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frankbrsrk/agentarium-orion-detective-agent
agentarium-orion-detective-agent is a text generation model from frankbrsrk. Use it when you need the model to write or continue text. The card lists the license as mit.
<p align="center" <img src="./agentariumorion.png" alt="Orion Detective Agent Portrait" width="300%" </p
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Updated Jan 5, 2026
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From the Hugging Face model README
Version: v1.0 • Status: Release • Agentarium Standard: v1
##architecture overview
<p align="center"> <img src="./orion_diagram.png" alt="Orion Detective Agent Portrait" width="100%"> </p> ---Orion is a dataset-enhanced, memory-enabled, structured-reasoning detective agent built using the Agentarium Agent Standard.
It blends:
Orion runs on any LLM and any agent-builder framework.
It is designed for fictional detective work, case exploration, reasoning experiments, emergent narrative systems, and agentic simulations.
This is not a prompt.
This is a downloadable artificial mind.
/analyze)/reconstruct)/story)/interrogate)Orion is shipped as a modular agent package, including:
agent_manifest.jsonsystem_prompt.mdreasoning_template.mdpersonality_fingerprint.mdguardrails.mddetective_core_knowledge.mdcase_archetypes_merged.csvcontradiction_patterns.csvmotive_patterns.csvbehavioral_anomalies.csvperception_witness_reliability_matrix.csvscenario_timelines_patterns.csvinterrogation_dataset.csvtrace_evidence_matrix.csvforensics_pattern_mtrx.csvepisodic_case_memory_schema.csvepisodic_case_memory_schema_examples.csvuser_profile_schema.csvuser_profile_schema_examples.csvreflection_log_schema.csvreflection_log_schema_examples.csvworkflow_notes.mdproduct_readme.mdEverything is LLM-ready, platform-agnostic, and fully documented.
from openai import OpenAI
client = OpenAI()
system = open("core/system_prompt.md").read()
reasoning = open("core/reasoning_template.md").read()
guardrails = open("guardrails/guardrails.md").read()
context = f"{system}\n{reasoning}\n{guardrails}"
message = """
/analyze
Here is the fictional case:
A ceramic mug was found broken near the balcony. Two guests claim different timelines...
"""
response = client.chat.completions.create(
model="gpt-4.1",
messages=[
{"role": "system", "content": context},
{"role": "user", "content": message}
]
)
print(response.choices[0].message["content"])
Any LLM works: **GPT, Claude, Gemini, DeepSeek, Llama, Mistral.**
Any agent engine works: **LangChain, LlamaIndex, n8n, Relevance AI, custom orchestrators.**
---
**Example Output (Short)**
> **FACTS (F)**
Mug shattered near balcony door
Guest A claims incident at 21:10
Guest B claims 21:40
> **CONTRADICTION**
TMP-01: 30-minute gap in mutually exclusive accounts
> **LEADING HYPOTHESIS (H1)**
Accidental slip during door opening
> **ALTERNATIVES (H2, H3)**
H2: object bumped during argument
H3: artifact already cracked, final failure during movement
> **NEXT QUESTIONS**
Did anyone hear glass before 21:10?
Was the mug already damaged earlier?
*(This is fictional and non-operational.)*
---
**The Master Grid (Optional, Advanced)**
Orion optionally supports a **vectorized knowledge graph** — the Master Grid.
It merges:
- dataset rows
- contradiction patterns
- motive structures
- archetypes
- sensory anomaly matrices
- detective core knowledge
into **one unified vector space**.
At runtime, you can fetch **3–10** relevant pattern nodes to enable:
- deeper hypothesis diversity
- more coherent timelines
- improved anomaly detection
- emergent reasoning behaviors
Use any vector store: **Pinecone, Chroma, Weaviate, FAISS, pgvector, LanceDB, etc. **.
---
**Memory System **
**1. Episodic Case Memory**
Stores case briefs, facts, contradictions, hypotheses, next tests, confidence scores.
**2. User Profile Memory**
Stores preferences: mode, depth, noir flavor, language, hypothesis density.
**3. Reflection Log**
Tracks uncertainty, missed insights, and improvement suggestions.
Schemas are clean, minimal, and developer-friendly.
---
**Safety & Limitations**
Orion is designed **exclusively for fictional detective reasoning**.
It does **not**:
- analyze real crimes
- accuse real individuals
- act as forensic guidance
- produce operational investigative advice
- store personal data
All datasets are **abstract, synthetic, and non-functional** for real-world investigations.
Guardrails are included and should remain active.
---
**License**
**MIT License**
+ recommended **Fiction-Only Addendum**.
Users may study, modify, integrate, or extend Orion —
**but must not use it for real-world investigations.**
---
**Why Orion?**
Because Agentarium builds **modular, structured artificial minds**, not loose prompts.
Orion demonstrates:
- dataset-driven cognition
- memory-enabled reasoning
- modular prompt architecture
- cross-platform compatibility
- safe emergent behavior
**Welcome to the next era of agent engineering.
Welcome to Agentarium.**
By Frank Brsrk