
Manifest is an open-source LLM router designed to intelligently orchestrate requests across multiple large language models so you can ship AI features faster and cheaper. Built for modern AI stacks, Manifest optimizes which model is called for each request based on latency, pricing, quality constraints, and your own routing rules. This allows teams to offload routine workloads to more cost-effective models while reserving premium models for critical prompts—cutting infrastructure spend by up to 70% without sacrificing user experience. Manifest integrates seamlessly into existing applications through a clean API and flexible configuration. You can define routing strategies, fallback logic, and model selection criteria in code or configuration files, then monitor performance through structured logs and metrics. Because it is fully open source, you retain control over data, deployment, and customization, whether you run it on your own servers, within Kubernetes, or alongside OpenClaw-based systems. Ideal for AI-first products, internal tools, and experimentation environments, Manifest helps engineering teams avoid hard-coding model choices and vendor lock-in. Swap providers, introduce new open-source models, or run A/B tests on different LLMs with minimal changes to your codebase. With Manifest, you get an extensible routing layer that makes your LLM usage more reliable, observable, and cost-efficient—so you can focus on building better AI experiences rather than managing complex model fleets.
Optimize chat and assistant backends by routing simple prompts to cheaper models while reserving high-end LLMs for complex reasoning.
Power AI developer platforms that need a unified interface to multiple commercial and open-source LLM providers.
Run A/B tests across different language models to compare quality, latency, and cost before standardizing on a stack.
Build internal tools that must meet strict budget constraints while still supporting reliable AI-driven workflows.
Migrate between LLM vendors or add new models without refactoring application logic or breaking client integrations.