“litellm”
A/B Testing Agent Variants: Prompts, Models, and Context
Set up A/B testing for agent variants using LiteLLM to compare prompts, models, and context strategies with statistical rigor and production-ready infrastructure.
Token Caps and Output Truncation: Containing Runaway Agents
Enforce strict token budgets, context limits, and output truncation using LiteLLM to prevent runaway cost spikes in multi-turn autonomous agent loops.
Load Testing Enterprise Agents: Simulating Concurrent User Load
Load test enterprise agent systems. Simulate concurrent users, measure throughput, identify bottlenecks, and ensure reliability at scale.
Token Budgets per Agent Run: Allocating, Tracking, and Enforcing
Manage token budgets across agent runs. Allocate, track, and enforce token limits to control costs and prevent runaway agent loops.
Model Routing for Multi-Agent Systems: Matching Tasks to the Right Model
Learn how to implement intelligent model routing in multi-agent systems using LiteLLM, matching each task to the optimal model for cost, speed, and quality.
LLM Gateways: Unified Routing, Quotas, and Observability for AI Traffic
Deploy an LLM gateway to unify routing, quotas, and observability across your AI infrastructure. Learn to set up LiteLLM as a production gateway for 100+ LLM providers.
Self-Hosting an LLM Gateway with LiteLLM
One OpenAI-compatible endpoint in front of every model your team uses, with routing, rate limits, spend caps, and auth. Deploy LiteLLM and stop hard-coding provider keys in your services.