ContextPilot

What ContextPilot does

Long-context workloads such as RAG, memory chat and tool-using agents repeatedly send overlapping context blocks that arrive reordered or duplicated, breaking the token prefix and triggering cache misses. ContextPilot maintains a Context Index of cached content and applies Reorder, aligning shared blocks into a common prefix, and Deduplicate, replacing repeated blocks with reference hints, plus cache-aware scheduling, before sending the optimized prompt through an OpenAI-compatible API.

It supports Hermes Agent as a native context-engine plugin alongside OpenClaw, vLLM, SGLang, llama.cpp, Mem0, PageIndex and LMCache, and also reorders multimodal content such as retrieved video frames or images to raise cache hits without changing answer accuracy. The project reports 4-12x cache hit improvements, 1.5-3x faster prefill and roughly 36% token savings across its tested workloads, and a paper describing the method was accepted to MLSys 2026.

Key features

• Context Index with reorder-and-deduplicate logic to maximize prefix-cache reuse
• Native context-engine plugin support for Hermes Agent and OpenClaw
• Drop-in support for vLLM, SGLang, llama.cpp, Mem0, PageIndex and LMCache
• Multimodal reordering for retrieved video frames and images
• OpenAI-compatible API with an /evict endpoint to keep the index in sync with KV cache

When to use it

• Cutting prefill latency and token cost for a Hermes agent with long-running memory context
• Reducing redundant KV recomputation in a RAG pipeline with overlapping retrieved chunks
• Raising prefix-cache hit rates for a multimodal agent that retrieves video frames

Who it is for: Teams running long-context RAG, memory chat or agentic workloads who want lower inference cost without a model or accuracy change.

How it fits with Hermes Agent

ContextPilot added Hermes Agent as a native context-engine plugin in a 2026 release, documented in its own guide alongside the project's other supported backends.

FAQ