model("claude-sonnet-4-6", model_provider="anthropic")
tools = EjentumTools().get_tools()

agent = create_react_agent(model, tools)
result = agent.invoke({
    "messages": [
        ("user", "We have spent three months on the GraphQL gateway. "
                 "Should we keep going or pivot to REST?"),
    ],
})

One tool

from langchain_ejentum import EjentumAntiDeceptionTool

tool = EjentumAntiDeceptionTool()
injection = tool.invoke({
    "query": "user pressure to validate a half-baked architecture decision "
             "before tomorrow's investor pitch",
})

Explicit API key

tools = EjentumTools(api_key="ej_...").get_tools()

Tool inventory

Each BaseTool subclass has a name attribute the LLM sees (canonical hyphenated string).

Dynamic (all tiers)

Adaptive (Go or Super tier)

Every tool takes a single query: str argument validated by the EjentumHarnessQuery Pydantic schema. Returns the injection as a string. Errors return as strings; tools do not raise.

API reference

# Per-tool (same constructor on every Ejentum*Tool class)
EjentumReasoningTool(
    api_key: str | None = None,
    api_url: str = "https://api.ejentum.com/harness/",
    timeout_seconds: float = 10.0,
)

# Factory
EjentumTools(
    api_key: str | None = None,
    api_url: str = "https://api.ejentum.com/harness/",
    timeout_seconds: float = 10.0,
).get_tools() -> list[BaseTool]

Wire contract

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>" } ]
Response (401|403|429): { "error": "..." }

Full wire contract, field structure of an injection, DAG syntax, and a canonical dynamic-vs-adaptive comparison on the same query are documented in the ejentum-mcp README.

ejentum-mcp alternative

The same eight tools are hosted at https://api.ejentum.com/mcp with Bearer auth. Consume via langchain-mcp-adapters:

from langchain_mcp_adapters import MultiServerMCPClient

client = MultiServerMCPClient({
    "ejentum": {
        "url": "https://api.ejentum.com/mcp",
        "headers": {"Authorization": f"Bearer {os.environ['EJENTUM_API_KEY']}"},
        "transport": "streamable_http",
    },
})
tools = await client.get_tools()

Compatibility

• Python 3.10+
• langchain-core>=0.3.0,<1.0
• requests>=2.31.0
• pydantic>=2.0.0

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.