| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides create_deep_agent() factory function that returns a fully-configured LangGraph compiled graph with planning, tool calling, and context management pre-wired. Eliminates manual prompt engineering and graph construction by bundling opinionated defaults for system prompts, tool schemas, and execution flow. Supports provider-agnostic LLM selection (Anthropic, OpenAI, Google, etc.) via LangChain's model registry.
Unique: Returns a LangGraph CompiledGraph directly rather than an agent class, enabling native streaming, checkpointing, and state persistence without wrapper abstractions. Bundles planning tool, filesystem backend, and context management into a single factory call instead of requiring manual middleware composition.
vs alternatives: Faster to production than AutoGPT or LangChain's AgentExecutor because it pre-configures planning, tool schemas, and memory in one call rather than requiring developers to manually wire each component.
Implements a composable middleware system that intercepts tool calls before execution, allowing custom logic injection for logging, validation, sandboxing, and result transformation. Middleware stack processes each tool invocation through registered handlers in sequence, with support for early termination and result eviction. Built on LangGraph's node-level hooks, enabling fine-grained control over tool execution without modifying core agent logic.
Unique: Middleware system operates at the LangGraph node level rather than as a wrapper around tool calls, enabling state-aware interception and result eviction without re-executing the agent's reasoning loop. Supports custom handlers that can modify, reject, or transform tool results before they're fed back to the LLM.
vs alternatives: More flexible than tool-wrapping approaches because middleware can access full agent state and modify execution flow, whereas simple tool decorators only see individual tool invocations in isolation.
Supports deploying agents as remote services via the 'deepagents deploy' command, exposing agents over HTTP/gRPC for client-server execution. Clients can invoke remote agents via a standardized protocol, with support for streaming responses and long-running tasks. Integrates with container orchestration platforms (Docker, Kubernetes) for scalable deployment.
Unique: Deployment is built into the framework via 'deepagents deploy' command, not a separate DevOps concern. Agents are deployed as-is without modification; the framework handles serialization, streaming, and protocol translation.
vs alternatives: Simpler than building custom API wrappers around agents because the framework handles protocol translation, streaming, and state management automatically.
Integrates with remote sandbox providers (Daytona, RunLoop, Modal, QuickJS) to execute code and tools in isolated environments rather than the agent's local process. Supports multiple sandbox backends with a unified interface; agents can switch providers at runtime. Enables safe execution of untrusted code or resource-intensive operations without impacting the agent's process.
Unique: Sandbox integration is abstracted through a unified interface; agents don't need to know which provider is being used. Supports multiple providers simultaneously for failover and load balancing.
vs alternatives: More flexible than single-provider sandboxing because it supports multiple backends and allows switching providers without changing agent code.
CLI agents can automatically discover and inject local files and directory context into the agent's system prompt, enabling agents to be aware of the current working directory and available files. Supports glob patterns for selective file inclusion and automatic content summarization for large files. Enables agents to understand the local environment without explicit file listing commands.
Unique: Context injection is integrated into the CLI agent creation flow, automatically discovering and summarizing local files without explicit agent configuration. Supports selective inclusion via glob patterns.
vs alternatives: More convenient than manually listing files because the agent discovers context automatically, and more efficient than having agents list files themselves because context is injected upfront.
Integrates with the Harbor evaluation framework to benchmark agent performance on standardized tasks and datasets. Supports defining evaluation tasks, running agents against them, and collecting metrics (success rate, latency, cost, tool usage). Enables comparing different agent configurations, models, and strategies on the same benchmarks.
Unique: Evaluation framework is integrated into the deepagents package, not a separate tool. Agents can be evaluated without modification; the framework handles task execution and metric collection.
vs alternatives: More integrated than external evaluation tools because it understands agent-specific metrics (tool usage, planning steps) and can evaluate agents without custom instrumentation.
Implements support for the Agent Client Protocol (ACP), a standardized protocol for client-agent communication. Enables deepagents to interoperate with other ACP-compliant tools and frameworks, allowing agents to be invoked from different clients and integrated into larger systems. Handles protocol translation and ensures compatibility with ACP specifications.
Unique: ACP support is built into the framework, not bolted on as a wrapper. Agents automatically expose ACP-compliant interfaces without modification.
vs alternatives: More standardized than custom integration protocols because ACP is a shared standard, enabling agents to work with multiple clients and frameworks without custom adapters.
Enables parent agents to spawn child agents (sub-agents) for specific subtasks, with automatic task decomposition and result aggregation. Sub-agents inherit parent's tools, memory, and configuration but execute in isolated contexts, allowing parallel or sequential delegation. Implemented via LangGraph's subgraph pattern, where each sub-agent is a compiled graph invoked as a node in the parent's execution flow.
Unique: Sub-agents are full LangGraph compiled graphs invoked as nodes in parent's graph, enabling true isolation and streaming support rather than simple function calls. Allows sub-agents to have their own planning loops, tool access, and memory while remaining coordinated by parent.
vs alternatives: More robust than sequential tool calling because sub-agents can reason independently and make their own tool decisions, whereas a single agent trying to handle all subtasks may lose focus or make suboptimal tool choices.
+7 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.
deepagents scores higher at 47/100 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