AgentKV: Arbitrary-Position Key-Value Cache Reuse for Multi-Agent LLMs

AgentKV is a high-throughput LLM KV cache management architecture designed to eliminate redundant prefill compute in collaborative multi-agent pipelines.

By exploiting the group-additive properties of Rotary Positional Embeddings (Delta-RoPE), AgentKV enables instant, bit-for-bit accurate Key-Value cache reuse at arbitrary sequence positions without re-running transformer layers.

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The Problem: The 0% Cache Hit Flaw in Multi-Agent Pipelines

In multi-agent systems (such as CrewAI, LangGraph, AutoGen, and modular subagent swarms), worker agents routinely share large, identical context blocks:

• 1,500+ token tool definitions and function-calling schemas
• Long system prompt instructions and shared governance rubrics
• Shared codebases or retrieved RAG reference documentation

However, each agent begins with an agent-specific prefix at sequence offset 0 (for example: Agent 1: Architect, Agent 2: Senior Implementer, Agent 3: Code Reviewer).

Existing state-of-the-art inference engines (vLLM, SGLang, TensorRT-LLM) rely on Radix Tree Automatic Prefix Caching (APC):

1. Cache lookup starts strictly from token position 0.
2. Any variation in prefix tokens invalidates cache matching for all subsequent tokens.
3. Every downstream agent is forced to re-prefill the entire 1,500+ token tool schema from scratch.
4. On consumer hardware like the NVIDIA GeForce RTX 3060 12GB (360 GB/s memory bandwidth), this redundant compute saturates the GPU and stalls agent responsiveness.

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The Solution: Delta-RoPE Group-Additive Invariant

AgentKV decouples positional encoding from raw prompt prefix alignment using two mathematical properties:

1. Value Vectors (V) are Position-Invariant

Transformers apply positional embeddings solely to Query and Key tensors. Value vectors contain zero positional encoding. A cached Value tensor at position $m$ is identical to a Value tensor at position $m'$. Value reuse requires 0 FLOPs (pure memory pointer remapping).

2. Key Vectors (K) Obey 2D Orthogonal Group Addition

Rotary Positional Embedding (RoPE) rotates Key coordinate pairs by an angle $\Theta_i$:

$$R(m\Theta_i) = \begin{bmatrix} \cos(m\theta_i) & -\sin(m\theta_i) \ \sin(m\theta_i) & \cos(m\theta_i) \end{bmatrix}$$

By trigonometric angle addition:

$$R(\Delta\Theta) \cdot [R(m\Theta) \mathbf{k}] = [R(\Delta\Theta) R(m\Theta)] \mathbf{k} = R((m + \Delta)\Theta) \mathbf{k} = \mathbf{k}_{m'}$$

Key Result: Rotating an already-computed Key cache block from sequence offset $m$ to target offset $m'$ requires only 4 FLOPs per coordinate pair (a single 2D Givens rotation by $\Delta = m' - m$).

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Empirical Benchmark: NVIDIA GeForce RTX 3060 12GB

Evaluated on Qwen 2.5 7B Instruct (AWQ / Marlin W4A16 GEMM kernel, 32 layers, hidden dimension 3584, 28/4 GQA):

Metric	Full Prefill (Radix Miss)	Delta-RoPE In-Place Rotation	Improvement
Compute Cost (1,500 tok)	22.118 TeraFLOPs	147.456 MegaFLOPs	149,995x fewer FLOPs
Rotation Kernel Time	~1,105.9 ms	0.28 ms	3,949x faster
Float64 Analytical Error	Reference	7.257e-13	Machine precision
Attention Output Cosine Sim	Reference	0.999931	Zero degradation

5-Agent Collaborative Pipeline Telemetry

Simulating a 5-agent team sharing a 1,500-token tool specification:

Agent Role	Unique Prefix	Shared Tool Chunk	Radix APC TTFT	AgentKV TTFT	Speedup
Agent 1 (Architect)	150 tok	1,500 tok (Init)	1,253.4 ms	1,253.4 ms	1.00x
Agent 2 (Implementer)	350 tok	1,500 tok (Delta=+200)	1,423.0 ms	317.3 ms	4.48x
Agent 3 (Reviewer)	80 tok	1,500 tok (Delta=-70)	1,194.4 ms	88.8 ms	13.46x
Agent 4 (Security Auditor)	220 tok	1,500 tok (Delta=+70)	1,312.4 ms	206.7 ms	6.35x
Agent 5 (Test Runner)	110 tok	1,500 tok (Delta=-40)	1,220.2 ms	114.6 ms	10.65x
Cumulative Pipeline	8,685 Total Tokens	6,000 Redundant Tokens Saved	6,403.5 ms (6.40s)	1,980.8 ms (1.98s)	3.23x Pipeline (7.08x TTFT)

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Interactive Visual Showcase

The repository includes a Swiss-designed, interactive hardware telemetry dashboard and vector math sandbox:

• Interactive Delta-RoPE Coordinate Sandbox: Live unit circle visualizer with customizable $\Delta$ angle slider.
• Side-by-Side Prefill Race: Real-time simulation of GPU SM core utilization (Radix APC vs. AgentKV).
• Multi-Agent Pipeline Telemetry: Cumulative token savings and TTFT breakdown.

To run locally:

python -m http.server 8089

Then navigate to http://localhost:8089.

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Quickstart & Reproduction

1. Standalone Zero-Dependency Mathematical Proof

Verify the analytical group identity with standard Python and NumPy:

python verify_rope.py

Expected output:

========================================================================
  AGENTKV / DELTA-ROPE MATHEMATICAL PROOF & REPRODUCIBILITY VERIFICATION
========================================================================

[1] Generating synthetic Key representations for 1500 tokens...
    - Original Position m: 120 .. 1619
    - Target Position m':   320 .. 1819 (Delta: +200)

[2] Computing Baseline (Full recalculation at position m')...
    - Computed in 101.881 ms

[3] Computing AgentKV (Delta-RoPE transformation of cached Key k_m)...
    - Transformed in 107.487 ms

[4] Numerical Invariance Audit (Bit-for-Bit Validation):
    - Maximum Absolute Error (Float64): 7.257e-13
    - Mean Absolute Error (Float64):    4.101e-15
    - Mathematical Identity Status:     PASSED (Exact Analytical Group Identity)

[5] FLOP Compute Complexity Comparison (32-Layer 7B Model):
    - Full Transformer Prefill:  25.519 TeraFLOPs
    - Delta-RoPE Rotation:       516.096 MegaFLOPs
    - Raw Arithmetic Cut:        49,445x Fewer Operations
    - 32-Layer Key Cache Size:   328.1 MB (FP16)
    - RTX 3060 Memory Bandwidth: 360 GB/s (Bus saturation latency < 0.5ms)

========================================================================
  VERDICT: 100% BIT-FOR-BIT REPRODUCIBLE (7.26e-13 max float64 error)
========================================================================

2. PyTorch Reference Kernel Test

Test the in-place tensor rotation kernel with PyTorch:

python test_kernel.py

3. Multi-Agent Pipeline Benchmark

Reproduce the 5-agent empirical latency benchmark:

python benchmark.py

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Repository Structure

AgentKV/
├── assets/           # Architectural diagrams and telemetry assets
├── index.html        # Interactive visualizer and live prefill race
├── kernel.py         # PyTorch Delta-RoPE in-place rotation reference kernel
├── test_kernel.py    # PyTorch numerical precision unit test
├── verify_rope.py    # Zero-dependency NumPy proof and FLOP benchmark
├── benchmark.py      # Multi-agent RTX 3060 empirical benchmark reproduction
└── README.md         # Architecture overview and mathematical proof

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Citation & Architecture Notes

If you use or reference AgentKV in your research or inference infrastructure:

@article{neve2026agentkv,
  title={AgentKV: Arbitrary-Position Key-Value Cache Reuse for Multi-Agent LLMs via Delta-RoPE},
  author={Neve, Sharvin},
  year={2026}
}