{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"agency-swarm","slug":"agency-swarm","name":"Agency Swarm","type":"framework","url":"https://github.com/VRSEN/agency-swarm","page_url":"https://unfragile.ai/agency-swarm","categories":["ai-agents","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"agency-swarm__cap_0","uri":"capability://planning.reasoning.hierarchical.agent.orchestration.with.agency.chart.based.communication","name":"hierarchical agent orchestration with agency-chart-based communication","description":"Organizes multiple AI agents into a hierarchical structure defined by an agency chart that specifies which agents can communicate with which other agents. The Agency class serves as the central orchestrator that creates and initializes agents, establishes dedicated communication threads between agents according to the chart topology, and routes messages through the defined hierarchy. This enables complex multi-agent workflows where agents delegate tasks up/down the chain of command rather than all agents communicating freely.","intents":["I want to create a multi-agent system where agents have specific roles and only communicate through defined channels","I need to organize agents in a hierarchical structure where some agents supervise others","I want to prevent agents from directly communicating with all other agents and instead enforce a communication topology"],"best_for":["teams building enterprise automation workflows with clear organizational hierarchies","developers creating agent systems where communication patterns need to be explicitly controlled","builders prototyping complex multi-agent systems with specialized agent roles"],"limitations":["Communication is constrained to defined agency chart — agents cannot dynamically discover or communicate with agents outside their defined connections","Hierarchical structure may become rigid for systems requiring fluid agent collaboration patterns","No built-in load balancing or failover if an agent in the communication chain becomes unavailable"],"requires":["Python 3.9+","OpenAI API key for Assistants API access","Agency chart definition specifying agent relationships"],"input_types":["agency chart (Python class definitions or configuration)","agent definitions with roles and instructions","user queries/messages"],"output_types":["agent responses","inter-agent messages","task delegation logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_1","uri":"capability://planning.reasoning.agent.to.agent.delegation.with.thread.based.message.passing","name":"agent-to-agent delegation with thread-based message passing","description":"Enables agents to delegate tasks to other agents through a thread-based message passing system where each agent pair has a dedicated Thread object that manages the conversation history and tool execution. When an agent needs to delegate work, it sends a message through the thread to another agent, which processes the message, executes tools if needed, and returns results back through the same thread. The Thread class handles OpenAI Assistants API integration, tool call processing, and maintains full conversation context.","intents":["I want agents to hand off tasks to specialized agents and receive results back","I need to maintain conversation history between specific agent pairs","I want agents to collaborate by passing messages and results through dedicated channels"],"best_for":["developers building workflows where task decomposition and delegation are core patterns","teams implementing multi-step processes that require different agent specializations","builders creating systems where agents need to maintain context across multiple interactions"],"limitations":["Thread objects are tied to specific agent pairs — creating N agents requires O(N²) potential thread objects","Message passing adds latency compared to direct function calls — each delegation round-trip involves OpenAI API calls","Thread state is not persisted by default — requires external storage for conversation history across sessions","No built-in message prioritization or queuing — messages are processed sequentially"],"requires":["OpenAI API key","Two or more Agent instances defined","Thread objects instantiated for each agent communication pair"],"input_types":["text messages from agents","task descriptions","tool execution results"],"output_types":["agent responses","tool call results","delegation status"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_10","uri":"capability://memory.knowledge.agent.state.management.and.configuration.persistence","name":"agent state management and configuration persistence","description":"Manages agent state including instructions, tools, model configuration, and conversation history. Agents maintain their own state objects that persist across interactions, storing role definitions, tool assignments, and model parameters. The framework enables agents to be configured once and reused across multiple conversations without reconfiguration.","intents":["I want agents to maintain consistent state and configuration across conversations","I need to configure agents once and reuse them without reconfiguration","I want agents to remember their role, instructions, and assigned tools"],"best_for":["developers building long-running agent systems","teams needing consistent agent behavior across multiple interactions","builders creating agent systems where agents are instantiated once and reused"],"limitations":["Agent state is in-memory by default — lost on process restart without explicit persistence","No built-in state serialization — custom code needed to save/load agent state","State changes during runtime are not automatically persisted","No versioning of agent configurations — difficult to track configuration changes over time"],"requires":["Agent class instantiation with configuration","Python 3.9+"],"input_types":["agent configuration","instructions","tool definitions"],"output_types":["agent state objects","configuration metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_11","uri":"capability://automation.workflow.observability.and.execution.tracking.with.callback.handlers","name":"observability and execution tracking with callback handlers","description":"Provides observability into agent execution through callback handlers that track agent actions, tool calls, and message flows. The framework includes LocalCallbackHandler for local logging and TrackingManager for centralized execution tracking. Callbacks are invoked at key points in the execution flow (agent initialization, message processing, tool execution) enabling monitoring and debugging of agent behavior.","intents":["I want to monitor what agents are doing and track their execution","I need to debug agent behavior by seeing detailed execution logs","I want to collect metrics about agent performance and tool usage"],"best_for":["developers debugging complex multi-agent workflows","teams monitoring agent system health and performance","builders implementing observability into production agent systems"],"limitations":["Callback handlers add overhead to execution — performance impact scales with number of callbacks","LocalCallbackHandler logs to local files — no built-in centralized logging to external systems","Callback data structure is framework-specific — integration with external monitoring tools requires custom adapters","No built-in sampling or filtering of callbacks — all events are tracked"],"requires":["Python 3.9+","Callback handler implementation"],"input_types":["execution events","agent actions","tool calls"],"output_types":["execution logs","metrics","tracking data"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_12","uri":"capability://automation.workflow.genesis.agency.template.system.for.rapid.agent.scaffolding","name":"genesis agency template system for rapid agent scaffolding","description":"Provides Genesis Agency as a pre-built template agency that can be used as a starting point for creating custom agencies. Genesis Agency comes with pre-configured agents and communication patterns that can be extended or modified. This enables developers to start with a working multi-agent system and customize it rather than building from scratch.","intents":["I want to quickly scaffold a multi-agent system without building from scratch","I need a reference implementation of a working agency to learn from","I want to extend a pre-built agency with custom agents and tools"],"best_for":["developers new to Agency Swarm wanting to learn by example","teams prototyping agent systems quickly","builders who want a working starting point to customize"],"limitations":["Genesis Agency is opinionated — may not match specific use case requirements","Customization requires understanding the template structure","Template agents may have unnecessary complexity for simple use cases","No built-in migration path from Genesis to custom agencies"],"requires":["Python 3.9+","OpenAI API key"],"input_types":["Genesis Agency class"],"output_types":["customized agency instances"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_13","uri":"capability://text.generation.language.agent.instruction.and.role.definition.with.natural.language.specifications","name":"agent instruction and role definition with natural language specifications","description":"Enables agents to be defined with natural language instructions and role descriptions that guide their behavior. Agents are instantiated with a name, description, and detailed instructions that specify their responsibilities, decision-making criteria, and interaction patterns. These instructions are sent to the OpenAI Assistants API and influence how the agent responds to messages and uses tools.","intents":["I want to define agent behavior through natural language instructions","I need agents to have clear roles and responsibilities","I want to guide agent decision-making through detailed instructions"],"best_for":["developers defining agent behavior through prompting","teams creating agents with specific domain expertise","builders who prefer natural language specification over code-based configuration"],"limitations":["Instruction quality directly impacts agent behavior — poorly written instructions lead to poor performance","No formal specification language — instructions are free-form text subject to interpretation","Instruction changes require agent redeployment","No built-in testing framework for validating instruction effectiveness"],"requires":["Natural language instruction text","Agent name and description"],"input_types":["instruction text","role descriptions"],"output_types":["agent behavior","tool usage patterns"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_2","uri":"capability://tool.use.integration.tool.system.with.pydantic.based.schema.validation.and.type.safety","name":"tool system with pydantic-based schema validation and type safety","description":"Provides a BaseTool class that agents can inherit from to define custom tools with Pydantic model-based input validation and automatic schema generation. Tools are defined as Python classes where the run() method contains the implementation, and Pydantic models define the input parameters with type hints and validation rules. The framework automatically converts these tool definitions into OpenAI function-calling schemas that agents can invoke, ensuring type safety and input validation before tool execution.","intents":["I want to create custom tools for agents with type-safe input validation","I need tools to automatically generate OpenAI function-calling schemas from Python code","I want to ensure agents can only call tools with valid, properly-typed inputs"],"best_for":["Python developers building agent systems who want type safety without manual schema definition","teams creating domain-specific tools that need input validation before execution","builders who want to avoid manual OpenAI schema definition and keep tool logic in Python"],"limitations":["Tool definitions are tightly coupled to Pydantic models — complex validation logic must fit within Pydantic constraints","Schema generation is automatic but may not capture all nuances of complex validation rules","Tools are synchronous by default — async tool execution requires additional implementation","No built-in tool versioning or deprecation mechanisms"],"requires":["Python 3.9+","Pydantic library","BaseTool class inheritance"],"input_types":["Python class definitions","Pydantic model definitions","method implementations"],"output_types":["OpenAI function-calling schemas","validated tool inputs","tool execution results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_3","uri":"capability://tool.use.integration.toolfactory.based.dynamic.tool.instantiation.and.discovery","name":"toolfactory-based dynamic tool instantiation and discovery","description":"Provides a ToolFactory class that dynamically discovers and instantiates tools from Python modules or class definitions, enabling agents to access tools without explicit registration. The factory introspects tool classes, validates they inherit from BaseTool, and creates instances with proper initialization. This allows tools to be discovered at runtime from directories or module paths, reducing boilerplate tool registration code and enabling plugin-like tool loading patterns.","intents":["I want agents to automatically discover and use tools from a directory without manual registration","I need to dynamically load tools at runtime based on agent roles or configuration","I want to reduce boilerplate code for tool registration in agent definitions"],"best_for":["developers building extensible agent systems where tools are added dynamically","teams using plugin architectures where tools are discovered from directories","builders prototyping agent systems and wanting to avoid manual tool registration"],"limitations":["Tool discovery relies on naming conventions and module structure — tools must be in expected locations","No built-in dependency injection for tool initialization — complex tool dependencies must be handled manually","Factory introspection adds startup latency proportional to number of tool modules","Tool discovery is one-time at initialization — dynamically adding tools at runtime requires framework restart"],"requires":["Python 3.9+","Tool classes inheriting from BaseTool","Tools organized in discoverable module structure"],"input_types":["module paths","directory paths","class definitions"],"output_types":["instantiated tool objects","tool metadata","tool availability status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_4","uri":"capability://tool.use.integration.openai.assistants.api.integration.with.function.calling.and.tool.execution","name":"openai assistants api integration with function calling and tool execution","description":"Provides deep integration with OpenAI's Assistants API, handling agent-to-API communication, function calling schema management, and tool execution result processing. The Agent class wraps the Assistants API, sending agent instructions and tools as function definitions, receiving function calls from the model, executing the corresponding tools, and feeding results back to the API. This abstraction handles the request-response loop, error handling, and schema conversion between Pydantic tool definitions and OpenAI function-calling format.","intents":["I want agents to use OpenAI's Assistants API without managing API calls directly","I need agents to call tools and have results fed back into the conversation context","I want to leverage OpenAI's function calling capabilities with my custom tools"],"best_for":["developers building agents that need access to OpenAI's latest models and features","teams using OpenAI as their primary LLM provider","builders who want managed conversation state through OpenAI's Assistants API"],"limitations":["Tightly coupled to OpenAI Assistants API — no support for other LLM providers (Anthropic, Ollama, etc.)","API rate limits and costs scale with number of agents and message volume","Assistants API has latency overhead compared to direct Chat Completions API calls","Tool execution happens on client side — no server-side tool execution or sandboxing"],"requires":["OpenAI API key with Assistants API access","Python 3.9+","Network connectivity to OpenAI API"],"input_types":["agent instructions","tool definitions","user messages"],"output_types":["model responses","function calls","tool execution results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_5","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.external.tool.access","name":"model context protocol (mcp) integration for external tool access","description":"Integrates with the Model Context Protocol to enable agents to access tools and resources exposed through MCP servers. The framework can connect to MCP servers, discover available tools and resources, and expose them to agents as callable functions. This allows agents to leverage tools from external systems (databases, APIs, file systems) that implement the MCP standard without requiring custom tool wrappers.","intents":["I want agents to access tools from MCP-compatible servers without writing custom tool wrappers","I need agents to interact with external systems that expose MCP interfaces","I want to leverage existing MCP ecosystem tools in my agent system"],"best_for":["developers integrating agents with MCP-compatible external systems","teams using standardized tool protocols across multiple systems","builders who want to avoid writing custom tool adapters for common services"],"limitations":["Requires external MCP servers to be running and accessible — adds infrastructure complexity","MCP tool discovery and execution adds network latency compared to local tools","Error handling for MCP server failures must be implemented at agent level","Limited to tools exposed through MCP protocol — proprietary APIs still require custom wrappers"],"requires":["MCP server running and accessible","MCP client library","Network connectivity to MCP server"],"input_types":["MCP server connection details","tool requests from agents"],"output_types":["tool results from MCP servers","resource data"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_6","uri":"capability://search.retrieval.specialized.browsingagent.for.web.search.and.content.retrieval","name":"specialized browsingagent for web search and content retrieval","description":"Provides a pre-built BrowsingAgent class that specializes in web search and content retrieval tasks. The agent comes pre-configured with tools for searching the web, fetching page content, and extracting information from web sources. This enables agents to research topics, gather information from the internet, and incorporate web data into agent workflows without requiring custom tool implementation.","intents":["I want an agent that can search the web and retrieve information without building custom tools","I need agents to gather current information from the internet for decision-making","I want to add web research capabilities to my agent system quickly"],"best_for":["developers building research or information-gathering agents","teams needing web search capabilities without custom tool development","builders prototyping agent systems that require internet access"],"limitations":["BrowsingAgent capabilities are fixed — customization requires subclassing","Web search results depend on external search provider availability and quality","No built-in caching of search results — repeated searches hit external APIs","Content extraction may fail on complex or dynamically-rendered web pages"],"requires":["OpenAI API key","Web search API credentials (if using external search provider)","Network connectivity for web access"],"input_types":["search queries","URLs"],"output_types":["search results","web page content","extracted information"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_7","uri":"capability://automation.workflow.synchronous.and.asynchronous.thread.based.message.processing","name":"synchronous and asynchronous thread-based message processing","description":"Provides both Thread (synchronous) and ThreadAsync (asynchronous) classes for processing agent messages and tool calls. The synchronous Thread class processes messages sequentially and waits for responses, while ThreadAsync enables concurrent message processing and tool execution. Both classes handle the OpenAI Assistants API interaction loop, tool call detection, tool execution, and result injection back into the conversation.","intents":["I want agents to process messages synchronously with blocking calls","I need agents to handle multiple messages concurrently without blocking","I want to choose between sync and async processing based on my workflow requirements"],"best_for":["developers building both blocking and non-blocking agent workflows","teams needing concurrent agent processing for high-throughput scenarios","builders who want flexibility in choosing sync vs async execution models"],"limitations":["Async processing adds complexity — requires async/await syntax throughout the codebase","Mixing sync and async threads in same agency can cause deadlocks if not carefully managed","Async thread overhead may not be worth it for low-concurrency scenarios","Error handling differs between sync and async — requires different exception handling patterns"],"requires":["Python 3.9+ (3.10+ recommended for better async support)","asyncio event loop for async threads","OpenAI API key"],"input_types":["messages","tool execution requests"],"output_types":["agent responses","tool results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_8","uri":"capability://memory.knowledge.file.search.and.retrieval.with.openai.file.handling","name":"file search and retrieval with openai file handling","description":"Integrates OpenAI's file search capabilities through FileSearch and Retrieval tools, enabling agents to search through uploaded documents and retrieve relevant content. Agents can upload files to OpenAI, search for content within those files, and retrieve specific passages. The framework handles file upload, indexing, and search query processing through the OpenAI API.","intents":["I want agents to search through uploaded documents and retrieve relevant information","I need agents to access knowledge from files without loading everything into context","I want to enable agents to work with large document collections"],"best_for":["developers building document-aware agents","teams needing agents to search through knowledge bases or document collections","builders creating research or analysis agents that work with files"],"limitations":["File search depends on OpenAI's indexing — search quality varies by document type and structure","File uploads incur storage costs with OpenAI","Search results may miss relevant content if documents are poorly structured","No built-in document preprocessing or cleaning — agents work with files as-is"],"requires":["OpenAI API key with file handling access","Files in supported formats (PDF, TXT, etc.)","File upload permissions"],"input_types":["files","search queries"],"output_types":["search results","retrieved content","file metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__cap_9","uri":"capability://automation.workflow.multi.interface.agent.interaction.terminal.web.ui.programmatic.api","name":"multi-interface agent interaction (terminal, web ui, programmatic api)","description":"Provides multiple interfaces for interacting with agencies: terminal CLI for command-line interaction, web UI (Gradio-based) for browser-based interaction, and programmatic Python API for integration into other systems. The Agency class exposes methods that work across all interfaces, allowing the same agent system to be accessed through different channels without code changes.","intents":["I want to interact with agents through a command-line interface","I need a web UI for non-technical users to interact with agents","I want to integrate agents into my Python application programmatically"],"best_for":["developers building agent systems that need multiple access patterns","teams supporting both technical and non-technical users","builders creating agent systems that integrate with existing applications"],"limitations":["Web UI is Gradio-based — limited customization without forking framework","Terminal interface requires manual prompt engineering for complex interactions","Programmatic API requires Python — no native REST API without additional implementation","Session management differs across interfaces — state may not sync between CLI and web UI"],"requires":["Python 3.9+","Gradio library for web UI","Terminal/shell for CLI interface"],"input_types":["terminal commands","web form inputs","Python function calls"],"output_types":["terminal output","web UI responses","Python return values"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agency-swarm__headline","uri":"capability://automation.workflow.collaborative.ai.agent.framework","name":"collaborative ai agent framework","description":"Agency Swarm is a framework designed for creating and managing collaborative AI agent systems, enabling complex workflow automation through specialized roles, tools, and communication channels among agents.","intents":["best collaborative AI agent framework","collaborative AI agents for workflow automation","how to create AI agent swarms","AI framework for multi-agent systems","tools for agent communication and collaboration"],"best_for":["developers building AI systems","teams automating workflows"],"limitations":[],"requires":["basic understanding of AI and programming"],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","OpenAI API key for Assistants API access","Agency chart definition specifying agent relationships","OpenAI API key","Two or more Agent instances defined","Thread objects instantiated for each agent communication pair","Agent class instantiation with configuration","Callback handler implementation","Natural language instruction text","Agent name and description"],"failure_modes":["Communication is constrained to defined agency chart — agents cannot dynamically discover or communicate with agents outside their defined connections","Hierarchical structure may become rigid for systems requiring fluid agent collaboration patterns","No built-in load balancing or failover if an agent in the communication chain becomes unavailable","Thread objects are tied to specific agent pairs — creating N agents requires O(N²) potential thread objects","Message passing adds latency compared to direct function calls — each delegation round-trip involves OpenAI API calls","Thread state is not persisted by default — requires external storage for conversation history across sessions","No built-in message prioritization or queuing — messages are processed sequentially","Agent state is in-memory by default — lost on process restart without explicit persistence","No built-in state serialization — custom code needed to save/load agent state","State changes during runtime are not automatically persisted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"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":null,"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=agency-swarm","compare_url":"https://unfragile.ai/compare?artifact=agency-swarm"}},"signature":"yKbZA8XW8UMfYgPByUVhpNljHOjTAx3FOFkBeLNHsYCd6AuDt8nZw/TtBISC711h2JoXJfvhuRH6Re/L5WE8Cw==","signedAt":"2026-06-23T14:52:53.723Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agency-swarm","artifact":"https://unfragile.ai/agency-swarm","verify":"https://unfragile.ai/api/v1/verify?slug=agency-swarm","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"}}