{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-auto-gpt","slug":"auto-gpt","name":"Auto-GPT","type":"agent","url":"https://github.com/Significant-Gravitas/AutoGPT","page_url":"https://unfragile.ai/auto-gpt","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-auto-gpt__cap_0","uri":"capability://planning.reasoning.autonomous.task.decomposition.and.execution","name":"autonomous-task-decomposition-and-execution","description":"Auto-GPT implements a loop-based autonomous agent that decomposes high-level user goals into discrete subtasks, executes them sequentially, and iteratively refines based on outcomes. The system uses GPT-4 as a reasoning engine to generate task plans, execute actions via tool integrations, and evaluate progress without human intervention between steps. This creates a self-directed workflow where the agent maintains context across multiple reasoning cycles and adapts its strategy based on intermediate results.","intents":["I want to give an AI a goal and have it figure out the steps needed to accomplish it without asking me for input","I need an agent that can break down complex multi-step problems and execute them autonomously","I want to automate workflows that require decision-making and adaptation based on intermediate results"],"best_for":["researchers exploring autonomous AI agent architectures","developers prototyping self-directed automation systems","teams building experimental LLM-powered task runners"],"limitations":["No built-in error recovery or rollback mechanisms — failed subtasks may leave system in inconsistent state","Context window limitations mean long task chains may lose early reasoning context","No persistent memory across sessions — each execution starts fresh without learning from previous attempts","Expensive token consumption due to repeated reasoning cycles and full context re-transmission"],"requires":["OpenAI API key with GPT-4 access","Python 3.8+","Network connectivity for API calls","Sufficient API quota for autonomous multi-step executions"],"input_types":["natural language goal/objective","system constraints and available tools"],"output_types":["task execution logs","intermediate results from subtask execution","final outcome or completion status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_1","uri":"capability://tool.use.integration.tool.integration.and.function.calling","name":"tool-integration-and-function-calling","description":"Auto-GPT provides a plugin architecture that allows GPT-4 to invoke external tools and APIs by generating structured function calls. The system maintains a registry of available tools (file operations, web search, code execution, etc.), passes this registry to the LLM as context, and parses the LLM's function-call responses to execute the requested operations. This enables the autonomous agent to interact with external systems and gather information needed to complete tasks.","intents":["I want the AI agent to be able to read and write files, execute code, and interact with external APIs","I need to extend the agent's capabilities by adding custom tools it can call","I want the agent to search the web or query databases to gather information for decision-making"],"best_for":["developers building extensible autonomous agents","teams needing agents that can interact with existing infrastructure and APIs","researchers exploring tool-use in LLM-based systems"],"limitations":["Tool registry must be passed in full context each reasoning cycle, consuming tokens","No built-in validation or sandboxing — malicious tool calls could execute arbitrary code","Tool execution errors may not be gracefully handled, causing agent to fail or loop","Limited to tools that can complete within API timeout windows"],"requires":["Python 3.8+","Tool implementations (file system access, web APIs, code execution environments)","OpenAI API key"],"input_types":["tool definitions (name, description, parameters)","LLM-generated function call requests"],"output_types":["tool execution results","structured data from tool responses","error messages or execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_2","uri":"capability://memory.knowledge.memory.and.context.management.across.reasoning.cycles","name":"memory-and-context-management-across-reasoning-cycles","description":"Auto-GPT maintains execution context across multiple reasoning cycles by storing task history, intermediate results, and agent state in memory structures that are passed back to GPT-4 in subsequent prompts. The system preserves a log of completed tasks, their outcomes, and current goals, allowing the agent to reference past decisions and avoid redundant work. This context window management is critical for maintaining coherence across long-running autonomous workflows.","intents":["I want the agent to remember what it has already done and not repeat tasks","I need the agent to reference previous results when making new decisions","I want visibility into the agent's reasoning history and decision trail"],"best_for":["developers debugging autonomous agent behavior","teams needing audit trails of agent decisions","researchers studying how agents maintain coherence across multiple reasoning steps"],"limitations":["Context is stored in memory only — lost on process restart without explicit persistence","No automatic context pruning — long execution histories consume tokens and may exceed context windows","No structured memory indexing — agent must search linearly through history to find relevant past decisions","Token cost grows linearly with task history length"],"requires":["Python 3.8+","Sufficient memory for storing task history and intermediate results","OpenAI API with large context window support"],"input_types":["task execution results","intermediate state snapshots","user feedback or corrections"],"output_types":["context summaries","execution history logs","state snapshots for checkpointing"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_3","uri":"capability://planning.reasoning.goal.refinement.and.progress.evaluation","name":"goal-refinement-and-progress-evaluation","description":"Auto-GPT uses GPT-4 to evaluate whether completed tasks have moved the agent closer to its original goal and to refine the goal or task plan based on intermediate results. After each task execution, the agent reasons about progress, identifies blockers or new information that changes the approach, and updates its task queue accordingly. This creates a feedback loop where the agent can adapt its strategy if initial assumptions prove incorrect.","intents":["I want the agent to evaluate its own progress and adjust its approach if it's not working","I need the agent to recognize when it has achieved the goal and stop executing tasks","I want the agent to identify and handle unexpected obstacles by replanning"],"best_for":["teams building adaptive autonomous systems","researchers exploring self-evaluation in LLM agents","developers prototyping goal-oriented automation"],"limitations":["Evaluation logic is entirely LLM-driven — no formal success criteria or metrics","Agent may incorrectly assess progress and continue executing unnecessary tasks","No built-in timeout or iteration limit — agent could loop indefinitely if unable to reach goal","Evaluation reasoning adds additional API calls and token consumption"],"requires":["OpenAI API key with GPT-4 access","Clear goal definition in natural language","Mechanism to measure or verify task completion"],"input_types":["original goal statement","task execution results","current system state"],"output_types":["progress assessment","refined goal or task plan","completion status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_4","uri":"capability://code.generation.editing.code.generation.and.execution","name":"code-generation-and-execution","description":"Auto-GPT can generate Python code to solve problems and execute it in a sandboxed environment, using code execution as a tool for information gathering, data processing, or task completion. The agent generates code based on the current goal and context, executes it, captures output and errors, and uses results to inform subsequent reasoning. This enables the agent to perform computational tasks and verify solutions programmatically.","intents":["I want the agent to write and run code to solve computational problems","I need the agent to test hypotheses by executing code and analyzing results","I want the agent to process data or perform calculations as part of task execution"],"best_for":["developers automating code-based workflows","researchers exploring code generation in autonomous agents","teams needing agents that can perform computational tasks"],"limitations":["Code execution is not sandboxed by default — generated code could access sensitive files or system resources","No built-in code validation or linting — generated code may be syntactically incorrect or inefficient","Execution errors may not be clearly communicated back to the agent, causing confusion","Limited to Python — cannot generate or execute code in other languages"],"requires":["Python 3.8+ runtime","Appropriate file system and network permissions for code execution","OpenAI API key"],"input_types":["natural language problem description","data or context needed for code generation"],"output_types":["generated Python code","code execution results","stdout/stderr output","error messages"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_5","uri":"capability://search.retrieval.web.search.and.information.retrieval","name":"web-search-and-information-retrieval","description":"Auto-GPT integrates web search capabilities to allow the agent to query the internet for information needed to complete tasks. The agent can formulate search queries based on current goals, retrieve search results, and parse them to extract relevant information. This enables the agent to access external knowledge and current information beyond its training data.","intents":["I want the agent to search the web for information it needs to complete tasks","I need the agent to find current data or recent events relevant to its goals","I want the agent to verify information or find solutions by searching online"],"best_for":["teams building agents that need access to current information","developers automating research or information-gathering tasks","researchers exploring information retrieval in autonomous agents"],"limitations":["Search results quality depends on query formulation — poor queries yield irrelevant results","No built-in result filtering or ranking — agent must parse raw search results","Search API rate limits may throttle agent execution","Results are time-dependent and may become stale"],"requires":["Web search API key (e.g., Google Custom Search, Bing Search)","Network connectivity","OpenAI API key"],"input_types":["search query formulated by agent","search parameters (number of results, language, etc.)"],"output_types":["search results (titles, snippets, URLs)","parsed information from results"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_6","uri":"capability://tool.use.integration.file.system.operations.and.persistence","name":"file-system-operations-and-persistence","description":"Auto-GPT provides tools for reading, writing, and manipulating files on the local file system, enabling the agent to persist data, load configurations, and manage artifacts generated during task execution. The agent can create files, read existing files, append data, and organize files in directories. This allows tasks to produce persistent outputs and the agent to maintain state across operations.","intents":["I want the agent to save results and artifacts to files for later use","I need the agent to read configuration files or input data from disk","I want the agent to organize and manage files as part of task execution"],"best_for":["developers automating file-based workflows","teams needing agents that produce persistent artifacts","researchers exploring file management in autonomous systems"],"limitations":["No built-in access control — agent can read/write any file it has permissions for","No transaction semantics — partial writes could leave files in inconsistent state","File operations are synchronous and may block agent execution","No built-in backup or versioning — overwritten files are lost"],"requires":["File system access with appropriate read/write permissions","Python 3.8+","Sufficient disk space for generated artifacts"],"input_types":["file paths","file content (text or binary)","directory paths"],"output_types":["file contents","directory listings","operation status (success/failure)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_7","uri":"capability://memory.knowledge.long.context.reasoning.with.token.optimization","name":"long-context-reasoning-with-token-optimization","description":"Auto-GPT manages token consumption across long reasoning chains by strategically summarizing context, pruning irrelevant history, and prioritizing recent task results in prompts sent to GPT-4. The system attempts to keep the most relevant information within the context window while discarding older or less relevant details. This optimization is critical for maintaining coherence and cost-efficiency in multi-step autonomous workflows.","intents":["I want the agent to handle long task sequences without exceeding token limits","I need to reduce API costs by optimizing token usage in autonomous workflows","I want the agent to maintain focus on recent progress rather than getting lost in historical context"],"best_for":["teams running long-duration autonomous tasks","developers optimizing LLM API costs","researchers studying context management in extended reasoning chains"],"limitations":["Aggressive context pruning may cause agent to lose important historical context","No principled algorithm for deciding what to keep/discard — heuristic-based","Summarization itself consumes tokens, reducing net savings","Agent may re-discover solutions it already tried if context is pruned"],"requires":["OpenAI API with GPT-4 access","Configurable context window size","Token counting utilities"],"input_types":["task history","intermediate results","current goal state"],"output_types":["pruned context","summarized history","token count estimates"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-auto-gpt__cap_8","uri":"capability://text.generation.language.natural.language.goal.specification.and.interpretation","name":"natural-language-goal-specification-and-interpretation","description":"Auto-GPT accepts high-level goals specified in natural language and uses GPT-4 to interpret them, extract constraints and success criteria, and translate them into executable task plans. The system parses the goal statement to identify what needs to be accomplished, what resources are available, and what constitutes success. This natural language interface makes the system accessible to non-technical users while leveraging LLM reasoning for goal interpretation.","intents":["I want to specify what I want the agent to do in plain English without formal syntax","I need the agent to understand ambiguous or complex goals and ask clarifying questions","I want the agent to infer constraints and success criteria from my goal description"],"best_for":["non-technical users directing autonomous agents","teams prototyping agent behavior without formal specifications","researchers exploring natural language interfaces for task automation"],"limitations":["Ambiguous goals may be misinterpreted by the LLM","No formal validation of goal specifications — agent may proceed with incorrect understanding","Complex goals may require multiple clarification cycles","LLM interpretation is non-deterministic — same goal may be interpreted differently on different runs"],"requires":["OpenAI API key with GPT-4 access","Clear goal statement in English"],"input_types":["natural language goal statement","optional constraints or context"],"output_types":["interpreted goal","extracted success criteria","task plan","clarifying questions (if needed)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4 access","Python 3.8+","Network connectivity for API calls","Sufficient API quota for autonomous multi-step executions","Tool implementations (file system access, web APIs, code execution environments)","OpenAI API key","Sufficient memory for storing task history and intermediate results","OpenAI API with large context window support","Clear goal definition in natural language","Mechanism to measure or verify task completion"],"failure_modes":["No built-in error recovery or rollback mechanisms — failed subtasks may leave system in inconsistent state","Context window limitations mean long task chains may lose early reasoning context","No persistent memory across sessions — each execution starts fresh without learning from previous attempts","Expensive token consumption due to repeated reasoning cycles and full context re-transmission","Tool registry must be passed in full context each reasoning cycle, consuming tokens","No built-in validation or sandboxing — malicious tool calls could execute arbitrary code","Tool execution errors may not be gracefully handled, causing agent to fail or loop","Limited to tools that can complete within API timeout windows","Context is stored in memory only — lost on process restart without explicit persistence","No automatic context pruning — long execution histories consume tokens and may exceed context windows","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=auto-gpt","compare_url":"https://unfragile.ai/compare?artifact=auto-gpt"}},"signature":"7i30ys3ZFwmAbDrzVk1LHI3zCo66QEnTtfvQLv9AOjzP/5BbR0EsrxPb7i1OC06qwG3OY0CF8gmWMXwEEeBeAQ==","signedAt":"2026-06-21T09:57:12.792Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/auto-gpt","artifact":"https://unfragile.ai/auto-gpt","verify":"https://unfragile.ai/api/v1/verify?slug=auto-gpt","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"}}