| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Nanobrowser decomposes user natural language requests into structured task plans using a Planner agent, then executes those plans through a Navigator agent that performs granular browser actions. The system uses a message-passing architecture (chrome-extension/src/background/index.ts) where the background script routes commands between agents, maintains execution state, and coordinates action sequencing. The Planner generates step-by-step workflows while the Navigator translates those steps into concrete browser interactions, enabling complex multi-step automation without requiring users to write code.
Unique: Uses a specialized two-tier agent architecture (Planner + Navigator) where the Planner generates structured task graphs and the Navigator executes them with real-time DOM interaction, rather than a single monolithic agent making all decisions. This separation enables better reasoning (planning) and precise execution (navigation) without conflating concerns.
vs alternatives: Outperforms single-agent approaches like OpenAI Operator by decomposing reasoning from execution, reducing hallucination in action selection and enabling more reliable multi-step workflows.
Nanobrowser abstracts LLM provider differences through a factory pattern (createChatModel in chrome-extension/src/background/agent/helper.ts) that maps 11+ providers (OpenAI, Anthropic, Gemini, Ollama, Groq, Cerebras, Azure, OpenRouter, DeepSeek, Grok, Llama) to LangChain chat model implementations. Users configure providers and models via the Options page UI, which persists settings to the storage layer (packages/storage/lib/settings/llmProviders.ts). At runtime, the factory instantiates the correct LangChain ChatModel class with provider-specific parameters (API keys, endpoints, deployment names), enabling seamless provider switching without code changes.
Unique: Implements a declarative provider configuration system stored in extension storage (llmProviderStore) that decouples provider setup from agent code. The factory pattern in helper.ts maps provider enums directly to LangChain classes, enabling new providers to be added by extending the configuration schema without modifying agent logic.
vs alternatives: More flexible than OpenAI Operator (which locks users into OpenAI) by supporting 11+ providers including local Ollama, and more maintainable than hardcoded provider conditionals by using a factory pattern that centralizes provider instantiation.
Nanobrowser manages browser contexts and pages through Puppeteer, maintaining a reference to the current active page and browser instance. The system handles page lifecycle events (navigation, load, close) and maintains DOM snapshots for agent decision-making. The Browser Context and Page Management layer (referenced in Architecture Overview) abstracts Puppeteer's API, providing a simplified interface for agents to query page state, execute JavaScript, and interact with the DOM. This enables agents to understand the current page context before executing actions, reducing errors from stale DOM references.
Unique: Abstracts Puppeteer's page management API to provide agents with a simplified interface for querying page state and executing actions. The system maintains DOM snapshots that agents can use for decision-making, reducing errors from stale references.
vs alternatives: More reliable than raw Puppeteer scripts because the abstraction layer handles page lifecycle events and provides agents with current DOM snapshots, reducing race conditions and stale reference errors.
The Executor (chrome-extension/src/background/agent/executor.ts) manages task execution lifecycle, maintaining state for in-progress tasks and coordinating between the Planner and Navigator agents. It tracks task progress, captures execution logs, and handles errors or task cancellation. The executor maintains a queue of pending actions and executes them sequentially, updating task state after each action. This enables users to monitor task progress through the UI and provides a foundation for resuming interrupted tasks. The executor also captures detailed logs of agent decisions and action results, enabling post-execution analysis and debugging.
Unique: Implements a state machine for task execution that tracks progress through multiple phases (planning, action execution, result capture). The executor maintains detailed logs of agent decisions and action results, enabling post-execution analysis without requiring external logging infrastructure.
vs alternatives: More transparent than black-box automation by providing detailed execution logs and progress tracking, enabling users to understand what happened during task execution and debug failures.
The Options page (pages/options/src/components/ModelSettings.tsx) provides a user-friendly interface for configuring LLM providers, assigning models to agents, and setting domain firewall rules. The UI is built with React and communicates with the storage layer to persist settings. Users can add/remove providers, test API credentials, and preview available models for each provider. The Options page also includes language selection and other extension-wide settings. All configuration changes are immediately persisted to extension storage and take effect on the next task execution.
Unique: Provides a React-based Options page that abstracts provider configuration complexity, allowing users to configure 11+ LLM providers through a unified UI without understanding provider-specific API details. The UI is tightly integrated with the storage layer, ensuring settings are immediately persisted.
vs alternatives: More user-friendly than JSON configuration files or command-line tools, and more discoverable than hidden settings because the Options page is accessible through the standard Chrome extension UI.
The Navigator agent executes browser actions (click, type, scroll, extract text) by translating natural language or planner directives into Puppeteer commands that interact with the live DOM. The system uses Puppeteer integration (chrome-extension/src/background/agent/agents/navigator.ts) with anti-detection measures to avoid triggering bot-detection systems on target websites. Actions are executed against the current browser context and page, with real-time DOM snapshots captured to inform subsequent action decisions. The action system maintains a registry of supported actions (click, fill form, navigate, extract data) that the Navigator can invoke with structured parameters.
Unique: Integrates Puppeteer directly into the Chrome extension background script (rather than spawning external processes) and applies anti-detection techniques at the action execution layer, making it harder to detect automation compared to naive Puppeteer scripts. The action system is extensible — new actions can be registered without modifying the Navigator agent.
vs alternatives: More stealthy than raw Puppeteer scripts due to built-in anti-detection measures, and more flexible than Selenium by supporting modern browser APIs and JavaScript execution within the extension context.
Nanobrowser maintains a persistent chat history stored in the extension's local storage (packages/storage/lib/settings/types.ts) that records user messages, agent responses, and execution logs. The Side Panel Interface displays this history with a replay system that allows users to re-execute previous tasks or inspect what actions were taken. Users can bookmark favorite conversations or task templates, which are stored separately in the Favorites storage layer. The chat history system captures not just text but also metadata (timestamps, agent decisions, action sequences), enabling users to audit automation decisions and reuse successful workflows.
Unique: Combines chat history with a replay system that re-executes previous tasks, and a separate bookmarking layer for saving templates. This three-tier approach (history, replay, bookmarks) enables both audit trails and workflow reuse without conflating concerns.
vs alternatives: More comprehensive than simple chat logging by including replay capability and template bookmarking, enabling users to turn successful one-off automations into reusable workflows.
The Side Panel Interface includes a speech-to-text input system that converts user voice commands into text task descriptions, which are then processed by the Planner agent. The system uses the browser's Web Speech API to capture audio and transcribe it into natural language, which is passed to the LLM for task decomposition. This enables hands-free task specification — users can describe complex workflows verbally without typing, and the system converts speech into structured task plans.
Unique: Integrates Web Speech API directly into the extension's Side Panel UI, allowing voice input to be converted to task descriptions without requiring external speech services. The transcribed text flows directly into the Planner agent for task decomposition.
vs alternatives: More integrated than external voice assistants (e.g., Alexa, Google Assistant) by keeping voice input within the extension context and directly connecting it to task automation, reducing latency and external dependencies.
+5 more capabilities
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.
nanobrowser scores higher at 46/100 vs Supabase at 42/100.
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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