the missing link

Video Intelligence
for AI Agents

The first formally verified, Rust-native video SDK with a built-in MCP server. Give your agents eyes and ears.

View Source → Explore Tools
rust-powered formally verified mcp-native
+ + KINO
fig. 01 — data prism

Agent-Native

Built-in MCP server for instant integration with Claude, GPT, and custom agents.

Performance

7-crate Rust workspace with zero-copy parsing and <4ms startup latency.

Correctness

State machines backed by TLA+ formal verification. verified

Versatility

Edge (WASM), Desktop (Tauri), CLI, Python pipelines. Deploy anywhere.

the problem

Video is the Last Unstructured Frontier

AI agents can read text, browse the web, and write code. But they can't watch a video stream.

Current State: The Blind Spot

  • Blind Agents — LLMs are text-centric; they cannot natively watch streams.
  • Unsafe Tooling — FFmpeg is unsafe, hard to wrap, and lacks semantic output.
  • Slow Clouds — API latency (100ms–10s) makes real-time reasoning impossible.

With Kino: Structured Vision

A drop-in primitive that allows agents to programmatically analyze, fingerprint, and QC video streams in real-time.

Semantic JSON Output
Anchor (Hash A)
Target (Hash B)
fig. 02 — spectral peak fingerprinting
agent toolset

8 MCP Functions for the LLM

Every tool returns structured JSON that agents can reason about directly.

Discovery
analyze_stream — Parse manifests, codecs, renditions. validate_stream — Check segment accessibility and DRM.
Quality
quality_check — Full QC report (bitrate ladder, captions). monitor_stream — Live latency and freshness checks.
Intelligence
fingerprint_audio — Content ID and duplicate detection. autotag_content — Detect BPM, genre, and mood.
Operations
compare_streams — Diff two streams for A/B testing. encode_video — Generate presets (Mobile/4K).
architecture

Modular 7-Crate Workspace

Each crate is independently versioned. Total: 14K lines of Rust. Zero-copy parsing.

kino-cli / kino-mcp Interface & Tooling
kino-core(HLS/DASH, ABR, DRM)
kino-frequency(FFT, Fingerprinting)
kino-wasm(Browser/Edge)
kino-desktop(GStreamer)
kino-tauri(Native App)
kino-python(Data Science)
fig. 03 — crate dependency tree
performance

Why Rust? Performance Meets Safety.

Kino (Rust)FFmpeg (C)Cloud APIs
Memory Safety ✓ Safe (Borrow Checker) Unsafe (Manual Mgmt) N/A
Latency<4ms StartupHeavy Overhead100ms - 10s
DeploymentSingle Binary / WASMShared LibrariesExternal Dep.

Process thousands of streams concurrently without memory leaks that crash Python pipelines.

developer experience

Integration in Minutes

Bash (CLI)
kino-cli analyze \
  https://example.com/master.m3u8 \
  --format json
JavaScript (WASM)
import { WasmPlayer } from './pkg';
player.load(url);
Python (Analysis)
analyzer = FrequencyAnalyzer(
  fft_size=2048
)
result = analyzer.analyze_file(
  "content.wav"
)
use cases

Real-World Agent Workflows

Content Moderation

📡

Agent scans live stream

↓
🔊

Fingerprints audio

↓
🛡

Flags violations before viewers see them

Transcoding Pipeline

📄

Agent analyzes input

↓
⚙️

Selects optimal preset

↓
✅

Validates output quality automatically

Event Monitoring

🕐

Agent watches 24/7

↓
📊

Monitors health metrics

↓
🚨

Alerts on latency spikes or DRM failures

business model

Open Core

Free for agents. Professional support for production. Enterprise for regulated media.

🙌 Community
Free — MIT/Apache
  • Full SDK & MCP Server
  • WASM Player
  • Audio Fingerprinting
  • All 8 Agent Tools
🤝 Professional
$12k–25k / year
  • Widevine DRM support
  • Priority support & SLA
  • Custom encoding presets
🏢 Enterprise
Custom
  • FairPlay & PlayReady DRM
  • White-label MCP server
  • Air-gapped deployment

Target: AI Platforms (Anthropic/OpenAI) · Streaming Tech (Cloudflare) · Media Tools (Frame.io)

The Missing Link for AI Video Intelligence

Rust-Powered. Formally Verified. Available Now.

For Agents

npm install -g kino-mcp

For Devs

cargo install kino-cli

Source

github.com/ExpertVagabond/kino