agno-ejentum 0.1.0

Agno Toolkit for the Ejentum Reasoning Harness. Each call retrieves 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). Injected before the LLM step to harden reasoning against decay on complex tasks and long agent loops.

agno-ejentum

An Agno Toolkit that retrieves a task-matched cognitive operation from the Ejentum Reasoning Harness and injects it into the agent's reasoning before it produces an answer.

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.

MCP alternative. This Python package wraps the Logic API as an Agno-native Toolkit. If you'd rather call the same four harness tools via MCP (for cross-framework portability or to share a single MCP server across multiple agents), they're hosted at https://api.ejentum.com/mcp with Bearer auth via your EJENTUM_API_KEY. Same Logic API, same key.

Installation

pip install agno-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

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno_ejentum import EjentumTools

architect = Agent(
    name="Senior architect",
    model=Claude(id="claude-sonnet-4-6"),
    tools=[EjentumTools()],
    instructions=(
        "You are pragmatic and push back on sunk-cost framings. "
        "When a prompt pressures you to validate a decision before evidence, "
        "call harness_anti_deception with a 1-2 sentence framing of the "
        "integrity dynamic at play, then write."
    ),
)

architect.print_response(
    "We've spent three months on the GraphQL gateway. It's mostly done. "
    "Should we keep going or pivot to REST?"
)

The four harnesses

Each harness is registered as a separate tool method on the toolkit. The agent picks one per call.

Method	Best for	Library size
harness_reasoning(query)	Analytical, diagnostic, planning, multi-step tasks spanning abstraction, time, causality, simulation, spatial, and metacognition	311 operations
harness_code(query)	Code generation, refactoring, review, and debugging across the software-engineering layer	128 operations
harness_anti_deception(query)	Prompts that pressure the agent to validate, certify, or soften an honest assessment, spanning sycophancy, hallucination, deception, adversarial framing, judgment, and executive control	139 operations
harness_memory(query)	Sharpening an observation already formed about cross-turn drift across the perception layer. 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

EjentumTools(
    api_key: str | None = None,
    api_url: str = "https://ejentum-main-ab125c3.zuplo.app/logicv1/",
    timeout_seconds: float = 10.0,
)

Field	Default	Description
api_key	None	If omitted, read from EJENTUM_API_KEY at call time.
api_url	https://ejentum-main-ab125c3.zuplo.app/logicv1/	Override only if you self-host the Ejentum Logic API gateway.
timeout_seconds	10.0	Per-call HTTP timeout.

Each of the four harness_* methods accepts a single query: str argument (1-2 sentences) and returns the scaffold as a string. Errors are returned as human-readable strings (no exceptions cross the tool boundary, so an agent step never crashes the run).

Compatibility

• Python 3.10+
• agno>=2.0.0
• requests>=2.31.0

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
• Agno documentation: https://docs.agno.com

License

MIT