{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-ai-legion","slug":"ai-legion","name":"AI Legion","type":"agent","url":"https://github.com/eumemic/ai-legion","page_url":"https://unfragile.ai/ai-legion","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-ai-legion__cap_0","uri":"capability://planning.reasoning.multi.agent.autonomous.decision.making.with.llm.based.reasoning","name":"multi-agent autonomous decision-making with llm-based reasoning","description":"Agents independently retrieve their event history from persistent memory, invoke LLMs (GPT-3.5/GPT-4) to generate decisions based on context, and record decisions back to memory before execution. Each agent maintains its own memory store and operates asynchronously, enabling parallel decision-making across multiple agents without blocking. The decision workflow converts unstructured LLM output into validated, executable action schemas through structured parsing and error recovery.","intents":["I want multiple AI agents to make independent decisions simultaneously without waiting for each other","I need agents to learn from their past decisions by maintaining persistent memory of all events","I want to understand why an agent made a decision by reviewing its memory and reasoning chain"],"best_for":["Teams building multi-agent systems where agents need independent reasoning","Developers prototyping autonomous workflows that require decision audit trails","Builders creating collaborative AI systems where agents must learn from history"],"limitations":["LLM invocation adds latency per decision cycle (typically 1-3 seconds for GPT-4)","Memory retrieval scales linearly with event history size; no built-in pagination or summarization","Action parsing relies on LLM output format consistency; malformed actions require manual intervention","No built-in conflict resolution when multiple agents make contradictory decisions"],"requires":["OpenAI API key (GPT-3.5-turbo or GPT-4)","Node.js 14+","Persistent storage backend (file system, database, or custom Store implementation)"],"input_types":["agent memory events (structured JSON)","agent context and goals (text)","LLM model specification (string)"],"output_types":["action objects (structured JSON with action type, parameters, metadata)","decision records (stored in memory with timestamp and reasoning)"],"categories":["planning-reasoning","multi-agent-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_1","uri":"capability://tool.use.integration.inter.agent.message.based.communication.via.messagebus","name":"inter-agent message-based communication via messagebus","description":"A centralized MessageBus component enables agents to send and receive messages asynchronously without direct coupling. Agents publish messages to the bus (targeting specific agents or broadcasting to all), and the bus routes messages to subscribed agents based on recipient filters. The system decouples agent communication from agent logic, allowing new agents to be added without modifying existing agent code, and supports both point-to-point and broadcast messaging patterns.","intents":["I want agents to coordinate on tasks by sending messages to each other without tight coupling","I need to broadcast information to all agents simultaneously (e.g., system updates, shared context)","I want to add new agents dynamically without rewriting existing agent communication logic"],"best_for":["Teams building loosely-coupled multi-agent systems","Developers prototyping agent swarms with dynamic agent addition/removal","Builders creating agent hierarchies where agents need to delegate tasks via messaging"],"limitations":["No message persistence; messages are lost if agents are offline or restart","No built-in message ordering guarantees across multiple agents","Broadcast messages scale linearly with agent count; no fan-out optimization","No message acknowledgment or delivery confirmation mechanism"],"requires":["Node.js 14+","Agent instances registered with MessageBus before communication"],"input_types":["message objects (sender ID, recipient ID or broadcast flag, message content as text/JSON)"],"output_types":["routed messages delivered to agent message handlers"],"categories":["tool-use-integration","communication-patterns"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_10","uri":"capability://tool.use.integration.openai.api.integration.with.model.selection.and.configuration","name":"openai api integration with model selection and configuration","description":"AI Legion integrates with OpenAI's API to invoke language models (GPT-3.5-turbo, GPT-4) for agent decision-making. The system handles API authentication through environment variables, supports model selection at startup, and manages API request/response formatting. The integration includes error handling for API failures, rate limiting, and token counting. Agents can be configured to use different models, enabling heterogeneous agent teams with varying capabilities and costs.","intents":["I want agents to use different LLM models (GPT-3.5 vs GPT-4) based on task complexity","I need to manage API costs by selecting cheaper models for simple tasks","I want agents to handle API failures gracefully without crashing"],"best_for":["Developers building agents that need LLM reasoning capabilities","Teams managing costs by selecting appropriate models per agent","Builders integrating with OpenAI's API ecosystem"],"limitations":["Requires valid OpenAI API key; no fallback to local models","API rate limits apply; high-frequency agent decision-making may hit limits","API latency (1-3 seconds per request) adds overhead to agent decision cycles","Token limits per request restrict context size; long agent memories may be truncated","API costs scale with agent count and decision frequency; no built-in cost optimization"],"requires":["Node.js 14+","OpenAI API key (set in .env file)","Network access to OpenAI API endpoints","Sufficient API quota for agent workload"],"input_types":["model name (string: 'gpt-3.5-turbo', 'gpt-4')","prompt (agent context and memory as text)"],"output_types":["LLM response (text containing action specification)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_11","uri":"capability://tool.use.integration.extensible.module.system.with.custom.module.creation","name":"extensible module system with custom module creation","description":"Developers can create custom modules by extending a base Module class and implementing action methods with typed parameters. Custom modules are registered with the ModuleManager and become available to all agents immediately. The module system provides a standardized interface for defining actions, validating parameters, and returning results. Modules can depend on external libraries or services, enabling integration with any capability (APIs, databases, ML models, etc.).","intents":["I want to add custom capabilities to agents without modifying core agent logic","I need to integrate agents with domain-specific services (e.g., payment processing, database queries)","I want to create reusable modules that multiple agents can use"],"best_for":["Developers building domain-specific agent systems with custom capabilities","Teams creating libraries of reusable agent modules","Builders extending AI Legion with specialized functionality"],"limitations":["Module development requires TypeScript/JavaScript knowledge","No standardized module distribution or package registry","Module dependencies must be manually managed and installed","No built-in module versioning or compatibility checking","Module actions are synchronous; async operations require wrapping in promises"],"requires":["Node.js 14+","TypeScript knowledge","Understanding of base Module class interface","External dependencies for custom functionality"],"input_types":["module class extending base Module","action method implementations with typed parameters"],"output_types":["registered module available to agents","action results returned to agent"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_12","uri":"capability://automation.workflow.configurable.agent.startup.with.cli.parameters.and.environment.variables","name":"configurable agent startup with cli parameters and environment variables","description":"AI Legion supports configuration through command-line parameters (agent count, model selection) and environment variables (.env file). Startup configuration controls the number of agents created, the LLM model used, API credentials, and storage backend. The system reads configuration at startup and initializes agents with the specified parameters. Configuration is centralized in .env.template, enabling easy setup and deployment across environments.","intents":["I want to start the system with different numbers of agents without code changes","I need to select different LLM models (GPT-3.5 vs GPT-4) for different deployments","I want to configure API credentials and storage backends through environment variables"],"best_for":["Developers deploying agents to different environments (dev, staging, production)","Teams managing agent configurations without code changes","Builders automating agent system deployment"],"limitations":["Configuration is static at startup; no runtime reconfiguration","CLI parameters are limited to agent count and model; other settings require .env editing","No configuration validation; invalid settings may cause runtime errors","No configuration versioning or rollback mechanism"],"requires":["Node.js 14+",".env file with required variables (OPENAI_API_KEY, etc.)","CLI access to start the system"],"input_types":["CLI arguments (agent count, model name)","environment variables (API keys, storage path)"],"output_types":["initialized agent system with specified configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_2","uri":"capability://tool.use.integration.modular.action.execution.with.pluggable.capability.modules","name":"modular action execution with pluggable capability modules","description":"A ModuleManager registry enables agents to execute actions through specialized modules (Core, Goals, Notes, Web, System, Messaging). Each module defines a set of callable actions with typed parameters and return values. When an agent decides on an action, the ActionHandler looks up the corresponding module, validates parameters against the module's schema, and executes the action. New modules can be created by extending a base Module class and registering with ModuleManager, allowing extensibility without modifying core agent logic.","intents":["I want agents to perform diverse tasks (web search, file I/O, note-taking) through a unified action interface","I need to add custom capabilities to agents without modifying the core agent decision logic","I want to validate action parameters before execution to catch malformed actions early"],"best_for":["Developers building agents with diverse capabilities (web, file system, memory management)","Teams creating domain-specific agent modules for specialized workflows","Builders prototyping agent systems where capabilities evolve frequently"],"limitations":["Module actions are synchronous; long-running operations block the agent's decision cycle","No built-in error recovery; failed module actions require explicit agent handling","Parameter validation is module-specific; no centralized schema enforcement","Module dependencies must be manually managed (e.g., Web module requires network access)"],"requires":["Node.js 14+","Module implementations extending base Module class","Action parameter types matching module schema"],"input_types":["action objects with type, module name, and parameters (JSON)","module schemas defining available actions and parameter types"],"output_types":["action execution results (text, structured data, or error messages)","side effects (file writes, network requests, memory updates)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_3","uri":"capability://memory.knowledge.persistent.agent.memory.with.event.sourcing.architecture","name":"persistent agent memory with event-sourcing architecture","description":"Each agent maintains a Store (file-based, database, or custom implementation) that records all events (messages received, decisions made, actions executed) in chronological order. Agents retrieve their full event history on each decision cycle, enabling them to understand context and learn from past actions. The event-sourcing pattern ensures complete auditability and allows agents to reconstruct their state at any point in time by replaying events. Memory is agent-specific; each agent has isolated storage preventing cross-agent memory leaks.","intents":["I want agents to learn from their past decisions by reviewing their complete action history","I need to audit why an agent made a specific decision by reviewing its memory at that point in time","I want agents to maintain long-term context across multiple decision cycles without losing information"],"best_for":["Teams building agents that must learn and improve over time","Developers debugging agent behavior by reviewing decision history","Builders creating agents that need to maintain context across long-running tasks"],"limitations":["Memory retrieval scales linearly with event history; no built-in pagination or filtering","No automatic memory summarization; agents receive full history even for long-running tasks","Event storage is agent-specific; no cross-agent memory sharing or knowledge bases","File-based storage (default) is not suitable for high-frequency event recording (>100 events/second per agent)"],"requires":["Node.js 14+","Persistent storage backend (file system by default, or custom Store implementation)","Sufficient disk space for event history (typically 1-10 KB per event)"],"input_types":["events (messages, decisions, actions) as structured JSON objects"],"output_types":["event history (array of chronologically ordered events)","agent state reconstructed from event replay"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_4","uri":"capability://automation.workflow.dynamic.agent.creation.and.lifecycle.management","name":"dynamic agent creation and lifecycle management","description":"Agents can be created at runtime through a factory pattern that initializes each agent with unique ID, isolated memory store, module manager, and message bus subscriptions. The system supports creating multiple agents with different configurations (model, modules, goals) without restarting the platform. Each agent operates independently in its own execution context, and the lifecycle is managed by the core system which handles agent startup, decision cycles, and graceful shutdown.","intents":["I want to create new agents dynamically based on runtime conditions (e.g., spawn agents for parallel tasks)","I need to configure agents differently (different models, different module sets) without code changes","I want to scale the number of agents up or down without restarting the entire system"],"best_for":["Developers building systems that spawn agents on-demand for parallel task execution","Teams creating agent pools that scale based on workload","Builders prototyping multi-agent systems with varying agent configurations"],"limitations":["No built-in load balancing; all agents compete for LLM API rate limits","Agent creation is synchronous; creating many agents sequentially blocks the system","No built-in agent pooling or reuse; each agent creation allocates new resources","Agent termination is not graceful; in-flight decisions may be lost if agent is killed"],"requires":["Node.js 14+","OpenAI API key with sufficient rate limits for multiple agents","Memory store backend configured for each agent"],"input_types":["agent configuration (ID, model, modules, goals, initial context)"],"output_types":["agent instance ready for operation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_5","uri":"capability://search.retrieval.web.search.and.page.content.extraction","name":"web search and page content extraction","description":"The Web module provides agents with capabilities to search the internet and extract content from web pages. Agents can invoke web search actions to find information, then fetch and parse page content to extract relevant data. The module integrates with external web APIs (search provider, page fetcher) and handles response parsing, error handling, and content extraction. Results are returned as structured data that agents can reason about in subsequent decision cycles.","intents":["I want agents to research topics by searching the web and reading relevant pages","I need agents to gather current information (news, prices, availability) that's not in their training data","I want agents to verify facts by checking multiple web sources"],"best_for":["Developers building research agents that need current information","Teams creating agents that monitor web content for changes","Builders prototyping agents that need real-time data from the internet"],"limitations":["Web search results depend on external API quality; results may be outdated or irrelevant","Page content extraction is fragile; complex layouts or JavaScript-rendered content may fail","Rate limiting on external APIs may throttle agent requests","No built-in caching; repeated searches for same query make redundant API calls","Privacy and legal concerns; agents may access restricted or copyrighted content"],"requires":["Node.js 14+","Web search API credentials (e.g., Google Search API, Bing Search API)","Network access to external web services","Page fetching library (e.g., Puppeteer, Cheerio)"],"input_types":["search query (text)","URL to fetch (string)"],"output_types":["search results (array of URLs and snippets)","page content (extracted text, structured data)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_6","uri":"capability://automation.workflow.file.system.operations.with.sandboxed.access","name":"file system operations with sandboxed access","description":"The System module provides agents with file system capabilities (read, write, list, delete files) through a sandboxed interface that restricts access to a designated directory. Agents can create and manage files, read configuration or data files, and organize information on disk. The module handles file I/O errors, path validation, and prevents directory traversal attacks by restricting operations to the sandbox directory.","intents":["I want agents to persist data to disk (logs, results, intermediate state) for later retrieval","I need agents to read configuration files or data files to inform their decisions","I want agents to organize their work by creating and managing files in a structured directory"],"best_for":["Developers building agents that need persistent local storage","Teams creating agents that manage files or logs","Builders prototyping agents that work with file-based workflows"],"limitations":["Sandboxing is directory-based; no fine-grained permission control per file","File I/O is synchronous; large file operations block the agent's decision cycle","No built-in file locking; concurrent access from multiple agents may cause conflicts","File system performance depends on underlying storage (HDD vs SSD)","No encryption; sensitive data stored in files is readable by any process with file access"],"requires":["Node.js 14+","Write permissions to sandbox directory","Sufficient disk space for agent files"],"input_types":["file path (relative to sandbox)","file content (text or binary)","directory path"],"output_types":["file content (text or binary)","directory listing (array of file names)","operation status (success/failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_7","uri":"capability://planning.reasoning.goal.based.task.tracking.and.completion.monitoring","name":"goal-based task tracking and completion monitoring","description":"The Goals module enables agents to set, track, and complete goals as part of their autonomous operation. Agents can create goals (e.g., 'research topic X', 'complete task Y'), mark goals as in-progress or completed, and review their goal history. Goals are stored in the agent's memory, enabling agents to maintain focus on long-term objectives across multiple decision cycles. The system supports goal hierarchies (parent/child goals) and tracks goal completion status.","intents":["I want agents to maintain focus on long-term objectives by tracking goals across multiple decision cycles","I need to understand what an agent is working toward by reviewing its active and completed goals","I want agents to break down complex tasks into sub-goals and track progress"],"best_for":["Developers building agents that work on multi-step tasks","Teams creating agents that need to maintain focus on objectives","Builders prototyping agents that decompose complex problems into sub-goals"],"limitations":["Goal completion is self-reported by agents; no external validation","No built-in goal prioritization; agents must decide which goals to work on","Goal hierarchies are flat; no deep nesting or complex dependencies","No automatic goal timeout or abandonment; agents may get stuck on impossible goals"],"requires":["Node.js 14+","Agent memory store for persisting goals"],"input_types":["goal description (text)","goal status (active, completed, abandoned)"],"output_types":["goal list (array of goals with status)","goal completion confirmation"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_8","uri":"capability://memory.knowledge.persistent.note.taking.and.knowledge.capture","name":"persistent note-taking and knowledge capture","description":"The Notes module enables agents to create, update, and retrieve notes as a form of persistent knowledge capture. Agents can write notes about findings, decisions, or important information, then retrieve and reference notes in subsequent decision cycles. Notes are stored in the agent's memory alongside other events, enabling agents to build up a knowledge base of insights over time. Notes can be tagged or categorized for easier retrieval.","intents":["I want agents to capture insights and findings in notes for later reference","I need agents to maintain a knowledge base of important information discovered during operation","I want agents to reference past notes when making decisions to avoid repeating work"],"best_for":["Developers building agents that accumulate knowledge over time","Teams creating research agents that need to document findings","Builders prototyping agents that learn from experience"],"limitations":["Notes are unstructured text; no schema enforcement or validation","No built-in search or retrieval optimization; finding relevant notes requires full history scan","Notes are agent-specific; no cross-agent knowledge sharing","No versioning; updating notes overwrites previous versions"],"requires":["Node.js 14+","Agent memory store for persisting notes"],"input_types":["note content (text)","note tags or categories (optional)"],"output_types":["note list (array of notes with metadata)","note retrieval (specific note content)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ai-legion__cap_9","uri":"capability://tool.use.integration.console.and.websocket.based.user.interaction.interface","name":"console and websocket-based user interaction interface","description":"AI Legion provides dual interfaces for user interaction: a console interface for local command-line interaction and a WebSocket server (port 8080) for remote clients. Users can send messages through either interface, which are broadcast to all agents via the MessageBus. Agents process messages as events and respond through the same interface. The WebSocket server enables real-time bidirectional communication with agents from web clients or external systems.","intents":["I want to interact with agents through a command-line interface for local development and testing","I need to integrate agents with web applications or external systems through WebSocket communication","I want to monitor agent activity in real-time through a web interface"],"best_for":["Developers testing agents locally through console interaction","Teams integrating agents with web applications","Builders creating dashboards or monitoring interfaces for agent systems"],"limitations":["Console interface is single-user; no multi-user support or authentication","WebSocket server has no built-in authentication or authorization","Messages are broadcast to all agents; no targeted message routing through UI","No message history or persistence in the UI; messages are lost on disconnect","WebSocket server runs on fixed port (8080); no configuration for custom ports"],"requires":["Node.js 14+","Port 8080 available for WebSocket server","WebSocket client library for remote interaction (e.g., ws, Socket.io)"],"input_types":["text messages from console or WebSocket client"],"output_types":["agent responses (text messages)","agent activity logs (real-time updates)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key (GPT-3.5-turbo or GPT-4)","Node.js 14+","Persistent storage backend (file system, database, or custom Store implementation)","Agent instances registered with MessageBus before communication","OpenAI API key (set in .env file)","Network access to OpenAI API endpoints","Sufficient API quota for agent workload","TypeScript knowledge","Understanding of base Module class interface","External dependencies for custom functionality"],"failure_modes":["LLM invocation adds latency per decision cycle (typically 1-3 seconds for GPT-4)","Memory retrieval scales linearly with event history size; no built-in pagination or summarization","Action parsing relies on LLM output format consistency; malformed actions require manual intervention","No built-in conflict resolution when multiple agents make contradictory decisions","No message persistence; messages are lost if agents are offline or restart","No built-in message ordering guarantees across multiple agents","Broadcast messages scale linearly with agent count; no fan-out optimization","No message acknowledgment or delivery confirmation mechanism","Requires valid OpenAI API key; no fallback to local models","API rate limits apply; high-frequency agent decision-making may hit limits","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":0.25,"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":"2026-06-17T09:51:02.370Z","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=ai-legion","compare_url":"https://unfragile.ai/compare?artifact=ai-legion"}},"signature":"AFSSP5xALoSOocpHjhkEBKietP+XACxPTlxb0P4YdmWcye0SajyzXk8vDw+sf5SoEDER8yxoDxeTId0WNCZwDQ==","signedAt":"2026-06-19T22:56:28.349Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-legion","artifact":"https://unfragile.ai/ai-legion","verify":"https://unfragile.ai/api/v1/verify?slug=ai-legion","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"}}