{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-agency-swarm","slug":"pypi-agency-swarm","name":"agency-swarm","type":"framework","url":"https://pypi.org/project/agency-swarm/","page_url":"https://unfragile.ai/pypi-agency-swarm","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-agency-swarm__cap_0","uri":"capability://planning.reasoning.hierarchical.multi.agent.orchestration.with.agency.based.organization","name":"hierarchical multi-agent orchestration with agency-based organization","description":"Organizes multiple AI agents into a hierarchical agency structure where agents are assigned specific roles, descriptions, and instructions that define their responsibilities. The Agency class serves as a central orchestrator that creates and initializes agents, establishes communication threads between them according to a defined agency chart, and routes user inputs through the appropriate agent chain. This hierarchical approach enables clear separation of concerns and scalable multi-agent systems where agents collaborate through structured message flows rather than direct peer-to-peer communication.","intents":["I want to create a team of specialized AI agents that each handle different aspects of a complex task","I need to define clear roles and responsibilities for agents in my system","I want to organize agents in a hierarchy where some agents delegate to others","I need agents to work together on tasks while maintaining a clear chain of responsibility"],"best_for":["teams building complex multi-agent systems with clear role separation","developers creating AI agent teams for enterprise workflows","builders prototyping hierarchical agent architectures"],"limitations":["Hierarchical structure may become complex to manage with >10 agents without careful design","Communication overhead increases with agency depth and agent count","Requires explicit agency chart definition upfront — no dynamic agent discovery"],"requires":["Python 3.9+","OpenAI API key for Assistants API access","agency-swarm package installed via pip"],"input_types":["natural language user queries","structured task definitions","agent role specifications"],"output_types":["agent responses","task completion status","message chains from agent collaboration"],"categories":["planning-reasoning","multi-agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_1","uri":"capability://tool.use.integration.thread.based.inter.agent.communication.with.message.routing","name":"thread-based inter-agent communication with message routing","description":"Implements a Thread system that creates and manages dedicated conversation channels between agents using OpenAI's API. Each thread maintains a message history and handles tool call execution, with messages flowing between agents according to the agency chart. The framework supports both synchronous (Thread class) and asynchronous (ThreadAsync class) communication modes, allowing agents to exchange messages, process tool results, and maintain context across multi-turn conversations. This abstraction decouples agent communication from the underlying OpenAI API details.","intents":["I want agents to communicate with each other through persistent conversation threads","I need to maintain message history and context across agent interactions","I want to handle tool calls and their results within agent conversations","I need both sync and async agent communication for different use cases"],"best_for":["developers building agent systems requiring persistent conversation state","teams implementing asynchronous agent workflows","builders needing fine-grained control over inter-agent message flows"],"limitations":["Thread state is tied to OpenAI API — no built-in local persistence or caching","Asynchronous mode adds complexity for error handling and retry logic","Message history grows unbounded — requires manual cleanup or archival strategy"],"requires":["OpenAI API key with Assistants API access","Python 3.9+ for async support","agency-swarm Thread or ThreadAsync classes"],"input_types":["agent messages","tool call results","user inputs routed through agency"],"output_types":["agent responses","tool execution results","message history"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_10","uri":"capability://code.generation.editing.tool.factory.with.dynamic.schema.generation.from.python.code","name":"tool factory with dynamic schema generation from python code","description":"The ToolFactory class dynamically generates OpenAI-compatible tool schemas from Python functions or classes without requiring manual JSON schema authoring. It introspects Python type hints and Pydantic models to automatically create function calling schemas that OpenAI's API can understand. This eliminates the error-prone process of manually writing JSON schemas and keeps tool definitions co-located with implementation. The factory handles complex types, nested models, and optional parameters, converting Python's type system directly to OpenAI's schema format.","intents":["I want to convert Python functions into agent tools without writing JSON schemas","I need automatic schema generation from type hints","I want to avoid manual JSON schema maintenance","I need to support complex parameter types in agent tools"],"best_for":["Python developers building agent tools","teams avoiding manual JSON schema maintenance","developers wanting type-safe tool definitions"],"limitations":["Complex nested types may not translate perfectly to OpenAI schemas","Schema generation is one-way — changes to OpenAI schemas don't update Python code","Some advanced Python type features may not be supported","Generated schemas are immutable after agent initialization"],"requires":["Python 3.9+ with type hints","ToolFactory from agency-swarm","Pydantic for complex type support"],"input_types":["Python functions with type hints","Pydantic model classes","class methods with annotations"],"output_types":["OpenAI function schemas","tool definitions ready for agents"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_11","uri":"capability://tool.use.integration.agent.communication.with.message.passing.and.tool.result.handling","name":"agent communication with message passing and tool result handling","description":"Implements a message-passing system where agents communicate through structured messages that flow through threads. When an agent needs to use a tool, the framework intercepts the tool call, executes it, and returns the result back to the agent through the message stream. This enables agents to collaborate by calling tools and sharing results without direct coupling. The system handles tool call parsing, execution, and result formatting, abstracting away the complexity of OpenAI's function calling protocol.","intents":["I want agents to call tools and receive results within conversations","I need agents to collaborate by sharing tool execution results","I want to handle tool errors and failures gracefully","I need to track which tools agents are calling and why"],"best_for":["developers building agent systems with tool-heavy workflows","teams needing visibility into agent tool usage","builders creating collaborative agent systems"],"limitations":["Tool call failures may not be handled gracefully — requires custom error handling","Tool result formatting is rigid — limited customization","No built-in tool call caching — repeated calls execute multiple times","Tool execution is synchronous — long-running tools block agent progress"],"requires":["agency-swarm Thread or ThreadAsync","BaseTool implementations for agents","OpenAI API key"],"input_types":["agent messages requesting tool calls","tool execution results"],"output_types":["tool call requests","formatted tool results","agent responses incorporating tool data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_12","uri":"capability://code.generation.editing.custom.agent.creation.through.inheritance.and.composition","name":"custom agent creation through inheritance and composition","description":"Enables developers to create custom agents by subclassing the Agent class and defining custom tools, instructions, and behaviors. Agents can be composed with specific tool sets and instructions that define their capabilities and expertise. The framework provides base classes and patterns for extending agents with domain-specific functionality, allowing teams to build reusable agent templates. Custom agents can override methods to customize initialization, message handling, or tool execution without modifying the core framework.","intents":["I want to create specialized agents for my domain","I need to build reusable agent templates for my team","I want to extend agents with custom tools and behaviors","I need agents with domain-specific expertise and instructions"],"best_for":["teams building domain-specific agent systems","developers creating reusable agent libraries","organizations standardizing on agent patterns"],"limitations":["Custom agents require Python knowledge — not suitable for non-technical users","Inheritance chains can become complex with many customizations","No built-in validation that custom agents follow framework conventions","Testing custom agents requires understanding framework internals"],"requires":["Python 3.9+","agency-swarm Agent base class","Understanding of agent architecture"],"input_types":["custom agent class definitions","tool implementations","instruction text"],"output_types":["specialized agent instances","custom agent behaviors"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-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 serves as the foundation for all agent tools, using Pydantic models for input validation and type checking. Tools are defined as Python classes inheriting from BaseTool, with method signatures automatically converted to OpenAI function schemas. The ToolFactory class dynamically generates tool definitions from Python functions or classes, handling schema generation and validation. This approach ensures type safety at the agent-tool boundary and enables automatic schema generation for OpenAI's function calling API without manual JSON schema writing.","intents":["I want to create tools for agents with automatic type validation","I need to convert Python functions into agent-callable tools without writing JSON schemas","I want type safety and IDE autocomplete for tool parameters","I need tools to validate inputs before execution"],"best_for":["Python developers building agent tools with strong typing requirements","teams wanting to avoid manual JSON schema maintenance","builders creating reusable tool libraries for agents"],"limitations":["Tool definitions are tightly coupled to Python type hints — limited support for complex nested types","Pydantic validation adds ~50-100ms overhead per tool call","No built-in tool versioning or deprecation management","Tool discovery is manual — requires explicit registration with agents"],"requires":["Python 3.9+","Pydantic library (included in agency-swarm dependencies)","BaseTool or ToolFactory from agency-swarm"],"input_types":["Python function signatures","Pydantic model definitions","class methods with type hints"],"output_types":["OpenAI function schemas","validated tool parameters","tool execution results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_3","uri":"capability://code.generation.editing.built.in.specialized.agents.with.pre.configured.capabilities","name":"built-in specialized agents with pre-configured capabilities","description":"Provides pre-built agent implementations like BrowsingAgent and Genesis Agency that come with pre-configured tools and instructions for common tasks. BrowsingAgent includes web browsing capabilities, while Genesis Agency provides code generation and file manipulation tools. These specialized agents can be instantiated directly or extended through inheritance, reducing boilerplate for common use cases. The framework includes agents like Devid with FileWriter tools, demonstrating the pattern of agents bundled with domain-specific tool sets.","intents":["I want to quickly add web browsing capabilities to my agent system","I need an agent that can generate and write code files","I want to extend pre-built agents with custom tools","I need agents with common capabilities without building from scratch"],"best_for":["rapid prototyping of agent systems with common capabilities","developers wanting to avoid reimplementing standard agent patterns","teams building on top of proven agent templates"],"limitations":["Pre-built agents have fixed instruction sets — customization requires subclassing","BrowsingAgent may have limitations with JavaScript-heavy or authenticated websites","Tool sets are opinionated — may not match all use cases","Updates to built-in agents require framework updates"],"requires":["agency-swarm package with specialized agent modules","Python 3.9+","API keys for any external services (e.g., web browsing APIs)"],"input_types":["user queries for browsing or code generation","file paths and content specifications"],"output_types":["browsed web content","generated code files","execution results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.external.tool.ecosystems","name":"model context protocol (mcp) integration for external tool ecosystems","description":"Integrates with the Model Context Protocol (MCP) standard, enabling agents to access tools and resources exposed through MCP servers. The framework includes MCP integration that allows agents to discover and call tools from external MCP-compatible services without requiring custom tool implementations. This enables agents to leverage existing tool ecosystems and third-party integrations through a standardized protocol, extending agent capabilities beyond built-in tools.","intents":["I want my agents to use tools from external MCP servers","I need to integrate with third-party tool ecosystems without custom code","I want agents to discover and call MCP-exposed resources","I need standardized tool integration across multiple agent systems"],"best_for":["teams integrating with MCP-compatible tool ecosystems","developers building agent systems that need extensibility through standards","organizations standardizing on MCP for tool integration"],"limitations":["MCP integration requires MCP servers to be running and accessible","Error handling for MCP tool failures may be opaque","No built-in MCP server discovery — requires manual configuration","MCP protocol overhead adds latency compared to direct tool calls"],"requires":["agency-swarm with MCP integration module","MCP servers running and accessible","MCP client library compatible with agency-swarm version"],"input_types":["MCP server endpoints","tool requests compatible with MCP protocol"],"output_types":["MCP tool results","resource data from MCP servers"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_5","uri":"capability://planning.reasoning.agent.instruction.and.role.definition.with.customizable.system.prompts","name":"agent instruction and role definition with customizable system prompts","description":"Allows developers to define agent behavior through customizable instructions, names, and descriptions that shape how agents interpret tasks and interact with tools. Each Agent class accepts a description and instructions parameter that define the agent's role, expertise, and behavioral guidelines. These instructions are passed to OpenAI's Assistants API as system prompts, enabling fine-grained control over agent personality, reasoning style, and decision-making without code changes. This approach separates agent behavior definition from implementation, enabling prompt engineering and behavioral tuning.","intents":["I want to define an agent's role and expertise through natural language instructions","I need to customize how an agent approaches tasks and uses tools","I want to tune agent behavior through prompt engineering without code changes","I need agents with different personalities or reasoning styles"],"best_for":["teams doing prompt engineering and behavioral tuning","developers creating specialized agents for specific domains","builders iterating on agent behavior without code deployment"],"limitations":["Instruction quality directly impacts agent performance — requires careful prompt engineering","No built-in validation that instructions are followed — relies on LLM behavior","Long instructions increase token usage and latency","Instruction changes require agent reinitialization"],"requires":["agency-swarm Agent class","OpenAI API key","Well-crafted instruction text"],"input_types":["agent name (string)","agent description (string)","agent instructions (string with natural language directives)"],"output_types":["agent behavior conforming to instructions","task responses reflecting defined role"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_6","uri":"capability://automation.workflow.multiple.user.interface.modes.terminal.web.ui.programmatic.api","name":"multiple user interface modes (terminal, web ui, programmatic api)","description":"Provides multiple ways to interact with an agency: terminal-based CLI interface for command-line usage, web UI (Gradio-based) for browser-based interaction, and programmatic API for integration into applications. The Agency class exposes methods that can be called directly from Python code, while the framework also includes CLI utilities and web interface wrappers. This multi-interface approach enables the same agency to be deployed across different channels without reimplementation, supporting different user preferences and deployment scenarios.","intents":["I want to interact with my agency through a command-line interface","I need a web UI for non-technical users to interact with agents","I want to integrate my agency into a Python application programmatically","I need to deploy the same agency across multiple interfaces"],"best_for":["teams deploying agents across multiple channels","developers building agent applications with diverse user bases","organizations needing both CLI and web interfaces for the same agency"],"limitations":["Web UI (Gradio) may have limitations for complex interactions or real-time updates","CLI interface requires terminal access — not suitable for web-only deployments","State management across interfaces requires careful design","Each interface adds maintenance burden"],"requires":["agency-swarm package","Gradio library for web UI (optional)","Python 3.9+ for programmatic API"],"input_types":["CLI arguments and stdin","web form inputs","Python function calls with parameters"],"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":"pypi_pypi-agency-swarm__cap_7","uri":"capability://automation.workflow.asynchronous.agent.processing.with.async.await.support","name":"asynchronous agent processing with async/await support","description":"Implements ThreadAsync class alongside the synchronous Thread class, enabling non-blocking agent execution and message processing. Agents can process multiple tasks concurrently using Python's asyncio, with the framework handling async context management and concurrent message flows. This enables building responsive applications where agent processing doesn't block user interactions, and multiple agents can work in parallel. The async implementation maintains the same message routing and tool execution semantics as the synchronous version.","intents":["I want agents to process tasks concurrently without blocking","I need to handle multiple user requests to the same agency simultaneously","I want responsive applications where agent processing doesn't freeze the UI","I need agents to work in parallel on independent tasks"],"best_for":["developers building high-concurrency agent systems","teams creating responsive web applications with agents","builders needing to handle multiple simultaneous user requests"],"limitations":["Async adds complexity to error handling and debugging","Concurrent agent execution may hit OpenAI API rate limits","State management across async tasks requires careful synchronization","Debugging async agent flows is more difficult than synchronous execution"],"requires":["Python 3.9+ with asyncio support","agency-swarm ThreadAsync class","async/await syntax knowledge"],"input_types":["async coroutines","concurrent task definitions"],"output_types":["async task results","concurrent agent responses"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_8","uri":"capability://safety.moderation.observability.and.tracking.with.callback.handlers","name":"observability and tracking with callback handlers","description":"Includes a tracking system with LocalCallbackHandler that captures agent execution events, tool calls, and message flows for observability and debugging. The TrackingManager coordinates callback handlers that log or process agent activities, enabling developers to monitor agent behavior, debug issues, and collect metrics. This abstraction allows plugging in different tracking backends (local logging, remote telemetry, etc.) without modifying agent code, supporting both development debugging and production monitoring.","intents":["I want to monitor what my agents are doing and which tools they're calling","I need to debug agent behavior and message flows","I want to collect metrics on agent performance and tool usage","I need to integrate agent execution events with external monitoring systems"],"best_for":["teams debugging complex multi-agent systems","developers monitoring agent performance in production","builders integrating agent metrics with observability platforms"],"limitations":["Callback overhead adds latency to agent execution","Local callback handler stores events in memory — no persistence by default","Tracking configuration is manual — no auto-discovery of tracking backends","Event schema may not capture all relevant context"],"requires":["agency-swarm tracking modules","LocalCallbackHandler or custom callback implementation"],"input_types":["agent execution events","tool call records","message flow data"],"output_types":["execution logs","performance metrics","debugging information"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agency-swarm__cap_9","uri":"capability://memory.knowledge.agent.state.management.and.configuration.persistence","name":"agent state management and configuration persistence","description":"Manages agent state including configuration, tools, instructions, and model settings through the Agent class. Agents maintain their own state and configuration that persists across message exchanges, enabling stateful interactions where agents remember their role and capabilities. The framework handles agent initialization with OpenAI's Assistants API, creating persistent assistant instances that maintain state server-side. This enables agents to be paused, resumed, and reused across sessions without losing context or configuration.","intents":["I want agents to maintain their configuration and state across conversations","I need to pause and resume agent conversations without losing context","I want to reuse agent instances across multiple user sessions","I need agents to remember their tools and instructions between interactions"],"best_for":["developers building long-running agent systems","teams needing persistent agent state across sessions","builders creating agent systems with session management"],"limitations":["Agent state is tied to OpenAI Assistants API — no local-first state management","State synchronization between local and API state can be complex","No built-in state versioning or rollback capabilities","Agent deletion requires explicit cleanup to avoid orphaned API resources"],"requires":["OpenAI API key with Assistants API access","agency-swarm Agent class","Python 3.9+"],"input_types":["agent configuration (name, description, instructions)","tool definitions","model settings"],"output_types":["agent state snapshots","configuration persistence","stateful agent instances"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","OpenAI API key for Assistants API access","agency-swarm package installed via pip","OpenAI API key with Assistants API access","Python 3.9+ for async support","agency-swarm Thread or ThreadAsync classes","Python 3.9+ with type hints","ToolFactory from agency-swarm","Pydantic for complex type support","agency-swarm Thread or ThreadAsync"],"failure_modes":["Hierarchical structure may become complex to manage with >10 agents without careful design","Communication overhead increases with agency depth and agent count","Requires explicit agency chart definition upfront — no dynamic agent discovery","Thread state is tied to OpenAI API — no built-in local persistence or caching","Asynchronous mode adds complexity for error handling and retry logic","Message history grows unbounded — requires manual cleanup or archival strategy","Complex nested types may not translate perfectly to OpenAI schemas","Schema generation is one-way — changes to OpenAI schemas don't update Python code","Some advanced Python type features may not be supported","Generated schemas are immutable after agent initialization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:12.847Z","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=pypi-agency-swarm","compare_url":"https://unfragile.ai/compare?artifact=pypi-agency-swarm"}},"signature":"wsrFby+txl81kW9PZfZbEqeRQPHnZlfy9eJf6U61HFGa1YzhCeNHHKSml36IbrLOYOiReQERchbDFYn0f5EtAg==","signedAt":"2026-06-21T01:53:34.015Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-agency-swarm","artifact":"https://unfragile.ai/pypi-agency-swarm","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}