{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-loop-gpt","slug":"loop-gpt","name":"Loop GPT","type":"repo","url":"https://github.com/farizrahman4u/loopgpt/tree/main","page_url":"https://unfragile.ai/loop-gpt","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-loop-gpt__cap_0","uri":"capability://planning.reasoning.autonomous.agent.orchestration.with.state.machine.lifecycle","name":"autonomous agent orchestration with state machine lifecycle","description":"Implements a core Agent class that coordinates language models, memory systems, and tool execution through a defined state machine lifecycle (initialization → planning → tool execution → reflection → completion). The agent maintains internal state including goals, constraints, and conversation history, orchestrating multi-step task decomposition and execution loops without requiring external orchestration frameworks. State transitions are driven by LLM reasoning outputs parsed into structured action directives.","intents":["I want to create an autonomous agent that can break down complex tasks and execute them step-by-step without human intervention","I need to build an agent that maintains context across multiple tool calls and learns from execution results","I want to implement an agent with a clear, inspectable lifecycle that I can pause, resume, and debug"],"best_for":["developers building autonomous task automation systems","teams implementing multi-step AI workflows without external orchestration platforms","researchers prototyping agent architectures with custom state management"],"limitations":["Agent state machine is synchronous — no native async/await support for concurrent tool execution","No built-in distributed execution — all state and execution happens in a single Python process","State transitions rely on LLM output parsing, which can fail on malformed model responses without fallback recovery"],"requires":["Python 3.8+","OpenAI API key or compatible LLM provider","Memory backend (in-process by default, external storage optional)"],"input_types":["natural language task descriptions","goal specifications (list of strings)","constraint definitions"],"output_types":["structured action directives (tool calls with parameters)","execution results with metadata","serialized agent state (JSON/pickle)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_1","uri":"capability://memory.knowledge.full.state.serialization.and.resumable.execution","name":"full state serialization and resumable execution","description":"Provides complete agent state persistence including agent configuration, conversation history, memory contents, and tool states, enabling pause-and-resume workflows without external databases. Serialization captures the entire execution context (goals, constraints, LLM choice, embedding provider) and conversation transcript, allowing agents to be checkpointed mid-execution and restored to continue from the exact point of interruption. Uses Python pickle and JSON serialization with custom handlers for non-serializable objects.","intents":["I want to pause an agent mid-task, save its state, and resume it later without losing context","I need to checkpoint agent execution for fault tolerance and recovery","I want to version and audit agent execution history by saving snapshots at each step"],"best_for":["long-running task automation where interruptions are expected","systems requiring audit trails and execution replay","development workflows where debugging requires inspecting agent state at specific points"],"limitations":["Serialization does not capture external tool state (e.g., open file handles, network connections) — only LoopGPT-managed state","Large conversation histories can produce multi-MB serialized states, impacting storage and deserialization latency","Custom tool implementations must implement __getstate__/__setstate__ to be serializable; default pickle may fail on complex objects"],"requires":["Python 3.8+","File system write access for state files","Compatible LLM and embedding provider instances available at deserialization time"],"input_types":["agent instance with populated state","file path for serialization target"],"output_types":["serialized state file (JSON or pickle)","restored agent instance with identical internal state"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_10","uri":"capability://automation.workflow.docker.containerization.for.isolated.agent.execution","name":"docker containerization for isolated agent execution","description":"Provides a Dockerfile and container configuration for running LoopGPT agents in isolated Docker containers. The container includes all dependencies, the LoopGPT framework, and a configured agent, enabling reproducible execution across environments. Supports volume mounting for persistent state and configuration, environment variable injection for API credentials, and network isolation. Enables agents to run in CI/CD pipelines, cloud platforms, and multi-tenant environments without dependency conflicts.","intents":["I want to run agents in isolated containers for reproducibility and security","I need to deploy agents to cloud platforms (Kubernetes, ECS, etc.) with minimal setup","I want to avoid dependency conflicts between agents and other services"],"best_for":["production deployments requiring isolation and reproducibility","CI/CD pipelines and cloud-native architectures","multi-tenant environments where agents must be isolated"],"limitations":["Container startup time adds latency compared to direct Python execution","File system isolation may prevent agents from accessing host resources (unless explicitly mounted)","Network isolation requires explicit port/volume configuration — default container is isolated","Container image size may be large if all dependencies are included — requires optimization for resource-constrained environments"],"requires":["Docker or compatible container runtime","Dockerfile in LoopGPT repository","API credentials injected via environment variables or mounted config files"],"input_types":["agent configuration (goals, model, tools)","environment variables for API credentials","mounted volumes for persistent state"],"output_types":["containerized agent execution results","logs written to stdout/stderr","persistent state files (if volumes are mounted)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_11","uri":"capability://tool.use.integration.multi.model.agent.switching.with.fallback.strategies","name":"multi-model agent switching with fallback strategies","description":"Enables agents to switch between multiple language models (OpenAI, open-source, custom) based on cost, latency, or capability requirements. The system supports fallback chains where if one model fails or is unavailable, the agent automatically tries the next model in the chain. Model selection can be dynamic based on task complexity or static based on configuration. Supports model-specific prompt optimization to maintain quality across different model families.","intents":["I want to optimize agent cost by using cheaper models (GPT-3.5) for simple tasks and GPT-4 for complex ones","I need fallback models in case my primary provider is unavailable","I want to experiment with different models without rewriting agent code"],"best_for":["cost-conscious deployments requiring model selection optimization","high-availability systems requiring fallback strategies","research and experimentation with multiple model families"],"limitations":["Model switching adds complexity to debugging — failures may be model-specific","Fallback chains increase latency if primary model fails — requires timeout configuration","Prompt optimization is manual per model family — no automatic prompt adaptation","Token counting varies by model — cost estimation may be inaccurate across fallback chain"],"requires":["Python 3.8+","API credentials for multiple model providers","Model-specific prompt templates (optional but recommended)"],"input_types":["list of models with fallback order","task complexity indicators (optional)","model-specific configuration"],"output_types":["model selection decision","execution results with model metadata","fallback chain execution logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_12","uri":"capability://planning.reasoning.agent.management.tools.for.self.delegation.and.sub.agent.creation","name":"agent management tools for self-delegation and sub-agent creation","description":"Provides tools enabling agents to create and delegate tasks to sub-agents, implementing hierarchical task decomposition. Agents can spawn child agents with specific goals and constraints, monitor their execution, and aggregate results. The system manages agent lifecycle (creation, execution, cleanup) and enables communication between parent and child agents through shared memory and result passing. Enables complex multi-agent workflows without external orchestration.","intents":["I want an agent to break down complex tasks by creating specialized sub-agents","I need hierarchical task decomposition where parent agents delegate to child agents","I want to implement multi-agent workflows with shared context and result aggregation"],"best_for":["complex task automation requiring hierarchical decomposition","multi-agent workflows with specialized sub-agents","systems where agents need to collaborate on shared goals"],"limitations":["Sub-agent creation adds overhead (initialization, LLM calls) — not suitable for fine-grained task delegation","Shared memory between agents can cause conflicts if not carefully managed — requires explicit synchronization","No built-in deadlock detection or timeout management — infinite delegation loops are possible","Result aggregation is manual — no automatic merging of sub-agent results"],"requires":["Python 3.8+","Sufficient API quota for multiple concurrent agents","Shared memory backend (in-process by default)"],"input_types":["sub-agent goals and constraints","parent agent context and memory","task specifications"],"output_types":["sub-agent execution results","aggregated results from multiple sub-agents","execution logs and metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_2","uri":"capability://tool.use.integration.pluggable.language.model.abstraction.with.multi.provider.support","name":"pluggable language model abstraction with multi-provider support","description":"Defines a BaseModel interface that abstracts language model interactions, enabling swappable implementations for OpenAI (GPT-3.5, GPT-4), open-source models (via Ollama, HuggingFace), and custom providers. The abstraction handles prompt formatting, token counting, and response parsing, allowing agents to switch models without code changes. Includes optimized prompts for GPT-3.5 to minimize token overhead while maintaining reasoning quality, and supports both chat and completion APIs.","intents":["I want to build an agent that works with GPT-3.5 without requiring GPT-4 access","I need to switch between OpenAI and open-source models (Llama, Mistral) without rewriting agent code","I want to implement custom model providers (e.g., fine-tuned models, proprietary APIs) with minimal integration effort"],"best_for":["teams optimizing for cost by using GPT-3.5 instead of GPT-4","organizations requiring on-premise or open-source model deployments","developers building multi-model agent systems with fallback strategies"],"limitations":["Token counting is approximate for non-OpenAI models — actual usage may vary by provider","Response parsing assumes structured output format (JSON action directives); malformed responses from weaker models may require custom error handling","No built-in retry logic or fallback to alternative models on failure — caller must implement"],"requires":["Python 3.8+","API credentials for chosen provider (OpenAI key, Ollama endpoint, HuggingFace token, etc.)","Network access to model provider or local Ollama instance"],"input_types":["prompt text (string)","system instructions","conversation history (list of messages)"],"output_types":["model response text","token usage metadata","parsed action directives (structured JSON)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_3","uri":"capability://tool.use.integration.extensible.tool.system.with.schema.based.function.calling","name":"extensible tool system with schema-based function calling","description":"Provides a pluggable tool registry where tools are defined as Python classes inheriting from a BaseTool interface, with automatic schema extraction for LLM function calling. Tools are organized hierarchically (web tools, code execution tools, agent management tools) and expose a standardized execute() method. The system automatically generates JSON schemas from tool signatures and passes them to the LLM for structured action generation, enabling the agent to invoke tools with validated parameters without manual prompt engineering.","intents":["I want to add custom tools to an agent without modifying core agent logic","I need the agent to call tools with validated parameters based on automatically-generated schemas","I want to organize tools hierarchically (web tools, file tools, code tools) and selectively enable/disable them"],"best_for":["developers building extensible agent systems with custom capabilities","teams integrating multiple external APIs (web search, code execution, file operations) into agents","researchers prototyping new tool types without forking the framework"],"limitations":["Schema generation assumes standard Python type hints — complex nested types may not serialize correctly to JSON schema","No built-in error recovery — if a tool execution fails, the agent must handle the error response and retry","Tool execution is synchronous — long-running tools (API calls, code execution) block the agent loop"],"requires":["Python 3.8+ with type hints support","BaseTool interface implementation for custom tools","JSON schema compatibility for tool signatures"],"input_types":["tool class definitions with execute() method","parameter specifications via Python type hints","tool configuration (enabled/disabled, timeout, etc.)"],"output_types":["JSON schema representations of tools","tool execution results (any type)","error messages on tool failure"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_4","uri":"capability://memory.knowledge.semantic.memory.with.embedding.based.retrieval","name":"semantic memory with embedding-based retrieval","description":"Implements an embedding-based memory system that stores agent interactions and retrieved information as vector embeddings, enabling semantic search and context-aware retrieval. The system uses a pluggable embedding provider (OpenAI embeddings, open-source models) to convert text to vectors, stores them in an in-memory vector store, and retrieves relevant context based on semantic similarity. Memory is integrated into the agent's prompt context, allowing the agent to reference past interactions and learned information without explicit recall instructions.","intents":["I want the agent to remember and retrieve relevant past interactions based on semantic similarity","I need to build long-running agents that learn from previous task executions","I want to implement context-aware retrieval that finds relevant information without keyword matching"],"best_for":["long-running agents that benefit from learning across multiple task executions","systems requiring semantic search over agent interaction history","applications where keyword-based retrieval is insufficient (e.g., conceptual similarity)"],"limitations":["In-memory vector store does not persist across process restarts — requires serialization to file or external database","Embedding generation adds latency (~100-500ms per embedding depending on provider) to memory operations","No built-in deduplication or cleanup — memory can grow unbounded without explicit pruning","Semantic similarity is approximate — irrelevant results may be retrieved if embedding space is poorly aligned with task domain"],"requires":["Python 3.8+","Embedding provider (OpenAI API key or local embedding model via Ollama)","Vector similarity library (likely numpy or similar for dot-product computation)"],"input_types":["text to embed (agent interactions, retrieved information)","query text for semantic search"],"output_types":["vector embeddings (float arrays)","retrieved memory entries with similarity scores","formatted context for agent prompt"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_5","uri":"capability://tool.use.integration.web.interaction.tools.with.browser.automation","name":"web interaction tools with browser automation","description":"Provides a suite of web tools enabling agents to browse the internet, search for information, and interact with web pages. Tools include web search (via external APIs), page scraping, link extraction, and form interaction. The system abstracts browser automation details, allowing agents to request web information through natural language instructions that are translated into tool calls. Results are parsed and formatted for agent consumption, handling HTML parsing, text extraction, and error cases.","intents":["I want my agent to search the web and retrieve current information","I need the agent to scrape web pages and extract specific data","I want the agent to interact with web forms and navigate multi-page workflows"],"best_for":["agents requiring real-time information from the internet","research and data gathering automation","web scraping and monitoring tasks"],"limitations":["Web scraping is fragile — page structure changes break extraction logic without updates","Rate limiting and blocking by websites may prevent tool execution — no built-in retry or proxy rotation","JavaScript-rendered content is not handled by basic scraping — requires headless browser for dynamic pages","Search results depend on external API availability and quality — no control over result ranking"],"requires":["Python 3.8+","Internet connectivity","Web search API credentials (e.g., Google Custom Search, Bing Search)","Optional: headless browser (Selenium, Playwright) for JavaScript rendering"],"input_types":["search queries (natural language)","URLs to scrape","form data for submission"],"output_types":["search results (title, URL, snippet)","extracted page content (text, links, structured data)","form submission responses"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_6","uri":"capability://code.generation.editing.code.execution.and.file.system.tools","name":"code execution and file system tools","description":"Provides tools for agents to execute Python code, manage files, and interact with the file system in a sandboxed manner. Includes code execution with output capture, file read/write operations, directory traversal, and command execution. The system enforces safety constraints (e.g., preventing access to sensitive directories) and captures execution results including stdout, stderr, and return values. Code execution results are formatted for agent consumption, enabling agents to test hypotheses and verify solutions.","intents":["I want the agent to execute Python code to solve problems or test solutions","I need the agent to read, write, and manipulate files on the system","I want the agent to run shell commands and capture their output"],"best_for":["agents performing data processing and analysis tasks","development automation and code generation workflows","system administration and file management tasks"],"limitations":["Code execution is not truly sandboxed — malicious code can access the entire file system and system resources","Long-running code execution blocks the agent loop — no timeout or resource limits by default","File operations are synchronous — large file I/O can cause latency spikes","No built-in version control or rollback for file modifications — destructive operations are permanent"],"requires":["Python 3.8+","File system write access for target directories","Shell access for command execution (optional)"],"input_types":["Python code (string)","file paths","shell commands"],"output_types":["code execution results (stdout, stderr, return value)","file contents (text or binary)","command output"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_7","uri":"capability://planning.reasoning.human.in.the.loop.feedback.and.course.correction","name":"human-in-the-loop feedback and course correction","description":"Enables human operators to provide feedback and corrections during agent execution, allowing course correction when the agent deviates from intended goals. The system pauses execution at defined checkpoints, presents the agent's current state and proposed actions to the human, and accepts feedback that is incorporated into the agent's memory and future decision-making. Feedback is stored in the agent's memory system for learning across multiple interactions.","intents":["I want to supervise agent execution and intervene if it goes off-track","I need to provide corrections that the agent learns from in future executions","I want to validate agent decisions before they are executed (especially for destructive operations)"],"best_for":["high-stakes automation where human oversight is required","iterative development workflows where feedback improves agent behavior","systems requiring audit trails of human decisions"],"limitations":["Feedback incorporation is not automatic — requires custom logic to parse and apply human input","No built-in UI for feedback collection — requires integration with external interfaces (CLI, web UI, etc.)","Feedback is stored in agent memory but does not update model behavior — agent must learn through in-context examples","Synchronous feedback collection blocks agent execution — not suitable for high-throughput scenarios"],"requires":["Python 3.8+","Human operator availability during execution","Feedback collection interface (CLI, web UI, etc.)"],"input_types":["human feedback (text corrections, approvals, rejections)","agent state and proposed actions"],"output_types":["updated agent memory with feedback","modified execution plan based on corrections"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_8","uri":"capability://automation.workflow.command.line.interface.for.agent.execution","name":"command-line interface for agent execution","description":"Provides a CLI interface for running agents directly from the terminal without writing Python code. The CLI accepts agent configuration (goals, model choice, tools to enable) as command-line arguments or interactive prompts, executes the agent, and displays results in human-readable format. Supports both one-shot execution and interactive mode where users can provide feedback and corrections during execution. The CLI is built on top of the Python API, enabling full customization through configuration files.","intents":["I want to run an agent from the command line without writing Python code","I need a quick way to test agent configurations and iterate on goals","I want to run agents in scripts or CI/CD pipelines with minimal setup"],"best_for":["non-technical users who want to use agents without coding","rapid prototyping and testing of agent configurations","integration into shell scripts and automation workflows"],"limitations":["CLI interface is less flexible than Python API — advanced customization requires Python code","Configuration via command-line arguments is verbose for complex setups — configuration files are recommended","Interactive mode requires terminal support — not suitable for headless or remote execution","Output formatting is fixed — no easy way to customize result presentation"],"requires":["Python 3.8+ with LoopGPT installed","Terminal/shell environment","API credentials for chosen LLM provider"],"input_types":["command-line arguments (goals, model, tools)","configuration files (YAML/JSON)","interactive user input"],"output_types":["formatted execution results (text)","agent state snapshots","execution logs"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-loop-gpt__cap_9","uri":"capability://code.generation.editing.ai.powered.function.decorator.for.llm.augmented.python.functions","name":"ai-powered function decorator for llm-augmented python functions","description":"Provides an @aifunc decorator that transforms regular Python functions into LLM-augmented versions. Decorated functions are executed by the LLM with access to the original function's implementation as a reference, enabling the LLM to generate improved or alternative implementations. The decorator handles function signature extraction, LLM invocation, and result validation, allowing developers to enhance functions with AI reasoning without rewriting them. Results are cached to avoid redundant LLM calls.","intents":["I want to enhance existing Python functions with AI reasoning without rewriting them","I need to generate alternative implementations of functions based on LLM suggestions","I want to use LLMs to improve function logic while maintaining backward compatibility"],"best_for":["developers enhancing existing codebases with AI capabilities","rapid prototyping of AI-augmented functions","scenarios where LLM reasoning can improve function logic"],"limitations":["LLM-generated implementations may not be correct or efficient — requires testing and validation","Decorator adds latency (LLM invocation time) to function calls — not suitable for performance-critical code","Caching is simple (no cache invalidation strategy) — stale results may be returned if function logic changes","No built-in error handling for LLM failures — function falls back to original implementation silently"],"requires":["Python 3.8+","LLM provider API credentials","Function must have clear docstring and type hints for LLM to understand intent"],"input_types":["Python function with docstring and type hints","function arguments (any type)"],"output_types":["function result (original or LLM-generated implementation)","execution metadata (which implementation was used)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","OpenAI API key or compatible LLM provider","Memory backend (in-process by default, external storage optional)","File system write access for state files","Compatible LLM and embedding provider instances available at deserialization time","Docker or compatible container runtime","Dockerfile in LoopGPT repository","API credentials injected via environment variables or mounted config files","API credentials for multiple model providers","Model-specific prompt templates (optional but recommended)"],"failure_modes":["Agent state machine is synchronous — no native async/await support for concurrent tool execution","No built-in distributed execution — all state and execution happens in a single Python process","State transitions rely on LLM output parsing, which can fail on malformed model responses without fallback recovery","Serialization does not capture external tool state (e.g., open file handles, network connections) — only LoopGPT-managed state","Large conversation histories can produce multi-MB serialized states, impacting storage and deserialization latency","Custom tool implementations must implement __getstate__/__setstate__ to be serializable; default pickle may fail on complex objects","Container startup time adds latency compared to direct Python execution","File system isolation may prevent agents from accessing host resources (unless explicitly mounted)","Network isolation requires explicit port/volume configuration — default container is isolated","Container image size may be large if all dependencies are included — requires optimization for resource-constrained environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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=loop-gpt","compare_url":"https://unfragile.ai/compare?artifact=loop-gpt"}},"signature":"LgEhBbi/sm/jE3joZezHcT+o0bm/IAHMWGqIp3Y0E0jvbMGkNtZiSI4LylCDnTSnRxpxHdfXitkUbjor5T0YCQ==","signedAt":"2026-06-22T08:25:24.187Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/loop-gpt","artifact":"https://unfragile.ai/loop-gpt","verify":"https://unfragile.ai/api/v1/verify?slug=loop-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"}}