{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mini-agi","slug":"mini-agi","name":"Mini AGI","type":"agent","url":"https://github.com/muellerberndt/mini-agi","page_url":"https://unfragile.ai/mini-agi","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mini-agi__cap_0","uri":"capability://planning.reasoning.autonomous.agent.loop.with.think.act.criticize.cycle","name":"autonomous agent loop with think-act-criticize cycle","description":"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.","intents":["Run an autonomous agent that can reason about complex objectives and break them into executable steps","Enable an agent to learn from its own actions and refine behavior through self-criticism","Build a self-contained agent that doesn't require external workflow engines or state management systems"],"best_for":["Solo developers building lightweight autonomous agents for research or prototyping","Teams needing a minimal-dependency agent framework that fits in a single Python file","Builders experimenting with agent behavior without committing to heavyweight frameworks like LangChain or AutoGPT"],"limitations":["No built-in persistence — agent state and memory are ephemeral unless manually serialized","Single-threaded execution loop blocks on LLM calls; no concurrent task execution","Context window management relies on manual summarization rather than intelligent retrieval, causing information loss on long-running tasks","Self-criticism is optional and not integrated into the core loop, requiring explicit enablement via ENABLE_CRITIC flag"],"requires":["Python 3.7+","OpenAI API key (OPENAI_API_KEY environment variable)","ThinkGPT library (custom dependency from repository)","Internet connectivity for LLM calls and web search"],"input_types":["text (objective/goal description)","environment variables (configuration)","user input (via talk_to_user command)"],"output_types":["text (agent thoughts, final results)","structured logs (action history, memory items)","side effects (executed Python code, shell commands, web searches)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_1","uri":"capability://memory.knowledge.context.aware.memory.summarization.with.token.budgeting","name":"context-aware memory summarization with token budgeting","description":"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.","intents":["Keep an agent operating within OpenAI token limits on long-running tasks without losing critical context","Automatically compress verbose command outputs (e.g., large file listings, API responses) into concise summaries","Maintain awareness of past actions while staying under configurable memory budgets"],"best_for":["Developers building agents that must run for many steps without hitting context window limits","Teams with strict token budgets who need to minimize API costs on long-running tasks","Researchers experimenting with memory compression strategies in autonomous agents"],"limitations":["Summarization is lossy — specific details in compressed observations may be discarded, potentially causing the agent to miss important information","Summarization adds latency (~500ms-2s per compression) and additional API calls, increasing total cost and execution time","No semantic indexing or retrieval — the agent cannot selectively recall specific past actions; it only sees the full compressed history","MAX_MEMORY_ITEM_SIZE and MAX_CONTEXT_SIZE are static; no dynamic adjustment based on task complexity or remaining tokens"],"requires":["OpenAI API key for summarizer model calls","Configuration of MAX_CONTEXT_SIZE (default 4000 tokens) and MAX_MEMORY_ITEM_SIZE (default 2000 tokens)","SUMMARIZER_MODEL environment variable (default gpt-3.5-turbo)"],"input_types":["text (observations from command execution)","structured memory items (action history)"],"output_types":["text (summarized observations)","structured history (compressed action sequence)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_10","uri":"capability://planning.reasoning.objective.driven.task.decomposition.via.llm.reasoning","name":"objective-driven task decomposition via llm reasoning","description":"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.","intents":["Enable an agent to break down a high-level objective into executable sub-tasks","Allow the agent to adapt its task decomposition based on observations and feedback","Build agents that can handle open-ended objectives without predefined workflows"],"best_for":["Developers building agents for open-ended problems with variable solution paths","Teams deploying agents for exploratory or research tasks","Builders experimenting with LLM-driven task planning"],"limitations":["Task decomposition is implicit in LLM reasoning; no explicit task graph or plan is generated, making it difficult to understand the overall strategy","No mechanism to validate that the decomposed tasks will actually achieve the objective; the agent may pursue irrelevant sub-tasks","LLM reasoning can be inefficient, causing the agent to take unnecessary steps or get stuck in loops","No backtracking or replanning if a sub-task fails; the agent must be prompted to recover","Objective completion is determined by the agent calling the done command; no external validation that the objective was actually achieved"],"requires":["OpenAI API key","MODEL environment variable (default gpt-4)","Clear, well-defined objective provided at initialization"],"input_types":["text (objective/goal description)"],"output_types":["text (final result when done command is called)","side effects (executed commands, created files, etc.)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_11","uri":"capability://automation.workflow.docker.containerization.for.isolated.execution","name":"docker containerization for isolated execution","description":"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.","intents":["Deploy an agent in a reproducible, isolated environment without dependency conflicts","Run agents in cloud platforms or Kubernetes clusters that support containerization","Simplify agent deployment by bundling all dependencies and configuration"],"best_for":["Teams deploying agents to cloud platforms (AWS, GCP, Azure)","Developers building multi-agent systems where isolation is important","Builders deploying agents in production environments"],"limitations":["Docker adds operational complexity; requires Docker runtime and knowledge of containerization","Container images are larger than the agent code itself, increasing storage and deployment time","No built-in orchestration; requires external tools (Kubernetes, Docker Compose) for multi-agent management","Container isolation is at the OS level; the agent still has full access to the container's file system and resources","Environment variables must be passed at container startup; no runtime configuration changes without restarting"],"requires":["Docker runtime installed","Dockerfile in the repository","Environment variables for configuration (OPENAI_API_KEY, MODEL, etc.)"],"input_types":["Docker image configuration","environment variables (passed at container startup)"],"output_types":["containerized agent executable"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_2","uri":"capability://tool.use.integration.multi.command.execution.engine.with.python.shell.and.web.search","name":"multi-command execution engine with python, shell, and web search","description":"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.","intents":["Enable an agent to execute arbitrary Python code to perform computations, data manipulation, or library calls","Allow an agent to run system commands for file operations, environment setup, or external tool invocation","Empower an agent to search the web for real-time information without being limited to training data","Enable an agent to request human guidance or approval before taking critical actions"],"best_for":["Developers building research agents that need to run experiments or data analysis","Teams deploying agents in environments where shell access is available and sandboxing is not required","Builders prototyping agent capabilities without security constraints (e.g., internal tools, research)"],"limitations":["execute_python runs code in the same process as the agent, creating security risks if the agent is compromised or behaves unexpectedly","No sandboxing or resource limits — a malicious or buggy command can consume unlimited CPU, memory, or disk","execute_shell has no built-in timeout; long-running commands can block the agent indefinitely","web_search implementation details are not specified in the architecture; likely uses a simple API wrapper without caching or rate-limiting","No command validation or approval mechanism beyond optional PROMPT_USER flag; agent can execute dangerous commands without warning"],"requires":["Python 3.7+ with subprocess module available","Bash or compatible shell for execute_shell","Internet connectivity and web search API credentials (implementation-dependent)","File system access for ingest_data and process_data"],"input_types":["text (Python code as string)","text (shell command as string)","text (search query)","text (file path or URL for ingest_data)","text (data and prompt for process_data)"],"output_types":["text (command output/stdout)","text (error messages/stderr)","structured data (search results)","file artifacts (from shell commands)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_3","uri":"capability://planning.reasoning.llm.driven.action.selection.with.structured.command.parsing","name":"llm-driven action selection with structured command parsing","description":"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.","intents":["Let the LLM decide which action to take based on the current objective and observations","Enable flexible, adaptive behavior where the agent can choose different command sequences for different problems","Implement a reasoning-first architecture where the agent explains its thinking before acting"],"best_for":["Developers building agents for open-ended problems where the solution path is not predetermined","Teams experimenting with chain-of-thought reasoning in autonomous agents","Researchers studying how LLMs make decisions in multi-step tasks"],"limitations":["Parsing LLM output is fragile — if the model deviates from the expected format, the agent fails silently or throws exceptions","No explicit validation of proposed commands before execution; the agent trusts the LLM to select valid commands","LLM reasoning is opaque — the agent's decision-making process is not easily interpretable or debuggable","No fallback mechanism if the LLM selects an invalid command; the agent must be prompted to retry","Token cost scales with reasoning depth — longer chains of thought consume more tokens per step"],"requires":["OpenAI API key","MODEL environment variable (default gpt-4)","Structured prompt engineering to ensure LLM outputs match expected format"],"input_types":["text (objective)","text (summarized history)","text (recent observations)"],"output_types":["text (agent thought)","text (selected command name)","text (command argument)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_4","uri":"capability://planning.reasoning.optional.self.criticism.mechanism.for.behavior.refinement","name":"optional self-criticism mechanism for behavior refinement","description":"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.","intents":["Enable an agent to second-guess its own decisions and avoid obviously incorrect actions","Implement a feedback loop where the agent learns from self-reflection","Add an extra layer of reasoning to improve decision quality on complex tasks"],"best_for":["Developers building agents for high-stakes tasks where incorrect actions have significant consequences","Teams experimenting with self-reflection in autonomous agents","Researchers studying how self-criticism affects agent performance and decision-making"],"limitations":["Self-criticism doubles the LLM calls per step (one for thinking, one for criticism), increasing latency and cost by ~50%","Criticism is generated but not automatically acted upon — the agent still executes the proposed command even if criticism suggests it's wrong","No mechanism to reject or modify actions based on criticism; the agent must be prompted to reconsider","Criticism quality depends on the LLM's ability to self-evaluate, which may be limited for complex or novel tasks","ENABLE_CRITIC is a binary flag with no granularity — criticism is either always on or always off"],"requires":["OpenAI API key","ENABLE_CRITIC environment variable set to true","MODEL environment variable (default gpt-4)"],"input_types":["text (proposed command)","text (proposed argument)","text (current objective and context)"],"output_types":["text (criticism feedback)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_5","uri":"capability://automation.workflow.user.approval.gating.with.interactive.prompts","name":"user approval gating with interactive prompts","description":"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.","intents":["Prevent an autonomous agent from executing dangerous or unintended commands without human oversight","Enable interactive debugging where a human can guide the agent's behavior in real-time","Implement a safety mechanism for agents running in production or high-stakes environments"],"best_for":["Developers building agents for interactive use cases where human oversight is required","Teams deploying agents in environments where safety and auditability are critical","Researchers studying human-in-the-loop agent behavior"],"limitations":["Requires synchronous user input via stdin; blocks the agent indefinitely if no input is provided","No timeout mechanism — the agent will wait forever for user approval, making it unsuitable for unattended operation","User approval is binary (yes/no); no mechanism for users to suggest alternative actions or provide detailed feedback","PROMPT_USER is a global flag that applies to all commands; no granular control over which commands require approval","Approval prompts are printed to stdout, making it difficult to integrate with non-interactive systems or APIs"],"requires":["Interactive terminal or stdin stream","PROMPT_USER environment variable (default true)","User availability to respond to prompts in real-time"],"input_types":["text (user approval/rejection via stdin)"],"output_types":["text (approval prompt to stdout)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_6","uri":"capability://automation.workflow.configurable.model.selection.with.cost.performance.optimization","name":"configurable model selection with cost-performance optimization","description":"The agent and summarizer can be configured to use different OpenAI models via MODEL and SUMMARIZER_MODEL environment variables. This allows builders to optimize cost and performance by using cheaper models (e.g., GPT-3.5-turbo) for summarization and more capable models (e.g., GPT-4) for reasoning. The configuration is applied at initialization time and affects all subsequent LLM calls.","intents":["Reduce API costs by using cheaper models for non-critical tasks like summarization","Improve reasoning quality by using more capable models for the main agent loop","Experiment with different model combinations to find optimal cost-performance tradeoffs"],"best_for":["Teams with strict API budgets who need to optimize model selection","Developers experimenting with different model capabilities and costs","Builders deploying agents at scale where model costs are a significant factor"],"limitations":["Model selection is static at initialization; no dynamic switching based on task complexity or remaining budget","No cost tracking or budgeting mechanism; builders must manually monitor API usage","Model capabilities vary significantly; using a weaker model for reasoning may cause the agent to fail on complex tasks","No fallback mechanism if the selected model is unavailable or deprecated","Configuration is environment-variable based, making it difficult to manage multiple agent instances with different configurations"],"requires":["OpenAI API key","MODEL environment variable (default gpt-4)","SUMMARIZER_MODEL environment variable (default gpt-3.5-turbo)","Access to the specified models via OpenAI API"],"input_types":["environment variables (model names)"],"output_types":["configuration state (selected models)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_7","uri":"capability://data.processing.analysis.file.and.url.data.ingestion.with.llm.based.processing","name":"file and url data ingestion with llm-based processing","description":"The ingest_data command loads data from files or URLs and returns the raw content. The process_data command takes ingested data and applies an LLM-based transformation using a user-provided prompt, enabling the agent to extract, summarize, or restructure data. This allows the agent to work with external data sources and apply semantic processing without requiring specialized data parsing libraries.","intents":["Enable an agent to load and process data from external files or web sources","Apply LLM-based transformations to unstructured data (e.g., summarization, extraction, reformatting)","Integrate external data sources into the agent's reasoning loop"],"best_for":["Developers building agents that need to work with external datasets or documents","Teams using agents for data analysis or information extraction tasks","Researchers experimenting with LLM-based data processing in autonomous agents"],"limitations":["No built-in support for structured data formats (JSON, CSV, XML); all data is treated as raw text","Large files may exceed token limits when passed to the LLM for processing, causing failures","No caching of ingested data; each ingest_data call re-fetches the data, wasting bandwidth and API calls","process_data relies on LLM interpretation of the user prompt; results may be inconsistent or incorrect for complex transformations","No error handling for malformed files or unreachable URLs; failures are not gracefully handled"],"requires":["File system access for local files","Internet connectivity for URL-based ingestion","OpenAI API key for process_data LLM calls"],"input_types":["text (file path or URL)","text (data and transformation prompt for process_data)"],"output_types":["text (raw file/URL content)","text (LLM-processed data)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_8","uri":"capability://automation.workflow.working.directory.isolation.and.file.system.scoping","name":"working directory isolation and file system scoping","description":"The WORK_DIR environment variable (default ~/miniagi) specifies a working directory where the agent operates. File operations and shell commands are scoped to this directory, providing basic isolation and preventing the agent from accidentally modifying files outside its intended scope. This is implemented via environment variable configuration rather than OS-level sandboxing.","intents":["Prevent an agent from accidentally modifying files outside its intended working directory","Organize agent artifacts and outputs in a dedicated directory","Provide basic isolation for multi-agent scenarios where different agents operate on different datasets"],"best_for":["Developers building agents that need basic file system isolation","Teams deploying multiple agents that should not interfere with each other's files","Builders prototyping agents without full containerization or sandboxing"],"limitations":["Isolation is not enforced at the OS level; the agent can still access files outside WORK_DIR via absolute paths or symlinks","No permission-based access control; the agent has the same file permissions as the user running the process","WORK_DIR is a single directory; no support for multiple isolated directories or hierarchical scoping","No audit trail of file operations; builders cannot track which files were modified or accessed","Symbolic links and relative path traversal (e.g., ../../../etc/passwd) can bypass the intended isolation"],"requires":["WORK_DIR environment variable (default ~/miniagi)","Write permissions to the specified directory","Bash or compatible shell for shell commands"],"input_types":["environment variables (WORK_DIR)"],"output_types":["file artifacts (created in WORK_DIR)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mini-agi__cap_9","uri":"capability://automation.workflow.debug.logging.and.execution.tracing","name":"debug logging and execution tracing","description":"When DEBUG is set to true, the agent prints detailed execution traces including thoughts, proposed commands, observations, and memory state to stdout. This provides visibility into the agent's decision-making process and helps developers understand why the agent is taking specific actions. Debug output is unstructured text rather than structured logs.","intents":["Debug agent behavior by observing its reasoning and decision-making in real-time","Understand why an agent is taking unexpected actions or failing on a task","Trace the agent's execution path through a multi-step task"],"best_for":["Developers building and debugging agents","Teams troubleshooting agent failures or unexpected behavior","Researchers studying agent reasoning and decision-making"],"limitations":["Debug output is unstructured text; no machine-readable format for parsing or analysis","Debug output is printed to stdout, making it difficult to separate from normal output or redirect to logs","No log levels or filtering; all debug information is printed regardless of relevance","DEBUG is a binary flag; no granular control over which components to debug","Debug output can be very verbose for long-running agents, making it difficult to find relevant information"],"requires":["DEBUG environment variable set to true"],"input_types":["environment variables (DEBUG)"],"output_types":["text (debug traces to stdout)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","OpenAI API key (OPENAI_API_KEY environment variable)","ThinkGPT library (custom dependency from repository)","Internet connectivity for LLM calls and web search","OpenAI API key for summarizer model calls","Configuration of MAX_CONTEXT_SIZE (default 4000 tokens) and MAX_MEMORY_ITEM_SIZE (default 2000 tokens)","SUMMARIZER_MODEL environment variable (default gpt-3.5-turbo)","OpenAI API key","MODEL environment variable (default gpt-4)","Clear, well-defined objective provided at initialization"],"failure_modes":["No built-in persistence — agent state and memory are ephemeral unless manually serialized","Single-threaded execution loop blocks on LLM calls; no concurrent task execution","Context window management relies on manual summarization rather than intelligent retrieval, causing information loss on long-running tasks","Self-criticism is optional and not integrated into the core loop, requiring explicit enablement via ENABLE_CRITIC flag","Summarization is lossy — specific details in compressed observations may be discarded, potentially causing the agent to miss important information","Summarization adds latency (~500ms-2s per compression) and additional API calls, increasing total cost and execution time","No semantic indexing or retrieval — the agent cannot selectively recall specific past actions; it only sees the full compressed history","MAX_MEMORY_ITEM_SIZE and MAX_CONTEXT_SIZE are static; no dynamic adjustment based on task complexity or remaining tokens","Task decomposition is implicit in LLM reasoning; no explicit task graph or plan is generated, making it difficult to understand the overall strategy","No mechanism to validate that the decomposed tasks will actually achieve the objective; the agent may pursue irrelevant sub-tasks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mini-agi","compare_url":"https://unfragile.ai/compare?artifact=mini-agi"}},"signature":"tzort/lFcVfGonAkbxsPxEw9h9bWqG/ItD0POz9F8kzKx7IrJ/IFdwxi3a2ti7lskJol5KPLy4Q+jKTATW72CQ==","signedAt":"2026-06-21T07:13:22.509Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mini-agi","artifact":"https://unfragile.ai/mini-agi","verify":"https://unfragile.ai/api/v1/verify?slug=mini-agi","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}