{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"autogpt","slug":"autogpt","name":"AutoGPT","type":"agent","url":"https://github.com/Significant-Gravitas/AutoGPT","page_url":"https://unfragile.ai/autogpt","categories":["ai-agents","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"autogpt__cap_0","uri":"capability://planning.reasoning.visual.agent.workflow.composition.with.block.based.dag.editor","name":"visual agent workflow composition with block-based dag editor","description":"Enables users to design autonomous agent workflows by dragging and dropping typed blocks (nodes) onto a canvas and connecting them with edges to define data flow. Built on React Flow for graph visualization with Zustand state management, supporting real-time graph serialization to JSON representing directed acyclic graphs (DAGs) of agent logic. The frontend communicates with a FastAPI backend that validates graph topology, manages block schemas via JSON Schema, and executes workflows through a distributed execution system.","intents":["I want to visually design an agent workflow without writing code","I need to connect multiple AI and integration blocks to create a multi-step automation","I want to see data flow between blocks and understand how my agent processes information"],"best_for":["non-technical founders and business users prototyping AI workflows","teams building internal automation tools without deep ML expertise","enterprises needing visual audit trails of agent logic"],"limitations":["Complex conditional logic requires custom block development; no native if/else visual primitives","Graph complexity scales poorly beyond ~50 blocks due to canvas rendering overhead","No built-in version control for workflows; requires external Git integration"],"requires":["Modern browser with ES2020+ support (Chrome 90+, Firefox 88+, Safari 14+)","Supabase authentication configured for user session management","Backend FastAPI service running with RabbitMQ message broker"],"input_types":["block definitions (JSON Schema)","graph topology (DAG serialized as JSON)","user credentials for block execution"],"output_types":["executable agent graph (JSON DAG)","block connection metadata","execution state snapshots"],"categories":["planning-reasoning","automation-workflow","visual-workflow-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_1","uri":"capability://automation.workflow.distributed.block.execution.with.rabbitmq.based.task.scheduling","name":"distributed block execution with rabbitmq-based task scheduling","description":"Executes agent workflows across distributed workers by decomposing the DAG into individual block tasks, queuing them via RabbitMQ message broker, and managing execution state through a centralized scheduler. The execution system tracks block inputs/outputs, handles inter-block data passing, manages credit consumption per execution, and provides WebSocket-based real-time status updates to clients. Supports both synchronous and asynchronous block execution with configurable timeouts and retry policies.","intents":["I want my agent workflows to execute reliably across multiple workers without blocking the UI","I need to track execution progress in real-time and see which blocks are running","I want to implement credit-based billing where complex operations consume more credits"],"best_for":["SaaS platforms monetizing agent execution via credit system","teams requiring horizontal scaling of agent workloads","applications needing real-time execution monitoring and audit logs"],"limitations":["RabbitMQ dependency adds operational complexity; no built-in fallback to synchronous execution","Execution state stored in database; no distributed transaction support for multi-block atomicity","Credit system requires manual configuration per block type; no automatic cost estimation"],"requires":["RabbitMQ 3.8+ running and accessible to backend services","PostgreSQL or compatible database for execution state persistence","Python 3.9+ for backend services","FastAPI 0.95+ for REST/WebSocket API"],"input_types":["serialized DAG (JSON)","block input parameters (JSON)","user credentials (encrypted)"],"output_types":["execution status events (WebSocket JSON)","block output data (JSON)","execution logs (structured text)","credit consumption records"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_10","uri":"capability://tool.use.integration.marketplace.and.agent.library.for.sharing.and.discovering.blocks","name":"marketplace and agent library for sharing and discovering blocks","description":"Provides a centralized marketplace where users can publish, discover, and install pre-built blocks and agent templates. Blocks are versioned, include documentation and usage examples, and can be rated/reviewed by the community. The library system manages block dependencies, handles version conflicts, and enables one-click installation into user projects. Supports both public blocks (shared with all users) and private blocks (team-only). Includes a search interface with filtering by category, rating, and compatibility.","intents":["I want to find pre-built blocks for common tasks instead of building from scratch","I want to share my custom blocks with my team or the community","I need to manage block versions and ensure compatibility with my workflows"],"best_for":["teams building multiple agents and wanting to reuse blocks","open-source communities contributing blocks to a shared library","enterprises managing internal block standards and governance"],"limitations":["No built-in code review or security scanning for published blocks; malicious blocks could be published","Version management is manual; no automatic dependency resolution","Search is basic; no advanced filtering by performance metrics or cost"],"requires":["User authentication for publishing blocks","Block schema validation before publishing","Storage backend for block metadata and code"],"input_types":["block definition (JSON schema + code)","documentation (markdown)","usage examples (JSON)"],"output_types":["published block (versioned, discoverable)","installation manifest (dependencies, version constraints)","search results (list of matching blocks)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_11","uri":"capability://safety.moderation.encrypted.credential.storage.and.multi.tenant.api.key.management","name":"encrypted credential storage and multi-tenant api key management","description":"Manages sensitive credentials (API keys, database passwords, OAuth tokens) for blocks and integrations with encryption at rest and in transit. Each user has isolated credential storage; credentials are encrypted with user-specific keys and never exposed to other users or the platform. Blocks reference credentials by name (e.g., 'openai_key') rather than storing them directly, enabling secure credential rotation without updating workflows. Supports credential expiration, audit logging of credential access, and integration with external secret managers (AWS Secrets Manager, HashiCorp Vault).","intents":["I want to securely store API keys without exposing them in my workflows","I need to rotate credentials without updating all my agents","I want to audit which blocks accessed which credentials and when"],"best_for":["multi-tenant SaaS platforms handling user credentials","enterprises with strict credential management policies","teams needing compliance with data protection regulations (GDPR, SOC 2)"],"limitations":["Encryption overhead adds ~10-20ms per credential lookup","No built-in credential sharing between users; each user must configure their own keys","Audit logs are stored in database; no integration with external SIEM systems"],"requires":["Encryption library (cryptography Python package)","Secure key derivation (e.g., PBKDF2, Argon2)","Database with encryption support","HTTPS for credential transmission"],"input_types":["credential name (text)","credential value (text, binary)","credential type (API key, OAuth token, etc.)"],"output_types":["decrypted credential (only to authorized blocks)","credential metadata (type, expiration, last accessed)","audit log entries (JSON)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_12","uri":"capability://automation.workflow.real.time.execution.monitoring.and.websocket.based.status.updates","name":"real-time execution monitoring and websocket-based status updates","description":"Provides real-time visibility into agent execution through WebSocket connections that stream execution events (block started, completed, failed) to connected clients. Clients receive structured JSON events containing block name, status, inputs, outputs, and timing information. Enables live dashboards showing execution progress, intermediate results, and error details. Supports filtering events by block type or execution ID. Includes execution history storage for post-execution analysis and debugging.","intents":["I want to see my agent executing in real-time and understand what it's doing","I need to debug failed executions by seeing which block failed and why","I want to build a live dashboard showing agent progress to end users"],"best_for":["applications requiring real-time user feedback during long-running tasks","debugging and development workflows where visibility is critical","customer-facing applications where transparency builds trust"],"limitations":["WebSocket connections are stateful; scaling to thousands of concurrent users requires connection pooling","Event streaming adds ~50-100ms latency per event due to serialization and transmission","No built-in event filtering on server side; clients receive all events and must filter locally"],"requires":["WebSocket support in FastAPI (starlette.websockets)","Client-side WebSocket library (ws, socket.io, etc.)","Event storage backend (database or message queue)"],"input_types":["execution ID (text)","event filter criteria (JSON, optional)"],"output_types":["execution events (WebSocket JSON stream)","event history (JSON array)","execution summary (timing, success/failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_13","uri":"capability://automation.workflow.credit.based.execution.billing.and.cost.tracking","name":"credit-based execution billing and cost tracking","description":"Implements a credit system where each block execution consumes credits based on complexity, LLM token usage, and external API calls. Credits are allocated to users, tracked per execution, and deducted from user balances. The system calculates credit costs based on configurable rates per block type and LLM provider. Includes usage reports showing credit consumption over time, cost breakdowns by block type, and alerts when users approach credit limits. Supports credit packages (e.g., 1000 credits for $10) and subscription-based credit allocation.","intents":["I want to monetize agent execution through a credit system","I need to track costs per user and per workflow for chargeback or billing","I want to prevent runaway costs by setting credit limits and alerts"],"best_for":["SaaS platforms monetizing agent execution","enterprises allocating costs to departments or projects","teams needing cost visibility and control over agent usage"],"limitations":["Credit rates must be manually configured per block type; no automatic cost estimation","LLM token counting varies by provider; credit calculations may be inaccurate","No built-in integration with payment processors; requires external billing system"],"requires":["Database for credit balance tracking","Configuration for credit rates per block type","LLM provider token counting (OpenAI, Anthropic, etc.)"],"input_types":["block execution details (type, LLM tokens, API calls)","credit rate configuration (JSON)"],"output_types":["credit cost (numeric)","updated user balance (numeric)","usage report (JSON)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_2","uri":"capability://tool.use.integration.dynamic.block.schema.generation.with.json.schema.and.rjsf.forms","name":"dynamic block schema generation with json schema and rjsf forms","description":"Automatically generates user input forms for blocks using React JSON Schema Form (RJSF) by parsing block definitions containing JSON Schema specifications. Each block declares its input parameters, types, validation rules, and UI hints (e.g., dropdown options, text area vs input field) in a schema object. The system validates user inputs against schemas before execution, provides IDE-like autocomplete for block connections, and enables dynamic field visibility based on conditional schema rules (e.g., show API key field only if auth type is 'API').","intents":["I want to create custom blocks without manually writing form code","I need input validation to prevent invalid data from reaching my blocks","I want users to see context-aware forms that hide irrelevant fields based on their selections"],"best_for":["developers building extensible block libraries with minimal boilerplate","platforms supporting user-contributed blocks with schema-based validation","teams needing consistent form UX across hundreds of block types"],"limitations":["RJSF has limited support for complex nested schemas; deeply nested objects render poorly","Custom validation logic requires writing JSON Schema constraints; no visual validation builder","Dynamic field dependencies only support simple conditional visibility, not complex branching logic"],"requires":["React 18+ for RJSF component rendering","JSON Schema Draft 7 or later for block definitions","Zustand for form state management in frontend"],"input_types":["block schema definition (JSON Schema object)","user input values (JSON)"],"output_types":["validated input object (JSON)","form validation errors (structured)","rendered form UI (React components)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_3","uri":"capability://tool.use.integration.llm.agnostic.block.integration.with.multi.provider.support","name":"llm-agnostic block integration with multi-provider support","description":"Abstracts LLM provider differences through a unified block interface that supports OpenAI, Anthropic, Ollama, and other providers via a provider registry pattern. Blocks declare their LLM requirements (model name, temperature, max tokens) in schema, and the execution system routes requests to the configured provider at runtime. Handles provider-specific response formats, token counting, cost calculation, and fallback logic when a provider is unavailable. Credentials are encrypted and stored per-user, enabling multi-tenant deployments where each user configures their own API keys.","intents":["I want to switch between OpenAI, Anthropic, and local Ollama models without rewriting blocks","I need to track LLM costs per execution and allocate them to user credits","I want my agents to work with any LLM provider without vendor lock-in"],"best_for":["multi-tenant SaaS platforms supporting user-provided API keys","enterprises with LLM provider contracts and cost optimization requirements","developers building LLM-agnostic agent frameworks"],"limitations":["Provider abstraction adds ~50-100ms latency per LLM call due to request translation layer","Not all providers support identical feature sets (e.g., vision, function calling); blocks must handle provider-specific capabilities","Token counting varies by provider; cost estimates may be inaccurate for non-OpenAI models"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Encryption library for credential storage (e.g., cryptography Python package)","Provider SDKs installed (openai, anthropic, ollama Python packages)"],"input_types":["block schema with LLM provider specification","user credentials (encrypted API keys)","prompt text and parameters"],"output_types":["LLM response (text, structured JSON)","token usage metrics","cost records (for credit system)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_4","uri":"capability://planning.reasoning.autonomous.agent.loop.with.self.prompting.and.tool.use","name":"autonomous agent loop with self-prompting and tool use","description":"Implements a reasoning loop where the agent generates thoughts, decides which tools/blocks to invoke, executes them, observes results, and iterates until reaching a goal or max iterations. The loop is driven by an LLM that receives the current state (available blocks, previous results, user goal) as context and outputs structured decisions (which block to call, with what parameters). Supports both deterministic block chains (DAGs) and dynamic agentic loops where the LLM decides execution order at runtime. Includes memory management to track intermediate results and prevent infinite loops via iteration limits.","intents":["I want my agent to autonomously decide which tools to use based on the task","I need the agent to reason about failures and retry with different approaches","I want to see the agent's thought process and understand why it made decisions"],"best_for":["complex multi-step tasks requiring dynamic decision-making (research, planning, debugging)","applications where the execution path cannot be predetermined","teams building agentic systems with interpretability requirements"],"limitations":["Agentic loops are non-deterministic; same input may produce different execution paths","No built-in long-term memory; agent context is limited to current execution window","Iteration limits prevent infinite loops but may truncate valid multi-step solutions","LLM hallucinations can cause agent to invoke non-existent blocks or misinterpret results"],"requires":["LLM provider with function calling support (OpenAI, Anthropic, etc.)","Block definitions with clear descriptions for LLM to understand capabilities","Structured output format (JSON) for LLM decisions"],"input_types":["user goal/task (text)","available blocks (schema definitions)","previous execution results (JSON)"],"output_types":["final result (task-dependent)","execution trace (list of block invocations)","agent reasoning (LLM thoughts)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_5","uri":"capability://search.retrieval.web.browsing.and.information.retrieval.within.agent.execution","name":"web browsing and information retrieval within agent execution","description":"Integrates web search and page scraping capabilities as executable blocks within agent workflows, allowing agents to fetch real-time information from the internet. Blocks handle URL fetching, HTML parsing, and search query execution through configured search providers. Results are returned as structured data (text, links, metadata) that subsequent blocks can process. Includes rate limiting, timeout handling, and user-agent rotation to avoid blocking. Supports both direct URL access and search-based discovery of relevant pages.","intents":["I want my agent to search the web and use current information in its reasoning","I need to scrape specific data from websites as part of an automated workflow","I want my agent to verify facts or find recent news relevant to a task"],"best_for":["research and analysis agents that need current information","competitive intelligence and market monitoring workflows","customer support agents that need to access knowledge bases or documentation"],"limitations":["Web scraping is fragile; page structure changes break extraction logic","Rate limiting and blocking by websites may cause failures; no built-in proxy rotation","Search results are non-deterministic and may vary by region/time","Large page content increases token usage and execution costs"],"requires":["Search provider API (Google Custom Search, Bing, DuckDuckGo, etc.)","HTTP client library (requests, httpx, etc.)","HTML parsing library (BeautifulSoup, lxml, etc.)"],"input_types":["search query (text)","URL (text)","CSS selectors or XPath for extraction (optional)"],"output_types":["search results (list of URLs, titles, snippets)","page content (text, structured data)","metadata (publication date, author, etc.)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_6","uri":"capability://code.generation.editing.code.execution.sandbox.with.python.runtime","name":"code execution sandbox with python runtime","description":"Allows agents to execute arbitrary Python code within a sandboxed environment as part of workflows. Code blocks receive input parameters, execute Python code with access to common libraries (pandas, numpy, requests), and return results. Execution is isolated from the main process using containerization or subprocess isolation to prevent malicious code from affecting the system. Includes timeout enforcement, memory limits, and output capture. Supports both inline code strings and references to uploaded Python files.","intents":["I want my agent to perform data transformations and calculations using Python","I need to execute custom logic that's not available as a pre-built block","I want to process structured data (CSV, JSON) using pandas within my workflow"],"best_for":["data processing and analytics workflows requiring custom transformations","research agents that need to run calculations or simulations","teams building internal tools with domain-specific logic"],"limitations":["Sandbox isolation adds ~200-500ms overhead per code execution","Limited library availability; only whitelisted packages can be imported for security","No persistent state between code blocks; each execution is isolated","Large data processing may hit memory limits in sandbox environment"],"requires":["Python 3.9+ runtime in execution environment","Docker or similar containerization for sandbox isolation","Whitelisted Python packages (pandas, numpy, requests, etc.)"],"input_types":["Python code (string)","input parameters (JSON, converted to Python objects)","file references (CSV, JSON, etc.)"],"output_types":["execution result (JSON-serializable Python objects)","stdout/stderr logs","generated files (CSV, JSON, etc.)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_7","uri":"capability://automation.workflow.file.system.operations.and.artifact.management","name":"file system operations and artifact management","description":"Enables agents to read, write, and manipulate files as part of workflows through dedicated blocks. Supports creating text files, reading file contents, appending data, and organizing files in directories. Files are stored in a user-isolated storage backend (local filesystem or cloud storage) with access control. Agents can generate artifacts (reports, code, data) and make them available for download or further processing. Includes file type validation and size limits to prevent abuse.","intents":["I want my agent to generate reports or documents as output artifacts","I need to process input files and produce modified versions","I want to organize agent outputs in a structured directory hierarchy"],"best_for":["document generation and report automation workflows","data processing pipelines that produce output files","teams needing audit trails of generated artifacts"],"limitations":["File storage is user-isolated; no cross-user file sharing without explicit export","Large file operations may timeout; no built-in streaming for multi-GB files","File system paths are abstracted; agents cannot access arbitrary system directories"],"requires":["File storage backend (local filesystem, S3, GCS, etc.)","User authentication for access control","File size limits configured per deployment"],"input_types":["file path (text)","file content (text, binary)","directory path (text)"],"output_types":["file content (text, binary)","file metadata (size, creation date, etc.)","directory listing (JSON)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_8","uri":"capability://planning.reasoning.agent.benchmarking.and.evaluation.framework.agbenchmark","name":"agent benchmarking and evaluation framework (agbenchmark)","description":"Provides a standardized testing framework for evaluating agent performance against predefined tasks and metrics. Includes a test suite with diverse tasks (coding, reasoning, web search, etc.), automated evaluation of agent outputs against expected results, and performance metrics (success rate, token usage, cost). Supports both deterministic checks (exact match, regex) and LLM-based evaluation (does the output accomplish the goal?). Results are aggregated and compared across agent versions to track improvements.","intents":["I want to measure how well my agent performs on standard tasks","I need to compare different agent configurations and see which is better","I want to ensure my agent improvements don't regress on existing tasks"],"best_for":["researchers developing and comparing agent architectures","teams implementing CI/CD for agent quality assurance","open-source projects needing standardized evaluation"],"limitations":["Benchmark tasks are static; real-world agent performance may differ significantly","LLM-based evaluation is non-deterministic; same output may be scored differently on different runs","Benchmarks don't measure latency, cost, or resource usage comprehensively","No built-in support for custom evaluation metrics; requires extending framework"],"requires":["Python 3.9+ for benchmark runner","LLM provider API for evaluation (OpenAI, Anthropic, etc.)","Agent implementation compatible with Agent Protocol"],"input_types":["agent implementation (Python class or API endpoint)","benchmark task definitions (JSON)","evaluation criteria (JSON)"],"output_types":["test results (pass/fail per task)","performance metrics (success rate, token usage, cost)","comparison reports (agent A vs agent B)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__cap_9","uri":"capability://code.generation.editing.agent.framework.and.sdk.for.custom.agent.development.forge","name":"agent framework and sdk for custom agent development (forge)","description":"Provides a Python SDK and development framework for building custom autonomous agents with minimal boilerplate. Includes base classes for agent implementation, decorators for tool registration, utilities for prompt engineering, and integration with the Agent Protocol standard. Developers write agent logic in Python, and Forge handles communication with the execution environment, tool invocation, and result formatting. Supports both simple agents (single LLM call) and complex agents (multi-step reasoning loops).","intents":["I want to build a custom agent without implementing the entire execution framework","I need to register tools/blocks and have them automatically available to my agent","I want my agent to be compatible with the AutoGPT ecosystem and benchmarks"],"best_for":["developers building specialized agents for specific domains","researchers prototyping new agent architectures","teams extending AutoGPT with custom agent implementations"],"limitations":["Forge is tightly coupled to AutoGPT architecture; limited portability to other frameworks","Documentation is sparse; learning curve is steep for developers unfamiliar with Agent Protocol","No built-in support for distributed execution; agents run in single process"],"requires":["Python 3.9+","AutoGPT Forge package (installed from repository)","LLM provider API key (OpenAI, Anthropic, etc.)","Understanding of Agent Protocol specification"],"input_types":["agent class definition (Python)","tool/block definitions (Python functions with decorators)","task specification (text)"],"output_types":["agent response (text, structured data)","execution trace (tool calls, results)","benchmark scores (if evaluated with agbenchmark)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogpt__headline","uri":"capability://automation.workflow.autonomous.ai.agent.for.goal.oriented.tasks","name":"autonomous ai agent for goal-oriented tasks","description":"AutoGPT is an autonomous AI agent that chains LLM thoughts to accomplish various goals, featuring web browsing, code execution, and self-prompting capabilities, making it ideal for developers looking to automate complex workflows.","intents":["best autonomous AI agent","AI agent for automating tasks","how to use AutoGPT for goal-oriented automation","best tools for building AI agents","AI agent frameworks for developers"],"best_for":["developers","automation enthusiasts"],"limitations":["requires internet access for web browsing"],"requires":["basic programming knowledge"],"input_types":["text prompts","code snippets"],"output_types":["automated task results","code execution outputs"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Modern browser with ES2020+ support (Chrome 90+, Firefox 88+, Safari 14+)","Supabase authentication configured for user session management","Backend FastAPI service running with RabbitMQ message broker","RabbitMQ 3.8+ running and accessible to backend services","PostgreSQL or compatible database for execution state persistence","Python 3.9+ for backend services","FastAPI 0.95+ for REST/WebSocket API","User authentication for publishing blocks","Block schema validation before publishing","Storage backend for block metadata and code"],"failure_modes":["Complex conditional logic requires custom block development; no native if/else visual primitives","Graph complexity scales poorly beyond ~50 blocks due to canvas rendering overhead","No built-in version control for workflows; requires external Git integration","RabbitMQ dependency adds operational complexity; no built-in fallback to synchronous execution","Execution state stored in database; no distributed transaction support for multi-block atomicity","Credit system requires manual configuration per block type; no automatic cost estimation","No built-in code review or security scanning for published blocks; malicious blocks could be published","Version management is manual; no automatic dependency resolution","Search is basic; no advanced filtering by performance metrics or cost","Encryption overhead adds ~10-20ms per credential lookup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"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=autogpt","compare_url":"https://unfragile.ai/compare?artifact=autogpt"}},"signature":"ZOdZS/WuYJg0wllImW3PrLblsnUY53qa2nvIiFl7DWLhiV6hMdp7Y4DPJGMSMGaqmhaqzDfNRDoSHLnQS5FKBw==","signedAt":"2026-06-20T08:20:14.351Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/autogpt","artifact":"https://unfragile.ai/autogpt","verify":"https://unfragile.ai/api/v1/verify?slug=autogpt","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"}}