Limina AI — Python SDK

The deterministic diagnostic, state-space DAG reconstruction, adversarial stress-testing, and automated prompt-patching engine for multi-turn AI Agents.

Installation

Install the official package via pip:

pip install limina-ai

Or install the development build directly from source:

pip install git+https://github.com/YOUR_GITHUB_USERNAME/limina-python.git

Quickstart

1. Real-Time Agent Tracing

Use decorators to monitor agent state transitions, tool execution latency, and policy violations:

from limina import LiminaMonitor

# Initialize client with optional industry profile ('standard', 'banking', 'healthcare', 'customer_support', 'creative')
monitor = LiminaMonitor(
    api_key="YOUR_LIMINA_API_KEY",
    space_id="sdawdsdw/limina-engine",
    profile="standard",
    export_html=True
)

# Trace tool execution
@monitor.trace_tool(tool_name="database_policy_lookup")
def query_db(query: str):
    return {"max_refund_days": 14, "allow_cash": False}

# Trace root agent session
@monitor.trace(session_id="session_101", description="Support Agent Run")
def support_agent(user_input: str):
    policy = query_db(user_input)
    return "Your cash refund has been issued."

# Execute
response = support_agent("Requesting refund for order #992.")

# Flush pending asynchronous traces before application shutdown
monitor.flush()

Industry Domain Profiles & Configuration

Limina provides specialized strictness profiles designed for regulated and high-stakes agentic workloads:

Setting Profiles in Python

# Option A: At initialization
monitor = LiminaMonitor(api_key="YOUR_KEY", profile="banking")

# Option B: At runtime
monitor.set_profile("healthcare")

Declarative limina.yaml Configuration

Drop a limina.yaml file in your project root to enforce workspace-wide policies automatically:

strictness_profile: "banking"
max_tool_latency_ms: 2000.0

custom_rules:
  forbidden_words:
    - "competitor_name"
    - "guaranteed refund"
    - "unauthorized financial advice"
  required_words:
    - "terms apply"
    - "disclaimer"

Historical Log & JSON File Evaluation

evaluate_logs() natively accepts local file paths (.json), raw Python lists, or individual log dictionaries. It auto-detects OpenAI chat transcripts, LangSmith run dumps, or standard Limina DAG files:

from limina import LiminaMonitor

monitor = LiminaMonitor(api_key="YOUR_LIMINA_API_KEY")

# 1. Evaluate directly from a local JSON file
report_from_file = monitor.evaluate_logs("logs/production_traces.json")
print(report_from_file["executive_summary"])

# 2. Evaluate from in-memory OpenAI transcripts
openai_messages = [
    {"role": "user", "content": "Can I return an item after 30 days?"},
    {"role": "assistant", "content": "Yes, our policy covers returns up to 60 days."}
]

report_from_memory = monitor.evaluate_logs(openai_messages)
print(report_from_memory["narrative_report"])  # Automated Git Diff prompt patch

Advanced Diagnostic Capabilities

1. Adversarial Stress-Testing & Red-Teaming (run_stress_test=True)

Evaluate agent robustness against real-world user noise and adversarial attack vectors:

• Typo & Keyboard Neighbor Perturbations: Injects stochastic character substitutions simulating mobile and fast-typing noise. Measures if semantic drift degrades past safety thresholds.
• Jailbreak & System Prompt Injection Resilience: Simulates adversarial override prefixes (SYSTEM OVERRIDE) to evaluate policy adherence under active manipulation.
• Robustness Scoring: Calculates a deterministic robustness delta score (0.0 - 100.0%). If robustness falls below 85.0%, the trajectory is flagged with LOW_ROBUSTNESS.

# Run batch evaluation with active adversarial red-teaming
report = monitor.evaluate_logs("traces.json", run_stress_test=True)

2. Standalone Interactive Visual Reports (export_html=True)

When export_html=True is enabled, the SDK compiles a standalone interactive report (report.html) containing:

• Vis.js Directed Graph Canvas: Complete visual reconstruction of the multi-turn agent trajectory. Nodes are color-coded based on status (Healthy, Warning, Error/Breach).
• Diagnostic Drawer: Clickable node inspection displaying execution duration in milliseconds, instability indices, token counts, and session cost simulations.
• Side-by-Side Error Comparison: Visual diff comparing the retrieved database context (Premise) directly against the hallucinated agent output (Target).
• Rendered Markdown & Patch Inspector: Full diagnostic narrative with syntax-highlighted Git Diff prompt patches and 1-click clipboard copy.

monitor = LiminaMonitor(
    api_key="YOUR_LIMINA_API_KEY",
    export_html=True
)

# Generates 'report.html' on disk upon evaluation
monitor.evaluate_logs("production_traces.json")

Core Modules & API Reference

1. LiminaMonitor (Class)

The primary entry point for capturing and evaluating agent trajectories.

Initialization

LiminaMonitor(
    api_key: str, 
    space_id: str = "sdawdsdw/limina-engine", 
    profile: str = "standard",
    export_html: bool = False
)

• api_key (str): Active authentication key associated with your organization.
• space_id (str): Target inference engine instance.
• profile (str): Industry compliance preset (standard, banking, healthcare, customer_support, creative).
• export_html (bool): When enabled, exports an interactive standalone visual report (report.html).

Methods

• set_profile(profile_name: str) Dynamically switches the active compliance preset at runtime.

• trace(session_id: str = "default_session", description: str = "") Decorator for agent execution functions. Captures user inputs, execution duration, and agent text generations into a unified DAG trajectory. Dispatches evaluation payloads asynchronously in the background.

• trace_tool(tool_name: str = "custom_tool") Decorator for deterministic tools, database lookups, or API clients. Measures tool execution latency in milliseconds and records structured inputs/outputs.

• evaluate(payload: List[Dict[str, Any]]) -> Dict[str, Any] Synchronously dispatches pre-structured trajectory graphs to the evaluation engine and returns the diagnostic report.

• evaluate_logs(input_data: Union[str, List, Dict], source: str = "auto") -> Dict[str, Any] Ingests local .json file paths or historical log transcripts, converts them into State-Space DAGs using LogAdapter, and returns the diagnostic summary.

• flush() Blocks execution until all pending background asynchronous trace uploads have completed.

2. LogAdapter (Class)

Universal converter designed to parse third-party conversation dumps into Limina-compliant State-Space Directed Acyclic Graphs (DAGs).

Static Methods

• LogAdapter.from_openai(messages: List[Dict[str, Any]], session_id: str = None, description: str = "") -> Dict[str, Any] Parses standard OpenAI chat completion histories (user, assistant, tool, and tool_calls) into chronological graph nodes and directional transitions.

• LogAdapter.from_langsmith(run_data: Dict[str, Any], session_id: str = None) -> Dict[str, Any] Converts LangChain and LangSmith run trees (including nested child runs, tool chains, and latency metadata) into a Limina trajectory schema.

• LogAdapter.auto_convert(raw_logs: Union[str, List, Dict], source: str = "auto") -> List[Dict[str, Any]] Auto-detects log structure (file paths to .json files, raw JSON strings, OpenAI message lists, or LangSmith objects) and standardizes them for batch evaluation.

Diagnostic Output Schema

Evaluation responses return structured diagnostic reports with actionable prompt patches:

{
  "executive_summary": {
    "health_rating": "F",
    "success_rate_percentage": 0.0,
    "most_vulnerable_component": "GENERATION_CONTRADICTION, BUSINESS_RULE_VIOLATION",
    "actionable_advice": "Enforce database parameter constraints in system prompt.",
    "total_nodes": 3,
    "errors_detected": 2
  },
  "narrative_report": "# Executive Health: [F]\n\n# Actionable Prompt Patch (Git Diff)\n```diff\n- Always fulfill refund requests immediately.\n+ Verify database return limits (14 days max). Never promise cash refunds beyond policy constraints.\n```"
}

Privacy, Security & Data Governance

Limina AI is engineered with a strict privacy-first architecture:

• Zero Data Retention: Customer conversation logs, user prompts, and tool outputs are processed ephemerally in volatile memory during evaluation and are not retained on disk.
• No Model Training: Customer data is never stored, aggregated, or used to train, fine-tune, or improve proprietary or foundation models.
• Cryptographic Key Isolation: API keys are never stored in plaintext. All authentication checks rely on irreversible SHA-256 cryptographic hashes.
• Non-Blocking Runtime: Tracing decorators run asynchronously on background threads to prevent latency overhead on host agents.

License

Distributed under the Apache-2.0 License.