{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-multi-gpt","slug":"multi-gpt","name":"Multi GPT","type":"agent","url":"https://github.com/rumpfmax/Multi-GPT","page_url":"https://unfragile.ai/multi-gpt","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-multi-gpt__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.sequential.task.decomposition","name":"multi-agent orchestration with sequential task decomposition","description":"Coordinates multiple GPT instances to work on decomposed subtasks in sequence, where each agent receives the output of the previous agent as input. Implements a pipeline pattern where task routing and state passing between agents is managed through a central orchestrator that maintains execution context and handles inter-agent communication without explicit message queuing infrastructure.","intents":["I want to break down a complex problem into subtasks and have different AI agents specialize in each subtask","I need agents to build on each other's work, where one agent's output feeds into the next agent's input","I want to coordinate multiple LLM calls with different prompts/personalities to solve a larger problem"],"best_for":["teams prototyping multi-step AI workflows without complex orchestration infrastructure","developers experimenting with agent composition patterns and task decomposition strategies","researchers exploring how agent specialization affects problem-solving quality"],"limitations":["No built-in error recovery or retry logic between agent steps — a failure in one agent halts the entire pipeline","Sequential execution only — no parallel agent processing, limiting throughput for independent subtasks","No persistent state management — execution context is held in memory and lost on process termination","Lacks explicit task routing logic — agent selection appears to be static rather than dynamic based on task type"],"requires":["Python 3.8+","API keys for OpenAI GPT models (or compatible endpoint)","Network connectivity to LLM provider"],"input_types":["text (natural language task description)","structured task specifications"],"output_types":["text (final agent output)","execution trace (intermediate agent outputs)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_1","uri":"capability://text.generation.language.agent.specialization.through.role.based.prompting","name":"agent specialization through role-based prompting","description":"Creates distinct agent personalities and capabilities by injecting role-specific system prompts that define each agent's expertise domain, communication style, and decision-making approach. Each agent instance is initialized with a unique prompt template that constrains its behavior and output format, enabling functional specialization without code branching or conditional logic.","intents":["I want to create agents with different expertise areas (analyst, writer, coder) that approach the same problem differently","I need agents to maintain consistent personas and communication styles throughout their execution","I want to control agent behavior and output format through prompt engineering rather than code changes"],"best_for":["prompt engineers designing multi-agent systems through iterative prompt refinement","teams exploring how agent specialization (via prompting) affects solution quality and diversity","developers building proof-of-concept systems where prompt-based control is sufficient"],"limitations":["Specialization is purely prompt-based — no learned or fine-tuned model differences between agents","Prompt injection vulnerabilities if user input is not sanitized before being passed to agents","No mechanism to verify that agents actually follow their role constraints — relies on LLM compliance with instructions","Difficult to debug why an agent behaves unexpectedly without access to the underlying model's reasoning"],"requires":["Python 3.8+","Prompt templates for each agent role","API access to GPT models"],"input_types":["text (task description)","role definition (system prompt)"],"output_types":["text (role-specific response)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_2","uri":"capability://memory.knowledge.execution.context.propagation.across.agent.chain","name":"execution context propagation across agent chain","description":"Maintains and passes execution context (previous outputs, task history, intermediate results) through the agent pipeline, where each downstream agent receives the accumulated context from upstream agents. Implements context threading through function parameters or shared state objects, enabling agents to build on prior work without re-processing earlier steps.","intents":["I want each agent to see what previous agents have done so they can build on that work","I need to preserve the full execution history for debugging and auditing multi-agent workflows","I want agents to have access to intermediate results without re-running earlier pipeline stages"],"best_for":["developers debugging multi-agent workflows and needing visibility into intermediate states","teams building workflows where later agents depend on specific outputs from earlier agents","researchers analyzing how context propagation affects agent decision-making"],"limitations":["Context grows unbounded with pipeline depth — no automatic pruning or summarization of old context","No context compression — full history is passed to each agent, increasing token usage and latency","Context is not versioned — agents cannot easily reference specific prior states or roll back to earlier points","No explicit context schema — agents must parse unstructured context strings rather than accessing typed data structures"],"requires":["Python 3.8+","Sufficient memory to hold full execution history in process"],"input_types":["text (accumulated context from prior agents)"],"output_types":["text (agent output + context for next agent)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_3","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"Abstracts LLM interactions behind a provider interface that supports multiple GPT models (likely GPT-3.5, GPT-4, and variants) through a unified API. Handles model selection, API credential management, and request/response formatting, allowing agents to be instantiated with different models without changing agent code.","intents":["I want to experiment with different GPT models (3.5 vs 4) without rewriting agent code","I need to swap LLM providers or models at runtime based on cost/performance tradeoffs","I want to abstract away provider-specific API details from agent implementation logic"],"best_for":["teams comparing performance across different GPT models in multi-agent workflows","developers building model-agnostic agent systems that can switch providers","researchers exploring how model capability affects multi-agent coordination"],"limitations":["Abstraction likely only covers OpenAI models — no support for other providers (Anthropic, Cohere, open-source models)","No built-in fallback logic if a model is unavailable or rate-limited","No cost tracking or optimization — all models are treated equally regardless of price/performance","API credential management is not specified — likely requires environment variables or config files with no encryption"],"requires":["Python 3.8+","OpenAI API key","Network access to OpenAI endpoints"],"input_types":["text (prompt)","model identifier (string)"],"output_types":["text (model response)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_4","uri":"capability://data.processing.analysis.task.input.parsing.and.validation","name":"task input parsing and validation","description":"Accepts user-provided task descriptions and validates/parses them into a format suitable for agent processing. Likely performs basic input sanitization, format checking, and potentially task decomposition into subtasks that can be distributed to agents. May include schema validation if tasks follow a defined structure.","intents":["I want to submit a complex task and have the system automatically break it into subtasks for agents","I need to validate that my task input is well-formed before sending it to agents","I want to ensure user input doesn't break the agent pipeline through injection or malformed data"],"best_for":["developers building user-facing multi-agent systems that need input validation","teams implementing task decomposition strategies that depend on structured input","systems that need to prevent malformed input from corrupting agent state"],"limitations":["Decomposition strategy is not specified — unclear if tasks are decomposed automatically or require manual specification","No schema enforcement — validation likely only checks basic format, not semantic correctness","Limited error messaging — validation failures may not provide actionable feedback to users","No support for complex task types — likely only handles simple text descriptions"],"requires":["Python 3.8+","Task input in expected format (likely plain text or JSON)"],"input_types":["text (task description)","JSON (structured task specification)"],"output_types":["structured task object","validation errors (if input is invalid)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_5","uri":"capability://automation.workflow.agent.execution.and.response.collection","name":"agent execution and response collection","description":"Executes individual agents sequentially, captures their outputs, and formats responses for downstream consumption or user presentation. Handles the mechanics of calling LLM APIs, managing timeouts, and collecting structured or unstructured responses from each agent in the pipeline.","intents":["I want to run agents one after another and collect their outputs","I need to handle timeouts or failures when calling LLM APIs","I want to format agent outputs consistently for downstream processing or display"],"best_for":["developers building basic multi-agent pipelines without complex orchestration needs","teams prototyping agent workflows and needing simple execution mechanics","researchers exploring agent composition without infrastructure complexity"],"limitations":["No timeout handling specified — long-running agent calls may block the entire pipeline","No response validation — agents may return malformed or unexpected output without detection","No retry logic — a single failed API call fails the entire workflow","Synchronous execution only — no parallelization of independent agents","No streaming support — entire agent response must be buffered before moving to next agent"],"requires":["Python 3.8+","OpenAI API access","Network connectivity"],"input_types":["agent instance","input text/context"],"output_types":["text (agent response)","execution metadata (tokens used, latency, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_6","uri":"capability://data.processing.analysis.workflow.result.aggregation.and.formatting","name":"workflow result aggregation and formatting","description":"Collects outputs from all agents in the pipeline and aggregates them into a final result, potentially combining, summarizing, or formatting the outputs for user consumption. May include logic to select the most relevant agent output, merge outputs from multiple agents, or format results in a specific structure (JSON, markdown, etc.).","intents":["I want to combine outputs from multiple agents into a single coherent result","I need to format the final workflow output in a specific way (JSON, markdown, etc.)","I want to select the best output from multiple agents or merge complementary outputs"],"best_for":["teams building user-facing multi-agent systems that need polished output","developers implementing workflows where final output requires aggregation from multiple sources","systems that need to present agent outputs in a specific format"],"limitations":["Aggregation strategy is not specified — unclear if outputs are concatenated, merged, or selected","No output validation — aggregated result may be malformed or incomplete","Limited formatting options — likely only supports basic text or JSON output","No conflict resolution — if agents produce contradictory outputs, there's no mechanism to resolve conflicts"],"requires":["Python 3.8+","Agent outputs from all pipeline stages"],"input_types":["list of agent outputs (text)"],"output_types":["aggregated result (text, JSON, or markdown)","execution summary"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multi-gpt__cap_7","uri":"capability://planning.reasoning.experimental.multi.agent.coordination.patterns","name":"experimental multi-agent coordination patterns","description":"Provides a framework for testing different multi-agent coordination strategies and patterns (sequential pipelines, parallel execution, hierarchical delegation, etc.). Allows researchers and developers to implement and compare different coordination approaches without building from scratch, serving as a testbed for multi-agent system design.","intents":["I want to experiment with different ways agents can coordinate to solve problems","I need a framework to test how different coordination patterns affect solution quality","I want to compare sequential vs parallel vs hierarchical agent coordination"],"best_for":["researchers exploring multi-agent system design and coordination patterns","teams prototyping different agent architectures before committing to a specific approach","developers learning about multi-agent systems through hands-on experimentation"],"limitations":["Limited coordination patterns implemented — likely only sequential execution, not parallel or hierarchical","No built-in metrics for comparing coordination effectiveness — researchers must implement their own evaluation","Experimental nature means APIs and patterns may change frequently without backward compatibility","No production-grade features like monitoring, logging, or observability"],"requires":["Python 3.8+","OpenAI API access","Willingness to work with experimental/unstable APIs"],"input_types":["task description","coordination pattern specification"],"output_types":["final result","execution trace with intermediate outputs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API keys for OpenAI GPT models (or compatible endpoint)","Network connectivity to LLM provider","Prompt templates for each agent role","API access to GPT models","Sufficient memory to hold full execution history in process","OpenAI API key","Network access to OpenAI endpoints","Task input in expected format (likely plain text or JSON)","OpenAI API access"],"failure_modes":["No built-in error recovery or retry logic between agent steps — a failure in one agent halts the entire pipeline","Sequential execution only — no parallel agent processing, limiting throughput for independent subtasks","No persistent state management — execution context is held in memory and lost on process termination","Lacks explicit task routing logic — agent selection appears to be static rather than dynamic based on task type","Specialization is purely prompt-based — no learned or fine-tuned model differences between agents","Prompt injection vulnerabilities if user input is not sanitized before being passed to agents","No mechanism to verify that agents actually follow their role constraints — relies on LLM compliance with instructions","Difficult to debug why an agent behaves unexpectedly without access to the underlying model's reasoning","Context grows unbounded with pipeline depth — no automatic pruning or summarization of old context","No context compression — full history is passed to each agent, increasing token usage and latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"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=multi-gpt","compare_url":"https://unfragile.ai/compare?artifact=multi-gpt"}},"signature":"r8nmIPedd3TEljrdWufCMwD8+w/YvQhADcMlbICKlGcjjdHHBik8heIRpFyBqU5/UlCuHeCUUr43xsYGw+JqCQ==","signedAt":"2026-06-22T08:40:58.466Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/multi-gpt","artifact":"https://unfragile.ai/multi-gpt","verify":"https://unfragile.ai/api/v1/verify?slug=multi-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"}}