EveryDev.ai
Subscribe
Home
Tools

5,118+ AI tools

  • New
  • Trending
  • Featured
  • Rate tools
  • Compare
  • Arena
Categories
  • Agents2616
  • Coding1923
  • Infrastructure904
  • Projects719
  • Research646
  • MCP613
  • Analytics497
  • Marketing497
  • Design422
  • Testing411
  • Data378
  • Security329
  • Integration259
  • Communication250
  • Prompts247
  • Extensions219
  • Commerce171
  • Voice171
  • DevOps168
  • Web123
  • Learning114
  • Finance35
AI Tools by Topic
  • AI Coding Assistants
  • Agent Frameworks
  • MCP Servers
  • AI Prompt Tools
  • Vibe Coding Tools
  • AI Design Tools
  • AI Database Tools
  • AI Website Builders
  • AI Testing Tools
  • LLM Evaluations
Follow Us
  • X / Twitter
  • LinkedIn
  • Reddit
  • Discord
  • Threads
  • Bluesky
  • Mastodon
  • YouTube
  • GitHub
  • Instagram
Get Started
  • Users
  • Rate Tools
  • About
  • Editorial Standards
  • Corrections & Disclosures
  • Community Guidelines
  • Advertise
  • Contact Us
  • Newsletter
  • Submit a Tool
  • Start a Discussion
  • Write A Blog
  • Share A Build
  • Terms of Service
  • Privacy Policy
Explore with AI
  • ChatGPT
  • Gemini
  • Claude
  • Grok
  • Perplexity
Agent Experience
  • llms.txt
Theme
With AI, Everyone is a Dev. EveryDev.ai ©
    1. Home
    2. Tools
    3. AKV
    AKV icon

    AKV

    Context Engineering

    Cache engineering for LLM agents that drops and repositions KV cache entries through agent-side policies and an SGLang-based inference system.

    Visit Website

    At a Glance

    Pricing
    Open Source

    Self-hosted AKV core (agent-side cache policies and SGLang-based system) released under the Apache License 2.0.

    Engagement

    Available On

    CLI
    SDK
    API

    Resources

    WebsiteDocsGitHubllms.txt

    Topics

    Context EngineeringAgent MemoryAI Infrastructure

    Alternatives

    Hy MemoryTabnine Enterprise Context EngineAcontext
    Developer
    NICS-EFC Lab of Tsinghua University

    Listed Oct 2026

    About AKV

    AKV is the official implementation of the paper "Cache Engineering for Agents" from the thu-nics organization. It brings KV cache engineering to LLM agents by pairing agent-side policies that decide what to drop and when to compact token positions with an SGLang-based inference system that executes those operations while reusing compatible KV cache.

    What It Is

    AKV has two components. akv.agent is the algorithm side: it decides which messages to drop from the KV cache and when to reposition retained tokens. akv.system is the execution side: it carries out cache operations through SGLang's OpenAI-compatible Chat Completions API. It is released under the Apache 2.0 license.

    How the Drop and Reposition Workflow Works

    A CachePolicy takes an OpenAI-compatible request payload and adds cache operation fields. drop_message names which messages' KV entries to drop and after which message, while reposition names when to compact the retained token positions. The augmented payload is sent to the AKV System server, which applies the drop first, then the reposition, and generates the next reply from the remaining context. Retained KV entries are reused without re-prefilling. Usage details report cached_tokens, drop_skipped_tokens and repos_tokens.

    Customizing Policies and Agent Integration

    Policies separate three components: a trigger (when to drop), a selector (what to drop), and a reposition rule. Built-in options cover turn-based, token-based and model-based approaches, and custom functions can be supplied. A CacheTool interface such as DropContextTool lets the agent schedule drops itself through tool calls. To integrate with an existing agent harness, pass the final serialized payload through CachePolicy.process before sending it to the system.

    Supported Models and Setup

    Drop and Reposition are enabled for text-model architectures including Qwen, Qwen1.5/2/2.5, Qwen3 (including MoE variants), GPT-OSS and the MiniMax-M2 family. Setup requires Rust and Cargo and a Python 3.12 environment installed with uv. The quick start launches the server with python -m akv.system.launch_server and the repo includes a demo script.

    Community Discussions

    Be the first to start a conversation about AKV

    Share your experience with AKV, ask questions, or help others learn from your insights.

    Pricing

    OPEN SOURCE

    Open Source

    Self-hosted AKV core (agent-side cache policies and SGLang-based system) released under the Apache License 2.0.

    • Apache License 2.0 (OSI-approved)
    • akv.agent cache policy component
    • akv.system executes cache operations via SGLang's OpenAI-compatible Chat Completions API
    • Self-hosted; user supplies own model and GPU infrastructure

    Capabilities

    Key Features

    • Agent-side CachePolicy with trigger, selector and reposition components
    • Drop operation removes selected messages' KV entries
    • Reposition compacts retained token positions
    • KV cache reuse without re-prefilling
    • Turn-based, token-based and model-based built-in policies
    • CacheTool interface for agent-managed cache via tool calls
    • OpenAI-compatible Chat Completions API through SGLang
    • Usage reporting of cached, drop-skipped and repositioned tokens
    • Custom policy functions

    Integrations

    SGLang
    OpenAI-compatible Chat Completions API
    Qwen
    GPT-OSS
    MiniMax-M2
    API Available
    View Docs

    Ratings & Reviews

    No ratings yet

    Be the first to rate AKV and help others make informed decisions.

    Rate other tools you’ve used

    Developer

    NICS-EFC Lab of Tsinghua University

    Read more about NICS-EFC Lab of Tsinghua University
    GitHub
    1 tool in directory

    Similar Tools

    Hy Memory icon

    Hy Memory

    Dual-system cognitive memory engine and Python SDK for LLM agents covering chat, working, and coding scenes.

    Tabnine Enterprise Context Engine icon

    Tabnine Enterprise Context Engine

    An enterprise context layer that gives AI coding agents structured understanding of your codebase, architecture, and standards to improve accuracy and reduce rework.

    Acontext icon

    Acontext

    Open-source skill memory layer that captures learnings from AI agent runs and stores them as readable Markdown skill files.

    Browse all tools

    Related Topics

    Context Engineering

    Techniques for optimizing context windows to improve AI responses.

    94 tools

    Agent Memory

    Memory layers, frameworks, and services that enable AI agents to store, recall, and manage information across sessions. These tools provide persistent, semantic, and contextual memory for agents, supporting personalization, long-term context retention, graph-based relationships, and hybrid RAG + memory workflows.

    216 tools

    AI Infrastructure

    Infrastructure designed for deploying and running AI models.

    520 tools
    Browse all topics
    Back to all toolsSuggest an edit
    ratings
    discussions