Comparison

AutoJev vs LiteLLM

LiteLLM is an open-source AI gateway you deploy as a server for a team. AutoJev is a desktop app that runs a gateway on your own machine for your own agents. They solve neighbouring problems, and they can be chained together.

LiteLLM: server-side proxy with virtual keys and budgets
AutoJev: one-click agent setup and per-request routing on your laptop
AutoJev can use a LiteLLM proxy as an OpenAI-compatible provider
LiteLLM facts checked against its README and docs, October 2026

Download AutoJev Star on GitHub

Route every request to the right model.

Connect an agent

Codex

Claude Code

Gemini CLI

AutoJev

autojev/quality

openrouter/anthropic/claude-sonnet

deepseek/deepseek-chat

ollama/qwen3:14b

Providers

Models

Routes

Agents

Requests today

1,284

Tokens today

18.6M

Different layers of the stack

LiteLLM describes itself as "an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers". You can use it as a Python SDK or deploy its proxy server with pip, Docker or Terraform. The proxy is built for platform teams: virtual keys per user, team or project, budgets and rate limits per key, spend tracking, guardrails, load balancing, fallbacks and an admin UI.

AutoJev is a desktop app for one developer. It runs a gateway at http://127.0.0.1:9527/v1, detects the coding agents on your machine and writes their config for you — Codex, Claude Code, Gemini CLI, OpenCode, Hermes and more. Each request is routed to a model by a route you define, with protocol conversion between Chat Completions, Responses and Anthropic Messages.

Put simply: LiteLLM governs how an organisation reaches models. AutoJev governs how the agents on your laptop reach models.

Feature comparison

LiteLLM facts come from its GitHub README and proxy docs as of October 2026. Check their docs for the latest.

Capability	AutoJev	LiteLLM
Deployment model	Desktop app; gateway listens on localhost only	Python SDK, or a proxy server deployed via pip/uv, Docker or Terraform
Primary user	An individual developer and their local agents	Teams and platforms sharing one gateway
UI	Native app: providers, models, routes, agents, debug, logs, usage	Admin UI in the browser for models, keys, spend and logs
Agent setup	Detects agents and writes their config in one click, with backup and restore	Clients such as Claude Code and Codex point at the gateway (see their client setup guide)
Providers	6 presets plus any OpenAI- or Anthropic-compatible endpoint	100+ LLM providers
Protocol conversion	Chat Completions ⇄ Responses ⇄ Anthropic Messages, all directions; Gemini bridge	OpenAI format across providers; also lists /responses and /messages endpoints
Routing strategies	Weighted load balancing, intelligent selection (Jev); direct <provider>/<model_id> calls	Load balancing, fallbacks and retries across deployments
Access control	None needed — single user, localhost only	Virtual keys with per-key models, budgets and rate limits
Speed tests	Scheduled first-content latency and tokens/s per model	— (check their docs)
Usage and cost	Local logs and usage by agent, model and provider with estimated cost	Spend per key, team, user and tag; export to Langfuse, Datadog, OpenTelemetry, Prometheus
Data path	Your machine → your providers; prompts not stored in logs	Clients → your LiteLLM server → providers
License	Free; MIT	MIT core; enterprise features under a commercial license

As of October 2026. LiteLLM is a trademark of its respective owner.

When to choose LiteLLM

Choose LiteLLM when you need a shared, server-side gateway: issuing virtual keys to colleagues, enforcing budgets and rate limits, attributing spend to teams, sending traces to an observability stack, or covering endpoints beyond chat such as embeddings, images and audio. AutoJev does none of that — it has no user accounts and only listens on 127.0.0.1.

LiteLLM also reaches far more providers out of the box and fits naturally into existing infrastructure such as Kubernetes or cloud deployments.

When to choose AutoJev

Nothing to deploy

Install the app, add a key, connect an agent. No server, database or container to run. Signed builds for macOS, Windows and Linux with auto-update.

Agents wired for you

AutoJev writes ~/.codex/config.toml, ~/.claude/settings.json and the other agent configs, backs them up first and restores them when you disconnect.

Routing that measures

Speed tests feed the "lower latency" preference; prices feed "lower cost"; quality and balanced routes ask the Jev decision model.

Use them together

If your company already runs a LiteLLM proxy, AutoJev can sit in front of it on your laptop.

1
Add LiteLLM as a provider
In Providers, choose OpenAI Compatible and enter your LiteLLM proxy URL and the virtual key your admin gave you.
2
Add models and routes
Add the model names your LiteLLM proxy exposes, mix them with other providers if you like, and build routes such as autojev/fast.
3
Connect your agents
Connect Codex or Claude Code in one click. Budgets and team spend still apply on the LiteLLM side; AutoJev adds local routing, speed data and per-agent logs.

What an agent sees

Whatever sits upstream, the agent talks to one local endpoint and asks for a route.

Chat Completions through AutoJev

curl http://127.0.0.1:9527/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "autojev/fast", "messages": [{"role": "user", "content": "hello"}]}'

Trademarks

LiteLLM and other product names are trademarks of their respective owners. AutoJev is not affiliated with or endorsed by BerriAI. Competitor details are summarized from public documentation as of October 2026 and may have changed.

Frequently asked questions

Is AutoJev a LiteLLM replacement?

Only for a single developer who wants a local router for their own agents. For shared gateways with virtual keys, budgets and team spend tracking, LiteLLM is the better tool.

Can AutoJev forward requests to a LiteLLM proxy?

Yes. Add it as an OpenAI Compatible provider with your proxy URL and key. AutoJev then routes between LiteLLM models and any other providers you add.

Does AutoJev support virtual keys or budgets?

No. AutoJev has no accounts or per-key limits. It is a single-user app listening on localhost. It records estimated cost per request so you can watch spend, but it does not enforce budgets.

Do I need Python or Docker for AutoJev?

No. AutoJev is a native desktop app built with Tauri and Rust. Download an installer for macOS, Windows or Linux and run it.

Which one converts Anthropic Messages for Claude Code?

AutoJev converts between Anthropic Messages, Chat Completions and Responses in all directions, so Claude Code can use OpenAI-compatible models. LiteLLM lists a /messages endpoint too — check their docs for the details of their implementation.

Keep exploring

FeaturesProtocol conversionConvert between Chat Completions, Responses and Anthropic Messages in all six directions, with streaming and tool calls.FeaturesOne-click agent setupConnect Codex, Claude Code, Gemini CLI, OpenCode and more. AutoJev edits each native config, backs it up and restores it.FeaturesUsage & request logsTokens, latency and estimated cost for every request, broken down by agent, model and provider. Metadata only, stored locally.Integrations

OpenAI-compatible APIsKimi, Qwen, SiliconFlow, MiniMax, vLLM, LM Studio — any OpenAI-compatible endpoint, available to all your agents.Use casesFor multi-agent developersCodex, Claude Code, Gemini CLI, OpenCode and more on one local gateway, with per-agent models and one request log.

Updated 2026-10-14

Give every agent the right model.

Install AutoJev, add the providers you already pay for, and connect your agents in one click. Everything stays on your machine.