Operating AI
is an infrastructure
problem.
Every major computing shift created a new infrastructure layer. AI Applications are no different.
Reliability, observability, governance, evaluation, and runtime controls — the infrastructure production AI systems require, built in from the ground up, not retrofitted after deployment.
pip install electripy-aiPublished as electripy-ai · Python 3.11+ · MIT License
LSAS Runtime Stack
5
Production Runtime Domains
9
LSAS Architecture Layers
MIT Open Source
License
Provider Agnostic
AI Provider Strategy
Early Access
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Compatible with every major provider
See what production adds
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See what production adds
Building is the start. Operating is the infrastructure problem.
Build the application
Most AI frameworks help teams assemble models, agents, tools, retrieval, and workflows. These layers are where AI product development starts — and where most frameworks stop.
Where most AI frameworks stopProduction changes the problem
Once the application serves real users and business workflows, a different class of requirements appears. Reliability, observability, governance, evaluation, and operational control are not optional enhancements — they are the infrastructure that makes AI operable.
Add the runtime layer
ElectriPy AI provides the operational infrastructure across model calls, tool execution, routing, policies, evaluations, and application workflows — beneath and across existing frameworks, not replacing them.
Operate with ElectriPy Cloud
The open-source runtime executes inside the application. ElectriPy Cloud provides the operational visibility teams need to understand, manage, and improve production AI systems across every deployment.
Coming SoonThe AI Transition
Every company is becoming an AI company.
The challenge is no longer access. It is operations.
The world's largest technology organizations have committed hundreds of billions of dollars to AI infrastructure, models, and platforms. Their enterprise customers are following at scale. The question that remains is not whether to adopt AI — it is whether organizations can operate AI systems safely, reliably, and at scale.
The question has shifted
projected global enterprise AI spending by 2028
IDC, 2024
of organizations now regularly using generative AI — doubled in one year
McKinsey Global Survey, 2024
of large enterprises will require formal AI governance programs by 2026
Gartner
projected healthcare AI market by 2026
Grand View Research
Governance and operational requirements by sector
PHI governance, HIPAA compliance, clinical decision auditability, PHI redaction at runtime.
Model risk management, regulatory examination readiness, explainability requirements.
Underwriting AI governance, claims automation controls, state regulatory scrutiny.
Procurement accountability requirements, public-sector AI policy, audit mandates.
Clinical trial data governance, FDA AI guidance, adverse event traceability.
Multi-tenant AI isolation, per-customer policy enforcement, compliance inheritance.
Market figures are sourced from publicly available research reports. ElectriPy AI makes no investment projections and does not imply partnership or affiliation with any third-party organization.
See production use casesThe Production Gap
What works in development.
What production actually requires.
Prototypes are successful by default — the conditions are controlled, the scale is minimal, and the consequences of failure are low. Production removes all three. Every AI team that reaches production eventually builds the same infrastructure, at significant cost, often under pressure.
Development Stack
Production Exposes
Five infrastructure gaps that prototypes don't encounter.
Without this: provider outages surface directly to users. SLA violations compound.
Without this: production failures are invisible until users report them.
Without this: there is no enforceable boundary between AI and sensitive operations.
Without this: model updates and prompt changes cause silent quality degradation.
Without this: cost is uncontrolled, provider lock-in is structural.
ElectriPy AI is the runtime infrastructure layer between the model and production.
Each of the five domains above is a structured runtime capability — not a library, not a utility, not a collection of patterns. Each one is engineered to operate in production, compose with the others, and emit the telemetry your team needs to understand what is actually happening in your AI systems.
Explore the full runtime platformThe Production Gap
Most AI frameworks stop at the model.
Production systems don't.
Models generate responses. Agents perform tasks. Systems run businesses. Once AI touches production traffic, teams need the infrastructure that frameworks don't provide.
The prototype problem
Prototypes work. Production systems expose what frameworks omit: reliability infrastructure, observability pipelines, governance controls, and evaluation gates.
The invisible failure problem
Without structured traces, there is no visibility into model decisions, tool calls, policy evaluations, or cost attribution. Failures are discovered in production, not before.
The governance problem
AI systems make consequential decisions. Without a policy layer, there are no enforceable constraints, no audit trails, and no path to compliance.
ElectriPy answers with five core value pillars
LSAS Runtime Stack
Every layer required
between prototype
and production.
LSAS — the Layered Safety & Abstraction Stack — defines nine layers of responsibility for production AI systems. Each layer has a clear contract. ElectriPy AI implements runtime primitives for every layer.
Teams adopt incrementally — starting with the layers their systems need today. The architecture scales with the system.
9 layers. One production runtime.
Platform Outcomes
Built for production, not demos.
Every component is designed around the concerns that matter once AI systems are serving real traffic.
Reliable by Design
Circuit breakers, retries, fallback chains, timeout propagation, rate limiting, workload routing.
Observable by Default
Structured traces, telemetry, OpenTelemetry export, redaction, session visibility, token and cost metadata.
Governable at Runtime
Policy gates, approvals, audit trails, evidence requirements, runtime enforcement.
Evaluated Before Deployment
Regression testing, scoring pipelines, quality gates, CI integration.
Composable by Design
MCP, skills, routing, realtime orchestration, reusable execution patterns.
Framework-Free Foundation
Minimal dependencies, typed APIs, incremental adoption.
Business Outcomes
What production-ready
AI actually means.
The difference between a prototype and a production system is not the model. It is the infrastructure around the model.
See use cases by industryFaster Production Deployments
Move from prototype to production without rebuilding infrastructure. Runtime controls for reliability, observability, and governance are ready on day one.
Reduced Operational Risk
Circuit breakers, fallback routing, and policy gates prevent provider failures, bad outputs, and unexpected behaviors from reaching users.
Compliance Readiness
Audit trails, approval workflows, governance controls, and observability are structural — not bolted on. Demonstrate compliance without retrofitting.
Lower Total Cost of Ownership
Every AI team eventually rebuilds the same infrastructure. ElectriPy AI eliminates that cost and lets teams focus on the AI system, not the plumbing.
Full Stack Visibility
Trace every model call, tool invocation, routing decision, and policy evaluation. No black boxes. No production surprises.
Incremental Adoption
Start with one layer — Observability or Reliability — and expand as your system matures. No framework lock-in. No minimum viable layer count.
Platform Capabilities
Six capability domains.
One runtime.
Reliability
- Circuit Breaker
- Retry + Backoff
- Timeout Propagation
- Rate Limiting
- Fallback Routing
Observability
- ObservabilityService
- OpenTelemetry Tracer
- Span Decorators
- PII Redaction
- Token Metadata
Governance
- PolicyEngine
- PolicyGateway
- Approval Workflows
- Audit Trails
- Violation Tracking
Orchestration
- WorkloadRouter
- Realtime Sessions
- MCP Client/Server
- Skills Registry
- Fallback Chains
Evaluation
- EvalService
- Retrieval Scorers
- Answer Scorers
- Regression Reporting
- CI Gates
Model Runtime
- LLM Gateway
- Provider Abstraction
- Structured Output Engine
Category Distinction
Frameworks solve orchestration.
ElectriPy AI solves production operations.
LangChain, LangGraph, LlamaIndex, and CrewAI are excellent tools for building AI systems. ElectriPy AI is the runtime infrastructure layer those systems need to operate in production. Use it alongside any orchestration choice, or as the foundation for your own.
Frameworks
Help you build AI
ElectriPy AI
Helps you operate AI
Used together
Framework + Runtime = production
| Capability | ElectriPy AI | LangChain | LangGraph | LlamaIndex | CrewAI |
|---|---|---|---|---|---|
| Category | AI Application Runtime | Orchestration Framework | Agent Workflow Framework | RAG Framework | Agent Collaboration Framework |
| Primary Role | Operate AI in production | Build AI chains and agents | Build stateful agent graphs | Build retrieval pipelines | Build multi-agent crews |
| Reliability Infrastructure | Circuit breakers · Retries · Fallback routing | — | — | — | — |
| Observability | OTEL-native · Full span traces · Cost metadata | LangSmith (paid, external) | LangSmith (paid, external) | LlamaCloud (paid, external) | Basic logging |
| Governance & Policy | Runtime policy engine · Approvals · Audit trails | — | — | — | — |
| Evaluation | Built-in scorers · CI gates · Regression tracking | LangSmith Evals (paid) | LangSmith Evals (paid) | Basic eval utilities | — |
| Framework lock-in | None — composable, incremental | High — chain execution model | High — graph execution model | High — pipeline owns execution | High — crew owns execution |
| Works alongside frameworks | Yes — designed to compose | N/A — is the framework | N/A — is the framework | N/A — is the framework | N/A — is the framework |
— indicates the capability is out of scope for that tool's category, not a limitation.
Real API Surface
# pip install electripy-ai && python quickstart.py
# Runs offline. No API key. No mocks. All five layers active.
from electripy.ai.llm_gateway import LlmGatewaySyncClient, LlmGatewaySettings
from electripy.ai.llm_gateway import LlmMessage, LlmRequest, OpenAiSyncAdapter
from electripy.ai.policy_gateway import PolicyGateway, PolicyRule
from electripy.ai.policy_gateway import PolicyStage, PolicyAction, PolicySeverity
from electripy.ai.policy_gateway import build_llm_policy_hooks
from electripy.concurrency import CircuitBreaker
from electripy.observability.observe import ObservabilityService, InMemoryTracer
from electripy.ai.cost_ledger import CostLedger
# L01 Governance — redact PII before the model sees it
gateway = PolicyGateway(rules=[
PolicyRule(rule_id="pii-email", code="PII_EMAIL",
description="Redact emails in prompts",
stage=PolicyStage.PREFLIGHT,
pattern=r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}",
action=PolicyAction.SANITIZE,
severity=PolicySeverity.MEDIUM),
])
# L03 Reliability — stop cascading failures before they propagate
breaker = CircuitBreaker(failure_threshold=3, recovery_timeout=30.0)
# L02 Observability — structured AI spans with ContextVar propagation
tracer = InMemoryTracer()
obs = ObservabilityService(tracer=tracer)
# L04 Model Runtime — gateway wired to policy hooks
request_hook, response_hook = build_llm_policy_hooks(gateway)
client = LlmGatewaySyncClient(
port=OpenAiSyncAdapter(api_key=os.environ["OPENAI_API_KEY"]),
settings=LlmGatewaySettings(
request_hook=request_hook, response_hook=response_hook
),
)
ledger = CostLedger(cost_per_1k_tokens=0.00015)
# The call — emails are redacted before the model sees them
with obs.start_llm_span(provider="openai", model="gpt-4o-mini") as span:
response = breaker.call(lambda: client.complete(LlmRequest(
model="gpt-4o-mini",
messages=[LlmMessage.user("Escalate to admin@acme.com ASAP")],
)))
span.set_attribute("gen_ai.usage.total_tokens", response.usage_total_tokens)
ledger.record(tokens=response.usage_total_tokens,
labels={"model": "gpt-4o-mini", "feature": "support"})
# Response: "Escalate to [REDACTED] ASAP"
# Span: 1 LLM span recorded, ContextVar propagated
# Circuit: CLOSED, 0/3 failures
# Cost: $0.000003 · 20 tokensElectriPy Cloud
Operational visibility for your AI systems.
The open-source runtime instruments your systems. ElectriPy Cloud surfaces that data as reliability scores, trace replays, policy analytics, and cost dashboards — without building the operational layer yourself.
Action send_email blocked — missing approval for external contact.
Open Source
Open source where it matters.
ElectriPy OSS provides the runtime primitives required to build production AI systems. The infrastructure layer is fully open, MIT licensed, and designed for incremental adoption.
View GitHubPricing
Free to build. Paid for scale.
The runtime is always open source. Cloud adds the operational layer.
Team
Coming SoonComing Soon
- Hosted Traces
- Session History
- Reliability Analytics
- Cost Monitoring
- Team Dashboards
Enterprise
Contact Us
- SSO
- Governance Controls
- Audit Trails
- Compliance Workflows
- Private Deployments
- Dedicated Support
Project signals
Get Started
AI Applications require
runtime infrastructure.
ElectriPy AI defines the category and provides the open source implementation. ElectriPy Cloud extends it with operational visibility, governance analytics, and enterprise-grade monitoring.
For Developers
Start building
Install the runtime, run the playground, and see production-grade AI infrastructure in action.
For Technical Leaders
Understand the architecture
The LSAS Architecture defines nine layers of AI runtime infrastructure. Read the full specification.
For Enterprise Teams
See production patterns
Healthcare, financial services, compliance, and agent orchestration — real operational patterns.