| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a continuous execution loop where the agent generates thoughts via LLM, selects and executes commands, processes observations, and optionally applies self-criticism to refine behavior. The loop maintains state across iterations through a MiniAGI orchestrator class that coordinates ThinkGPT instances for reasoning and action generation, enabling multi-step task decomposition without external orchestration frameworks.
Unique: Uses a dual-ThinkGPT architecture where one instance generates agent actions and the other independently summarizes history, decoupling reasoning from memory compression and allowing different model configurations (e.g., GPT-4 for agent, GPT-3.5-turbo for summarizer) to optimize cost-performance tradeoffs.
vs alternatives: Lighter and more transparent than AutoGPT or BabyAGI because the entire loop is implemented in ~500 lines of Python with explicit state management, making it easier to understand, modify, and debug compared to framework-based alternatives.
Maintains a summarized_history buffer that condenses lengthy observations and action sequences to stay within a configurable MAX_CONTEXT_SIZE token limit. When observations exceed MAX_MEMORY_ITEM_SIZE, the summarizer ThinkGPT instance compresses them; when total history grows, older items are summarized and replaced. This approach preserves semantic meaning of past actions while freeing tokens for new reasoning, implemented via explicit summarization calls rather than sliding-window or retrieval-based approaches.
Unique: Implements a two-tier memory system where individual observations are summarized when they exceed MAX_MEMORY_ITEM_SIZE, and the entire history is re-summarized when approaching MAX_CONTEXT_SIZE, creating a cascading compression strategy that avoids sudden context drops.
vs alternatives: More explicit and controllable than RAG-based memory systems (e.g., LangChain's ConversationSummaryMemory) because token budgets are hard-coded and summarization is deterministic, making behavior predictable for cost-sensitive applications.
The agent is initialized with a user-provided objective (goal) and uses the think-act-criticize loop to decompose it into sub-tasks and execute them sequentially. The LLM reasons about what steps are needed to achieve the objective, selects appropriate commands, and iterates until the objective is complete (signaled by the done command). This approach enables flexible, adaptive task decomposition without requiring explicit task graphs or workflows.
Unique: Implements task decomposition implicitly through LLM reasoning rather than explicitly generating a task graph, allowing the agent to adapt its plan based on observations but making the overall strategy opaque to external observers.
vs alternatives: More flexible than predefined workflows because the agent can adapt its approach based on observations, but less transparent and potentially less efficient than explicit task planning systems.
MiniAGI can be deployed in a Docker container with environment variables and dependencies pre-configured. The Dockerfile specifies Python runtime, dependency installation, and entry point configuration, enabling reproducible agent execution across different environments. This provides OS-level isolation and dependency management without requiring manual setup.
Unique: Provides a pre-configured Docker setup that bundles the agent, dependencies, and runtime configuration, enabling one-command deployment without manual environment setup.
vs alternatives: Simpler than manual deployment because dependencies are pre-installed, but adds operational overhead compared to running the agent directly on the host system.
Provides a Commands class that exposes six executable actions: execute_python (runs arbitrary Python code in the agent's process), execute_shell (runs bash/shell commands), web_search (queries the web for information), talk_to_user (prompts for human input), ingest_data (loads files or URLs), and process_data (applies LLM-based transformation to data). The agent selects which command to execute based on LLM reasoning, and each command returns structured observations that feed back into the reasoning loop.
Unique: Integrates Python code execution directly into the agent loop without requiring separate sandboxing or containerization, allowing the agent to leverage the full Python ecosystem (numpy, pandas, requests, etc.) for data processing and computation within a single process.
vs alternatives: More flexible than tool-calling APIs (OpenAI functions, Anthropic tools) because it allows arbitrary Python code execution rather than predefined schemas, but trades safety and reproducibility for expressiveness.
The agent's think() method prompts the LLM to generate a thought, proposed_command, and proposed_arg in a structured format. The LLM output is parsed to extract the command name and argument, which are then validated against the Commands registry and executed. This approach uses the LLM as a decision-making engine that reasons about which action to take next, rather than using predefined workflows or decision trees.
Unique: Uses the LLM as a stateful decision engine that maintains context across multiple steps, allowing it to reason about the current state and select actions adaptively, rather than using a fixed decision tree or rule-based system.
vs alternatives: More flexible than ReAct-style agents because it doesn't require predefined tool schemas; the agent can reason about any command in the Commands registry without explicit tool definitions, but less robust than schema-validated function calling.
When ENABLE_CRITIC is set to true, the agent generates a criticism of its proposed action before execution, allowing it to reflect on whether the action is appropriate. The criticism is stored and can inform future decisions. This is implemented as an optional post-thinking step that calls the agent ThinkGPT instance again to evaluate the proposed command, adding an extra LLM call per step.
Unique: Implements self-criticism as an optional post-thinking step that evaluates the proposed action before execution, creating a two-stage reasoning process where the agent first decides what to do, then critiques its own decision.
vs alternatives: Simpler than multi-agent debate systems (e.g., LLM-based consensus) because it uses a single agent instance for both reasoning and criticism, reducing complexity and cost, but less robust because the agent may not effectively critique its own flawed reasoning.
When PROMPT_USER is enabled (default true), the agent pauses before executing each command and prompts the user for approval via stdin. The user can approve the action, provide feedback, or reject it. This implements a human-in-the-loop mechanism that prevents the agent from executing unintended or dangerous commands without explicit authorization.
Unique: Implements approval gating at the command execution level rather than at the planning level, meaning the agent completes its reasoning and selects an action before asking for approval, allowing humans to see the agent's full reasoning before deciding whether to allow execution.
vs alternatives: More transparent than silent autonomous execution because it exposes the agent's decisions to human review, but less efficient than fully autonomous agents because it introduces latency and requires human availability.
+4 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.
Supabase scores higher at 42/100 vs Mini AGI at 23/100.
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