Vibecode Soccial AI
track this build5 steps, step by step0%KINDA · weekend project
The code is the easy part; Meta is the hard part. The core loop, an LLM that auto-replies to your Instagram DMs, is a small webhook server any coding agent writes in an afternoon. But it only runs against a Meta developer app: you need an Instagram professional account, a public HTTPS endpoint that is up 24/7 (DMs arrive while your laptop is closed), webhook signature verification, and token refresh. And your app stays in development mode, which is fine for your own account but serving anyone else means Meta App Review plus business verification. What the subscription actually sells is connector upkeep across Instagram, Facebook, Shopify and GoHighLevel, hosted always-on webhooks, and a CRM wrapped around the conversations.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Soccial AI. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Soccial AI indie build ## Goal Build the smallest trustworthy replacement for the core Soccial AI workflow for one developer or a tiny team. ## Scope A small webhook server that receives your Instagram DMs and auto-replies through an LLM using your own system prompt. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Anyone-but-you: without Meta App Review your app only serves accounts you add as testers - Shopify and GoHighLevel context behind replies, and the CRM around the conversations - Comment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes - Someone else babysitting webhook uptime, token refresh, and Meta API deprecations If those capabilities are essential, use Chatwoot instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a self-hosted Instagram DM auto-responder for my own Instagram professional account, in this empty folder. Stack: Node 22 + Hono + better-sqlite3 + the Anthropic SDK (claude-sonnet-5). One small server, no frontend. Secrets in .env: IG_ACCESS_TOKEN, IG_ACCOUNT_ID, META_APP_SECRET, META_VERIFY_TOKEN, ANTHROPIC_API_KEY. Endpoints: - GET /webhook: Meta's hub.challenge verification handshake. - POST /webhook: verify X-Hub-Signature-256 with META_APP_SECRET, ACK 200 immediately, process async. On each incoming DM: skip echoes of my own outbound messages (is_echo); dedupe by message id in SQLite; load the last 10 messages of that conversation from SQLite as context; generate a reply with Claude using the editable system prompt in persona.md; send it via POST https://graph.instagram.com/v23.0/me/messages with recipient.id set to the sender's IGSID; log both sides to SQLite. Guardrails: never reply twice to the same message id; hard cap of one auto-reply per user per two minutes; if the model's reply contains the token HUMAN, send nothing and log it for me instead. Write a README that walks through the Meta side, which is the actual hard part: create a Meta app, add the Instagram API with Instagram Login, generate a long-lived access token for my account, subscribe the app to the messages webhook field, and expose this server with a Cloudflare Tunnel. Note plainly that in development mode this works only for my own account, and that is the point. Deliberately out of scope: comment automation, multiple accounts, any CRM, Shopify or calendar integrations, and Meta App Review. ## Required capabilities - Meta developer app (free) + Instagram professional account - Anthropic or OpenAI API key - Always-on public HTTPS endpoint (Cloudflare Tunnel on a Pi, or a $5 VPS) ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Soccial AI. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Soccial AI indie build ## Goal Build the smallest trustworthy replacement for the core Soccial AI workflow for one developer or a tiny team. ## Scope A small webhook server that receives your Instagram DMs and auto-replies through an LLM using your own system prompt. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Anyone-but-you: without Meta App Review your app only serves accounts you add as testers - Shopify and GoHighLevel context behind replies, and the CRM around the conversations - Comment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes - Someone else babysitting webhook uptime, token refresh, and Meta API deprecations If those capabilities are essential, use Chatwoot instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a self-hosted Instagram DM auto-responder for my own Instagram professional account, in this empty folder. Stack: Node 22 + Hono + better-sqlite3 + the Anthropic SDK (claude-sonnet-5). One small server, no frontend. Secrets in .env: IG_ACCESS_TOKEN, IG_ACCOUNT_ID, META_APP_SECRET, META_VERIFY_TOKEN, ANTHROPIC_API_KEY. Endpoints: - GET /webhook: Meta's hub.challenge verification handshake. - POST /webhook: verify X-Hub-Signature-256 with META_APP_SECRET, ACK 200 immediately, process async. On each incoming DM: skip echoes of my own outbound messages (is_echo); dedupe by message id in SQLite; load the last 10 messages of that conversation from SQLite as context; generate a reply with Claude using the editable system prompt in persona.md; send it via POST https://graph.instagram.com/v23.0/me/messages with recipient.id set to the sender's IGSID; log both sides to SQLite. Guardrails: never reply twice to the same message id; hard cap of one auto-reply per user per two minutes; if the model's reply contains the token HUMAN, send nothing and log it for me instead. Write a README that walks through the Meta side, which is the actual hard part: create a Meta app, add the Instagram API with Instagram Login, generate a long-lived access token for my account, subscribe the app to the messages webhook field, and expose this server with a Cloudflare Tunnel. Note plainly that in development mode this works only for my own account, and that is the point. Deliberately out of scope: comment automation, multiple accounts, any CRM, Shopify or calendar integrations, and Meta App Review. ## Required capabilities - Meta developer app (free) + Instagram professional account - Anthropic or OpenAI API key - Always-on public HTTPS endpoint (Cloudflare Tunnel on a Pi, or a $5 VPS) ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Soccial AI. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Soccial AI product brief ## Problem The code is the easy part; Meta is the hard part. The core loop, an LLM that auto-replies to your Instagram DMs, is a small webhook server any coding agent writes in an afternoon. But it only runs against a Meta developer app: you need an Instagram professional account, a public HTTPS endpoint that is up 24/7 (DMs arrive while your laptop is closed), webhook signature verification, and token refresh. And your app stays in development mode, which is fine for your own account but serving anyone else means Meta App Review plus business verification. What the subscription actually sells is connector upkeep across Instagram, Facebook, Shopify and GoHighLevel, hosted always-on webhooks, and a CRM wrapped around the conversations. ## Product outcome A small webhook server that receives your Instagram DMs and auto-replies through an LLM using your own system prompt. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Meta developer app (free) + Instagram professional account - Anthropic or OpenAI API key - Always-on public HTTPS endpoint (Cloudflare Tunnel on a Pi, or a $5 VPS) ## Explicit non-goals for v1 - Anyone-but-you: without Meta App Review your app only serves accounts you add as testers - Shopify and GoHighLevel context behind replies, and the CRM around the conversations - Comment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes - Someone else babysitting webhook uptime, token refresh, and Meta API deprecations ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a self-hosted Instagram DM auto-responder for my own Instagram professional account, in this empty folder. Stack: Node 22 + Hono + better-sqlite3 + the Anthropic SDK (claude-sonnet-5). One small server, no frontend. Secrets in .env: IG_ACCESS_TOKEN, IG_ACCOUNT_ID, META_APP_SECRET, META_VERIFY_TOKEN, ANTHROPIC_API_KEY. Endpoints: - GET /webhook: Meta's hub.challenge verification handshake. - POST /webhook: verify X-Hub-Signature-256 with META_APP_SECRET, ACK 200 immediately, process async. On each incoming DM: skip echoes of my own outbound messages (is_echo); dedupe by message id in SQLite; load the last 10 messages of that conversation from SQLite as context; generate a reply with Claude using the editable system prompt in persona.md; send it via POST https://graph.instagram.com/v23.0/me/messages with recipient.id set to the sender's IGSID; log both sides to SQLite. Guardrails: never reply twice to the same message id; hard cap of one auto-reply per user per two minutes; if the model's reply contains the token HUMAN, send nothing and log it for me instead. Write a README that walks through the Meta side, which is the actual hard part: create a Meta app, add the Instagram API with Instagram Login, generate a long-lived access token for my account, subscribe the app to the messages webhook field, and expose this server with a Cloudflare Tunnel. Note plainly that in development mode this works only for my own account, and that is the point. Deliberately out of scope: comment automation, multiple accounts, any CRM, Shopify or calendar integrations, and Meta App Review. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Soccial AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Soccial AI indie build ## Goal Build the smallest trustworthy replacement for the core Soccial AI workflow for one developer or a tiny team. ## Scope A small webhook server that receives your Instagram DMs and auto-replies through an LLM using your own system prompt. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Anyone-but-you: without Meta App Review your app only serves accounts you add as testers - Shopify and GoHighLevel context behind replies, and the CRM around the conversations - Comment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes - Someone else babysitting webhook uptime, token refresh, and Meta API deprecations If those capabilities are essential, use Chatwoot instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a self-hosted Instagram DM auto-responder for my own Instagram professional account, in this empty folder. Stack: Node 22 + Hono + better-sqlite3 + the Anthropic SDK (claude-sonnet-5). One small server, no frontend. Secrets in .env: IG_ACCESS_TOKEN, IG_ACCOUNT_ID, META_APP_SECRET, META_VERIFY_TOKEN, ANTHROPIC_API_KEY. Endpoints: - GET /webhook: Meta's hub.challenge verification handshake. - POST /webhook: verify X-Hub-Signature-256 with META_APP_SECRET, ACK 200 immediately, process async. On each incoming DM: skip echoes of my own outbound messages (is_echo); dedupe by message id in SQLite; load the last 10 messages of that conversation from SQLite as context; generate a reply with Claude using the editable system prompt in persona.md; send it via POST https://graph.instagram.com/v23.0/me/messages with recipient.id set to the sender's IGSID; log both sides to SQLite. Guardrails: never reply twice to the same message id; hard cap of one auto-reply per user per two minutes; if the model's reply contains the token HUMAN, send nothing and log it for me instead. Write a README that walks through the Meta side, which is the actual hard part: create a Meta app, add the Instagram API with Instagram Login, generate a long-lived access token for my account, subscribe the app to the messages webhook field, and expose this server with a Cloudflare Tunnel. Note plainly that in development mode this works only for my own account, and that is the point. Deliberately out of scope: comment automation, multiple accounts, any CRM, Shopify or calendar integrations, and Meta App Review. ## Required capabilities - Meta developer app (free) + Instagram professional account - Anthropic or OpenAI API key - Always-on public HTTPS endpoint (Cloudflare Tunnel on a Pi, or a $5 VPS) ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Soccial AI product brief ## Problem The code is the easy part; Meta is the hard part. The core loop, an LLM that auto-replies to your Instagram DMs, is a small webhook server any coding agent writes in an afternoon. But it only runs against a Meta developer app: you need an Instagram professional account, a public HTTPS endpoint that is up 24/7 (DMs arrive while your laptop is closed), webhook signature verification, and token refresh. And your app stays in development mode, which is fine for your own account but serving anyone else means Meta App Review plus business verification. What the subscription actually sells is connector upkeep across Instagram, Facebook, Shopify and GoHighLevel, hosted always-on webhooks, and a CRM wrapped around the conversations. ## Product outcome A small webhook server that receives your Instagram DMs and auto-replies through an LLM using your own system prompt. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Meta developer app (free) + Instagram professional account - Anthropic or OpenAI API key - Always-on public HTTPS endpoint (Cloudflare Tunnel on a Pi, or a $5 VPS) ## Explicit non-goals for v1 - Anyone-but-you: without Meta App Review your app only serves accounts you add as testers - Shopify and GoHighLevel context behind replies, and the CRM around the conversations - Comment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes - Someone else babysitting webhook uptime, token refresh, and Meta API deprecations ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a self-hosted Instagram DM auto-responder for my own Instagram professional account, in this empty folder. Stack: Node 22 + Hono + better-sqlite3 + the Anthropic SDK (claude-sonnet-5). One small server, no frontend. Secrets in .env: IG_ACCESS_TOKEN, IG_ACCOUNT_ID, META_APP_SECRET, META_VERIFY_TOKEN, ANTHROPIC_API_KEY. Endpoints: - GET /webhook: Meta's hub.challenge verification handshake. - POST /webhook: verify X-Hub-Signature-256 with META_APP_SECRET, ACK 200 immediately, process async. On each incoming DM: skip echoes of my own outbound messages (is_echo); dedupe by message id in SQLite; load the last 10 messages of that conversation from SQLite as context; generate a reply with Claude using the editable system prompt in persona.md; send it via POST https://graph.instagram.com/v23.0/me/messages with recipient.id set to the sender's IGSID; log both sides to SQLite. Guardrails: never reply twice to the same message id; hard cap of one auto-reply per user per two minutes; if the model's reply contains the token HUMAN, send nothing and log it for me instead. Write a README that walks through the Meta side, which is the actual hard part: create a Meta app, add the Instagram API with Instagram Login, generate a long-lived access token for my account, subscribe the app to the messages webhook field, and expose this server with a Cloudflare Tunnel. Note plainly that in development mode this works only for my own account, and that is the point. Deliberately out of scope: comment automation, multiple accounts, any CRM, Shopify or calendar integrations, and Meta App Review. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Soccial AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent
Because the product is the plumbing. Instagram automation is 10% LLM calls and 90% staying connected: OAuth token lifecycles, webhook receivers that never sleep, Meta App Review, deprecation churn across Instagram, Facebook, Shopify and GoHighLevel APIs, and a CRM so conversations turn into customers instead of scrollback.
xAnyone-but-you: without Meta App Review your app only serves accounts you add as testers
xShopify and GoHighLevel context behind replies, and the CRM around the conversations
xComment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes
xSomeone else babysitting webhook uptime, token refresh, and Meta API deprecations
share on X ↗"I just replaced Soccial AI ($139/mo) with one prompt"
Soccial AI pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| starter | $34 | $27.20 | 40 messages/day, Instagram only, 30-day conversation history |
| professional | $139 | $111.20 | 200 messages/day, 4 integrations, 500 Deep Think runs/month, 20 AI images/month |
| enterprise | $419 | $335.20 | 800 messages/day, 4 integrations, unlimited Deep Think, 100 premium AI images/month |
| managed soccial crm + done-for-you setup | $1049 | $839.42 | Everything in Enterprise plus dedicated CRM, custom domain, workflow build-out and 30-day onboarding |
free tierFree start allowance is 50 messages; the page does not state that it renews
billingmonthly + annual (-20%); early-adopter rates are locked for the lifetime of an uninterrupted subscription; cancel anytime
hidden costsEarly-adopter prices are temporary for new signups; managed CRM also has a one-time custom setup fee; daily message and monthly AI/image caps apply.
verified 2026-08-14 · source ↗
Vibecode Soccial AI
Kinda. The core of Soccial AI is buildable in a weekend with the prompt on this page, but there are real gaps: Anyone-but-you: without Meta App Review your app only serves accounts you add as testers, Shopify and GoHighLevel context behind replies, and the CRM around the conversations. Read the honest list above before committing.
How much does Soccial AI cost?
Soccial AI costs about $139/month (Professional, checked 2026-08-03), which is $1668 per year.
What do I lose by replacing Soccial AI?
Honestly: Anyone-but-you: without Meta App Review your app only serves accounts you add as testers; Shopify and GoHighLevel context behind replies, and the CRM around the conversations; Comment-to-DM automations, scheduled follow-ups, and human-approval gates on AI writes; Someone else babysitting webhook uptime, token refresh, and Meta API deprecations. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Soccial AI?
Yes: Chatwoot (open-source support inbox with an official Instagram DM channel, bring-your-own Meta app). Using prior art is also vibecoding; the prompt is for when you want it exactly your way.