{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-beebot","slug":"beebot","name":"BeeBot","type":"agent","url":"https://github.com/AutoPackAI/beebot","page_url":"https://unfragile.ai/beebot","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-beebot__cap_0","uri":"capability://planning.reasoning.multi.task.agent.orchestration.with.llm.routing","name":"multi-task agent orchestration with llm routing","description":"BeeBot routes incoming requests to specialized task handlers through an LLM-based decision layer that analyzes task intent and selects appropriate execution paths. The system maintains a registry of task types and uses language model reasoning to decompose complex requests into sequential or parallel subtasks, with built-in error handling and fallback mechanisms for failed task execution.","intents":["I need to automate a workflow that spans multiple different types of tasks without writing separate integrations","I want an agent that can intelligently decide which tool or handler to use based on the input request","I need to chain multiple operations together where the output of one task feeds into the next"],"best_for":["teams building autonomous agents for heterogeneous task domains","developers prototyping multi-step automation workflows","organizations migrating from rigid rule-based automation to LLM-driven decision making"],"limitations":["LLM routing adds latency per task decision (typically 500ms-2s depending on model)","No built-in cost optimization for repeated routing decisions — each request incurs full LLM inference","Task registry must be manually maintained; no automatic discovery of available handlers","Early-stage project with limited production hardening for high-throughput scenarios"],"requires":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","Task handler implementations must conform to BeeBot's handler interface"],"input_types":["natural language task descriptions","structured task specifications (JSON)","code snippets for code-related tasks"],"output_types":["task execution results (format depends on handler)","structured execution logs","error reports with fallback suggestions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_1","uri":"capability://code.generation.editing.code.execution.and.generation.with.sandboxed.runtime","name":"code execution and generation with sandboxed runtime","description":"BeeBot provides a sandboxed execution environment for running generated or user-provided code snippets with resource isolation and timeout enforcement. The system integrates with code generation models to produce executable code and validates syntax before execution, capturing stdout/stderr and execution results for downstream task handlers.","intents":["I want to safely execute dynamically generated code without risking the host system","I need to generate code snippets and immediately test them within an isolated environment","I want to capture execution output and errors from code runs for logging and debugging"],"best_for":["developers building code generation agents with safety requirements","teams running untrusted or dynamically generated code","automation systems that need to validate code before deploying to production"],"limitations":["Sandbox overhead adds 100-300ms per execution compared to direct Python execution","Limited to Python code execution in early versions; other languages require custom runtime handlers","No built-in support for long-running processes — timeout enforcement may interrupt legitimate long-duration tasks","Sandbox isolation may prevent legitimate inter-process communication or system calls needed by some code"],"requires":["Python 3.8+","Docker or similar containerization (if using container-based sandboxing)","Sufficient system resources for parallel code execution"],"input_types":["Python code strings","code AST representations","code generation model outputs"],"output_types":["execution results (return values)","stdout/stderr capture","execution metadata (duration, memory usage)","error traces and exceptions"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_10","uri":"capability://automation.workflow.task.performance.profiling.and.optimization.recommendations","name":"task performance profiling and optimization recommendations","description":"BeeBot profiles task execution performance (latency, memory usage, handler selection frequency) and generates optimization recommendations based on observed patterns. The system identifies slow handlers, inefficient routing decisions, and bottlenecks in task chains, providing actionable suggestions (switch to faster provider, cache results, parallelize tasks). Profiling data is collected continuously with minimal overhead and can be exported for analysis.","intents":["I want to identify which tasks are slowing down my automation workflows","I need recommendations for optimizing my task chain execution","I want to understand the cost/latency tradeoffs of different handler choices"],"best_for":["teams optimizing production automation systems for latency and cost","developers debugging performance issues in complex task chains","organizations managing large-scale automation deployments"],"limitations":["Profiling adds 2-5% overhead to task execution time","Recommendations are heuristic-based; they may not account for domain-specific constraints","No built-in A/B testing framework to validate optimization recommendations","Profiling data requires external storage and analysis tools for actionable insights"],"requires":["Python 3.8+","Profiling data collection enabled (default)","Analysis tools or external observability platform for interpreting recommendations"],"input_types":["task execution metrics","handler performance data","routing decision logs"],"output_types":["performance profiles","optimization recommendations","cost/latency tradeoff analysis"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_2","uri":"capability://tool.use.integration.task.handler.plugin.system.with.dynamic.registration","name":"task handler plugin system with dynamic registration","description":"BeeBot implements a plugin architecture where task handlers are registered at runtime through a handler registry interface. Handlers expose metadata (name, description, input schema, output schema) that the routing layer uses to match incoming requests, enabling extensibility without modifying core framework code. The system supports both synchronous and asynchronous handlers with automatic execution model detection.","intents":["I want to add new task types to my automation system without forking or modifying BeeBot core","I need to integrate custom business logic as pluggable handlers that the agent can discover and use","I want to share task handlers across multiple BeeBot instances through a handler registry"],"best_for":["teams building extensible automation platforms","organizations with domain-specific task types not covered by built-in handlers","developers creating reusable task handler libraries"],"limitations":["Handler discovery is static at startup; dynamic handler registration during runtime requires framework restart","No built-in versioning for handlers — breaking changes to handler interfaces require manual migration","Handler metadata schema is not validated at registration time, leading to potential runtime errors","No built-in handler dependency management — circular dependencies between handlers are not detected"],"requires":["Python 3.8+","Handler implementations must inherit from BeeBot's BaseHandler class or implement required interface","Handlers must provide JSON-serializable metadata (name, description, schemas)"],"input_types":["handler class definitions","handler metadata dictionaries","task specifications matching handler input schemas"],"output_types":["handler execution results","handler metadata for discovery","registration confirmations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_3","uri":"capability://text.generation.language.multi.provider.llm.abstraction.with.fallback.chains","name":"multi-provider llm abstraction with fallback chains","description":"BeeBot abstracts multiple LLM providers (OpenAI, Anthropic, local Ollama) behind a unified interface, allowing requests to be routed to different models based on cost, latency, or availability constraints. The system implements fallback chains where if one provider fails or times out, requests automatically retry against alternative providers with configurable backoff strategies.","intents":["I want to use multiple LLM providers without rewriting my agent code for each one","I need to optimize costs by routing simple requests to cheaper models and complex requests to more capable ones","I want automatic failover to backup LLM providers if my primary provider is unavailable"],"best_for":["teams managing multi-provider LLM deployments","cost-conscious organizations optimizing LLM spend across models","systems requiring high availability with automatic provider failover"],"limitations":["Fallback chains add latency on provider failures (retry delay + second provider latency)","No built-in load balancing across providers — all requests go to primary until it fails","Provider-specific features (vision, function calling) may not be uniformly abstracted, requiring conditional code","Cost tracking across providers requires manual aggregation; no built-in billing integration"],"requires":["Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, or local Ollama)","Network connectivity to provider endpoints (or local Ollama instance)"],"input_types":["prompts (text)","messages (conversation history)","provider configuration (model name, API key, endpoint)"],"output_types":["LLM completions (text)","structured outputs (JSON from function calling)","provider metadata (model used, tokens consumed)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_4","uri":"capability://data.processing.analysis.structured.task.result.validation.and.schema.enforcement","name":"structured task result validation and schema enforcement","description":"BeeBot validates task handler outputs against declared output schemas (JSON Schema, Pydantic models) before returning results to downstream consumers. The validation layer catches malformed outputs early, provides detailed error messages about schema violations, and can optionally coerce or transform outputs to match expected schemas using configurable validators.","intents":["I want to ensure task outputs conform to expected schemas before passing them to downstream tasks","I need detailed error messages when a task handler returns malformed data","I want to automatically transform or coerce task outputs to match expected types"],"best_for":["teams building reliable automation pipelines with strict data contracts","systems integrating multiple task handlers with incompatible output formats","developers debugging data flow issues in complex task chains"],"limitations":["Schema validation adds 10-50ms overhead per task result depending on schema complexity","No built-in schema inference — schemas must be manually defined for each handler","Coercion rules are not standardized; custom validators require per-handler implementation","Schema evolution is not supported — changing handler output schemas requires manual migration of downstream tasks"],"requires":["Python 3.8+","Output schema definitions (JSON Schema or Pydantic models)","Validator implementations for custom coercion rules"],"input_types":["task handler outputs (any Python object)","output schema definitions (JSON Schema or Pydantic)","validator functions"],"output_types":["validated results","validation error reports","coerced/transformed outputs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_5","uri":"capability://automation.workflow.task.execution.logging.and.observability.with.structured.traces","name":"task execution logging and observability with structured traces","description":"BeeBot captures detailed execution traces for each task including routing decisions, handler selection, input/output data, execution duration, and error information. Traces are structured as JSON and can be exported to observability platforms (Datadog, New Relic, custom backends) for monitoring and debugging. The system includes built-in metrics collection for latency, error rates, and handler performance.","intents":["I want to understand why a particular task was routed to a specific handler","I need to debug failures in multi-step task chains by examining execution traces","I want to monitor handler performance and identify bottlenecks in my automation system"],"best_for":["teams operating production automation systems requiring observability","developers debugging complex multi-task workflows","organizations needing compliance audit trails for automated decisions"],"limitations":["Trace collection adds 5-20% overhead to task execution time","Structured logging requires external storage (files, databases, observability platforms) for long-term retention","No built-in sampling — high-volume task execution can generate excessive trace data","Trace schema is not versioned; schema changes may break downstream log parsing"],"requires":["Python 3.8+","Storage backend for traces (file system, database, or observability platform)","Optional: observability platform SDK (Datadog, New Relic, etc.)"],"input_types":["task execution events","handler metadata","routing decisions","error information"],"output_types":["structured JSON traces","execution metrics (latency, error rates)","observability platform exports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_6","uri":"capability://planning.reasoning.conditional.task.branching.and.flow.control","name":"conditional task branching and flow control","description":"BeeBot supports conditional execution paths where task results determine which subsequent tasks execute. The system evaluates conditions (based on task output, error status, or explicit predicates) and branches execution to different handlers, enabling complex workflows like error recovery, A/B testing, or multi-path processing. Branching logic is declarative and can be composed with sequential and parallel task chains.","intents":["I want to execute different tasks based on the output of a previous task","I need to implement error recovery paths that execute only if a task fails","I want to run multiple parallel task branches and merge results based on conditions"],"best_for":["teams building complex automation workflows with conditional logic","systems implementing error recovery and fallback strategies","organizations needing A/B testing or multi-path processing in automation"],"limitations":["Branching logic is not visually represented — complex workflows become difficult to understand without documentation","No built-in deadlock detection for circular task dependencies","Condition evaluation is synchronous; async conditions require custom implementations","No built-in support for dynamic branching based on runtime data (e.g., number of items in a list)"],"requires":["Python 3.8+","Task handlers for each branch path","Condition predicates (functions or declarative rules)"],"input_types":["task results","error information","explicit condition predicates"],"output_types":["branched task execution results","merged results from parallel branches","execution path metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_7","uri":"capability://automation.workflow.parallel.task.execution.with.result.aggregation","name":"parallel task execution with result aggregation","description":"BeeBot executes multiple independent tasks concurrently and aggregates their results using configurable merge strategies. The system manages thread/async pools, handles partial failures (some tasks succeed while others fail), and provides result aggregation functions (collect all, wait for first, merge dictionaries). Execution is non-blocking and supports both CPU-bound and I/O-bound tasks through async/await patterns.","intents":["I want to run multiple independent tasks in parallel to reduce total execution time","I need to aggregate results from parallel tasks into a single output","I want to handle partial failures where some parallel tasks fail but others succeed"],"best_for":["teams optimizing automation latency through parallelization","systems processing multiple independent data items concurrently","organizations running I/O-bound tasks (API calls, database queries) in parallel"],"limitations":["Thread pool overhead adds 50-200ms for small task counts; parallelization only beneficial for 3+ tasks","Python GIL limits true parallelism for CPU-bound tasks; async/await required for I/O-bound tasks","Partial failure handling requires explicit merge strategy configuration; default behavior may not match expectations","No built-in load balancing across parallel tasks — uneven task durations can cause stragglers"],"requires":["Python 3.8+","Async-compatible task handlers for I/O-bound parallelization","Result aggregation functions matching expected output format"],"input_types":["list of independent tasks","task parameters","merge strategy configuration"],"output_types":["aggregated results from all tasks","partial failure reports","execution timing metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_8","uri":"capability://automation.workflow.task.state.persistence.and.resumption","name":"task state persistence and resumption","description":"BeeBot persists task execution state (intermediate results, execution progress, handler selections) to enable resumption after failures or interruptions. The system checkpoints state at configurable intervals and can restore execution from the last checkpoint, skipping already-completed tasks. State is stored in pluggable backends (file system, database, Redis) and includes metadata for debugging and audit trails.","intents":["I want to resume long-running task chains after a failure without restarting from the beginning","I need to checkpoint execution state for audit and compliance purposes","I want to pause and resume task execution on demand"],"best_for":["teams running long-duration automation workflows with failure recovery requirements","systems with compliance requirements for execution audit trails","organizations needing pause/resume capabilities for resource management"],"limitations":["State persistence adds 50-200ms per checkpoint depending on state size and backend","No built-in state versioning — schema changes to task state require manual migration","Resumption assumes task handlers are idempotent; non-idempotent handlers may produce duplicate side effects","State storage requires external infrastructure (database, Redis, file system); no built-in cleanup of old state"],"requires":["Python 3.8+","State persistence backend (file system, database, or Redis)","Idempotent task handlers for safe resumption"],"input_types":["task execution state","intermediate results","execution progress metadata"],"output_types":["persisted state snapshots","resumption checkpoints","state recovery metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beebot__cap_9","uri":"capability://automation.workflow.human.in.the.loop.task.approval.and.intervention","name":"human-in-the-loop task approval and intervention","description":"BeeBot pauses task execution at designated checkpoints and requests human approval before proceeding. The system presents task context (input, proposed output, routing decision) to human reviewers through a pluggable interface (web UI, Slack, email) and waits for approval/rejection/modification. Approved tasks resume execution; rejected tasks trigger error handlers or alternative paths.","intents":["I want to require human approval before executing high-risk tasks","I need to allow humans to modify task inputs or outputs before proceeding","I want to integrate human review into my automation workflow without stopping the entire system"],"best_for":["teams automating high-stakes processes (financial transactions, data deletion, policy changes)","organizations with compliance requirements for human oversight","systems where human judgment is needed for edge cases or exceptions"],"limitations":["Human approval adds unpredictable latency (minutes to hours) to task execution","No built-in timeout for approval requests; unapproved tasks can block indefinitely","Approval interface requires custom implementation for each notification channel (web UI, Slack, etc.)","No built-in audit trail for approval decisions; compliance tracking requires external logging"],"requires":["Python 3.8+","Approval interface implementation (web UI, Slack bot, email handler, etc.)","Human reviewer availability and notification mechanism"],"input_types":["task context (input, proposed output, routing decision)","approval request metadata","human reviewer feedback"],"output_types":["approval decisions (approved, rejected, modified)","modified task inputs/outputs","approval audit trail"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","Task handler implementations must conform to BeeBot's handler interface","Docker or similar containerization (if using container-based sandboxing)","Sufficient system resources for parallel code execution","Profiling data collection enabled (default)","Analysis tools or external observability platform for interpreting recommendations","Handler implementations must inherit from BeeBot's BaseHandler class or implement required interface","Handlers must provide JSON-serializable metadata (name, description, schemas)","API keys for at least one LLM provider (OpenAI, Anthropic, or local Ollama)"],"failure_modes":["LLM routing adds latency per task decision (typically 500ms-2s depending on model)","No built-in cost optimization for repeated routing decisions — each request incurs full LLM inference","Task registry must be manually maintained; no automatic discovery of available handlers","Early-stage project with limited production hardening for high-throughput scenarios","Sandbox overhead adds 100-300ms per execution compared to direct Python execution","Limited to Python code execution in early versions; other languages require custom runtime handlers","No built-in support for long-running processes — timeout enforcement may interrupt legitimate long-duration tasks","Sandbox isolation may prevent legitimate inter-process communication or system calls needed by some code","Profiling adds 2-5% overhead to task execution time","Recommendations are heuristic-based; they may not account for domain-specific constraints","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=beebot","compare_url":"https://unfragile.ai/compare?artifact=beebot"}},"signature":"+q4e4FTHaj3ghvmiB4WHjtLzjZA/GledX7XG1bTP/SCSjiLSXfUS2C2f21O27x36xI86sHIj0QoSzsFFdXsKAg==","signedAt":"2026-06-20T11:23:01.241Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/beebot","artifact":"https://unfragile.ai/beebot","verify":"https://unfragile.ai/api/v1/verify?slug=beebot","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"}}