{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-nlsom","slug":"nlsom","name":"NLSOM","type":"repo","url":"https://github.com/mczhuge/NLSOM","page_url":"https://unfragile.ai/nlsom","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-nlsom__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.via.natural.language.society.composition","name":"multi-agent orchestration via natural language society composition","description":"Orchestrates multiple specialized AI agents as a 'society of mind' where agents are defined, coordinated, and communicate through natural language specifications rather than explicit code. Each agent maintains its own role, context, and decision-making logic, with a central coordinator parsing natural language instructions to route tasks, aggregate responses, and manage inter-agent dependencies. The system uses LLM-based interpretation of agent capabilities and constraints to dynamically compose agent teams for complex reasoning tasks.","intents":["I want to decompose a complex problem into specialized agent roles and have them collaborate automatically","I need agents to communicate and share reasoning without explicit API definitions","I want to define agent behaviors and constraints in plain English rather than code","I need to dynamically adjust which agents participate in solving a problem based on task requirements"],"best_for":["researchers prototyping multi-agent reasoning systems","teams building complex AI workflows without rigid agent interfaces","developers exploring emergent behavior from agent societies"],"limitations":["Natural language parsing of agent roles introduces non-deterministic behavior and potential ambiguity in agent selection","No built-in persistence or state management across agent sessions — requires external storage for conversation history","Scaling to 10+ agents may cause exponential growth in coordination overhead and token consumption","Debugging agent interactions is difficult because routing decisions are implicit in LLM interpretation rather than explicit rules"],"requires":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","Natural language descriptions of agent roles and capabilities"],"input_types":["natural language task descriptions","agent role specifications (text)","structured problem statements"],"output_types":["aggregated reasoning from multiple agents","structured task decomposition","agent interaction traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_1","uri":"capability://planning.reasoning.dynamic.agent.role.inference.and.capability.matching","name":"dynamic agent role inference and capability matching","description":"Analyzes incoming tasks and automatically infers which agent roles from a society are best suited to handle them by matching task semantics against natural language agent descriptions. Uses LLM-based semantic similarity and constraint satisfaction to select and compose agent subsets without explicit routing rules. The system maintains a registry of agent capabilities expressed in natural language and performs real-time matching to determine optimal agent participation.","intents":["I want the system to automatically pick the right agents for a task without me specifying which ones","I need agents to be selected based on semantic similarity to task requirements","I want to add new agents and have them automatically participate in relevant tasks"],"best_for":["teams building self-organizing agent systems","researchers exploring emergent task allocation","developers prototyping flexible agent composition"],"limitations":["Semantic matching can fail for ambiguous or novel task types not well-represented in agent descriptions","No explicit constraint propagation — agent selection is based on LLM interpretation which may miss hard constraints","Requires well-written natural language descriptions of agent capabilities; poor descriptions lead to suboptimal agent selection","Cold-start problem: system has no learning mechanism to improve matching accuracy over time"],"requires":["Python 3.8+","LLM API access","Natural language descriptions of agent capabilities and constraints"],"input_types":["task descriptions (natural language)","agent capability registry (text)"],"output_types":["ranked list of selected agents","matching confidence scores","agent composition rationale"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_2","uri":"capability://planning.reasoning.natural.language.task.decomposition.into.agent.subtasks","name":"natural language task decomposition into agent subtasks","description":"Decomposes complex natural language tasks into subtasks that can be distributed across specialized agents in the society. Uses LLM-based reasoning to identify task dependencies, parallelizable components, and required agent capabilities. The system generates a task graph with explicit dependencies and assigns each subtask to agents capable of handling it, enabling coordinated execution across the agent society.","intents":["I want to break down a complex problem into subtasks that agents can work on in parallel","I need to understand task dependencies before assigning work to agents","I want to generate a task execution plan that respects agent capabilities and constraints"],"best_for":["teams building hierarchical multi-agent systems","researchers studying task decomposition strategies","developers implementing complex reasoning workflows"],"limitations":["Decomposition quality depends on LLM reasoning capability; complex tasks may be decomposed suboptimally","No learning from previous decompositions — each task is decomposed from scratch","Dependency inference is heuristic-based and may miss subtle task ordering constraints","Exponential growth in subtask count for deeply nested problems can overwhelm coordination"],"requires":["Python 3.8+","LLM API access with strong reasoning capabilities","Task descriptions in natural language"],"input_types":["complex task descriptions (natural language)","agent capability registry"],"output_types":["task dependency graph","subtask assignments to agents","execution plan with ordering constraints"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_3","uri":"capability://planning.reasoning.agent.response.aggregation.and.consensus.reasoning","name":"agent response aggregation and consensus reasoning","description":"Collects responses from multiple agents working on the same or related subtasks and synthesizes them into a coherent final answer through consensus-based reasoning. Uses LLM-based analysis to identify agreement, resolve conflicts, and weight responses based on agent expertise and confidence. The system can apply voting mechanisms, confidence-weighted averaging, or hierarchical aggregation depending on task requirements.","intents":["I want to combine outputs from multiple agents into a single coherent answer","I need to resolve disagreements between agents and pick the best response","I want to weight agent responses based on their expertise or confidence levels"],"best_for":["teams building ensemble reasoning systems","researchers exploring consensus mechanisms in multi-agent systems","developers implementing robust decision-making across agent societies"],"limitations":["Consensus mechanisms are heuristic-based and may not scale well with many agents (10+ agents)","No principled way to weight agent expertise — relies on LLM interpretation of confidence signals","Aggregation can mask important minority viewpoints or edge cases","Requires all agents to produce responses in compatible formats for meaningful aggregation"],"requires":["Python 3.8+","LLM API access","Multiple agent responses in structured or semi-structured format"],"input_types":["multiple agent responses (text or structured)","agent metadata (expertise, confidence scores)"],"output_types":["aggregated response","confidence score","reasoning trace showing how responses were combined"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_4","uri":"capability://memory.knowledge.agent.context.and.memory.management.across.interactions","name":"agent context and memory management across interactions","description":"Maintains and manages context for each agent within the society, including conversation history, task state, and learned information from previous interactions. Implements context windowing and selective memory retrieval to keep agent context within token limits while preserving relevant historical information. Agents can access shared context (visible to all agents) and private context (agent-specific), enabling both collaboration and specialization.","intents":["I want agents to remember information from previous interactions without token explosion","I need agents to share context selectively based on task requirements","I want to manage agent memory efficiently across long-running sessions"],"best_for":["teams building long-running multi-agent systems","researchers studying memory management in agent societies","developers implementing stateful agent interactions"],"limitations":["No built-in persistence — context is lost between sessions unless explicitly saved","Context selection is heuristic-based and may discard important historical information","Shared context can become a bottleneck if many agents access it simultaneously","No automatic cleanup of stale or irrelevant context — requires manual pruning"],"requires":["Python 3.8+","LLM API with sufficient context window (8k+ tokens recommended)","External storage for persistent context (optional but recommended)"],"input_types":["agent interactions (text)","task state (structured data)","historical context (text or structured)"],"output_types":["agent-specific context windows","shared context summaries","context relevance scores"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_5","uri":"capability://text.generation.language.natural.language.agent.instruction.and.behavior.specification","name":"natural language agent instruction and behavior specification","description":"Allows defining agent behaviors, constraints, and instructions entirely through natural language specifications rather than code. Agents interpret their role descriptions and constraints at runtime, adapting their behavior based on task context and society dynamics. The system uses LLM-based instruction following to implement agent behaviors without requiring explicit code for each agent variant.","intents":["I want to define agent behaviors in plain English without writing code","I need to quickly prototype different agent personalities or expertise levels","I want to modify agent behavior by editing text descriptions rather than code"],"best_for":["non-technical domain experts designing agent societies","researchers prototyping agent behavior variations","teams iterating rapidly on agent specifications"],"limitations":["Behavior specification quality depends entirely on natural language clarity — ambiguous descriptions lead to unpredictable agent behavior","No formal verification of agent constraints — agents may violate specified constraints if instructions are unclear","Debugging agent misbehavior is difficult because behavior is implicit in LLM interpretation","No type safety or schema validation for agent outputs"],"requires":["Python 3.8+","LLM API access","Well-written natural language specifications of agent roles and constraints"],"input_types":["natural language agent role descriptions","constraint specifications (text)","behavior examples (optional)"],"output_types":["agent behavior implementations (implicit in LLM)","agent response outputs"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_6","uri":"capability://tool.use.integration.inter.agent.communication.and.message.routing","name":"inter-agent communication and message routing","description":"Enables agents to communicate with each other through a message-passing system that routes messages based on natural language specifications of communication patterns. Agents can send messages to specific agents, broadcast to all agents, or send to agents matching certain role descriptions. The system handles message queuing, ordering, and delivery semantics without requiring explicit routing configuration.","intents":["I want agents to send messages to each other without explicit routing rules","I need to broadcast information to relevant agents based on their roles","I want to implement request-response patterns between agents"],"best_for":["teams building collaborative multi-agent systems","researchers studying agent communication patterns","developers implementing agent-to-agent workflows"],"limitations":["Message routing based on natural language role matching can be ambiguous — multiple agents may match a single message specification","No guaranteed message ordering or delivery semantics — messages may be processed out of order","No built-in message persistence — messages are lost if agents don't process them immediately","Circular message dependencies can cause infinite loops without explicit cycle detection"],"requires":["Python 3.8+","LLM API access for message routing decisions","Agent role specifications for routing logic"],"input_types":["agent messages (text or structured)","recipient specifications (natural language or agent IDs)","message metadata (priority, timestamp)"],"output_types":["routed messages","delivery confirmation","message routing decisions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_7","uri":"capability://planning.reasoning.society.level.reasoning.and.emergent.behavior.coordination","name":"society-level reasoning and emergent behavior coordination","description":"Coordinates reasoning across the entire agent society to enable emergent behaviors that arise from agent interactions rather than being explicitly programmed. Implements mechanisms for agents to influence each other's reasoning, share insights, and collectively solve problems that no single agent could solve alone. The system monitors agent interactions and reasoning patterns to identify and amplify beneficial emergent behaviors.","intents":["I want agents to learn from each other and improve their reasoning through interaction","I need to enable emergent problem-solving behaviors that arise from agent collaboration","I want to observe and understand how agent interactions lead to novel solutions"],"best_for":["researchers studying emergent behavior in multi-agent systems","teams exploring novel reasoning approaches through agent societies","developers building self-improving agent systems"],"limitations":["Emergent behaviors are unpredictable and difficult to control or reproduce","No formal guarantees about behavior quality or safety — emergent behaviors may be suboptimal or harmful","Requires significant computational resources to simulate and monitor agent interactions","Difficult to debug or explain why specific emergent behaviors occur"],"requires":["Python 3.8+","LLM API access with strong reasoning capabilities","Multiple agents with diverse roles and capabilities","Monitoring and logging infrastructure"],"input_types":["agent interactions (text)","reasoning traces","task descriptions"],"output_types":["emergent behavior patterns","society-level reasoning traces","interaction analysis"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nlsom__cap_8","uri":"capability://automation.workflow.natural.language.configuration.and.society.definition","name":"natural language configuration and society definition","description":"Allows defining entire agent societies through natural language configuration files or descriptions rather than code. Specifies agent roles, relationships, communication patterns, and coordination rules in plain English. The system parses these descriptions and instantiates the agent society at runtime, enabling rapid prototyping and modification of agent societies without code changes.","intents":["I want to define a multi-agent system by writing descriptions rather than code","I need to quickly experiment with different agent society configurations","I want non-technical stakeholders to be able to modify agent society structure"],"best_for":["teams prototyping agent societies rapidly","non-technical domain experts designing agent systems","researchers exploring different agent society structures"],"limitations":["Configuration parsing is heuristic-based and may fail on ambiguous or complex descriptions","No validation of configuration consistency — invalid configurations may only be detected at runtime","Limited expressiveness compared to code-based configuration — complex constraints may be impossible to specify","No version control or diff tools designed for natural language configurations"],"requires":["Python 3.8+","LLM API access for configuration parsing","Natural language descriptions of agent society structure"],"input_types":["natural language configuration descriptions","agent role specifications","relationship and communication pattern descriptions"],"output_types":["instantiated agent society","configuration parsing trace","validation results"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":20,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","Natural language descriptions of agent roles and capabilities","LLM API access","Natural language descriptions of agent capabilities and constraints","LLM API access with strong reasoning capabilities","Task descriptions in natural language","Multiple agent responses in structured or semi-structured format","LLM API with sufficient context window (8k+ tokens recommended)","External storage for persistent context (optional but recommended)"],"failure_modes":["Natural language parsing of agent roles introduces non-deterministic behavior and potential ambiguity in agent selection","No built-in persistence or state management across agent sessions — requires external storage for conversation history","Scaling to 10+ agents may cause exponential growth in coordination overhead and token consumption","Debugging agent interactions is difficult because routing decisions are implicit in LLM interpretation rather than explicit rules","Semantic matching can fail for ambiguous or novel task types not well-represented in agent descriptions","No explicit constraint propagation — agent selection is based on LLM interpretation which may miss hard constraints","Requires well-written natural language descriptions of agent capabilities; poor descriptions lead to suboptimal agent selection","Cold-start problem: system has no learning mechanism to improve matching accuracy over time","Decomposition quality depends on LLM reasoning capability; complex tasks may be decomposed suboptimally","No learning from previous decompositions — each task is decomposed from scratch","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.18,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.27,"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":"inactive","updated_at":"2026-06-17T09:51:03.579Z","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=nlsom","compare_url":"https://unfragile.ai/compare?artifact=nlsom"}},"signature":"E2G7KzbPJWq4gKJ5Io/9Ni/z3Jitt41PS0gOdbkV2vdnSApH2GEEZ1FwRIFhke4b6TehZQI9j+oobZ81b2y/Dw==","signedAt":"2026-06-20T12:09:52.566Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nlsom","artifact":"https://unfragile.ai/nlsom","verify":"https://unfragile.ai/api/v1/verify?slug=nlsom","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"}}