Transcribe and decode any public Instagram profile — hooks, CTAs, and script patterns — locally and for free.

npx -y reelrecon

Your agent can already write scripts. Now it can study the competition first: "Transcribe @competitor's latest 10 Reels and break down their hook formulas" — one tool call away.

🤖 Agent Setup · 🚀 Quick Start · 🔍 Use Cases · 🧰 Tool Reference · 🖥️ Web UI

🎯 Why this exists

Today, analyzing a competitor's Reels means either paying a per-minute transcription SaaS, or uploading videos one by one to a multimodal model and burning tokens while it watches. ReelRecon is the third option: free, open source, and local — an MCP-native pipeline built for the part that actually matters for content strategy, the spoken word:

1. Your agent calls one tool with a public Instagram profile URL.
2. The server grabs the latest 10 videos, extracts audio, and transcribes every word with OpenAI Whisper — locally. No subscriptions, no per-minute fees, no tokens spent on video frames.
3. The agent gets back structured JSON: full transcripts plus mined hooks, CTAs, sentiment, keyword clusters, title ideas, and a cross-video strategy overview.

ReelRecon doesn't analyze visuals — scripts, hooks, and CTAs live in the audio, and that's what it mines for patterns and content ideas. It pairs perfectly with video-capable models: triage all ten Reels here for free in minutes, then send only the one or two that matter to a multimodal model for full visual breakdown.

Built agent-tough: structured errors instead of exceptions, progress notifications, job queueing with hard timeouts, context-window-friendly response trimming, and a check_health tool so your agent can self-diagnose a broken install instead of hallucinating around it.

🤖 Drop it into your agent stack

The server speaks stdio and streamable-HTTP MCP, so anything MCP-capable can use it. No MCP? There's a JSON-mode CLI any framework can shell out to.

⚡ One command, no clone: npx

With Node 18+, Python 3.10+ (3.11 recommended), and ffmpeg installed:

npx -y reelrecon

That starts the MCP server on stdio. The first run provisions a private Python environment in ~/.reelrecon (Whisper + friends — a few minutes and a few GB, once); every start after that is instant. One-off CLI runs work too:

npx -y reelrecon transcribe "https://www.instagram.com/<username>/" --json

Already have Python + deps? Set REELRECON_PYTHON=/path/to/python to skip provisioning and use your own environment.

Package not on npm yet in your region/registry? Run it straight from GitHub — same launcher: npx -y github:4nw3rprod/ReelRecon

🔄 Upgrading

• npx users: pin reelrecon@latest in your config (as in the snippets below) and every server start runs the newest published version. If plain npx -y reelrecon keeps serving you a stale cached copy, run npx -y reelrecon@latest once or clear the cache with npm cache clean --force.
• GitHub-direct: npx -y github:4nw3rprod/ReelRecon always runs the latest main — no npm release needed.
• Local clone: git pull. That's it — the private Python env in ~/.reelrecon is reused automatically and only reinstalls when requirements.txt changes.

If Instagram login-walled you on a public reel, upgrade to v1.2.1+ and (optionally) hand the server your own session in the MCP config:

{
  "mcpServers": {
    "reelrecon": {
      "command": "npx",
      "args": ["-y", "reelrecon@latest"],
      "env": { "REELRECON_COOKIES_FILE": "/absolute/path/to/cookies.txt" }
    }
  }
}

| Agent / Framework | Integration | |---|---| | Claude Code (CLI) | claude mcp add reelrecon -- npx -y reelrecon | | Claude Desktop | mcpServers entry in config | | ChatGPT / Codex CLI | mcp_servers entry in ~/.codex/config.toml | | Gemini CLI | mcpServers entry in ~/.gemini/settings.json | | Cursor / Windsurf / Cline | Standard MCP server config (stdio) | | OpenClaw, Hermes & other open agent frameworks | Point the framework's MCP client at npx -y reelrecon (stdio) or the HTTP endpoint | | LangChain / CrewAI / custom loops | Use an MCP adapter, or shell out to the CLI with --json |

claude mcp add reelrecon -- npx -y reelrecon

{
  "mcpServers": {
    "reelrecon": {
      "command": "npx",
      "args": ["-y", "reelrecon"]
    }
  }
}

[mcp_servers.reelrecon]
command = "npx"
args = ["-y", "reelrecon"]

{
  "mcpServers": {
    "reelrecon": {
      "command": "npx",
      "args": ["-y", "reelrecon"]
    }
  }
}

Clone the repo, install the Python deps (Quick Start), then point your MCP client at the launcher script:

{
  "mcpServers": {
    "reelrecon": {
      "command": "/absolute/path/to/ReelRecon/run_mcp_server.sh"
    }
  }
}

./run_mcp_server.sh --transport streamable-http --host 127.0.0.1 --port 8001

Then point the client at http://127.0.0.1:8001/mcp.

./run_latest_reel_transcription.sh "https://www.instagram.com/<username>/" --json

stdout is a single JSON object on success; non-zero exit + {"status":"error","error":"..."} on failure. Trivially parseable from any language.

Then just prompt your agent:

"Use reelrecon to transcribe the latest Reels from @competitor. Compare their hooks against my last 5 scripts and tell me what patterns I'm missing."

🔍 What your agent can do with it

Point any LLM at the structured output and competitive content research becomes a conversation:

• 🪝 Hook mining — the opening line of a competitor's last 10 videos, side by side. Your agent extracts the formula.
• 📣 CTA patterns — every "follow / comment / link in bio / DM me" detected and counted per batch.
• 🧬 Script structure — full transcripts expose pacing: hook → context → payoff → CTA. Steal the skeleton, not the words.
• 🔑 Topic clusters — recurring keywords across recent videos = a creator's actual content pillars.
• 📈 Trend triangulation — run 3–5 competitors and let the LLM diff what they're all suddenly talking about.
• ♻️ Repurposing engine — each video ships with ready-made content angles and title suggestions for your own spin.
• 🕵️ Scheduled watching — pair with your agent's cron/loop feature: "check these 3 profiles every morning and brief me."

Fair use, please: public profiles only (private accounts are detected and refused), and it's built for research and inspiration — study patterns, don't plagiarize scripts. Instagram may rate-limit anonymous requests; be a good citizen.

⚙️ How it works

flowchart LR
    A["🤖 Agent / LLM<br/>MCP tool call"] --> B["📱 Public IG profile<br/>latest 10 videos"]
    A --> C["🔗 Single video URL"]
    A --> D["🎙️ Local audio file"]
    B --> E["⬇️ yt-dlp<br/>audio extraction"]
    C --> E
    D --> F
    E --> F["📝 Whisper<br/>local transcription"]
    F --> G["🧠 AI insights<br/>hooks · CTAs · keywords"]
    G --> H["📦 Structured JSON<br/>back to the agent"]

🚀 Quick Start

Fastest path (no clone): npx -y reelrecon — see agent setup above.

Manual setup — requirements: Python 3.11+, ffmpeg on your PATH, network access.

git clone https://github.com/4nw3rprod/ReelRecon.git
cd ReelRecon
python3.11 -m venv .venv
.venv/bin/pip install -r requirements.txt

Optional (Groq-powered insights instead of the built-in heuristics):

cp .env.example .env.local   # then set GROQ_API_KEY

Connect your agent (see configs above), or run it by hand:

# A competitor's latest 10 videos
./run_latest_reel_transcription.sh "https://www.instagram.com/nike/" --json

# A single Reel, with model + language hints
./run_latest_reel_transcription.sh "https://www.instagram.com/reel/<id>/" --json --model small --language en

🧰 MCP tool reference

| Tool | What it does | |---|---| | transcribe_input | Profile URL → latest 10 videos, or any single video URL yt-dlp supports | | transcribe_local_audio | Transcribe a local audio file + generate insights | | list_recent_batches | Browse saved runs | | read_batch_manifest | Load a full batch result | | read_video_output | Load one video's transcript + metadata | | check_health | Self-diagnose ffmpeg/Whisper/yt-dlp, disk, and job status |

Resources: reelrecon://server · reelrecon://recent-batches · reelrecon://manifest/{group}/{label} · reelrecon://transcript/{group}/{label}/{video_id}

The contract your agent can rely on:

• Tools never raise for expected failures — every call returns status: "ok" or a structured error: error_type (invalid_input, not_found, pipeline_error, dependency_error, server_busy, timeout, …), a message, and a hint the agent can act on.
• Progress streams as MCP notifications during long batches.
• Context-window friendly: include_transcript_text=false or max_transcript_chars=N trims responses; full transcripts always stay on disk and behind resources.
• Partial success: in a 10-video batch, one broken video is recorded (failed_videos) instead of sinking the other nine.
• Jobs are queued with hard timeouts; limits are env-tunable (below).

📦 What comes out

outputs/
└── instagram_profiles/
    └── nike/
        ├── manifest.json          ← batch result + AI overview
        └── <video_id>/
            ├── audio.mp3
            ├── transcript.txt     ← the gold
            └── metadata.json      ← caption, timestamps, insights

{
  "status": "ok",
  "input_kind": "instagram_profile",
  "total_videos": 10,
  "completed_videos": 10,
  "videos": [
    {
      "title": "You don't need motivation…",
      "transcript_text": "...",
      "ai_insights": {
        "hook": "You don't need motivation, you need a system.",
        "cta": "follow",
        "sentiment": "positive",
        "keywords": ["system", "habits", "training"],
        "title_suggestions": ["..."],
        "content_angles": ["..."]
      }
    }
  ],
  "ai_overview": {
    "recurring_keywords": ["..."],
    "top_hooks": ["..."],
    "cta_patterns": [["follow", 6], ["link in bio", 3]]
  },
  "manifest_file": "outputs/instagram_profiles/nike/manifest.json"
}

🖥️ The Dashboard (for humans)

Agents get MCP; you get a live dashboard:

./run_ui.sh

Builds the frontend, picks an open localhost port, opens your browser. Paste a profile/Reel URL or upload audio (mp3, wav, m4a, aac, flac, ogg, webm), pick the Whisper model, watch live progress through every pipeline stage, and browse transcript + insight history.

🎛️ Tuning

All optional, via environment variables:

| Variable | Default | Purpose | |---|---|---| | GROQ_API_KEY | — | Enables GroqCloud AI insights (heuristic fallback otherwise) | | REELRECON_OUTPUT_DIR | <repo>/outputs | Where results are written | | REELRECON_JOB_TIMEOUT_SECONDS | 3600 | Hard per-job timeout (MCP) | | REELRECON_QUEUE_TIMEOUT_SECONDS | 900 | Max wait for a job slot (MCP) | | REELRECON_MAX_CONCURRENT_JOBS | 1 | Parallel transcription jobs (MCP) | | REELRECON_MAX_UPLOAD_BYTES | 2 GiB | Max local audio file size (MCP) | | REELRECON_EXTRA_MODELS | — | Comma-separated extra Whisper model names to allow | | REELRECON_COOKIES_FILE | — | Path to a cookies.txt export of your own Instagram session (fallback when Instagram login-walls anonymous access) | | REELRECON_COOKIES_FROM_BROWSER | — | Read your session straight from a browser, e.g. chrome or firefox:ProfileName | | REELRECON_HTTP_TIMEOUT_SECONDS | 30 | Instagram/Groq/yt-dlp socket timeout | | REELRECON_FETCH_RETRIES | 3 | Instagram profile fetch attempts (with backoff) |

Legacy IG_TRANSCRIBER_* variable names are still honored, so existing setups keep working.

Whisper model cheat sheet: tiny = fastest, base = default sweet spot, small/medium = better accuracy, large-v3 = best (needs RAM/time).

✅ Tests

The MCP server and pipeline helpers ship with a lightweight suite (no Whisper/torch download needed):

.venv/bin/pip install pytest
.venv/bin/python -m pytest tests/ -q

📝 Good to know

• Public profiles only — private accounts are detected and refused.
• Instagram may rate-limit anonymous requests; the tool retries with backoff, but if it's blocked, wait and rerun.
• Hitting Instagram's login wall on a public reel? Public videos normally work anonymously (the same access you get after dismissing the login popup in a browser). If Instagram keeps refusing, supply your own logged-in session: set REELRECON_COOKIES_FILE to a cookies.txt export, or REELRECON_COOKIES_FROM_BROWSER=chrome. Your session, your account, your responsibility — keep it to research-scale use.
• Whisper models are cached after first load; already-transcribed videos are reused on reruns.
• Everything runs locally. The only network calls are to Instagram/video hosts, and (optionally) GroqCloud with your key.
• Agent-facing docs live in CLAUDE.md — most MCP-aware coding agents pick it up automatically.

Wiring this into your agent? ⭐ Star the repo — it's free and it helps others find it.

Built with Whisper, yt-dlp, FastAPI, React + shadcn/ui, and the Model Context Protocol.