Recipes
End-to-end working examples showing how ElectriPy Studio components compose. Each recipe is a runnable, commented Python file.
CLI Tool
Build typed CLI tools with ElectriPy's CLI framework. Subcommands, typed arguments, help generation, and config loading wired together.
When to use
When you need a production-grade CLI over your AI pipeline or toolkit.
LLM Gateway
Provider-agnostic LLM gateway with sync/async support. Wraps OpenAI, Anthropic, Ollama, and HTTP-JSON providers behind a unified interface.
When to use
When you want to swap LLM providers without changing application code.
Policy-Governed LLM Flow
LLM requests with pre/post policy hooks. Block, warn, or transform requests and responses at runtime using a declarative policy chain.
When to use
When you need guardrails, content filtering, or compliance enforcement on LLM traffic.
Agent Collaboration Runtime
Multi-agent coordination patterns. Orchestrate specialist agents with a shared context, handoff protocol, and result aggregation.
When to use
When a single LLM call is insufficient and you need multi-agent decomposition.
Policy + Collaboration E2E
End-to-end flow combining policy enforcement with multi-agent collaboration. Demonstrates how safety and orchestration layers compose.
When to use
When building governed multi-agent systems that require both safety seams and coordination.
RAG Evaluation Runner
Evaluate retrieval-augmented generation pipelines with configurable scorers. Measures retrieval quality, answer correctness, and coherence.
When to use
When you need systematic evaluation of RAG systems before or after deployment.
AI Telemetry
Instrument AI workloads with telemetry and cost tracking. Captures latency, token usage, model metadata, and structured outcome events.
When to use
When you need visibility into cost, latency, and quality across AI calls in production.