# Video Extract Agents > AI-powered video extraction platform - CrewAI agents, MCP/SSE tools, Claude (Anthropic), AWS Bedrock, Angular, Node.js, Python, FFmpeg, Azure Container Apps - **Type:** MCP server - **Install:** `agentstack add mcp-cibis-video-extract-agents` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [cibis](https://agentstack.voostack.com/s/cibis) - **Installs:** 0 - **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [cibis](https://github.com/cibis) - **Source:** https://github.com/cibis/video-extract-agents ## Install ```sh agentstack add mcp-cibis-video-extract-agents ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About This project is a complete redesign of the original Video Extract tool. Original implementation: https://github.com/cibis/video_extract --- # Video Extract Agents A prompt-driven video extraction platform. Upload a video, describe what you want in plain English, and AI agents extract and compile the relevant segments into a highlight reel. > **Example:** *"Extract all kitesurfing jumps from this video and compile them into a highlight reel."* --- ## Why This Exists This project exists because of two problems. The first: I'm mildly obsessed with agentic AI, the idea that you give a system a goal in plain English and a crew of AI agents figures out how to get there. The second: I have *hours* of kitesurfing footage and zero patience for scrubbing through it frame by frame. The obvious solution was to build an enterprise-grade, cloud-native, multi-agent video extraction platform. So here we are: a full Azure microservices stack, CrewAI orchestration, MCP tool servers, and FFmpeg keyframe pipelines, all so I can type *"find the jumps"* and go back to the beach. --- ## Table of Contents - [Overview](#overview) - [How It Works](#how-it-works) - [Tech Stack](#tech-stack) - [Quick Start (Local Dev)](#quick-start-local-dev) - [Running Tests](#running-tests) - [Documentation](#documentation) - [External Agents](#external-agents) - [Repository Structure](#repository-structure) --- ## Overview The platform combines agentic AI orchestration (CrewAI + Claude), MCP tool servers over SSE transport, and a cloud-native Azure microservices architecture to enable natural-language-driven video processing at scale. **Home — session active with completed job history and chat** **Session History — completed and failed jobs with output files** Key capabilities: - Upload videos up to 10 GB directly to Azure Blob Storage - Describe what to extract in natural language via a chat interface - AI agents (planner → analysis → processing) orchestrate the full pipeline - FFmpeg keyframe pre-processing dramatically reduces AI token costs - Output videos delivered via signed CDN URLs and email notification - All processing services auto-scale to zero when idle (KEDA on Azure Container Apps) --- ## How It Works ``` Upload video (Angular → Blob Storage via SAS token) ↓ Pre-processing worker (FFmpeg keyframe extraction → PostgreSQL index) ↓ User submits prompt (LibreChat iframe → API Gateway → Agent Orchestrator) ↓ CrewAI crew: Planner → Analysis Agent (MCP tools) → Processing Agent (MCP tools) ↓ Output video written to Blob Storage ↓ Signed download URL delivered via SSE stream + email notification ``` All steps are asynchronous and fault-tolerant via Azure Service Bus queues. --- ## External Agents The platform's MCP tools can be used directly from Claude Desktop or the LibreChat official image via an MCP bridge (port 8300) that translates standard MCP JSON-RPC to the platform's SSE tool protocol. **Claude Desktop:** ```bash # Start MCP bridge bash external-agents/claude-desktop/scripts/start-mcp-bridge.sh # Install config (Windows PowerShell) .\external-agents\claude-desktop\scripts\install.ps1 # Restart Claude Desktop — Tools icon should show 19 tools ``` | Session started — upload link provided | Job complete — extraction summary and download link | |---|---| | | | **LibreChat (official image):** ```bash cp external-agents/librechat/.env.example external-agents/librechat/.env # Set ANTHROPIC_API_KEY and generate random secrets (see docs/getting-started.md §13.2) cd external-agents/librechat && docker compose up -d # Open http://localhost:3081 ``` | Agent running MCP tool calls (ingest → detect → clip) | Extraction complete — final output URL | |---|---| | | | See [docs/getting-started.md § External agents](docs/getting-started.md#13-external-agents-librechat-official--claude-desktop) for the full walkthrough. --- ## Tech Stack | Layer | Technology | |---|---| | Frontend | Angular 19 + LibreChat (forked, iframe embed) | | API / BFF | Node.js + Express (TypeScript) | | AI Orchestration | Python + CrewAI + FastAPI | | LLM | Any LiteLLM-compatible model (Anthropic Claude, OpenAI, AWS Bedrock, and more) | | Tool Protocol | MCP over SSE transport | | Container Platform | Azure Container Apps + KEDA | | Infrastructure as Code | Terraform | | Storage | Azure Blob Storage | | Database | PostgreSQL 15 (ACA container, Azure Files backed) | | Messaging | Azure Service Bus | | Auth | Azure Entra External ID (magic link / JWT) | | Local Dev Emulation | Docker Compose + Azurite | | CI/CD | GitLab CI (mirrored to GitHub) | --- ## Quick Start (Local Dev) Requires Docker Desktop (≥ 4.30) with WSL 2. See [docs/getting-started.md](docs/getting-started.md) for full prerequisites and Azure setup. **1. Copy environment files:** ```bash cp backend/api-gateway/.env.example backend/api-gateway/.env cp backend/agent-orchestrator/.env.example backend/agent-orchestrator/.env cp backend/preprocessing-worker/.env.example backend/preprocessing-worker/.env cp mcp-servers/mcp-server-analysis/.env.example mcp-servers/mcp-server-analysis/.env cp mcp-servers/mcp-server-processing/.env.example mcp-servers/mcp-server-processing/.env cp frontend/librechat/.env.example frontend/librechat/.env ``` Edit `backend/agent-orchestrator/.env` and set `ANTHROPIC_API_KEY`. **2. Start the stack:** ```bash cd infrastructure/docker-compose docker compose up --build ``` **3. Create Service Bus queues (once, after stack is up):** ```bash export SERVICE_BUS_CONNECTION_STRING="Endpoint=sb://localhost;SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=SAS_KEY_VALUE;UseDevelopmentEmulator=true;" python scripts/create_service_bus_queues.py ``` **4. Verify services:** ```bash curl http://localhost:8000/health # API Gateway curl http://localhost:8001/health # Agent Orchestrator curl http://localhost:8100/tools # MCP Analysis tools curl http://localhost:8200/tools # MCP Processing tools ``` Services run on: | Service | Port | |---|---| | Angular Shell | http://localhost:4200 | | LibreChat | http://localhost:3080 | | API Gateway | http://localhost:8000 | | Agent Orchestrator | http://localhost:8001 | | MCP Analysis | http://localhost:8100 | | MCP Processing | http://localhost:8200 | | Azurite (Blob) | http://localhost:10000 | | PostgreSQL | localhost:5433 | --- ## Running Tests ### E2E tests (fully containerised) ```bash scripts/run-e2e-local.sh # With frontier vision tools: ANTHROPIC_API_KEY=sk-... scripts/run-e2e-local.sh ``` --- ## Documentation | Document | Description | |---|---| | [docs/architecture.md](docs/architecture.md) | System design, data flows, service responsibilities, component details, deployment diagrams | | [docs/getting-started.md](docs/getting-started.md) | Full setup guide — prerequisites, GitLab/GitHub/Azure configuration, local dev bootstrap, CI/CD variables, secrets reference, troubleshooting | | [docs/local-development.md](docs/local-development.md) | Day-to-day local development — starting the stack, running services and tests, common tasks | | [docs/e2e-tests.md](docs/e2e-tests.md) | End-to-end pipeline tests | | [docs/azure-production-deployment.md](docs/azure-production-deployment.md) | Azure production deployment reference — services, roles, inter-service communication, scaling, CI/CD | | [docs/azure-credentials.md](docs/azure-credentials.md) | Azure credentials setup — every credential the platform needs, how to create and configure each | | [docs/terraform.md](docs/terraform.md) | Terraform layout, modules, environments, and how the pieces connect | | [docs/ai-containers-deep-dive.md](docs/ai-containers-deep-dive.md) | Deep dive into each AI container — inputs, outputs, and position in the job processing sequence | | [docs/gitlab-pipeline.md](docs/gitlab-pipeline.md) | CI/CD pipeline — every stage and job, environment lifecycle, and SDLC workflow | | [docs/instant-compilation-errors.md](docs/instant-compilation-errors.md) | Getting immediate type and syntax error feedback during local development without Docker rebuilds | | [docs/local-containers-report.md](docs/local-containers-report.md) | Local container architecture report | | [external-agents/claude-desktop/README.md](external-agents/claude-desktop/README.md) | Claude Desktop MCP integration | | [external-agents/librechat/README.md](external-agents/librechat/README.md) | LibreChat official image MCP integration | --- ## Repository Structure ``` backend/ api-gateway/ Node.js + Express (TypeScript) — auth, SAS tokens, SSE, chat proxy agent-orchestrator/ Python + CrewAI (FastAPI) — planner/analyst/processor agents preprocessing-worker/ Python — FFmpeg keyframe extraction mcp-servers/ mcp-server-analysis/ Port 8100 — ingest_video, extract_frames, detect_motion, detect_motion_sports, detect_objects, detect_objects_vision, analyze_scene, transcribe_audio, estimate_height_above_surface, read_asset, query_asset, write_query_asset, write_segments_asset mcp-server-processing/ Port 8200 — split_video, extract_clip, extract_clips_bulk, merge_clips, transform_video, write_asset, query_asset, write_query_asset frontend/ angular-shell/ Angular 19 — upload UI, job dashboard, LibreChat iframe host librechat/ Forked LibreChat — custom endpoint, branding, job status postMessage bridge external-agents/ mcp-bridge/ Standard MCP server (port 8300) — SSE + stdio transports claude-desktop/ Claude Desktop config + install scripts librechat/ LibreChat official image stack agent-instructions/ System prompt for external agents infrastructure/ docker-compose/ Full local dev stack terraform/ modules/ aca, storage, database (reusable modules) envs/ dev, test (ephemeral per CI pipeline) tests/ e2e/ End-to-end tests (ephemeral Azure + local Docker Compose) scripts/ init_db.py Create all database tables init_storage.py Create Blob Storage containers create_service_bus_queues.py Create all Service Bus queues run-e2e-local.sh Run E2E tests locally (fully containerised) bootstrap-dev.sh First-time local dev setup smoke-test.sh Quick smoke test against running stack teardown.sh Stop and clean up local stack repair_job_output.py Repair job output records in PostgreSQL collect_test_logs.py Collect logs from CI test run check_e2e_threshold.py Assert E2E test pass rate meets threshold docs/ architecture.md System architecture reference getting-started.md Full setup and deployment guide ``` ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [cibis](https://github.com/cibis) - **Source:** [cibis/video-extract-agents](https://github.com/cibis/video-extract-agents) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-cibis-video-extract-agents - Seller: https://agentstack.voostack.com/s/cibis - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.