{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-babybeeagi","slug":"babybeeagi","name":"BabyBeeAGI","type":"agent","url":"https://yoheinakajima.com/babybeeagi-task-management-and-functionality-expansion-on-top-of-babyagi/","page_url":"https://unfragile.ai/babybeeagi","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-babybeeagi__cap_0","uri":"capability://planning.reasoning.unified.task.management.via.single.llm.prompt","name":"unified task management via single llm prompt","description":"Consolidates all task orchestration logic into a single GPT-4 prompt that receives the complete task list state as JSON, evaluates task completion status, determines dependencies, assigns tools, and decides whether new tasks are needed. This replaces the original BabyAGI's distributed prompting approach with a monolithic decision point that maintains full context of the objective and all prior task decisions in a single LLM invocation.","intents":["I want to manage multi-step task workflows where the AI understands the full task state and can make holistic decisions about what to do next","I need the AI to determine task dependencies and execution order without manual specification","I want to reduce the number of LLM calls by consolidating task planning into a single decision point"],"best_for":["researchers and developers experimenting with agentic AI frameworks","task automation engineers building close-ended multi-step workflows","teams prototyping AI-driven project management systems"],"limitations":["Single prompt approach creates a token bottleneck — context window limits maximum task list size before performance degrades","No parallel task execution support; all tasks execute sequentially even if independent","Slower processing speeds than original BabyAGI due to increased prompt complexity and context size","Occasional errors in task state management acknowledged but not quantified"],"requires":["OpenAI API key with GPT-4 access","Python 3.7+ (inferred from BabyAGI base)","Objective statement and initial task list in text format"],"input_types":["text (objective statement)","JSON (task list state with completion status and metadata)"],"output_types":["JSON (structured task list with status, dependencies, tool assignments, and new task decisions)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_1","uri":"capability://memory.knowledge.json.based.task.state.persistence.across.iterations","name":"json-based task state persistence across iterations","description":"Maintains task list state as a global JSON variable that persists across all LLM invocations and tool executions, replacing the original BabyAGI's vector database approach. Each iteration reads the current JSON state, passes it to the task management prompt, receives updated JSON output, and stores it for the next iteration. This creates a deterministic, inspectable state machine where all task history and decisions are visible in structured form.","intents":["I want to inspect and debug the exact state of all tasks at any point in the workflow","I need task state to persist reliably across multiple LLM calls without relying on vector similarity search","I want to manually edit or override task state if the AI makes incorrect decisions"],"best_for":["developers debugging agentic workflows who need full visibility into state changes","teams building deterministic task pipelines where state must be auditable","researchers studying how task state evolves through multi-step AI reasoning"],"limitations":["JSON state grows linearly with task count; no pruning or summarization mechanism documented, risking context window overflow on large task lists","No built-in versioning or rollback capability for task state","Requires external persistence layer (file system, database) — no built-in state storage documented","No conflict resolution if multiple agents attempt to modify state simultaneously"],"requires":["Python 3.7+ with JSON serialization support","File system or in-memory storage for JSON state variable","OpenAI API key for GPT-4"],"input_types":["JSON (current task list state)"],"output_types":["JSON (updated task list state with new completion status, dependencies, and tool assignments)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_10","uri":"capability://planning.reasoning.objective.driven.task.decomposition.and.planning","name":"objective-driven task decomposition and planning","description":"Given a high-level objective, the framework decomposes it into a task list that the task management prompt iteratively refines. The prompt analyzes the objective, current task list, and execution results to determine what tasks are needed, in what order, and with what tools. This creates a goal-driven planning process where task decomposition happens iteratively rather than upfront.","intents":["I want to specify a high-level objective and have the AI decompose it into tasks","I need the task list to evolve as the workflow progresses and new information emerges","I want the AI to plan task execution order based on dependencies and objective requirements"],"best_for":["exploratory projects where the full task list is not known upfront","research workflows where task decomposition is iterative","goal-driven automation where the AI must reason about task necessity"],"limitations":["Task decomposition quality depends entirely on the objective statement clarity","No validation that decomposed tasks are necessary or well-formed","Risk of task explosion or circular task creation if the LLM generates redundant tasks","No mechanism to prevent duplicate tasks across iterations","Decomposition decisions are not logged; no audit trail of planning decisions"],"requires":["OpenAI API key with GPT-4 access","Clear, well-articulated objective statement"],"input_types":["text (objective statement)"],"output_types":["JSON (decomposed task list with dependencies and tool assignments)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_2","uri":"capability://planning.reasoning.task.dependency.graph.construction.and.sequencing","name":"task dependency graph construction and sequencing","description":"The task management prompt analyzes the objective and current task list to determine which tasks must complete before others can begin, outputting a dependency graph embedded in the JSON task state. Tasks are then executed sequentially in dependency order, with the LLM deciding which task to execute next based on completion status and prerequisite satisfaction. This enables multi-step workflows where later tasks depend on outputs from earlier ones.","intents":["I want the AI to automatically figure out the correct order to execute tasks based on their logical dependencies","I need to ensure that a task doesn't start until its prerequisite tasks are complete","I want to model workflows where task B requires the output of task A before it can proceed"],"best_for":["information gathering workflows where research tasks must complete before synthesis tasks","multi-step problem-solving where each step builds on previous results","project management scenarios with clear task prerequisites"],"limitations":["No parallel execution of independent tasks; even tasks with no dependencies execute sequentially, reducing throughput","Dependency graph is implicit in the JSON state; no explicit graph visualization or validation tool documented","LLM must infer dependencies from task descriptions; no formal dependency specification language provided","Circular dependency detection not documented; could cause infinite loops if LLM creates circular task dependencies"],"requires":["OpenAI API key with GPT-4 access","Task descriptions that clearly indicate prerequisites and outputs","Sequential execution environment"],"input_types":["text (objective and task descriptions)","JSON (task list with completion status)"],"output_types":["JSON (task list with dependency relationships and execution order)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_3","uri":"capability://search.retrieval.web.search.tool.assignment.and.execution","name":"web search tool assignment and execution","description":"The task management prompt can assign web search as a tool to specific tasks, which are then executed by a web search function that retrieves results from the internet. Results are returned as text and fed back into the global JSON state for the next iteration. The LLM decides when web search is needed and what queries to use based on task requirements.","intents":["I want the AI to search the web for information needed to complete a task","I need to gather current information from the internet as part of a multi-step workflow","I want the AI to decide when web search is necessary and what to search for"],"best_for":["information gathering workflows requiring current web data","research tasks where the AI needs to find specific information online","workflows combining web search with synthesis or analysis tasks"],"limitations":["Web search tool implementation details not documented; unclear if it uses a specific search API (Google, Bing, etc.) or a generic web scraper","No control over search result ranking, filtering, or result count","Search results are unstructured text; no structured data extraction from search results documented","No caching of search results; repeated searches for the same query will re-execute","Search quality depends entirely on query formulation by the LLM; no query optimization or refinement mechanism"],"requires":["OpenAI API key with GPT-4 access","Internet connectivity for web search execution","Web search API credentials (if using external search service; not documented)"],"input_types":["text (search query generated by task management prompt)"],"output_types":["text (unstructured web search results)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_4","uri":"capability://search.retrieval.web.scraping.tool.assignment.and.execution","name":"web scraping tool assignment and execution","description":"The task management prompt can assign web scraping as a tool to specific tasks, which extracts structured or unstructured content from specified web pages. Scraped content is returned as text and incorporated into the global JSON state for subsequent task processing. The LLM determines when scraping is needed and which URLs to scrape.","intents":["I want the AI to extract specific content from web pages as part of a task","I need to gather data from specific websites that web search alone cannot provide","I want the AI to decide when to scrape a page and what content to extract"],"best_for":["workflows requiring content extraction from specific known URLs","data gathering tasks where search results must be followed to source pages","information synthesis workflows combining search and scraping"],"limitations":["Web scraping implementation not documented; unclear if it handles JavaScript-rendered content, authentication, or dynamic pages","No HTML parsing or structured data extraction; results are likely raw text","No respect for robots.txt or rate limiting documented; could violate website terms of service","Scraping failures (404, timeouts, blocked requests) handling not documented","No caching of scraped content; repeated scrapes of the same URL will re-execute"],"requires":["OpenAI API key with GPT-4 access","Internet connectivity for web scraping execution","Target URLs accessible without authentication (unless auth mechanism is documented)"],"input_types":["text (URL and optional scraping instructions from task management prompt)"],"output_types":["text (unstructured or semi-structured scraped content)"],"categories":["search-retrieval","tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_5","uri":"capability://planning.reasoning.task.completion.status.tracking.and.evaluation","name":"task completion status tracking and evaluation","description":"The task management prompt evaluates whether each task in the list is complete or incomplete based on task description, assigned tools, execution results, and progress toward the objective. Completion status is stored in the JSON state and used to determine which tasks to execute next. The LLM makes the final determination of completion, not automated metrics or exit conditions.","intents":["I want the AI to determine when a task has been successfully completed","I need to track which tasks are done and which still need work","I want the AI to decide when enough information has been gathered to move to the next task"],"best_for":["workflows where task completion is subjective and requires reasoning","multi-step processes where completion depends on quality of results, not just execution","scenarios where the AI must decide if a task is 'good enough' to proceed"],"limitations":["Completion evaluation is entirely LLM-based; no objective metrics or automated validation","No explicit completion criteria defined; LLM infers from task description and objective","Risk of premature task completion if LLM overestimates result quality","No mechanism to mark tasks as 'blocked' or 'failed'; only complete/incomplete states","Completion decisions are not logged or auditable; no explanation of why a task was marked complete"],"requires":["OpenAI API key with GPT-4 access","Clear task descriptions that enable the LLM to evaluate completion"],"input_types":["text (task description and execution results)","JSON (current task state)"],"output_types":["JSON (updated task state with completion status)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_6","uri":"capability://planning.reasoning.dynamic.task.creation.based.on.objective.gaps","name":"dynamic task creation based on objective gaps","description":"The task management prompt analyzes the current task list and objective to determine whether new tasks are needed to reach the goal. If gaps are identified, the prompt outputs new tasks to be added to the task list. This enables the workflow to dynamically expand the task list as the AI discovers what additional work is required, rather than requiring all tasks to be specified upfront.","intents":["I want the AI to identify what additional work is needed to reach the objective","I need the workflow to automatically create new tasks as gaps are discovered","I want to start with a high-level objective and let the AI decompose it into tasks"],"best_for":["open-ended problem-solving where the full task list is not known upfront","research workflows where new questions emerge as information is gathered","exploratory projects where task decomposition happens iteratively"],"limitations":["Task creation is entirely LLM-driven; no validation that new tasks are necessary or well-formed","No limit on task list growth; LLM could create redundant or circular tasks","Task creation decisions are not logged; no audit trail of why tasks were created","Risk of task explosion if LLM creates many tasks per iteration","No mechanism to prevent duplicate tasks; LLM could create the same task multiple times"],"requires":["OpenAI API key with GPT-4 access","Clear objective statement that enables the LLM to identify gaps"],"input_types":["text (objective statement)","JSON (current task list)"],"output_types":["JSON (updated task list with new tasks added)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_7","uri":"capability://planning.reasoning.close.ended.workflow.termination","name":"close-ended workflow termination","description":"Unlike the original BabyAGI's infinite task loop, BabyBeeAGI is designed to terminate when the objective is achieved. The task management prompt evaluates whether the objective has been met based on completed tasks and their results, and signals workflow completion when no new tasks are needed and the objective is satisfied. This creates a bounded, goal-driven execution model.","intents":["I want the workflow to stop when the objective is achieved, not run indefinitely","I need to know when the AI has completed the work and can provide final results","I want to avoid wasting tokens on unnecessary iterations after the goal is met"],"best_for":["goal-driven workflows with clear success criteria","one-off tasks or projects with defined endpoints","scenarios where continuous operation is not desired"],"limitations":["Termination decision is entirely LLM-based; no objective success metrics","Risk of premature termination if LLM overestimates objective achievement","No explicit termination criteria; LLM infers from objective and task results","No mechanism to resume a terminated workflow if additional work is discovered later","Termination decisions are not logged; no explanation of why the workflow ended"],"requires":["OpenAI API key with GPT-4 access","Clear objective statement that enables the LLM to evaluate completion"],"input_types":["text (objective statement)","JSON (completed task list and results)"],"output_types":["text or JSON (final results and termination signal)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_8","uri":"capability://planning.reasoning.gpt.4.based.task.reasoning.and.decision.making","name":"gpt-4 based task reasoning and decision-making","description":"All task orchestration, planning, and decision-making is performed by GPT-4 via a single complex prompt that receives task state and objective. The LLM reasons about task completion, dependencies, tool assignments, new task creation, and workflow termination. This centralizes all intelligence in the language model rather than distributing logic across multiple agents or heuristics.","intents":["I want to leverage GPT-4's reasoning capabilities for task planning and orchestration","I need flexible, context-aware decision-making that can adapt to different objectives","I want to avoid hardcoding task logic and instead use LLM reasoning"],"best_for":["teams with GPT-4 API access and budget","workflows where flexible reasoning is more important than predictability","research projects exploring LLM-based task orchestration"],"limitations":["Requires OpenAI API key and GPT-4 access; no support for other LLM providers documented","High token consumption due to large prompts and full task state context","Reasoning quality depends on prompt engineering; no optimization documented","Non-deterministic outputs; same input can produce different task decisions across runs","Vendor lock-in to OpenAI; no fallback to other models or local alternatives"],"requires":["OpenAI API key with GPT-4 access","Sufficient API quota and budget for GPT-4 token usage","Internet connectivity for API calls"],"input_types":["text (task management prompt with objective and task state)"],"output_types":["JSON (task decisions, assignments, and state updates)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babybeeagi__cap_9","uri":"capability://tool.use.integration.sequential.task.execution.with.tool.integration","name":"sequential task execution with tool integration","description":"Tasks are executed one at a time in dependency order, with each task assigned a specific tool (web search, web scrape, or implicit reasoning). Tool execution results are captured as text and fed back into the global JSON state for the next iteration. The task management prompt then decides which task to execute next based on completion status and dependencies.","intents":["I want to execute tasks sequentially with clear tool assignments","I need tool execution results to be captured and incorporated into task state","I want the AI to decide which task to execute next based on dependencies and completion"],"best_for":["workflows where sequential execution is acceptable and dependencies are clear","information gathering tasks combining search, scraping, and synthesis","scenarios where tool results must be incorporated into subsequent task decisions"],"limitations":["No parallel execution; independent tasks cannot run concurrently, reducing throughput","Tool set is limited to web search and web scrape; no custom tools documented","Tool execution errors are not documented; unclear how failures are handled","Tool results are unstructured text; no structured data extraction or validation","No tool result caching; repeated tool executions for the same input will re-run"],"requires":["OpenAI API key with GPT-4 access","Internet connectivity for web search and scraping tools","Sequential execution environment"],"input_types":["text (task description and tool assignment from task management prompt)"],"output_types":["text (tool execution results)","JSON (updated task state with results)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4 access","Python 3.7+ (inferred from BabyAGI base)","Objective statement and initial task list in text format","Python 3.7+ with JSON serialization support","File system or in-memory storage for JSON state variable","OpenAI API key for GPT-4","Clear, well-articulated objective statement","Task descriptions that clearly indicate prerequisites and outputs","Sequential execution environment","Internet connectivity for web search execution"],"failure_modes":["Single prompt approach creates a token bottleneck — context window limits maximum task list size before performance degrades","No parallel task execution support; all tasks execute sequentially even if independent","Slower processing speeds than original BabyAGI due to increased prompt complexity and context size","Occasional errors in task state management acknowledged but not quantified","JSON state grows linearly with task count; no pruning or summarization mechanism documented, risking context window overflow on large task lists","No built-in versioning or rollback capability for task state","Requires external persistence layer (file system, database) — no built-in state storage documented","No conflict resolution if multiple agents attempt to modify state simultaneously","Task decomposition quality depends entirely on the objective statement clarity","No validation that decomposed tasks are necessary or well-formed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-05-03T14:00:10.321Z","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=babybeeagi","compare_url":"https://unfragile.ai/compare?artifact=babybeeagi"}},"signature":"tfWPWQQldQGq6oNV39bNpAR0Kt7tJZdScP3H7rH/2JSQuwLHeTV0rtPXj4jEfp9WVJvT815bizw7topBKo8jAw==","signedAt":"2026-06-22T19:05:19.037Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/babybeeagi","artifact":"https://unfragile.ai/babybeeagi","verify":"https://unfragile.ai/api/v1/verify?slug=babybeeagi","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"}}