OptiLLM Inference Optimizer
A zero-training OpenAI-compatible proxy that boosts reasoning accuracy by 2-10x using 20+ inference optimization techniques.
- Source repo
- algorithmicsuperintelligence/optillm
- Stars
- ★ 4.3k
- Last updated
- 7d ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- 45/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI APIClaude API (Partial support)
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- Data scientists use OptiLLM's MARS technique to improve accuracy on math benchmarks like AIME.
- Not a fit if
- Users who cannot supply paid OpenAI/Anthropic API keys
- Latency- or cost-sensitive workloads that cannot afford multiple model calls
- Source review
- 45/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
OptiLLM is an open-source inference proxy that acts as a drop-in replacement for the OpenAI API, applying over 20 state-of-the-art techniques (MARS, CePO, MOA, MCTS, etc.) to dramatically improve LLM reasoning on math, coding, and logic tasks—without any fine-tuning. It supports multiple providers (OpenAI, Anthropic, Google, Cerebras) and local models, can be deployed locally or via Docker, and includes a suite of plugins for extended capabilities like memory, web search, and MCP integration. The repository provides extensive benchmark results showing substantial accuracy gains on tasks like AIME and GPQA.
OptiLLM runs as a proxy server that accepts OpenAI-compatible /v1/chat/completions requests. It parses the requested technique (e.g., moa, mcts, bon) from the model name, calls the underlying model (via provider APIs or local inference), performs multiple generations, self-reflections, or planning steps, and returns the best or aggregated response. It supports environment-variable configuration for API keys, routing to OpenAI, Cerebras, Azure OpenAI, and other providers via LiteLLM.
- Data scientists use OptiLLM's MARS technique to improve accuracy on math benchmarks like AIME.
- Software engineers leverage PlanSearch or CePO to increase pass rates in code generation tasks.
- Enterprises deploy OptiLLM as a unified API gateway to multiple LLM providers.
- Researchers compare different reasoning techniques quickly via OptiLLM.
- Developers use plugins like web_search and memory to extend LLM capabilities for long-context tasks.
How do you install or deploy this agent?
Install via pip: pip install optillm, or use Docker: docker pull ghcr.io/algorithmicsuperintelligence/optillm:latest. For source installation, clone the repo and run pip install -r requirements.txt.
How do you use this agent?
After installation, set environment variables (e.g., export OPENAI_API_KEY=...), then run optillm to start the service. Modify your OpenAI client's base_url to http://localhost:8000/v1, and prepend technique slugs to the model name (e.g., moa-gpt-4o-mini) to activate optimization. Alternatively, use extra_body or special tags in the prompt.
What are this agent's strengths and limitations?
- Significant accuracy improvements without training, as demonstrated on benchmarks (e.g., +30 points on AIME).
- Supports multiple optimization techniques and plugins (MCTS, MOA, CePO, etc.).
- OpenAI API-compatible, facilitating integration with existing tools.
- Offers Docker images and a local inference server, supporting multiple providers.
- Inference-time compute overhead increases latency and cost.
- Requires API keys and environment variable configuration.
- Some techniques (e.g., MOA) may make multiple model calls, increasing usage.
- Advanced customization (e.g., developing new plugins) has a learning curve.
How does this agent compare with similar options?
OptiLLM is similar to LiteLLM but focuses on reasoning optimization. LiteLLM primarily provides a unified interface, while OptiLLM adds multiple inference enhancement techniques.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| OptiLLM Inference Optimizer This agent | 45 · Major gaps | CLIFree + model costs | ★ 4.3k | 7d ago | Python | OpenAI API |
| Lynkr | 62 · Some gaps | CLIFree + model costs | ★ 634 | 5d ago | JavaScript | Codex · Claude Code · OpenAI API |
| Aster | 77 · Good | CLIFree + model costs | ★ 129 | today | Rust | OpenAI API |
| rpi: Rust Coding-Agent Runtime | 64 · Some gaps | CLIFree + model costs | ★ 116 | today | Rust | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: binds to 127.0.0.1 by default, offers API key authentication, has privacy plugin for PII anonymization, and SSL configuration notes. But lacks user confirmation mechanisms, data flow transparency is limited, dependencies are not pinned, external effects (like code execution, URL reading) lack clear warnings, rollback is not mentioned, and publisher identity is unverified. Deductions: missing user confirmation, incomplete data flow description, insufficient dependency security.
Evidence shows: consistent naming in README and code, test scripts exist but depend on external server, failure messages handled in tests. Deductions: dependency availability unverified, failure messages insufficient in docs.
Evidence shows: targets developers and researchers, supports multiple scenarios, multiple trigger methods (prefix, extra_body, prompt tags), supports various environments (pip, docker, local inference). Deductions: capability boundaries incomplete, environment fit has warnings but not comprehensive.
Evidence shows: clear README structure, detailed install notes, stable naming, examples and FAQ present, license clear, version exists but no changelog, maintenance responsibility unclear. Deductions: known limitations insufficient, versioning changelog missing, maintenance responsibility unclear.
Evidence shows: output is OpenAI-compatible, easy to use, provides multiple optimization techniques with marginal value. Deductions: cost-benefit analysis insufficient, benchmark results not independently verified.
Evidence shows: benchmark results in README, but no reproduction details, cross-source verification insufficient, facts and inferences not clearly separated. Deductions: weak claim traceability, insufficient cross-source corroboration, unclear fact-inference separation.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Dependencies are not pinned, posing supply chain risks.
- External effects (like code execution, URL reading) lack clear warnings; use with caution.
- Publisher identity is unverified; assess trustworthiness yourself.
- Benchmark results are not independently verified; treat performance claims with caution.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
What's Changed Fix: proxy plugin config, logging and agent clients (#330, #331) - Proxy target ignored: without ~/.optillm/proxy_config.yaml, the proxy silently loaded the bundled example config and routed to its sample providers. It now creates an empty template instead. A single failed request no longer sends all later traffic to --base-url: 4xx errors don't mark a provider unhealthy, and unhealthy providers are retried before falling back. - /v1/models lists the configured providers' models (plus model_map aliases) with --approach proxy. …View on GitHub ↗ -
Release notes excerpt
What's Changed Fix: autothink, thinkdeeper and deepconf for transformers >= 5 (#320) Since transformers 5, apply_chat_template(return_tensors="pt") returns a BatchEncoding rather than a plain tensor, so passing the result straight to model(input_ids=...) raised. The tensor is now unwrapped via .input_ids: - optillm/thinkdeeper.py - optillm/autothink/processor.py - optillm/deepconf/processor.py …View on GitHub ↗ -
Release notes excerpt
What's Changed Fix: broken PyPI wheel — import optillm failed with ModuleNotFoundError: No module named 'math_verify' (#318) The published wheel is built from pyproject.toml [project.dependencies], which had drifted from requirements.txt, so pip install optillm shipped a package that couldn't even start: - math-verify was missing from pyproject.toml — optillm/cepo/cepo.py imports it at import time, so importing optillm crashed. (Affected 0.3.16–0.3.20.) …View on GitHub ↗