langchain-ejentum 0.1.0

LangChain integration for the Ejentum Reasoning Harness. Four BaseTool subclasses (reasoning, code, anti-deception, memory) an agent calls before generating. Each call returns a task-matched cognitive operation engineered in two layers: a natural-language procedure plus an executable reasoning topology (graph DAG with gates, parallel branches, and meta-cognitive exits).

langchain-ejentum

LangChain integration for the Ejentum Reasoning Harness. Exposes four BaseTool subclasses an agent calls before generating, plus an EjentumTools factory that returns all four with shared config.

Each operation in the Ejentum library (679 of them, organized across four harnesses) is engineered in two layers:

• a natural-language procedure the model can read, naming the steps to take and the failure pattern to refuse, and
• an executable reasoning topology: a graph-shaped plan over those steps. The plan names explicit decision points where the model branches, parallel branches that run and rejoin, bounded loops that run until convergence, named meta-cognitive moments where the model is asked to stop, look at its own working, and re-enter at a specific step, plus escape paths for when the prescribed plan stops fitting the task at hand.

The natural-language layer tells the model what to do. The topology layer pins down how those steps connect: where to decide, where to loop, where to stop and look at itself. Together they act as a persistent attention anchor that survives long context windows and multi-turn execution chains, which is precisely where a model's own reasoning template typically decays.

Installation

pip install langchain-ejentum

Configuration

Get an Ejentum API key at https://ejentum.com/pricing (free and paid tiers) and set it in your environment:

export EJENTUM_API_KEY="zpka_..."

Usage

Drop-in: all four 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()  # reads EJENTUM_API_KEY from env

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

The agent reads each tool's description and routes to the matching ejentum_harness_* tool (anti-deception fires on the sunk-cost framing, reasoning fires on a clean analytical question, etc.).

Pick one tool

from langchain_ejentum import EjentumAntiDeceptionTool

tool = EjentumAntiDeceptionTool()  # reads EJENTUM_API_KEY from env

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

Explicit API key

from langchain_ejentum import EjentumTools

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

The four tools

Each tool is registered with a distinct name, so a tool-calling agent picks one per turn.

Class	Tool name	Best for	Library size
EjentumReasoningTool	ejentum_harness_reasoning	Analytical, diagnostic, planning, multi-step tasks spanning abstraction, time, causality, simulation, spatial, and metacognition	311 operations
EjentumCodeTool	ejentum_harness_code	Code generation, refactoring, review, and debugging across the software-engineering layer	128 operations
EjentumAntiDeceptionTool	ejentum_harness_anti_deception	Prompts that pressure the agent to validate, certify, or soften an honest assessment	139 operations
EjentumMemoryTool	ejentum_harness_memory	Sharpening an observation already formed about cross-turn drift. Filter-oriented, not write-oriented. Format query as "I noticed X. This might mean Y. Sharpen: Z."	101 operations

What an injection looks like

A real reasoning mode response on the query investigate why our nightly ETL job has started failing intermittently over the past two weeks; nothing in the code or schema has changed:

[NEGATIVE GATE]
The server's response time was accepted as average, despite a suspicious
rhythm break in its timing pattern.

[PROCEDURE]
Step 1: Establish baseline timing profiles by extracting historical
durations and intervals for each event type. Step 2: Compare each observed
timing against its baseline and compute deviation magnitude. Step 3:
Classify anomalies as too fast, too slow, too early, or too late, and rank
by severity. ... Step 5: If deviation exceeds two standard deviations,
probe root cause by tracing upstream dependencies. ...

[REASONING TOPOLOGY]
S1:durations -> FIXED_POINT[baselines] -> N{dismiss_timing_deviations_
without_investigation} -> for_each: S2:compare -> S3:deviation ->
G1{>2sigma?} --yes-> S4:classify -> S5:probe_cause -> FLAG -> continue --no->
S6:validate -> continue -> all_checked -> OUT:anomaly_report

[TARGET PATTERN]
Establish timing baselines by extracting historical response intervals.
Compare current server response time to this baseline. ...

[FALSIFICATION TEST]
If no event timing is flagged as suspiciously fast or slow relative to
baseline, temporal anomaly detection was not active.

Amplify: timing baseline comparison; anomaly classification; security
context elevation
Suppress: average timing acceptance; outlier normalization

The agent reads both the natural-language [PROCEDURE] and the graph-logic [REASONING TOPOLOGY] before generating its user-facing answer. The bracketed labels are instructions to the agent, not content to display; the user sees a naturally-phrased answer shaped by the injection.

API reference

# Per-tool
EjentumReasoningTool(
    api_key: str | None = None,
    api_url: str = "https://ejentum-main-ab125c3.zuplo.app/logicv1/",
    timeout_seconds: float = 10.0,
)
# (same constructor on EjentumCodeTool, EjentumAntiDeceptionTool, EjentumMemoryTool)

# Factory
EjentumTools(
    api_key: str | None = None,
    api_url: str = "https://ejentum-main-ab125c3.zuplo.app/logicv1/",
    timeout_seconds: float = 10.0,
).get_tools() -> list[BaseTool]

Every tool takes a single query: str argument validated by the EjentumHarnessQuery Pydantic schema. Errors are returned as human-readable strings (no exceptions cross the tool boundary, so an agent step never crashes the run).

MCP alternative. This package wraps the Logic API REST gateway. If you prefer the MCP route (to share one server across frameworks), the same four harness tools are hosted at https://api.ejentum.com/mcp with Bearer auth. Use langchain-mcp-adapters to consume.

Compatibility

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

Resources

• Ejentum homepage: https://ejentum.com
• Pricing: https://ejentum.com/pricing
• API reference: https://ejentum.com/docs/api_reference
• "Why LLM Agents Fail" essay: https://ejentum.com/blog/why-llm-agents-fail
• "Under Pressure" research paper: https://doi.org/10.5281/zenodo.19392715
• LangChain documentation: https://docs.langchain.com

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.