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optillm

algorithmicsuperintelligence/optillm

Optimizing inference proxy for LLMs

GitHub

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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4.2k stars385 forksLast push Jul 18, 2026 Python Apache-2.0

Decision brief

optillm is an optimizing inference proxy for LLMs that provides enhanced deployment options through Docker, supporting both full and lightweight configurations.

Good fit when

Use optillm when you require automatic optimization of the server approach to enhance reasoning capabilities with large language models.
Consider opting for optillm if your project involves agents or complex workflows managed via API gateway services.

Avoid when

Avoid optillm when your application does not require proxy server optimization for large language models; simpler serving setups may suffice.
Do not use optillm if your deployment environment strictly prohibits the use of Docker images or containers, given that this tool heavily relies on Docker for its various configurations.

Hosting:: self hosted - This open-source proxy supports diverse hosting environments and can be run via Docker for flexibility in deployment.
Pricing:: freemium - optillm is available under the Apache-2.0 license, which makes it free to use and distribute without cost.

Observed Jul 14, 2026 · Source: enrich:decision_facts

optillm alternatives Trust report

optillm vs airllm optillm vs rtk optillm vs vllm

Verify the decision

Maintenance and security

Full trust report

Maintenance: Steady (30d since push); As of Aug 17, 2026
Provenance: Not a fork · Organization account; As of Aug 17, 2026
Security (OSV): 1 low (1 low); As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install optillm

PyPI

How it fits your stack(7)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

vllm

Both OptiLLM and vLLM aim to optimize the performance of LLMs, but they approach it differently. While OptiLLM focuses on optimizing inference without requiring training or fine-tuning, vLLM provides an easy framework for serving LLMs efficiently.

alternative

rtk

OptiLLM and RTK both act as proxies to reduce token consumption and improve LLM efficiency, offering similar performance benefits.

alternative

airllm

OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups.

alternative

Integrates

litellm

LitELM acts as an AI gateway supporting multiple models, and it could integrate with OptiLLM for optimized inference service across different LLMs.

integrates

sglang

OptiLLM could integrate with the sglang framework for serving large language models, providing both optimized performance and a robust serving infrastructure.

integrates

litgpt

OptiLLM could potentially integrate with litgpt, as it focuses on high-performance LLM deployment and pretraining, while OptiLLM can enhance inference performance by applying techniques that do not require retraining.

integrates

ollama

Both platforms allow users to run various LLMs, but they serve slightly different purposes; Ollama focuses on deployment setup while OptiLLM provides an optimizing proxy.

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Similar tools

Same-category neighbours not already linked as typed edges.

omlx

LLM inference server with continuous batching and SSD caching for Apple Silicon

22k★

TensorRT-LLM

Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs

15k★

LLMsPracticalGuide

Curated list of practical guide resources for LLMs

10k★

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A Python-based tool that optimizes the proxy servers for large language models (LLMs) and supports various deployment options including Docker.

Capability facts

Deploy: Self-host
Source: dockerfile:Dockerfile · Aug 17, 2026
Docker: Dockerfile present
Source: dockerfile:Dockerfile · Aug 17, 2026
CLI: CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 17, 2026
Languages: python
Source: github.language+pyproject.toml · Aug 17, 2026

Graph entities

LiteLLMframework Smolagentsframework

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

OpenAI APIOpenAI API

Source: README excerpt (regex_v1, Aug 17, 2026)

tory and add any environment variables you want to set. For example, to set the OpenAI API key, add the following line to the `.env` file:

Source link

Python runtimePython

Source: README excerpt (regex_v1, Aug 17, 2026)

python3 -m venv .venv

Source link

README

🚀 Quick Start Get powerful reasoning improvements in 3 simple steps: bash docker pull ghcr.io/algorithmicsuperintelligence/optillm:latest docker run p 8000:8000 ghcr.io/algorithmicsuperintelligence/optillm:latest 2024 10 22 07:45:05,612 INFO Loaded plugin: privacy 2024 10 22 07:...

For agents

This page has a .md twin and JSON over the API.

/tools/algorithmicsuperintelligence-optillm.md GET /api/graphcanon/tools/algorithmicsuperintelligence-optillm GET /api/graphcanon/graph?tool=algorithmicsuperintelligence-optillm

optillm

Decision brief

Good fit when

Avoid when

Verify the decision

Maintenance and security

Install

How it fits your stack(7)

Alternative

Integrates

Related

Relationship graph

Similar tools

Evidence and technical details

Overview

Capability facts

Categories

Graph entities

Compatibility

Tags

README

For agents