{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-bambooai","slug":"bambooai","name":"BambooAI","type":"repo","url":"https://github.com/pgalko/BambooAI","page_url":"https://unfragile.ai/bambooai","categories":["data-analysis"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-bambooai__cap_0","uri":"capability://code.generation.editing.natural.language.to.python.code.generation.for.data.analysis","name":"natural language to python code generation for data analysis","description":"Converts natural language questions about datasets into executable Python code by routing queries through a specialized code-generation agent that understands pandas/numpy/matplotlib APIs. The system maintains transparency by returning visible, editable generated code alongside execution results, enabling users to inspect and modify the analysis logic without requiring programming knowledge.","intents":["I want to ask a question about my CSV data without writing Python code","I need to generate analysis code that I can review and edit before running","I want to explore a dataset interactively through conversational queries"],"best_for":["Non-technical business analysts exploring datasets","Data scientists prototyping analysis workflows quickly","Teams needing transparent, auditable data analysis code"],"limitations":["Code generation quality depends on LLM model capability; complex statistical analyses may require manual refinement","Generated code executes in isolated Python environment with no persistent state between queries unless explicitly managed","Limited to Python ecosystem libraries (pandas, numpy, matplotlib, scikit-learn); cannot generate code for R, SQL, or other languages"],"requires":["Python 3.8+","API key for OpenAI, Google Gemini, or Anthropic","CSV/JSON/Parquet dataset loaded into memory or accessible via file path","LLM_CONFIG.json configured with model provider credentials"],"input_types":["natural language text query","CSV/JSON/Parquet file path","pandas DataFrame object"],"output_types":["Python code string","Execution result (numeric, text, or visualization)","Error messages with debugging suggestions"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_1","uri":"capability://planning.reasoning.multi.agent.orchestration.for.complex.data.analysis.workflows","name":"multi-agent orchestration for complex data analysis workflows","description":"Coordinates 11 specialized agents (planner, code generator, executor, debugger, etc.) in a pipeline pattern where each agent handles a specific phase of analysis: query understanding, planning, code generation, execution, error correction, and result synthesis. The BambooAI orchestrator manages message passing, context propagation, and agent sequencing based on query complexity and execution outcomes.","intents":["I need to break down a complex analysis question into steps and execute them sequentially","I want different agents to specialize in different tasks (planning vs coding vs debugging)","I need to handle errors gracefully by routing failed queries to a debugging agent"],"best_for":["Teams building multi-step data analysis pipelines","Organizations needing specialized agent roles for different analysis phases","Systems requiring transparent agent-level logging and cost tracking"],"limitations":["Agent coordination adds ~200-500ms latency per workflow phase due to sequential LLM calls","No built-in load balancing across agents; all agents use same LLM provider unless manually configured","Agent state is not persisted between sessions; complex multi-turn workflows require external state management"],"requires":["LLM_CONFIG.json with 11 agent configurations (each with model, temperature, system prompt)","API credentials for at least one LLM provider (OpenAI, Gemini, Anthropic)","Python 3.8+","Message queue or in-memory context store for agent communication"],"input_types":["user query (text)","dataset context (schema, sample rows)","previous agent outputs (code, execution results)"],"output_types":["agent execution logs","intermediate code artifacts","final analysis result","token usage and cost metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_10","uri":"capability://automation.workflow.flask.web.application.with.workflow.management.ui","name":"flask web application with workflow management ui","description":"Provides a browser-based web interface (Flask backend + JavaScript frontend) enabling non-technical users to upload datasets, ask questions, view generated code, execute analyses, and navigate analysis workflows. The UI includes dataset preview, code editor, result visualization, and workflow history management. Backend handles file uploads, code execution, and result streaming.","intents":["I want a web interface for data analysis without command-line usage","I need to share analysis workflows with non-technical team members","I want to visualize results and manage analysis history through a browser UI"],"best_for":["Non-technical business users","Teams needing collaborative analysis interfaces","Organizations deploying BambooAI as a web service"],"limitations":["Web UI adds infrastructure complexity; requires Flask server, file storage, and session management","Large file uploads are slow; no built-in chunked upload or streaming","Real-time result streaming requires WebSocket or Server-Sent Events; adds complexity","UI is single-user by default; multi-user support requires session management and access control"],"requires":["Flask 2.0+","Python 3.8+","JavaScript runtime (browser)","File storage (local filesystem or cloud storage)","Web server (Flask development server or production WSGI server)"],"input_types":["CSV/JSON/Parquet file upload","natural language query (text input)","code editor modifications (user-edited code)"],"output_types":["HTML/CSS/JavaScript UI","generated code (displayed in editor)","execution results (displayed in UI)","visualizations (rendered in browser or saved as images)","workflow history (JSON or database records)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_11","uri":"capability://automation.workflow.streaming.and.real.time.result.updates","name":"streaming and real-time result updates","description":"Implements streaming of code execution results and LLM responses to the frontend in real-time, enabling users to see analysis progress without waiting for full completion. Uses Server-Sent Events (SSE) or WebSocket to push updates from Flask backend to browser, displaying intermediate results, code generation progress, and execution logs as they occur.","intents":["I want to see analysis progress in real-time instead of waiting for completion","I need to monitor long-running queries and see intermediate results","I want to cancel queries if I see early results are incorrect"],"best_for":["Web UI users analyzing large datasets with long execution times","Teams needing visibility into analysis progress","Interactive analysis workflows where early results inform subsequent queries"],"limitations":["Streaming adds complexity to backend (event handling, connection management)","Browser compatibility issues with older browsers (IE11 and earlier)","Streaming can increase server resource usage; requires connection pooling","Intermediate results may be incomplete or incorrect; users must validate final results"],"requires":["Flask 2.0+ with streaming support","Server-Sent Events (SSE) or WebSocket implementation","Browser with SSE/WebSocket support (all modern browsers)","Connection management and timeout handling"],"input_types":["code execution process (subprocess or async task)","LLM response stream (if using streaming LLM APIs)"],"output_types":["streamed events (JSON format)","intermediate results (code, execution logs, partial results)","final result (complete analysis output)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_12","uri":"capability://tool.use.integration.multi.model.provider.abstraction.with.configurable.model.assignment","name":"multi-model provider abstraction with configurable model assignment","description":"Abstracts LLM provider differences (OpenAI, Google Gemini, Anthropic, Ollama) behind a unified interface, enabling users to configure which model each agent uses via LLM_CONFIG.json. Supports model-specific features (function calling, streaming, vision) and enables cost optimization by assigning cheap models to simple tasks and expensive models to complex tasks. Handles provider-specific API differences transparently.","intents":["I want to use different LLM providers for different agents based on cost/capability","I need to switch between OpenAI, Gemini, and Anthropic without code changes","I want to use local models (Ollama) for privacy-sensitive queries"],"best_for":["Organizations using multiple LLM providers","Cost-conscious teams optimizing model selection per task","Teams with privacy requirements needing local model support"],"limitations":["Model feature parity is not guaranteed; some providers lack function calling or streaming","API differences require provider-specific error handling; failures may be provider-specific","Model performance varies significantly; switching providers may require prompt tuning","Ollama/local models require local infrastructure; adds deployment complexity"],"requires":["LLM_CONFIG.json with provider and model configuration for each agent","API credentials for each provider (OpenAI key, Gemini key, Anthropic key, etc.)","Python SDK for each provider (openai, google-generativeai, anthropic, ollama)","Network access to provider APIs (or local Ollama server)"],"input_types":["LLM_CONFIG.json configuration","provider API credentials","model name and parameters"],"output_types":["unified LLM response interface","provider-agnostic error handling","token usage metrics per provider"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_13","uri":"capability://planning.reasoning.prompt.template.customization.for.agent.behavior.control","name":"prompt template customization for agent behavior control","description":"Enables customization of system prompts for each of the 11 agents via configuration files, allowing users to modify agent behavior, output format, and reasoning style without code changes. Prompts can be templated with variables (dataset schema, user context, previous results) and versioned for experimentation. Supports prompt engineering best practices like few-shot examples and chain-of-thought instructions.","intents":["I want to customize how the code generation agent writes code (style, libraries, patterns)","I need to add domain-specific instructions to agents without modifying source code","I want to experiment with different prompts to improve analysis quality"],"best_for":["Teams fine-tuning agent behavior for specific domains","Organizations with custom coding standards or analysis patterns","Researchers experimenting with prompt engineering"],"limitations":["Prompt customization requires understanding LLM behavior; poor prompts degrade quality","No automatic prompt optimization; requires manual iteration and testing","Prompt changes are not versioned by default; difficult to track which prompt produced which result","Prompt injection vulnerabilities if user input is not sanitized before injection into prompts"],"requires":["Prompt template files (YAML, JSON, or text format)","Understanding of LLM prompt engineering best practices","Configuration management for prompt versioning","Testing framework to validate prompt changes"],"input_types":["prompt template text with variables","variable values (dataset schema, user context, examples)"],"output_types":["rendered prompt (with variables substituted)","LLM response based on customized prompt"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_14","uri":"capability://planning.reasoning.message.management.and.context.propagation.across.agents","name":"message management and context propagation across agents","description":"Manages message passing between agents in the multi-agent pipeline, maintaining conversation history, context windows, and state across agent transitions. Implements context compression to fit large histories into LLM token limits, selective context inclusion to reduce noise, and message formatting for agent-specific requirements. Enables agents to reference previous agent outputs and build on prior analysis.","intents":["I want agents to have access to previous agent outputs and conversation history","I need to manage context size to stay within LLM token limits","I want to ensure agents can reference and build on prior analysis steps"],"best_for":["Complex multi-step analysis workflows requiring agent coordination","Teams needing transparent agent communication and context flow","Systems with long conversation histories requiring context management"],"limitations":["Context compression may lose important information; requires careful tuning","Message formatting overhead adds latency (~50-100ms per agent transition)","No automatic context relevance detection; all context is included unless explicitly filtered","Token limit management is manual; no automatic context truncation"],"requires":["Message queue or in-memory context store","Context compression strategy (summarization, truncation, or selective inclusion)","Message formatting templates for each agent","Token counting to monitor context size"],"input_types":["agent output (code, results, analysis)","conversation history (previous messages)","context metadata (timestamp, agent, relevance)"],"output_types":["formatted messages for next agent","context window (selected messages within token limit)","context metadata for tracking"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_2","uri":"capability://memory.knowledge.episodic.memory.via.vector.database.for.solution.reuse","name":"episodic memory via vector database for solution reuse","description":"Stores previously generated code solutions and their execution results in a vector database (embeddings-based), enabling semantic similarity matching to retrieve relevant past solutions when new queries are submitted. When a new query arrives, the system embeds it, searches the vector database for semantically similar past queries, and can reuse or adapt cached solutions, reducing redundant LLM calls and improving response latency.","intents":["I want to reuse analysis code from similar past queries without regenerating it","I need to reduce API costs by caching and retrieving similar solutions","I want to speed up response time by leveraging previously computed analyses"],"best_for":["Organizations with repetitive data analysis patterns","Teams analyzing similar datasets across multiple projects","Cost-sensitive deployments where reducing LLM calls is critical"],"limitations":["Vector database requires external setup (Pinecone, Weaviate, Chroma, or local FAISS); no built-in persistence","Semantic similarity matching may retrieve false positives if query intent is ambiguous; requires manual validation","Cached solutions may become stale if underlying data schema changes; no automatic invalidation mechanism","Embedding quality depends on embedding model; poor embeddings reduce retrieval accuracy"],"requires":["Vector database instance (Pinecone, Weaviate, Chroma, or FAISS)","Embedding model API (OpenAI embeddings, Hugging Face, or local model)","Previous query-solution pairs to seed the vector database","Similarity threshold configuration for retrieval"],"input_types":["natural language query (text)","embedding vector (768-1536 dimensions depending on model)"],"output_types":["retrieved past solutions (code + results)","similarity scores","adapted code for current query"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_3","uri":"capability://memory.knowledge.semantic.memory.via.owl.rdf.ontologies.for.domain.knowledge","name":"semantic memory via owl/rdf ontologies for domain knowledge","description":"Encodes domain-specific knowledge (data model relationships, business rules, metric definitions) as OWL/RDF ontologies that are injected into agent prompts during query processing. The system uses ontology reasoning to enrich query context with relevant domain concepts, enabling agents to generate more semantically correct code that respects business logic and data relationships.","intents":["I want to encode business rules and data relationships so the LLM respects them when generating code","I need to ensure generated analyses use correct metric definitions and calculations","I want to provide domain knowledge without modifying agent prompts manually"],"best_for":["Enterprise teams with complex data models and business rules","Organizations with domain-specific terminology and calculation standards","Teams needing to enforce data governance rules in generated code"],"limitations":["OWL/RDF ontology creation and maintenance requires domain expertise; no automatic ontology generation from data","Ontology reasoning adds computational overhead; large ontologies may slow query processing","LLM integration with ontologies is prompt-based; no native semantic reasoning in LLM itself","Ontology updates require manual editing and redeployment; no dynamic ontology learning from user corrections"],"requires":["OWL/RDF ontology file (Turtle, RDF/XML, or JSON-LD format)","RDF reasoning engine (Apache Jena, Protégé, or Python rdflib)","Domain expertise to author and maintain ontologies","Integration point in agent prompt templates to inject ontology context"],"input_types":["OWL/RDF ontology file","user query (text)","dataset schema"],"output_types":["enriched agent prompts with ontology context","semantically validated code","ontology-aware analysis results"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_4","uri":"capability://code.generation.editing.self.healing.error.correction.with.iterative.debugging","name":"self-healing error correction with iterative debugging","description":"Automatically detects code execution errors (syntax, runtime, logic) and routes failed queries to a specialized debugging agent that analyzes the error, regenerates corrected code, and re-executes it in a loop until success or max retries. The system maintains error history and context to inform subsequent regeneration attempts, improving code quality without user intervention.","intents":["I want the system to automatically fix code errors instead of returning failures to the user","I need iterative debugging that learns from previous errors in the same query","I want to reduce manual debugging cycles for generated analysis code"],"best_for":["Non-technical users who cannot debug generated code","High-volume analysis pipelines where manual error handling is infeasible","Teams prioritizing user experience over cost (debugging adds LLM calls)"],"limitations":["Iterative debugging can consume 2-5x more LLM tokens than single-pass generation; significantly increases costs","Max retry limit (typically 3-5) means some errors remain unresolved; complex logic errors may exceed retry budget","Debugging agent may generate incorrect fixes that mask underlying data issues; no validation that fixed code is semantically correct","Error context is limited to execution output; silent failures (incorrect results) are not detected or corrected"],"requires":["Debugging agent configured in LLM_CONFIG.json","Python code execution environment with error capture","Max retry limit configuration (default typically 3-5)","Error message parsing to extract actionable debugging information"],"input_types":["generated Python code (string)","execution error message (traceback, exception)","dataset context (schema, sample data)"],"output_types":["corrected Python code","execution result (if successful)","error history and retry count","final failure message (if max retries exceeded)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_5","uri":"capability://automation.workflow.dual.execution.modes.local.and.remote.code.execution","name":"dual execution modes: local and remote code execution","description":"Supports both local Python execution (code runs in user's environment with direct data access) and remote execution (code runs on isolated server, suitable for untrusted code). The system abstracts execution mode selection, enabling users to choose based on security/performance trade-offs. Local mode provides fast iteration and data privacy; remote mode provides sandboxing and audit trails.","intents":["I want to run generated code locally for fast iteration and data privacy","I need to execute untrusted generated code in a sandboxed environment","I want to choose execution mode based on security and performance requirements"],"best_for":["Individual data scientists running code locally on their machines","Enterprise deployments requiring code sandboxing and audit trails","Teams with mixed security requirements (some queries local, some remote)"],"limitations":["Local execution requires Python environment with all dependencies installed; no dependency isolation","Remote execution adds network latency (100-500ms per execution) and requires external compute infrastructure","Local execution has no audit trail; remote execution requires logging infrastructure","Switching execution modes requires code path changes; no transparent fallback if one mode fails"],"requires":["For local: Python 3.8+ with pandas, numpy, matplotlib installed","For remote: Docker container or serverless function (AWS Lambda, Google Cloud Functions)","Execution mode configuration in BambooAI initialization","For remote: API endpoint and authentication credentials"],"input_types":["Python code string","dataset (file path or DataFrame)","execution mode flag (local/remote)"],"output_types":["execution result (numeric, text, or visualization)","execution logs and timing","error messages with traceback"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_6","uri":"capability://search.retrieval.web.search.integration.for.research.queries","name":"web search integration for research queries","description":"Integrates web search capabilities (via search agent) that enable queries combining real-time web data with local dataset analysis. When a query requires external information (market data, news, competitor info), the search agent retrieves relevant web results, synthesizes them with local data analysis, and generates code that incorporates both sources. Results are cached to avoid redundant searches.","intents":["I want to analyze my sales data in context of current market trends from the web","I need to combine local dataset analysis with real-time external data sources","I want to research competitor data and correlate it with my own metrics"],"best_for":["Business analysts combining internal data with market intelligence","Research teams correlating datasets with external sources","Organizations needing real-time context for data analysis"],"limitations":["Web search results are unstructured and may contain outdated or inaccurate information; requires validation","Search result parsing is fragile; changes to search engine HTML/API may break integration","Web search adds latency (2-5 seconds per search) and external API dependencies","Search results are not automatically integrated into generated code; requires manual synthesis by agent"],"requires":["Web search API (Google Search, Bing, or custom search engine)","Search API credentials and rate limits","Search agent configured in LLM_CONFIG.json","Result caching mechanism (Redis, in-memory, or vector database)"],"input_types":["natural language query (text)","local dataset","search query keywords (auto-extracted from user query)"],"output_types":["web search results (URLs, snippets, metadata)","synthesized analysis combining web data and local data","generated code incorporating both sources"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_7","uri":"capability://data.processing.analysis.multi.dataset.analysis.with.auxiliary.data.source.integration","name":"multi-dataset analysis with auxiliary data source integration","description":"Enables analysis across multiple datasets by loading auxiliary data sources (lookup tables, reference data, external CSVs) and making them available to the code generation agent. The system manages dataset relationships, handles joins/merges, and generates code that combines data from multiple sources. Dataset schemas are tracked and injected into agent context.","intents":["I want to analyze my main dataset joined with reference data from another CSV","I need to correlate metrics across multiple datasets in a single analysis","I want to enrich my data with lookup tables before analysis"],"best_for":["Data analysts working with denormalized or multi-source datasets","Teams combining internal data with external reference data","Organizations with complex data relationships requiring joins"],"limitations":["Multiple dataset loading increases memory footprint; large datasets may cause out-of-memory errors","Dataset relationship inference is not automatic; users must specify join keys and relationships","Code generation for complex multi-dataset joins may produce inefficient or incorrect merge logic","No built-in data validation; mismatched schemas or join keys can produce silent failures"],"requires":["Primary dataset (CSV, JSON, Parquet, or DataFrame)","Auxiliary datasets (same formats)","Dataset schema definitions (column names, types, join keys)","Sufficient memory to load all datasets simultaneously"],"input_types":["primary dataset file path or DataFrame","auxiliary dataset file paths or DataFrames","dataset relationship definitions (join keys, relationship type)"],"output_types":["merged/joined dataset","generated code for multi-dataset analysis","analysis results across combined data"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_8","uri":"capability://automation.workflow.token.usage.tracking.and.cost.optimization","name":"token usage tracking and cost optimization","description":"Tracks LLM token consumption across all agents and queries, providing detailed cost breakdowns by agent, model, and query type. The system logs token usage for every LLM call, enables cost-per-query reporting, and supports cost optimization strategies like model selection (cheap models for planning, expensive for code generation) and caching to reduce redundant calls.","intents":["I want to understand how much each analysis query costs in LLM API fees","I need to optimize costs by using cheaper models for certain agent tasks","I want to track token usage trends and identify cost-saving opportunities"],"best_for":["Organizations with large-scale data analysis workloads","Teams managing LLM API budgets and cost controls","Cost-sensitive deployments requiring detailed usage metrics"],"limitations":["Token tracking adds logging overhead (~10-20ms per LLM call); impacts latency","Cost calculations depend on accurate model pricing; pricing changes require manual updates","Token counts are approximate for some models; actual API charges may vary","No automatic cost optimization; requires manual model selection and configuration"],"requires":["Logging infrastructure (file, database, or cloud logging service)","Model pricing configuration (tokens per dollar for each model)","Token counting library (tiktoken for OpenAI, or model-specific counters)","Cost reporting dashboard or export capability"],"input_types":["LLM API responses with token counts","model pricing configuration","query metadata (agent, model, timestamp)"],"output_types":["token usage logs (per call, per query, per agent)","cost reports (total, by agent, by model, by query type)","cost optimization recommendations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bambooai__cap_9","uri":"capability://text.generation.language.interactive.cli.conversation.loop.for.exploratory.analysis","name":"interactive cli conversation loop for exploratory analysis","description":"Provides a command-line interface (via pd_agent_converse() method) that enables multi-turn conversational analysis where users ask sequential questions about a dataset, building on previous context. The CLI maintains conversation history, manages dataset state across turns, and enables iterative refinement of analyses without reloading data or restarting the session.","intents":["I want to ask follow-up questions about my data without reloading the dataset","I need an interactive conversation where each question builds on previous context","I want to explore data iteratively through a natural conversation interface"],"best_for":["Data explorers preferring command-line interfaces","Jupyter notebook users integrating BambooAI into analysis workflows","Teams using BambooAI as a Python library in scripts"],"limitations":["CLI interface is text-only; no visualization display in terminal (plots saved to files)","Conversation history is in-memory; not persisted across sessions unless explicitly saved","Multi-turn context can grow large, increasing token usage for subsequent queries","No built-in session management; users must manually manage state for complex workflows"],"requires":["Python 3.8+ with BambooAI installed","Dataset loaded into memory or accessible via file path","LLM API credentials configured","Terminal/console for CLI interaction"],"input_types":["natural language query (text input from user)","dataset (CSV, JSON, or DataFrame)","previous conversation context (implicit)"],"output_types":["generated code (displayed in CLI)","execution results (printed to CLI)","visualizations (saved to files, paths printed)","conversation history (in-memory or exported)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for OpenAI, Google Gemini, or Anthropic","CSV/JSON/Parquet dataset loaded into memory or accessible via file path","LLM_CONFIG.json configured with model provider credentials","LLM_CONFIG.json with 11 agent configurations (each with model, temperature, system prompt)","API credentials for at least one LLM provider (OpenAI, Gemini, Anthropic)","Message queue or in-memory context store for agent communication","Flask 2.0+","JavaScript runtime (browser)","File storage (local filesystem or cloud storage)"],"failure_modes":["Code generation quality depends on LLM model capability; complex statistical analyses may require manual refinement","Generated code executes in isolated Python environment with no persistent state between queries unless explicitly managed","Limited to Python ecosystem libraries (pandas, numpy, matplotlib, scikit-learn); cannot generate code for R, SQL, or other languages","Agent coordination adds ~200-500ms latency per workflow phase due to sequential LLM calls","No built-in load balancing across agents; all agents use same LLM provider unless manually configured","Agent state is not persisted between sessions; complex multi-turn workflows require external state management","Web UI adds infrastructure complexity; requires Flask server, file storage, and session management","Large file uploads are slow; no built-in chunked upload or streaming","Real-time result streaming requires WebSocket or Server-Sent Events; adds complexity","UI is single-user by default; multi-user support requires session management and access control","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":"2026-06-17T09:51:02.371Z","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=bambooai","compare_url":"https://unfragile.ai/compare?artifact=bambooai"}},"signature":"VBNdpABDOBcBzO0qoDZPjEwYgwLxscWSwWyIS4g9VdyYWdzVd605bCEHsBTSsD/gezcbWtlL7OQKnzzCJb9zBA==","signedAt":"2026-06-19T19:58:55.340Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bambooai","artifact":"https://unfragile.ai/bambooai","verify":"https://unfragile.ai/api/v1/verify?slug=bambooai","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"}}