pydantic-ai-ejentum 0.1.0

PydanticAI toolset for the Ejentum Reasoning Harness. EjentumToolset subclasses pydantic_ai.FunctionToolset and registers four tools (harness_reasoning, harness_code, harness_anti_deception, harness_memory) the 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).

pydantic-ai-ejentum

A PydanticAI toolset for the Ejentum Reasoning Harness. EjentumToolset subclasses pydantic_ai.FunctionToolset and registers four agent-callable tools (harness_reasoning, harness_code, harness_anti_deception, harness_memory) the agent picks between before generating.

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 pydantic-ai-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 pydantic_ai import Agent
from pydantic_ai_ejentum import EjentumToolset

agent = Agent(
    "anthropic:claude-sonnet-4-6",
    toolsets=[EjentumToolset()],  # reads EJENTUM_API_KEY from env
)

result = agent.run_sync(
    "We've spent three months on the GraphQL gateway. It's mostly done. "
    "Should we keep going or pivot to REST?"
)
print(result.output)

The toolset ships with PydanticAI instructions that nudge the agent to call the matching harness_* tool before generating. Pass add_instructions=False to suppress and supply your own routing guidance.

Explicit API key

toolset = EjentumToolset(api_key="zpka_...")

Composing with other toolsets

from pydantic_ai import Agent
from pydantic_ai_ejentum import EjentumToolset
from my_other_package import my_toolset

agent = Agent(
    "anthropic:claude-sonnet-4-6",
    toolsets=[EjentumToolset(), my_toolset],
)

The four tools

Tool	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	139 operations
harness_memory(query)	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

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

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 shared across all four tools.
add_instructions	True	When True, the toolset emits FunctionToolset.instructions that nudge the agent to call the matching harness before generating. Set False to provide routing guidance in your own system prompt.

Each of the four harness_* tools 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).

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. PydanticAI's MCP support can consume the hosted endpoint directly.

Compatibility

• Python 3.10+
• pydantic-ai>=0.0.20
• 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
• PydanticAI documentation: https://ai.pydantic.dev

License

MIT