“litellm”
Failure Injection for LLM Calls: Simulating Model Outages Before They Simulate Themselves
Happy-path mocks never catch resilience bugs. Inject timeouts, 5xx, 429s, mid-stream cuts, and malformed JSON into your LLM calls with deterministic failure plans, then assert what recovery actually did.
Cost-Aware Model Routing: Fallback Chains That Trade Quality for Price
Design model routing strategies that balance quality, latency, and cost — weighted routing, fallback chains, and budget guards for production LLM applications.
Retry Strategies That Respect Model and Rate Limits
Design LLM retries that respect rate limits: LiteLLM Router backoff, RetryPolicy, cooldowns, and why naive retry loops make 429s worse.
Rate-Limiting Agent Tools: 429s, Throttling, and Graceful Degradation
A 429 is the one error an agent cannot think its way out of. Where limits come from, how to honor them at the gateway with LiteLLM, and what your agent should do when the queue is full.
Circuit Breakers for LLM and Tool Dependencies
Apply the circuit-breaker pattern specifically to LLM providers and agent tool endpoints — failure classification, per-dependency state machines, LiteLLM gateway wiring, and fault-injection tests that prove the breaker trips when it should.
Alerting on Agent Anomalies: Drift, Hallucinations, and Stalls
A practical alerting strategy for production agents — what to detect, what thresholds to set, and how to wire LiteLLM alerting into Slack, email, and webhooks.
Caching for Agentic RAG: Index Refresh and Query Caching
Agentic RAG loops re-retrieve constantly. Layer query caching, response caching, and index refresh policies to cut latency and LLM bills without serving stale answers.
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.