| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Receives incoming Telegram messages via polling or webhook, forwards them to OpenAI's ChatGPT API, and streams responses back using Server-Sent Events (SSE). The bot maintains a message processing loop that captures user input, sends it to ChatGPT's streaming endpoint, and progressively updates the Telegram message as tokens arrive, reducing perceived latency compared to waiting for full response completion.
Unique: Implements SSE-based streaming with in-place Telegram message editing rather than sending multiple separate messages, reducing chat clutter and providing a native streaming UX within Telegram's constraints. Uses Go's lightweight concurrency model to handle multiple user conversations simultaneously without blocking.
vs alternatives: Faster perceived response time than polling-based bots because streaming tokens update the same message in real-time; more efficient than webhook-based approaches because it maintains persistent connections to OpenAI's SSE stream.
Launches a headless browser window when no stored session token exists, guides the user through OpenAI's login flow, automatically extracts the session token from browser cookies, and persists it to the local config file for future use. This eliminates manual token extraction and handles session refresh transparently, supporting both interactive setup and programmatic authentication.
Unique: Uses browser automation to capture session tokens directly from cookies rather than requiring users to manually extract them, reducing setup friction. Stores tokens in platform-specific config directories (XDG_CONFIG_HOME on Linux, AppData on Windows) following OS conventions.
vs alternatives: More user-friendly than manual token extraction (which requires browser DevTools knowledge); more reliable than API key-based auth because it uses the same session mechanism as the web interface, avoiding API-specific limitations.
Manages bot configuration through a hybrid approach: environment variables (.env file) for runtime settings like TELEGRAM_TOKEN and EDIT_WAIT_SECONDS, combined with persistent JSON storage for stateful data like OpenAI session tokens. Configuration is loaded on startup, with environment variables taking precedence, and persistent state is written back to platform-specific config directories after authentication or updates.
Unique: Separates transient configuration (Telegram token, edit wait time) from stateful data (OpenAI session token) across two storage layers, allowing environment-based deployment while maintaining session persistence. Uses platform-specific config directories (XDG_CONFIG_HOME, AppData, Library) rather than hardcoded paths.
vs alternatives: More flexible than single-file config because it supports both containerized (env vars) and local (persistent JSON) deployments; more secure than embedding secrets in code, though less secure than encrypted vaults.
Buffers streaming ChatGPT tokens and updates the Telegram message at configurable intervals (default ~1 second via EDIT_WAIT_SECONDS) rather than on every token, respecting Telegram's rate limits (~1 edit per second per message). This prevents API throttling errors and reduces network overhead while maintaining perceived real-time streaming by batching multiple tokens into single edit operations.
Unique: Implements configurable token batching with a timer-based approach rather than fixed batch sizes, allowing operators to tune streaming feel without code changes. Respects Telegram's documented 1-edit-per-second limit by design rather than retrying on throttle errors.
vs alternatives: More predictable than naive streaming (which hits rate limits immediately); more responsive than sending complete responses as separate messages because updates happen in-place.
Optionally restricts bot access to a single Telegram user by checking the incoming message sender's ID against a configured TELEGRAM_ID value. When set, only messages from that user ID are processed; all others are silently ignored. This provides a simple access control mechanism without requiring a full authentication system, suitable for personal bot deployments.
Unique: Provides optional single-user allowlisting via environment variable rather than requiring a full user database or authentication system. Fails open (accepts all users) if TELEGRAM_ID is not set, making it opt-in rather than forcing configuration.
vs alternatives: Simpler than OAuth-based access control for personal deployments; more secure than no access control, though less flexible than role-based systems.
Provides pre-compiled binaries for macOS (Intel/ARM), Linux (x86/ARM), and Windows, eliminating the need for users to compile from source. Additionally offers a Docker image (ghcr.io/m1guelpf/chatgpt-telegram) that bundles the binary with runtime dependencies, allowing deployment via container orchestration with volume mounts for persistent config and environment variable injection for secrets.
Unique: Distributes pre-compiled binaries for 5 platform variants (macOS Intel/ARM, Linux x86/ARM, Windows) alongside a Docker image, eliminating compilation friction for both local and containerized deployments. Uses GitHub Releases for binary hosting and ghcr.io for container registry.
vs alternatives: Faster to deploy than source-based installation because no compilation is required; more portable than Docker-only distribution because it supports bare-metal and local development.
Processes each incoming Telegram message independently without maintaining conversation history or context between messages. Each message is sent to ChatGPT as a standalone request, and responses are isolated to that single message. This stateless design simplifies deployment and avoids memory leaks from unbounded conversation history, but requires users to provide full context in each message if they want multi-turn conversations.
Unique: Deliberately avoids conversation state management, treating each message as independent. This simplifies deployment and prevents memory leaks, but trades off multi-turn conversation capability. Contrasts with stateful bots that maintain conversation history.
vs alternatives: More memory-efficient and simpler to deploy than stateful bots because no history storage is needed; less capable for multi-turn conversations, making it suitable only for single-query use cases.
Communicates with OpenAI's ChatGPT API using session-based authentication (session tokens extracted from browser cookies) rather than API keys. Sends user messages to OpenAI's streaming endpoint, receives Server-Sent Events (SSE) with token-by-token responses, and handles streaming response parsing. This approach mirrors the web interface's authentication mechanism, avoiding API key management and supporting the same session lifecycle as the browser.
Unique: Uses session token authentication (reverse-engineered from browser behavior) instead of official OpenAI API keys, allowing users to leverage existing web accounts. Implements SSE parsing to handle streaming responses token-by-token rather than waiting for complete responses.
vs alternatives: Avoids API key management and works with free OpenAI accounts; less reliable than official API because it's not officially supported and may break if OpenAI changes their web interface.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Go Telegram bot at 20/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities