Metadata-Version: 2.4
Name: agno-ejentum
Version: 0.2.0
Summary: Agno Toolkit for the Ejentum Reasoning Harness. Eight agent-callable methods: four dynamic (reasoning, code, anti_deception, memory) and four adaptive (adaptive_reasoning, adaptive_code, adaptive_anti_deception, adaptive_memory) that pre-fit the operation to the task via an adapter LLM. Each call retrieves a structured cognitive injection: a natural-language procedure plus an executable reasoning topology.
Project-URL: Homepage, https://ejentum.com
Project-URL: Documentation, https://ejentum.com/docs/api_reference
Project-URL: Repository, https://github.com/ejentum/agno-ejentum
Project-URL: Issues, https://github.com/ejentum/agno-ejentum/issues
Project-URL: Changelog, https://github.com/ejentum/agno-ejentum/blob/main/CHANGELOG.md
Project-URL: Pricing, https://ejentum.com/pricing
Author-email: Ejentum <info@ejentum.com>
License-Expression: MIT
License-File: LICENSE
Keywords: agentic-ai,agno,agno-tools,ai,anti-deception,cognitive-scaffold,ejentum,llm,reasoning-harness
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: <3.14,>=3.10
Requires-Dist: agno>=2.0.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: requests>=2.31.0
Provides-Extra: dev
Requires-Dist: build>=1.2.0; extra == 'dev'
Requires-Dist: pytest-cov>=5.0.0; extra == 'dev'
Requires-Dist: pytest>=8.0.0; extra == 'dev'
Requires-Dist: ruff>=0.6.0; extra == 'dev'
Description-Content-Type: text/markdown

# agno-ejentum

[Agno](https://agno.com) Toolkit for the Ejentum Reasoning Harness. `EjentumTools()` registers eight agent-callable methods: four dynamic (`reasoning`, `code`, `anti_deception`, `memory`) and four adaptive (`adaptive_reasoning`, `adaptive_code`, `adaptive_anti_deception`, `adaptive_memory`).

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.

Dynamic methods return the top-1 abstract operation; adaptive methods additionally run an adapter LLM that rewrites the operation with task-specific identifiers. Adaptive methods require the Go or Super tier.

Method symbols use underscores because Python identifiers cannot contain hyphens. The on-wire API mode strings stay hyphenated (`anti-deception`, `adaptive-anti-deception`); the translation is internal to each method.

## Install

```bash
pip install agno-ejentum
```

## Configuration

```bash
export EJENTUM_API_KEY="ej_..."
```

Or pass it explicitly: `EjentumTools(api_key="...")`. Get a key at [ejentum.com/pricing](https://ejentum.com/pricing).

## Usage

```python
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=(
        "Pragmatic; pushes back on sunk-cost framings. "
        "Call anti_deception (or adaptive_anti_deception for high-stakes cases) "
        "before evaluating any decision the prompt pressures you to validate."
    ),
)

architect.print_response(
    "We have spent three months on the GraphQL gateway. "
    "Should we keep going or pivot to REST?"
)
```

## Tool inventory

The Agno agent sees the method name verbatim (underscored form).

### Dynamic (all tiers)

| Method | Mode string (on wire) | Library size |
|---|---|---:|
| `reasoning(query)` | `reasoning` | 311 |
| `code(query)` | `code` | 128 |
| `anti_deception(query)` | `anti-deception` | 139 |
| `memory(query)` | `memory` | 101 |

### Adaptive (Go or Super tier)

| Method | Mode string (on wire) |
|---|---|
| `adaptive_reasoning(query)` | `adaptive-reasoning` |
| `adaptive_code(query)` | `adaptive-code` |
| `adaptive_anti_deception(query)` | `adaptive-anti-deception` |
| `adaptive_memory(query)` | `adaptive-memory` |

Each method accepts a single `query: str` argument and returns the injection as a string. For `memory` and `adaptive_memory`, format as `"I noticed X. This might mean Y. Sharpen: Z."`.

Errors return as human-readable strings; methods do not raise.

## API reference

```python
EjentumTools(
    api_key: str | None = None,
    api_url: str = "https://api.ejentum.com/harness/",
    timeout_seconds: float = 10.0,
    **toolkit_kwargs,
)
```

| 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. |
| `**toolkit_kwargs` | | Forwarded to `agno.tools.Toolkit`. |

## 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](https://github.com/ejentum/ejentum-mcp#wire-contract).

## ejentum-mcp alternative

The same eight tools are exposed as MCP tools at `https://api.ejentum.com/mcp`. If you prefer that route, configure Agno with the MCP client of your choice.

## Compatibility

- Python 3.10+
- `agno>=2.0.0`
- `requests>=2.31.0`

## License

[MIT](./LICENSE)


## Measured effects

The Ejentum harness is benchmarked publicly under CC BY 4.0 at [github.com/ejentum/benchmarks](https://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.
