On a 21 task subset of Terminal-Bench 2.0, an auto router matched Claude Opus-5 solve rate at 27% lower cost. Adding the last 3 user messages as classifier context raised relative quality 14% but cost 44% more; adding assistant replies made both worse.
A new Auto-Router Usage tab in Cost Optimization, per-request classifier cost reporting, preset matching against your own deployments, and two routing fixes.
Six changes to the Auto-Router: 1-click Anthropic and OpenAI presets, a one-line agent setup skill, Test Routing in the UI, a replaceable classifier prompt, customizable tiers, and configurable reminder markers.
v1.97 adds cost and usage benchmarks for the auto router, gives the LLM classifier a window of prior turns, and turns session affinity off by default. Across 5,600 live classifier calls, prior turns raised tier agreement on referential follow-ups from 14% to 78% at under a tenth of a cent per request, with no measurable latency change.
The most common objection to auto-routing is that switching models throws away your prompt cache. We measured it across five datasets, including real gateway traffic with the provider's own cache accounting, and the answer is no.
Two independent evaluations of a four-tier Auto Router config against an all-frontier baseline: 8,619 graded prompts, 14,000 simulated real conversations, and what the cost and quality numbers actually depend on.
A recap of the July LiteLLM town hall: 38 security fixes, 317 bug fixes, 140 feature commits, new Rust gateway benchmarks, and the launch of Autorouter V2.
AIGatewayBench measures the overhead an AI gateway adds on top of the upstream model, isolated against a deterministic mock, across LiteLLM (Rust), LiteLLM (Python v1), Portkey, and Bifrost. The LiteLLM Rust gateway has the lowest overhead and memory footprint of the four.
Router plugins are now live on LiteLLM. Configure a plugin pipeline to determine which models to pick for a given input. Plugins can be chained as well
Auto Router v2 folds LiteLLM's complexity, semantic, and adaptive routers into a single router with an LLM classifier, keyword tiers, multi-model pools, and adaptive Thompson sampling.
A two-week product quality update. We addressed two major issues (MCP credential resolution and pass-through memory) and shipped 134 bug fixes in total. Plus our next goal: 95% end-to-end test coverage.
A recap of the June LiteLLM town hall covering security hardening, our zero-regression commitment, 78 feature commits, and the gradual migration of the gateway to Rust.
Keep the OpenAI code_interpreter tool in your requests, run the code in your own sandbox. LiteLLM intercepts the tool call and routes it to E2B or OpenSandbox; no client changes.
LiteLLM is moving its AI gateway to Rust: 15x throughput, 11x less memory, and sub-1ms per-request overhead. No v2, no migration, your config stays the same.
Starting Monday, June 29, 2026, LiteLLM actively supports the four most recent stable minor lines. Older lines reach end of life, and the window rolls forward as new stable lines ship.
LiteLLM now supports semantic prompt caching on Valkey clusters running the valkey-search module, including AWS ElastiCache for Valkey, with no RediSearch, Redis Stack, or Qdrant required.
Disclosure of a Host-header authentication bypass in the LiteLLM proxy. Addressed in v1.84.0. Very limited deployments are potentially affected, and no LiteLLM Cloud customers were affected.
How we built a background agent on the LiteLLM AI Gateway that merges PRs with no human in the loop (the infra, harness, and credential-scoping calls behind it).
A recap of the May LiteLLM town hall covering 89 security fixes, new release versioning, MCP toolsets, performance wins, and the LiteLLM Agent Platform.
On May 11, 2026, a malicious version of the mistralai PyPI package was published as part of a coordinated supply chain attack. LiteLLM is not affected β we call Mistral exclusively via httpx, never by importing the mistralai SDK.
Dropping `-stable` and `-nightly` suffixes. Weekly releases bump MINOR; PATCH is now reserved for actual hotfixes. Old releases keep their tags forever; new ones start with `1.84.0`.
Use generally available gemini-embedding-2 for multimodal embeddings on LiteLLM via Gemini API and Vertex AIβthe same flows as preview, stable model id.
How LiteLLM's production AI Gateway handles Redis degradation at scale without cascading failures β circuit breaker pattern, 0ms fast-fail, automatic recovery.
Generate embeddings from text, images, audio, video, and PDFs with gemini-embedding-2-preview on LiteLLM via Gemini API (one vector per input, OpenAI-compatible) and Vertex AI (single unified vector per request).