pydantic-ai-ejentum 0.2.0

PydanticAI toolset for the Ejentum Reasoning Harness. EjentumToolset subclasses pydantic_ai.FunctionToolset and registers eight tools: four dynamic (reasoning, code, anti-deception, memory) plus four adaptive variants (adaptive-reasoning, adaptive-code, adaptive-anti-deception, adaptive-memory) that pre-fit the cognitive operation to the caller's task via an adapter LLM.

pydantic-ai-ejentum

PydanticAI toolset for the Ejentum Reasoning Harness. EjentumToolset subclasses pydantic_ai.FunctionToolset and registers eight agent-callable 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.

PydanticAI accepts hyphenated tool names via @tool_plain(name="anti-deception"). The Python method symbols use underscores (anti_deception), but the LLM-facing names registered with the agent use the canonical hyphenated form.

Install

pip install pydantic-ai-ejentum

Configuration

export EJENTUM_API_KEY="ej_..."

Or pass api_key= to EjentumToolset(...). Get a key at ejentum.com/pricing.

Usage

from pydantic_ai import Agent
from pydantic_ai_ejentum import EjentumToolset

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

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

EjentumToolset ships with FunctionToolset.instructions that nudge the agent to call the matching tool before generating. Pass add_instructions=False to suppress and supply routing guidance in your own system prompt.

Explicit API key

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

Composing with other toolsets

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

Tool inventory

Dynamic (all tiers)

Tool name (LLM-visible)	Mode string	Library size
reasoning	reasoning	311
code	code	128
anti-deception	anti-deception	139
memory	memory	101

Adaptive (Go or Super tier)

Tool name	Mode string
adaptive-reasoning	adaptive-reasoning
adaptive-code	adaptive-code
adaptive-anti-deception	adaptive-anti-deception
adaptive-memory	adaptive-memory

Each tool accepts a single query: str argument. Returns the injection as a string. For memory and adaptive-memory, format the query as "I noticed X. This might mean Y. Sharpen: Z.".

Errors return as strings; tools do not raise.

API reference

EjentumToolset(
    api_key: str | None = None,
    api_url: str = "https://api.ejentum.com/harness/",
    timeout_seconds: float = 10.0,
    add_instructions: bool = True,
)

Field	Default	Description
api_key	None	If unset, read from EJENTUM_API_KEY at call time.
api_url	https://api.ejentum.com/harness/	Override for self-hosted gateway.
timeout_seconds	10.0	Per-call HTTP timeout.
add_instructions	True	Emit FunctionToolset.instructions nudging the agent to call the matching tool before generating.

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 as an MCP server at https://api.ejentum.com/mcp. PydanticAI's MCP support can consume the endpoint directly.

Compatibility

• Python 3.10+
• pydantic-ai>=0.0.20
• 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.