{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-brainsoup","slug":"brainsoup","name":"BrainSoup","type":"product","url":"https://www.nurgo-software.com/products/brainsoup","page_url":"https://unfragile.ai/brainsoup","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-brainsoup__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.role.based.task.delegation","name":"multi-agent orchestration with role-based task delegation","description":"BrainSoup enables users to create and manage multiple AI agents with distinct roles and responsibilities that work collaboratively on complex tasks. The system uses a role-definition framework where each agent is configured with specific instructions, capabilities, and behavioral constraints, then coordinates their execution through a task queue and inter-agent messaging system. Agents can hand off work to each other based on task requirements, enabling hierarchical problem decomposition without requiring manual workflow definition.","intents":["I want to create specialized AI agents that each handle different parts of my workflow","I need multiple AI assistants to collaborate on complex projects without manual coordination","I want to delegate different types of tasks to agents optimized for those specific domains"],"best_for":["solo developers and small teams building internal automation systems","non-technical users wanting to automate multi-step business processes","teams managing content creation, research, or analysis workflows"],"limitations":["Agent coordination overhead increases latency for highly interdependent tasks","No built-in persistence layer for agent state across sessions — requires manual checkpointing","Limited visibility into agent decision-making process and reasoning chains","Scaling to 10+ concurrent agents may require manual load balancing configuration"],"requires":["Windows PC or compatible system running BrainSoup","At least one configured LLM backend (OpenAI, Anthropic, or local model)","Basic understanding of task decomposition and role definition"],"input_types":["natural language task descriptions","structured task specifications","file paths and document references"],"output_types":["task completion reports","structured results from agent execution","inter-agent communication logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_1","uri":"capability://tool.use.integration.local.llm.backend.integration.with.multi.provider.support","name":"local llm backend integration with multi-provider support","description":"BrainSoup provides a unified interface for connecting to multiple LLM providers (OpenAI, Anthropic, local models) through an abstraction layer that normalizes API differences and handles provider-specific authentication. The system maintains connection pooling and request queuing to manage concurrent agent requests across different backends, allowing users to route different agents to different models based on cost, latency, or capability requirements.","intents":["I want to use multiple LLM providers without rewriting agent logic for each API","I need to run some agents on local models for privacy and others on cloud APIs for capability","I want to switch between LLM providers without reconfiguring my entire agent team"],"best_for":["developers building cost-optimized agent systems","teams with privacy requirements needing local model support","users wanting to experiment with different LLM providers"],"limitations":["Local model performance depends on hardware — GPU with 8GB+ VRAM recommended for reasonable latency","No automatic model selection or fallback routing if primary provider fails","API key management is manual — no built-in secrets management or rotation","Latency variance between local and cloud models may require per-agent tuning"],"requires":["API keys for cloud providers (OpenAI, Anthropic) if using cloud backends","For local models: Ollama or compatible runtime installed","Network connectivity for cloud providers, or local GPU for on-device inference","8GB+ RAM minimum, 16GB+ recommended for concurrent multi-agent execution"],"input_types":["API credentials and configuration","model selection parameters","provider-specific settings"],"output_types":["normalized LLM responses","token usage metrics","provider-specific metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_10","uri":"capability://automation.workflow.error.handling.and.task.retry.logic","name":"error handling and task retry logic","description":"BrainSoup implements automatic error detection and recovery mechanisms for failed agent tasks, including configurable retry strategies with exponential backoff, fallback agent assignment, and manual intervention workflows. 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The system enables users to set cost budgets, monitor spending in real-time, and identify optimization opportunities (e.g., using cheaper models for simple tasks). Cost data is aggregated and visualized to help users understand their LLM spending patterns.","intents":["I want to track how much my agent team costs to run","I need to optimize agent execution to reduce LLM API spending","I want to set budgets and alerts for agent team costs"],"best_for":["organizations managing LLM costs at scale","teams optimizing agent efficiency and cost-effectiveness","users wanting visibility into automation expenses"],"limitations":["Cost tracking is approximate — depends on accurate token counting from LLM providers","No automatic cost optimization — requires manual agent configuration changes","Budget enforcement is advisory only — no hard limits on spending","Cost data requires manual export for integration with accounting systems"],"requires":["API keys with usage tracking enabled (OpenAI, Anthropic)","Understanding of token pricing for different models","Regular monitoring of cost reports and trends"],"input_types":["LLM API usage data","model pricing information","budget parameters"],"output_types":["cost reports and breakdowns","usage analytics","optimization recommendations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_2","uri":"capability://memory.knowledge.persistent.agent.memory.and.context.management","name":"persistent agent memory and context management","description":"BrainSoup maintains agent-specific memory stores that persist across sessions, enabling agents to retain knowledge from previous interactions and build context over time. The system implements a hybrid memory architecture combining short-term conversation context (in-memory for current session) with long-term knowledge storage (persisted to disk), allowing agents to reference past decisions and accumulated information without manual context injection.","intents":["I want my agents to remember previous tasks and decisions across multiple sessions","I need agents to build institutional knowledge over time without manual documentation","I want to query what my agents have learned and accomplished historically"],"best_for":["teams running continuous automation workflows over weeks or months","users building knowledge-accumulating systems (research assistants, project managers)","organizations needing audit trails of agent decisions and reasoning"],"limitations":["Memory storage grows unbounded without manual pruning — no automatic retention policies","No built-in deduplication of similar memories — may accumulate redundant information","Memory retrieval latency increases linearly with stored context size","No cross-agent memory sharing by default — each agent has isolated memory store"],"requires":["Local disk storage for persistent memory (100MB+ recommended for typical usage)","Periodic maintenance to archive or prune old memory entries","Understanding of memory management patterns to avoid context bloat"],"input_types":["task execution results","agent decisions and reasoning","user-provided context and annotations"],"output_types":["retrieved memory entries","memory summaries and indexes","historical decision logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_3","uri":"capability://planning.reasoning.task.decomposition.and.execution.planning","name":"task decomposition and execution planning","description":"BrainSoup analyzes complex user requests and automatically breaks them into subtasks that can be distributed across the agent team, with dependency tracking and execution ordering. The system uses a planning engine that builds a directed acyclic graph (DAG) of task dependencies, identifies parallelizable work, and sequences execution to minimize total completion time while respecting data dependencies between subtasks.","intents":["I want to give my agent team a complex goal and have them figure out the steps","I need to parallelize independent subtasks across multiple agents automatically","I want to see the execution plan before agents start working on a task"],"best_for":["teams with complex multi-step workflows (research, content creation, analysis)","users wanting to minimize manual task specification overhead","organizations needing visibility into how tasks are decomposed"],"limitations":["Planning overhead adds 2-5 seconds per complex task before execution begins","No dynamic replanning if subtasks fail — requires manual intervention or predefined fallbacks","Decomposition quality depends on LLM capability — may miss optimal task boundaries","Circular dependencies or complex interdependencies may not be detected automatically"],"requires":["Clear, well-specified task descriptions for reliable decomposition","Understanding of task dependencies to validate generated plans","LLM backend with reasoning capability (GPT-4 or equivalent recommended)"],"input_types":["natural language task descriptions","structured task specifications with constraints","domain-specific context and requirements"],"output_types":["task execution plan (DAG structure)","subtask specifications","dependency graph visualization"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_4","uri":"capability://planning.reasoning.agent.behavior.customization.and.instruction.management","name":"agent behavior customization and instruction management","description":"BrainSoup allows users to define and modify agent behavior through a system prompt and instruction framework, where each agent can be configured with specific guidelines, constraints, and behavioral patterns. The system supports instruction versioning and templates, enabling users to create agent archetypes (researcher, writer, analyst) that can be instantiated with domain-specific customizations without code changes.","intents":["I want to create specialized agent personas with different expertise and communication styles","I need to enforce specific constraints on how agents behave (tone, format, safety guidelines)","I want to reuse agent configurations across multiple projects with minor customizations"],"best_for":["non-technical users building agent teams through UI configuration","teams standardizing agent behavior across multiple workflows","organizations with specific compliance or brand voice requirements"],"limitations":["Instruction effectiveness depends on LLM's instruction-following capability — not guaranteed","No automated testing of instruction compliance — requires manual validation","Complex behavioral constraints may require iterative refinement through trial and error","Instruction conflicts between agents may cause unexpected behavior in multi-agent scenarios"],"requires":["Understanding of prompt engineering principles for effective instruction design","Iterative testing and refinement process to validate agent behavior","Clear specification of desired agent behavior and constraints"],"input_types":["natural language instructions and guidelines","behavioral constraints and rules","example outputs and reference materials"],"output_types":["agent configuration profiles","instruction templates","behavior validation reports"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_5","uri":"capability://automation.workflow.workflow.monitoring.and.execution.logging","name":"workflow monitoring and execution logging","description":"BrainSoup provides real-time visibility into agent execution through comprehensive logging of all agent actions, decisions, and outputs. The system captures execution traces including LLM prompts, responses, token usage, and timing information, storing them in a queryable log that enables debugging, auditing, and performance analysis of agent workflows.","intents":["I want to see what my agents are doing in real-time as they execute tasks","I need to debug why an agent made a particular decision or produced unexpected output","I want to analyze agent performance metrics and identify bottlenecks"],"best_for":["developers debugging agent behavior and decision-making","teams needing audit trails for compliance or accountability","organizations optimizing agent performance and cost"],"limitations":["Logging overhead increases memory usage — may impact performance with high-volume agent execution","Log storage grows rapidly with verbose logging enabled — requires periodic archival","No built-in log analysis tools — requires manual inspection or external tools for insights","Sensitive data in logs (API keys, user information) requires careful handling and redaction"],"requires":["Local disk storage for log files (1GB+ for typical usage)","Log rotation and archival strategy to manage disk usage","Tools or scripts for log analysis and querying"],"input_types":["agent execution events","LLM API calls and responses","task completion status"],"output_types":["execution logs and traces","performance metrics","debugging information"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_6","uri":"capability://data.processing.analysis.file.and.document.processing.with.agent.access","name":"file and document processing with agent access","description":"BrainSoup enables agents to read, analyze, and process files and documents from the local filesystem, with support for multiple formats (text, PDF, images, code). The system provides agents with file I/O capabilities through a sandboxed interface that prevents unauthorized access while allowing agents to work with project documents, code repositories, and data files as part of their tasks.","intents":["I want agents to analyze documents and code files in my project directory","I need agents to extract information from PDFs and other document formats","I want agents to process multiple files and generate reports based on their content"],"best_for":["teams automating document analysis and processing workflows","developers using agents to analyze and refactor code","organizations processing batches of files without manual review"],"limitations":["File access is limited to configured directories — requires explicit path whitelisting","Large file processing may timeout or consume excessive memory — no streaming support","PDF extraction quality varies by document structure — may require manual cleanup","No built-in file format conversion — requires external tools for unsupported formats"],"requires":["Files stored in local filesystem accessible to BrainSoup process","Appropriate file permissions configured for agent access","Supported file formats (text, PDF, common code formats)"],"input_types":["file paths","document content","code files and repositories"],"output_types":["extracted text and structured data","analysis reports","processed file outputs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_7","uri":"capability://automation.workflow.agent.team.configuration.and.management.ui","name":"agent team configuration and management ui","description":"BrainSoup provides a graphical interface for creating, configuring, and managing multiple agents without requiring code. The UI enables users to define agent roles, assign capabilities, set behavioral parameters, and organize agents into teams through visual configuration tools. The system stores agent configurations persistently, allowing users to save, load, and modify agent teams across sessions.","intents":["I want to create and manage multiple agents through a visual interface","I need to configure agent capabilities and roles without writing code","I want to save and reuse agent team configurations across projects"],"best_for":["non-technical users building agent teams","teams standardizing agent configurations across projects","organizations wanting to manage agents without developer involvement"],"limitations":["UI may not expose all advanced configuration options available programmatically","Complex agent behaviors may be difficult to express through UI alone","No version control integration for agent configuration changes","Limited ability to perform bulk operations on multiple agents"],"requires":["Windows PC with BrainSoup installed","Basic understanding of agent concepts and task types","No coding knowledge required"],"input_types":["agent name and description","role and capability definitions","behavioral parameters and constraints"],"output_types":["agent configuration profiles","team definitions","configuration export/import files"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_8","uri":"capability://planning.reasoning.inter.agent.communication.and.collaboration","name":"inter-agent communication and collaboration","description":"BrainSoup implements a message-passing system that allows agents to communicate with each other, request assistance, and share results. Agents can query other agents' outputs, ask for specialized help, and coordinate on complex tasks through a structured communication protocol that maintains context and prevents circular dependencies.","intents":["I want agents to ask each other for help when they need specialized expertise","I need agents to share results and build on each other's work","I want to enable agents to collaborate on tasks that require multiple perspectives"],"best_for":["teams with complex workflows requiring agent collaboration","organizations building knowledge-sharing agent systems","projects where agents need to validate or review each other's work"],"limitations":["Inter-agent communication adds latency — each message round-trip requires LLM inference","No built-in consensus mechanism — agents may disagree without resolution","Circular communication patterns can cause infinite loops without explicit guards","Message context grows with each communication round, increasing token usage"],"requires":["Multiple agents configured and running","Clear communication protocols and message formats","Timeout and recursion depth limits to prevent runaway communication"],"input_types":["agent queries and requests","task results and outputs","collaboration parameters"],"output_types":["agent responses and assistance","shared results and findings","collaboration logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-brainsoup__cap_9","uri":"capability://automation.workflow.scheduled.and.triggered.task.execution","name":"scheduled and triggered task execution","description":"BrainSoup supports scheduling agents to run tasks on a recurring basis (hourly, daily, weekly) or triggering execution based on external events or file system changes. The system maintains a task scheduler that manages agent execution timing, handles missed runs, and provides retry logic for failed tasks. Users can define trigger conditions and execution schedules through the UI without requiring cron syntax or code.","intents":["I want agents to run daily reports or monitoring tasks automatically","I need agents to process new files as they appear in a directory","I want to trigger agent execution based on external events or conditions"],"best_for":["teams automating recurring tasks and monitoring workflows","organizations running continuous background processes","users wanting hands-off automation without manual triggering"],"limitations":["Scheduler runs only while BrainSoup is active — no background execution if application closes","No distributed scheduling — limited to single-machine execution","Missed scheduled runs are not automatically caught up — may require manual intervention","Complex trigger conditions require custom logic — limited to simple file/event patterns"],"requires":["BrainSoup application running continuously for scheduled tasks","Clear schedule definitions (frequency, time, trigger conditions)","Sufficient system resources to handle scheduled agent execution"],"input_types":["schedule definitions (frequency, time)","trigger conditions and patterns","task specifications"],"output_types":["execution schedules","trigger logs","execution history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Windows PC or compatible system running BrainSoup","At least one configured LLM backend (OpenAI, Anthropic, or local model)","Basic understanding of task decomposition and role definition","API keys for cloud providers (OpenAI, Anthropic) if using cloud backends","For local models: Ollama or compatible runtime installed","Network connectivity for cloud providers, or local GPU for on-device inference","8GB+ RAM minimum, 16GB+ recommended for concurrent multi-agent execution","Clear error classification and retry policies","Fallback agents or escalation procedures defined","Monitoring and alerting setup for critical failures"],"failure_modes":["Agent coordination overhead increases latency for highly interdependent tasks","No built-in persistence layer for agent state across sessions — requires manual checkpointing","Limited visibility into agent decision-making process and reasoning chains","Scaling to 10+ concurrent agents may require manual load balancing configuration","Local model performance depends on hardware — GPU with 8GB+ VRAM recommended for reasonable latency","No automatic model selection or fallback routing if primary provider fails","API key management is manual — no built-in secrets management or rotation","Latency variance between local and cloud models may require per-agent tuning","Retry logic cannot fix fundamental issues (missing files, API errors) — only transient failures","No automatic root cause analysis — requires manual investigation of failures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"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=brainsoup","compare_url":"https://unfragile.ai/compare?artifact=brainsoup"}},"signature":"THrRavlX9ug7nWE+dse0UOlG9mOkO0q3QetVy7oeSmaOM20VDkIzcXr0GbD5R6+J5k1GnP5NU6cOyLOjeDEECQ==","signedAt":"2026-06-20T08:21:22.543Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/brainsoup","artifact":"https://unfragile.ai/brainsoup","verify":"https://unfragile.ai/api/v1/verify?slug=brainsoup","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"}}