Quickstart · Why · Benchmarks · How It Works · Install · Full Install Guide · Paper

---

AI agents can't see across sessions.

Your agent takes a screenshot, analyzes it, and forgets. Next session — blank slate. It can't compare what a page looks like now versus yesterday. It can't recall what the error dialog said three conversations ago. It can't search its own visual history.

Text-based memory exists. Visual memory doesn't — until now.

AgenticVision gives AI agents persistent visual memory. Capture images, embed them with CLIP ViT-B/32, store them in a compact binary format, and query them by similarity, time, or description. Every capture is a first-class MCP resource that any LLM can access.

cargo install agentic-vision-mcp

One binary. 11 MCP tools. Persistent .avis files. Works with Claude Desktop, VS Code, Cursor, Windsurf, and any MCP-compatible client.

---

Benchmarks

Rust core. CLIP ViT-B/32 via ONNX Runtime. Binary .avis format. Real numbers from cargo test --release:

Operation	Time	Notes
Image capture (file → embed → store)	47 ms	CLIP ViT-B/32, 512-dim
Similarity search (top-5)	1-2 ms	Brute-force cosine, f64 precision
Visual diff (pixel-level)	<1 ms	8×8 grid region detection
MCP tool round-trip	7.2 ms	Including process startup (~6.1 ms)
Storage per capture	~4.26 KB	Embedding + JPEG thumbnail
Capacity per GB	~250K	Observations

All benchmarks on Apple M4, macOS 26.2, Rust 1.90.0 --release. ONNX Runtime for CLIP inference. Fallback mode available when ONNX model is not present.

---

Why AgenticVision

Agents need visual continuity. A debugging agent should remember what the UI looked like before and after a code change. A monitoring agent should detect visual regressions. A research agent should build a visual knowledge base over time.

Capture once, query forever. Every image is embedded into a 512-dimensional CLIP vector and stored with its JPEG thumbnail, timestamp, and description. Query by cosine similarity, time range, or text search — in milliseconds.

Binary format, not a database. The .avis file is a single portable binary — 64-byte header, JSON payload, JPEG thumbnails. Copy it, share it, back it up. No server, no database, no dependencies.

Works with every MCP client. AgenticVision-MCP exposes 11 tools, 6 resources, and 4 prompts via the Model Context Protocol. Any LLM that speaks MCP gains visual memory automatically.

Links to AgenticMemory. The vision_link tool connects visual captures to AgenticMemory cognitive graph nodes — bridging what an agent sees with what it knows.

---

How It Works

1. Capture — vision_capture accepts images from files, base64, screenshots, or the system clipboard. Each image is resized, embedded via CLIP ViT-B/32 into a 512-dimensional vector, compressed to JPEG thumbnail, and stored in the .avis binary file. Screenshots support optional region capture; clipboard reads the current image from the OS clipboard.

2. Query — vision_query retrieves captures by time range, description, recency, and quality constraints (min_quality, sort_by). Results include capture metadata, quality scores, thumbnails, and similarity scores.

3. Compare — vision_compare places two captures side-by-side for LLM analysis. vision_diff performs pixel-level differencing with 8×8 grid region detection to identify exactly what changed.

4. Link — vision_link connects captures to AgenticMemory nodes, bridging visual observations with the agent's cognitive graph. An agent can recall "what did the UI look like when I made that decision?"

The .avis binary format uses a 64-byte fixed header (magic 0x41564953, version, counts, timestamps) followed by a JSON payload containing captures with embedded JPEG thumbnails and 512-dim float vectors. Single-file, portable, no external dependencies.

MCP surface area

11 Tools:

Tool	Description
vision_capture	Capture and embed an image (file, base64, screenshot, clipboard), with metadata redaction and quality scoring
vision_compare	Side-by-side comparison of two captures
vision_query	Query captures by time, description, recency
vision_ocr	Extract text from a captured image
vision_similar	Find visually similar captures (cosine similarity)
vision_track	Track visual changes to a target over time
vision_diff	Pixel-level diff between two captures
vision_health	Quality + staleness + memory-link coverage summary
vision_link	Link a capture to an AgenticMemory node
session_start	Begin a named observation session
session_end	End the current session

6 Resources:

URI	Description
avis://capture/{id}	Single capture with metadata and thumbnail
avis://session/{id}	All captures in a session
avis://timeline/{start}/{end}	Captures within a time range
avis://similar/{id}	Visually similar captures
avis://stats	Storage statistics and counts
avis://recent	Most recent captures

4 Prompts:

Prompt	Description
observe	Guided visual observation workflow
compare	Structured comparison between captures
track	Change tracking over time
describe	Detailed image description

---

Install

One-liner (desktop profile, backwards-compatible):

curl -fsSL https://agentralabs.tech/install/vision | bash

Environment profiles (one command per environment):

# Desktop MCP clients (auto-merge Claude Desktop + Claude Code when detected)
curl -fsSL https://agentralabs.tech/install/vision/desktop | bash

# Terminal-only (no desktop config writes)
curl -fsSL https://agentralabs.tech/install/vision/terminal | bash

# Remote/server hosts (no desktop config writes)
curl -fsSL https://agentralabs.tech/install/vision/server | bash

Channel	Command	Result
GitHub installer (official)	curl -fsSL https://agentralabs.tech/install/vision | bash	Installs release binaries when available, otherwise source fallback; merges MCP config
GitHub installer (desktop profile)	curl -fsSL https://agentralabs.tech/install/vision/desktop | bash	Explicit desktop profile behavior
GitHub installer (terminal profile)	curl -fsSL https://agentralabs.tech/install/vision/terminal | bash	Installs binaries only; no desktop config writes
GitHub installer (server profile)	curl -fsSL https://agentralabs.tech/install/vision/server | bash	Installs binaries only; server-safe behavior
crates.io + Cargo deps (official)	cargo install agentic-vision-mcp + cargo add agentic-vision	Installs MCP server binary and adds the core library crate to your project

Server auth and artifact sync

For cloud/server runtime:

export AGENTIC_TOKEN="$(openssl rand -hex 32)"

All MCP clients must send Authorization: Bearer <same-token>. If .avis/.amem/.acb files are on another machine, sync them to the server first.

MCP Server (for Claude Desktop, VS Code, Cursor, Windsurf):

cargo install agentic-vision-mcp

Core library (for Rust projects):

cargo add agentic-vision

Configure Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "vision": {
      "command": "agentic-vision-mcp",
      "args": ["--vision", "~/.vision.avis", "serve"]
    }
  }
}

See INSTALL.md for full installation guide, VS Code / Cursor configuration, build from source, and troubleshooting.

Do not use /tmp for vision files — macOS and Linux clear this directory periodically. Use ~/.vision.avis for persistent storage.

Deployment Model

• Standalone by default: AgenticVision is independently installable and operable. Integration with AgenticMemory or AgenticCodebase is optional, never required.
• Autonomic operations by default: daemon/runtime maintenance uses safe profile-based defaults with cache hygiene, migration safeguards, and health-ledger snapshots.

Area	Default behavior	Controls
Autonomic profile	Conservative local-first posture	`CORTEX_AUTONOMIC_PROFILE=desktop
Cache + registry maintenance	Periodic expiry cleanup and registry GC	CORTEX_MAINTENANCE_TICK_SECS, CORTEX_REGISTRY_GC_EVERY_TICKS, CORTEX_REGISTRY_GC_KEEP_DELTAS
Storage migration	Policy-gated with checkpointed auto-safe path	`CORTEX_STORAGE_MIGRATION_POLICY=auto-safe
Storage budget policy	20-year projection + capture rollup under pressure	`CORTEX_STORAGE_BUDGET_MODE=auto-rollup
Maintenance throttling	SLA-aware under sustained cache pressure	CORTEX_SLA_MAX_CACHE_ENTRIES_BEFORE_GC_THROTTLE
Health ledger	Periodic operational snapshots (default: ~/.agentra/health-ledger)	CORTEX_HEALTH_LEDGER_DIR, AGENTRA_HEALTH_LEDGER_DIR, CORTEX_HEALTH_LEDGER_EMIT_SECS

---

Quickstart

MCP (Claude Desktop, VS Code, Cursor)

After configuring the MCP server (see Install), ask your agent:

"Take a screenshot and remember it."

The LLM calls vision_capture automatically. Then later:

"What did the screen look like earlier?"

The LLM calls vision_query to retrieve and display past captures.

Rust API

use agentic_vision::{VisionStore, CaptureSource};

let mut store = VisionStore::open("observations.avis")?;

// Capture from file
let id = store.capture(
    CaptureSource::File("screenshot.png"),
    "Homepage after deploy"
)?;

// Find similar
let matches = store.similar(id, 5)?;
for m in matches {
    println!("  {} (similarity: {:.3})", m.description, m.score);
}

---

Validation

Suite	Tests	Notes
Rust core (agentic-vision)	38	Unit + integration (includes screenshot/clipboard)
Python SDK tests	47	Edge cases, format validation
MCP integration suite	3	Python → Rust stdio transport
Multi-agent suite	3	Shared file, vision-memory linking, rapid handoff
Total	91	All passing

Two research papers:

• Paper I: Cortex — Web Cartography (10 pages, 8 figures, 13 tables)
• Paper II: AgenticVision-MCP — Persistent Visual Memory via MCP (8 pages, 4 figures, 7 tables)

---

Repository Structure

This is a Cargo workspace monorepo containing the core library and MCP server.

agentic-vision/
├── Cargo.toml                    # Workspace root
├── crates/
│   ├── agentic-vision/           # Core library (crates.io: agentic-vision v0.1.0)
│   └── agentic-vision-mcp/       # MCP server (crates.io: agentic-vision-mcp v0.1.0)
├── tests/                        # Integration tests (Python → Rust, multi-agent)
├── models/                       # ONNX model directory (CLIP ViT-B/32)
├── publication/                  # Research papers (I, II)
├── assets/                       # SVG diagrams and visuals
└── docs/                         # Guides and reference

Running Tests

# All workspace tests (unit + integration)
cargo test --workspace

# Core library only
cargo test -p agentic-vision

# MCP server only
cargo test -p agentic-vision-mcp

# Python integration tests
python tests/integration/test_mcp_clients.py
python tests/integration/test_multi_agent.py

MCP Server Quick Start

cargo install agentic-vision-mcp

Configure Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "vision": {
      "command": "agentic-vision-mcp",
      "args": ["--vision", "~/.vision.avis", "serve"]
    }
  }
}

agentic-vision-mcp supports both line-delimited JSON-RPC and Content-Length framed MCP stdio messages.

---

Roadmap: v0.2.0 — Remote Server Support

The next release is planned to add HTTP/SSE transport for remote deployments. Track progress in #2.

Feature	Status
--token bearer auth	Planned
--multi-tenant per-user vision files	Planned
/health endpoint	Planned
--tls-cert / --tls-key native HTTPS	Planned
OCR with Tesseract (--features ocr)	Planned
Clipboard TIFF fix	Planned
delete / export / compact CLI commands	Planned
Docker image + compose	Planned
Remote deployment docs	Planned

Planned CLI shape (not available in current release):

agentic-vision-mcp serve-http --port 8081 --token "<token>"
agentic-vision-mcp serve-http --multi-tenant --data-dir /data/users --port 8081 --token "<token>"

---

Contributing

See CONTRIBUTING.md. The fastest ways to help:

1. Try it and file issues
2. Add an MCP tool — extend the visual memory surface
3. Write an example — show a real use case
4. Improve docs — every clarification helps someone

---

_{Built by Agentra Labs}