| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Accepts voice messages via Telegram, transcribes them to text using configurable STT providers (Whisper, Google Cloud Speech-to-Text, or local alternatives), sends the transcribed prompt to OpenCode as a coding task, and streams back responses with optional TTS synthesis for voice playback. The pipeline integrates grammy's voice message handling with the @opencode-ai/sdk's event stream, buffering audio chunks and managing provider-specific authentication and format conversion.
Unique: Implements a bidirectional voice pipeline that bridges Telegram's voice message API with OpenCode's SSE event stream, supporting multiple STT/TTS providers via environment-based configuration and managing audio format conversion (Telegram OGG → provider-specific format) without intermediate file storage.
vs alternatives: Unlike OpenClaw's web-only interface, this bot enables voice-first mobile interaction with local OpenCode execution, reducing context switching for developers on the go.
Consumes Server-Sent Events (SSE) from the OpenCode SDK's event stream, aggregates multi-event sequences (task start, model selection, context consumption, file changes, task completion) into a single coherent state, and maintains a persistent pinned Telegram message that updates in-place with live metrics: token usage, context window consumption, list of modified files, and agent status. Uses a SummaryAggregator class to deduplicate events, calculate deltas, and format structured data into Telegram's MarkdownV2 syntax.
Unique: Implements a SummaryAggregator pattern that deduplicates and coalesces SSE events into a single mutable pinned message, avoiding Telegram chat spam while maintaining real-time visibility. Uses MarkdownV2 formatting with careful escaping to render structured metrics (token counts, file diffs) in a mobile-friendly compact layout.
vs alternatives: Provides better observability than OpenClaw's web dashboard for mobile users by consolidating multi-event sequences into a single pinned status, reducing API calls and chat clutter while maintaining real-time updates.
Supports running the bot as a background daemon process on Linux/macOS using systemd or similar process managers. Provides configuration templates and setup guides for systemd service files, environment variable management, and log rotation. Enables the bot to start automatically on system boot and restart on failure, making it suitable for always-on local execution.
Unique: Provides systemd service templates and setup guides that enable the bot to run as a background daemon with automatic restart on failure, suitable for always-on local execution without manual intervention.
vs alternatives: Enables production-grade deployment of the bot as a local service, unlike OpenClaw's web-only model which requires manual server management.
Implements comprehensive error handling for common failure scenarios: OpenCode server unavailable, invalid session/project, task submission errors, SSE connection drops, and API rate limits. Translates technical errors into user-friendly Telegram messages with suggested remediation steps (e.g., 'Server is offline, please check localhost:8000'). Includes retry logic for transient failures and graceful degradation when features are unavailable.
Unique: Translates technical errors into user-friendly Telegram messages with remediation suggestions, implementing retry logic for transient failures and graceful degradation for unavailable features.
vs alternatives: Provides better error visibility and recovery than OpenClaw's web interface, with mobile-friendly error messages and automatic retry logic for common failures.
Provides a command-line interface (CLI) for starting the bot with configurable options: Telegram token, OpenCode server URL, STT/TTS provider selection, locale, and logging level. Parses arguments using a custom args parser, validates configuration, and loads environment variables from .env files. Supports both global npm installation (via npx) and direct execution, with clear error messages for missing or invalid configuration.
Unique: Implements a custom CLI argument parser that validates configuration and loads environment variables, supporting both npx and global npm installation with clear error messages for missing or invalid options.
vs alternatives: Provides flexible configuration management that OpenClaw's web interface doesn't support, allowing developers to customize bot behavior via CLI arguments and environment variables.
Implements a state machine that intercepts OpenCode agent questions and permission requests (e.g., 'Should I modify this file?', 'Which model should I use?') via SSE events, renders them as Telegram inline keyboard buttons, captures user responses, and sends them back to OpenCode via the SDK's interaction API. The Interaction Guard class manages state transitions, prevents concurrent interactions, and ensures responses are routed to the correct agent context (session, project, task).
Unique: Uses a dedicated Interaction Guard state machine that maps Telegram callback_query events to OpenCode SDK interaction responses, preventing concurrent interactions and ensuring responses are routed to the correct task context. Integrates grammy's callback_query handler with the SDK's interaction API, managing the full round-trip from question to response.
vs alternatives: Enables mobile-first approval workflows that OpenClaw's web interface doesn't support, allowing developers to respond to agent questions from anywhere without returning to their desktop.
Provides commands to list, create, and switch between OpenCode sessions and projects, mirroring the TUI's session management. Internally uses the OpenCode SDK to query available projects, manage git worktrees (creating isolated working directories for parallel work), and maintain session state (current project, branch, uncommitted changes). Stores session context in memory and persists it across bot restarts via environment variables or a local state file.
Unique: Mirrors OpenCode TUI's session management by wrapping the SDK's project and session APIs, providing Telegram commands that abstract away git worktree creation and branch switching. Maintains session state in memory with optional persistence, allowing users to manage multiple projects without manual git operations.
vs alternatives: Provides mobile-friendly project switching that OpenClaw doesn't expose, allowing developers to manage multiple concurrent feature branches directly from Telegram without returning to the CLI.
Accepts natural language scheduling descriptions (e.g., 'every Monday at 9am', 'daily at 3pm', 'once tomorrow at 2pm') via Telegram message, parses them using a scheduling library (likely node-cron or similar), generates cron expressions, and registers recurring or one-time tasks with the OpenCode server. The bot stores scheduled task definitions and executes them on a schedule, submitting the associated coding prompt to OpenCode at the specified time.
Unique: Implements natural language scheduling that converts user-friendly descriptions into cron expressions, storing task definitions and executing them on a schedule. Integrates with OpenCode's task submission API to run coding tasks at specified times without requiring manual CLI invocation.
vs alternatives: Provides lightweight task scheduling without a full CI/CD pipeline, allowing developers to automate routine coding tasks directly from Telegram with natural language syntax instead of cron syntax.
+5 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs opencode-telegram-bot at 38/100. However, opencode-telegram-bot offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities