{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-autogen","slug":"pypi-autogen","name":"autogen","type":"framework","url":"https://github.com/ag2ai/ag2","page_url":"https://unfragile.ai/pypi-autogen","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-autogen__cap_0","uri":"capability://planning.reasoning.multi.agent.conversation.orchestration.with.conversableagent.base","name":"multi-agent conversation orchestration with conversableagent base","description":"Implements a unified agent abstraction (ConversableAgent) that handles bidirectional message passing, reply function composition, and state management across heterogeneous agent types. Uses a pluggable reply function registry pattern where agents register handlers for different message types, enabling dynamic behavior composition without inheritance chains. Agents maintain conversation history, manage turn-taking logic, and support both synchronous and asynchronous message exchange through a standardized interface.","intents":["I want to build multi-agent systems where agents can communicate with each other using a consistent message protocol","I need agents to have customizable response logic that can be composed from multiple handlers","I want to manage conversation state and history across multiple agents without tight coupling"],"best_for":["teams building complex multi-agent workflows with 3+ agents","developers needing pluggable agent behavior without modifying core agent classes","researchers prototyping novel agent interaction patterns"],"limitations":["Reply function ordering matters — first matching handler wins, no priority system for conflicting handlers","Conversation history stored in-memory by default — no built-in persistence layer for long-running agents","Synchronous message passing can create blocking bottlenecks in large agent networks (10+ agents)"],"requires":["Python 3.9+","LLM provider API key (OpenAI, Anthropic, or compatible)","ag2 package installed from PyPI"],"input_types":["text messages","structured agent messages with metadata","function call results"],"output_types":["text responses","structured agent messages","function call requests"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_1","uri":"capability://planning.reasoning.group.chat.with.dynamic.speaker.selection.and.eligibility.policies","name":"group chat with dynamic speaker selection and eligibility policies","description":"Orchestrates multi-agent conversations where 3+ agents participate in a shared chat context. Implements a speaker selection mechanism that determines which agent speaks next based on eligibility policies (rules that filter which agents can respond to specific messages). Uses a GroupChat object that maintains shared conversation history and applies policies like round-robin, relevance-based selection, or custom predicates. Supports nested chats where a group chat can be invoked as a single turn in another conversation.","intents":["I want to run a meeting-like scenario where multiple agents discuss a problem and reach consensus","I need to control which agents can respond to certain types of messages (e.g., only code agents respond to code errors)","I want to implement complex orchestration patterns like manager-worker hierarchies or specialist routing"],"best_for":["teams simulating multi-stakeholder decision-making processes","developers building specialist agent networks with role-based routing","researchers studying emergent behavior in agent collectives"],"limitations":["Speaker selection policies are evaluated sequentially — complex eligibility rules can add 50-200ms per turn","No built-in deadlock detection — misconfigured policies can cause infinite loops or agent starvation","Shared context grows linearly with conversation length — no automatic context pruning or summarization","Max agent count in a single group chat not formally specified but performance degrades significantly beyond 10 agents"],"requires":["Python 3.9+","Multiple agent instances (minimum 3 for meaningful group chat)","Eligibility policy definitions (can be custom predicates or built-in policies)"],"input_types":["text messages","agent messages with sender metadata","eligibility policy configurations"],"output_types":["group chat conversation history","final consensus or decision","per-agent contribution logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_10","uri":"capability://automation.workflow.runtime.logging.and.opentelemetry.tracing.for.agent.execution","name":"runtime logging and opentelemetry tracing for agent execution","description":"Implements comprehensive logging and tracing for agent execution using Python's logging module and OpenTelemetry. Captures agent messages, function calls, LLM requests/responses, and execution timing. Integrates with OpenTelemetry for distributed tracing, enabling visualization of agent execution flows across multiple services. Supports structured logging with JSON output for log aggregation systems.","intents":["I want to debug agent behavior by examining detailed execution logs","I need to trace agent execution across multiple services for distributed debugging","I want to monitor agent performance and identify bottlenecks"],"best_for":["teams debugging complex multi-agent systems","developers monitoring agent performance in production","organizations using distributed tracing infrastructure (Jaeger, Datadog, etc.)"],"limitations":["Verbose logging can impact performance — logging overhead can be 5-10% of execution time","OpenTelemetry integration requires additional dependencies and configuration","Log volume can be very large for long-running agents — requires log aggregation and filtering","Sensitive information (API keys, user data) can be logged — requires careful configuration to avoid leaks"],"requires":["Python 3.9+","logging module (built-in)","OpenTelemetry libraries (optional, for distributed tracing)","Log aggregation system (optional, for production use)","ag2 package with logging modules"],"input_types":["logging configuration","OpenTelemetry exporter configuration","log level specifications"],"output_types":["structured logs","execution traces","performance metrics","error reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_11","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.standardized.tool.interfaces","name":"mcp (model context protocol) integration for standardized tool interfaces","description":"Implements integration with the Model Context Protocol (MCP), a standardized protocol for tools and resources. Agents can discover and invoke MCP-compatible tools without custom integration code. Supports both local MCP servers and remote MCP endpoints. Implements automatic schema translation between MCP tool definitions and agent function calling interfaces.","intents":["I want agents to use tools from the MCP ecosystem without custom integration","I need to standardize tool interfaces across different agent systems","I want to build agent systems that can discover and use new tools dynamically"],"best_for":["teams building tool-agnostic agent systems","developers integrating with MCP-compatible services","organizations standardizing on MCP for tool interfaces"],"limitations":["MCP server discovery requires network access or configuration — not suitable for offline-only systems","Schema translation can lose MCP-specific features — complex tool definitions may not translate perfectly","MCP server reliability affects agent reliability — no built-in fallback for unavailable servers","Authentication for MCP servers requires careful credential management"],"requires":["Python 3.9+","MCP server (local or remote)","MCP client library","ag2 package with MCP integration modules"],"input_types":["MCP server configurations","tool discovery requests","tool invocation parameters"],"output_types":["discovered tools with schemas","tool execution results","MCP protocol responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_12","uri":"capability://tool.use.integration.a2a.protocol.and.ag.ui.adapter.for.agent.to.agent.communication","name":"a2a protocol and ag-ui adapter for agent-to-agent communication","description":"Implements the A2A (Agent-to-Agent) protocol, a standardized message format for agent communication. Provides an AG-UI adapter that enables agents to communicate through a web-based UI. Supports both direct agent-to-agent communication and communication through a central UI server. Implements message serialization and deserialization for the A2A protocol.","intents":["I want agents to communicate using a standardized protocol","I need to build web-based interfaces for agent systems","I want to enable human-in-the-loop interaction with agents through a UI"],"best_for":["teams building agent systems with web-based interfaces","developers implementing human-in-the-loop agent workflows","organizations standardizing on A2A protocol for agent communication"],"limitations":["A2A protocol adds serialization overhead — not suitable for high-frequency agent communication","AG-UI adapter requires web server setup and maintenance","Message ordering is not guaranteed in distributed A2A deployments","No built-in authentication for A2A protocol — requires custom security implementation"],"requires":["Python 3.9+","Web server for AG-UI adapter (optional)","A2A protocol definitions","ag2 package with A2A modules"],"input_types":["A2A protocol messages","agent communication requests","UI interaction events"],"output_types":["A2A protocol responses","UI updates","agent communication logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_13","uri":"capability://automation.workflow.cli.tool.for.agent.project.scaffolding.and.management","name":"cli tool for agent project scaffolding and management","description":"Provides a command-line interface for creating, configuring, and managing AG2 projects. Supports project scaffolding with templates, configuration management, and local development workflows. Implements commands for running agents, managing dependencies, and deploying agent systems. Integrates with the AG2 documentation and examples.","intents":["I want to quickly scaffold a new AG2 project with best practices","I need to manage agent configurations and dependencies from the command line","I want to run and test agents locally before deployment"],"best_for":["developers getting started with AG2","teams managing multiple AG2 projects","developers preferring CLI-based workflows"],"limitations":["CLI is primarily for local development — not suitable for production deployments","Limited customization of project templates — may require manual adjustments","No built-in CI/CD integration — requires custom scripts for automated deployments","Documentation for CLI commands is sparse — requires trial and error for advanced usage"],"requires":["Python 3.9+","ag2 package installed","Command-line shell (bash, zsh, PowerShell, etc.)"],"input_types":["CLI commands and arguments","project configuration files","template specifications"],"output_types":["scaffolded project files","configuration outputs","execution logs"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_14","uri":"capability://automation.workflow.beta.agent.framework.with.middleware.and.observer.patterns","name":"beta agent framework with middleware and observer patterns","description":"Implements an experimental beta agent framework that uses middleware and observer patterns for extensibility. Agents can register middleware that intercepts and modifies messages before/after processing. Observers can subscribe to agent lifecycle events (message received, response generated, etc.). Supports both synchronous and asynchronous middleware/observers.","intents":["I want to extend agent behavior through middleware without modifying core agent code","I need to observe agent execution and react to lifecycle events","I want to implement cross-cutting concerns (logging, monitoring, validation) as middleware"],"best_for":["developers building extensible agent systems","teams implementing cross-cutting concerns as middleware","researchers experimenting with novel agent architectures"],"limitations":["Beta framework is experimental — API may change in future versions","Middleware ordering matters — incorrect ordering can cause unexpected behavior","Observer notifications are synchronous — slow observers can block agent execution","No built-in middleware composition utilities — complex middleware chains require manual management"],"requires":["Python 3.9+","ag2 package with beta modules","Understanding of middleware and observer patterns"],"input_types":["middleware implementations","observer implementations","lifecycle event specifications"],"output_types":["modified messages","lifecycle event notifications","middleware execution logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_15","uri":"capability://memory.knowledge.document.agent.for.multi.document.analysis.and.synthesis","name":"document agent for multi-document analysis and synthesis","description":"Implements DocumentAgent, a specialized agent type for analyzing and synthesizing information from multiple documents. Automatically chunks documents, creates embeddings, and retrieves relevant sections for analysis. Supports both single-document and cross-document analysis. Implements automatic summarization and synthesis of information across documents.","intents":["I want to analyze information across multiple documents without manual chunking","I need to synthesize information from different documents to answer complex questions","I want to build systems that can compare and contrast information across documents"],"best_for":["teams building document analysis systems","developers creating research and synthesis tools","applications needing to extract and combine information from multiple sources"],"limitations":["Document chunking strategy affects analysis quality — poor chunking can lose context","Cross-document analysis can be slow for large document collections — no built-in optimization","Synthesis quality depends on LLM capabilities — may produce hallucinated connections between documents","No built-in support for non-text documents (PDFs, images) — requires preprocessing"],"requires":["Python 3.9+","Document corpus in text format","Embedding model for document chunking","Vector database for chunk storage","ag2 package with document agent modules"],"input_types":["document files or text","analysis queries","chunking parameters","synthesis instructions"],"output_types":["analysis results","synthesized summaries","cross-document insights","source citations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_2","uri":"capability://tool.use.integration.unified.llm.provider.abstraction.with.multi.model.configuration","name":"unified llm provider abstraction with multi-model configuration","description":"Provides a unified client interface (OpenAIWrapper and ModelClient V2) that abstracts away provider-specific API differences for OpenAI, Anthropic, Gemini, Ollama, and other LLM providers. Agents configure LLM behavior through a config_list (list of model configurations) that specifies model names, API keys, parameters, and fallback strategies. Implements automatic provider detection, request marshaling to provider-specific formats, and response normalization to a UnifiedResponse format that all agents consume uniformly.","intents":["I want to switch between different LLM providers without rewriting agent code","I need to configure multiple models with different parameters and use them as fallbacks","I want to track costs and token usage across different providers in a unified way"],"best_for":["teams evaluating multiple LLM providers for cost/quality tradeoffs","developers building provider-agnostic agent systems","organizations with multi-cloud or hybrid LLM deployments"],"limitations":["Response normalization to UnifiedResponse loses provider-specific features (e.g., OpenAI's logprobs, Anthropic's thinking blocks) — custom handling required for advanced features","Config list fallback strategy is sequential, not intelligent — doesn't learn which providers work best for which tasks","No built-in rate limiting or quota management — applications must implement their own throttling","Streaming responses require provider-specific handling — not fully abstracted in UnifiedResponse"],"requires":["Python 3.9+","API keys for at least one supported LLM provider","config_list with valid model configurations","Network access to provider APIs"],"input_types":["model configuration dictionaries","prompt text","function schemas for tool calling","message history"],"output_types":["UnifiedResponse objects with normalized structure","token usage statistics","cost tracking data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_3","uri":"capability://tool.use.integration.function.calling.and.tool.registration.with.dependency.injection","name":"function calling and tool registration with dependency injection","description":"Enables agents to invoke external functions and APIs through a schema-based function registry. Agents register Python functions with JSON schema descriptions, and the LLM generates function calls that are validated against schemas and executed with automatic type coercion. Implements dependency injection for tools, allowing functions to receive context (agent state, conversation history) without explicit parameter passing. Supports both synchronous and asynchronous function execution.","intents":["I want agents to call external APIs and tools without hardcoding function references","I need to validate function calls against schemas before execution to prevent errors","I want to inject agent context into tool functions without polluting the function signature"],"best_for":["developers building agents that interact with external services (APIs, databases, file systems)","teams needing strict validation of agent-generated function calls","applications requiring audit trails of which tools agents invoked"],"limitations":["Schema generation from Python functions requires explicit type hints — untyped functions fail silently or generate incomplete schemas","Dependency injection only works for registered context types — custom context objects require manual registration","Async function execution requires event loop management — mixing sync and async tools can cause deadlocks","No built-in retry logic for failed function calls — applications must implement their own error handling"],"requires":["Python 3.9+","Python functions with type hints for schema generation","JSON schema definitions for complex parameter types","ag2 package with tools module"],"input_types":["Python functions","JSON schema definitions","function call requests from LLM","context objects for dependency injection"],"output_types":["function execution results","error messages with validation details","execution logs with timing and resource usage"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_4","uri":"capability://code.generation.editing.code.execution.environment.with.jupyter.kernel.integration","name":"code execution environment with jupyter kernel integration","description":"Provides a sandboxed code execution environment where agents can write and run Python code. Uses Jupyter kernels as the execution backend, enabling stateful code execution where variables persist across multiple code blocks. Implements code validation, output capture, and error handling. Supports both local execution and remote kernel connections. Integrates with the code generation pipeline to execute generated code and provide feedback to agents.","intents":["I want agents to write and execute Python code to solve problems","I need code execution to be stateful so agents can build on previous computations","I want to capture code output and errors to feed back to agents for debugging"],"best_for":["teams building data analysis and scientific computing agents","developers creating code generation agents that need to validate their output","researchers building agents that perform computational tasks"],"limitations":["Jupyter kernel startup adds 2-5 second latency per agent initialization","No built-in resource limits — runaway code can consume unlimited CPU/memory","Kernel state is not isolated between agents — shared state can cause interference","Remote kernel connections require network setup and authentication — adds operational complexity"],"requires":["Python 3.9+","Jupyter installed (pip install jupyter)","IPython kernel for code execution","ag2 package with coding module"],"input_types":["Python code strings","code execution requests with timeout parameters","kernel configuration"],"output_types":["code execution results","stdout/stderr output","error tracebacks","variable state snapshots"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_5","uri":"capability://planning.reasoning.swarm.orchestration.with.dynamic.agent.routing","name":"swarm orchestration with dynamic agent routing","description":"Implements SwarmAgent, a specialized agent type that dynamically routes tasks to other agents based on message content and agent capabilities. Uses a routing function that examines incoming messages and selects the most appropriate agent to handle the request. Supports hierarchical routing where a swarm agent can delegate to other swarm agents. Implements automatic context propagation so agents in the swarm have access to shared state and conversation history.","intents":["I want to build a system where a coordinator agent routes tasks to specialist agents","I need dynamic routing that adapts based on task content rather than static rules","I want to implement hierarchical agent structures where agents can delegate to sub-agents"],"best_for":["teams building task routing systems with dynamic agent selection","developers creating hierarchical agent architectures","applications needing intelligent workload distribution across specialist agents"],"limitations":["Routing function is synchronous — complex routing logic can block message processing","No built-in load balancing — routing doesn't consider agent workload or availability","Context propagation is shallow — nested swarms can lose context from parent levels","Routing decisions are not logged by default — difficult to debug why a task was routed to a specific agent"],"requires":["Python 3.9+","Multiple agent instances to route to","Routing function that returns agent selection","ag2 package with SwarmAgent class"],"input_types":["text messages","agent messages with metadata","routing function definitions"],"output_types":["routed task results","routing decision logs","agent selection traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_6","uri":"capability://text.generation.language.realtime.agent.communication.with.streaming.llm.responses","name":"realtime agent communication with streaming llm responses","description":"Enables agents to receive and process LLM responses as they stream in, rather than waiting for complete responses. Implements streaming clients for OpenAI Realtime API and Google Gemini Realtime API that handle audio/text input and output. Agents can process partial responses incrementally, enabling lower-latency interactions and more responsive behavior. Supports both text and audio modalities for input and output.","intents":["I want agents to respond to user input with lower latency by processing streaming responses","I need agents to handle audio input and output for voice-based interactions","I want to build interactive applications where agents provide real-time feedback"],"best_for":["teams building conversational AI applications with low-latency requirements","developers creating voice-enabled agent systems","applications requiring real-time user interaction with agents"],"limitations":["Streaming responses cannot be easily retried — partial responses are lost if connection drops","Audio processing requires additional dependencies (audio libraries) — adds complexity to deployment","Realtime APIs have different latency characteristics than batch APIs — performance varies by provider","Token counting for streaming responses is approximate — cost tracking is less accurate"],"requires":["Python 3.9+","OpenAI API key for Realtime API or Google Cloud credentials for Gemini Realtime","Audio libraries (e.g., pyaudio) for audio input/output","ag2 package with realtime_agent module"],"input_types":["text messages","audio streams","realtime API configurations"],"output_types":["streaming text responses","audio output streams","partial response chunks"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_7","uri":"capability://memory.knowledge.rag.enhanced.agent.chat.with.vector.database.integration","name":"rag-enhanced agent chat with vector database integration","description":"Implements RetrieveChat, an agent type that augments conversations with retrieved documents from vector databases. Before responding, the agent retrieves relevant documents based on the user query, then uses those documents as context for generating responses. Supports multiple vector database backends (Chroma, Weaviate, Pinecone) through a pluggable retrieval interface. Implements query expansion and re-ranking to improve retrieval quality.","intents":["I want agents to answer questions based on a large knowledge base without fine-tuning","I need agents to cite sources when answering questions from retrieved documents","I want to build question-answering systems that combine LLM reasoning with document retrieval"],"best_for":["teams building knowledge-base question-answering systems","developers creating customer support agents with access to documentation","applications needing to ground agent responses in retrieved documents"],"limitations":["Retrieval quality depends on embedding model and vector database — poor embeddings lead to irrelevant results","Query expansion and re-ranking add latency (100-500ms per query) — impacts response time","Vector database setup and maintenance adds operational overhead — requires separate infrastructure","No built-in handling of document updates — stale documents can persist in vector database"],"requires":["Python 3.9+","Vector database (Chroma, Weaviate, Pinecone, or compatible)","Embedding model (OpenAI embeddings, local embeddings, or compatible)","Document corpus to index","ag2 package with RAG modules"],"input_types":["user queries","document corpus","vector database configurations","embedding model specifications"],"output_types":["agent responses with citations","retrieved document snippets","retrieval scores and metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_8","uri":"capability://memory.knowledge.graph.based.rag.with.knowledge.graph.traversal","name":"graph-based rag with knowledge graph traversal","description":"Implements Graph RAG, an advanced retrieval approach that builds knowledge graphs from documents and retrieves information by traversing graph edges. Instead of vector similarity, Graph RAG uses graph structure to find related entities and relationships. Supports both local graph construction and integration with external knowledge graphs. Implements multi-hop reasoning where agents traverse multiple edges to answer complex questions.","intents":["I want agents to answer complex questions that require reasoning across multiple documents","I need to retrieve information based on entity relationships rather than text similarity","I want to build systems that can explain reasoning paths through a knowledge graph"],"best_for":["teams building complex question-answering systems with multi-hop reasoning","developers creating knowledge graph-based applications","applications needing explainable retrieval with reasoning paths"],"limitations":["Knowledge graph construction is computationally expensive — requires NLP for entity extraction and relationship detection","Graph traversal can be slow for large graphs — no built-in query optimization","Multi-hop reasoning can hallucinate connections between distant entities — requires careful validation","Integration with external knowledge graphs requires custom mapping logic"],"requires":["Python 3.9+","Document corpus for graph construction","NLP models for entity extraction (spaCy, transformer-based models)","Graph database or in-memory graph structure","ag2 package with graph RAG modules"],"input_types":["document corpus","entity extraction configurations","graph construction parameters","user queries"],"output_types":["agent responses with reasoning paths","traversed graph edges","entity relationship chains","confidence scores for retrieved information"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-autogen__cap_9","uri":"capability://memory.knowledge.context.variables.and.shared.state.management.across.agents","name":"context variables and shared state management across agents","description":"Implements ContextVariables, a mechanism for sharing state across agents in a group chat or multi-agent system. Context variables are key-value pairs that agents can read and write, enabling coordination without explicit message passing. Supports variable scoping (global, group-level, agent-level) and change notifications so agents can react to state updates. Implements automatic serialization for complex state objects.","intents":["I want agents to share state without explicit message passing","I need to coordinate agents by having them read/write shared variables","I want to implement agent behaviors that react to state changes"],"best_for":["teams building coordinated multi-agent systems with shared state","developers implementing agent behaviors that depend on global state","applications needing implicit communication channels between agents"],"limitations":["No built-in conflict resolution for concurrent writes — last write wins, can cause data loss","Variable scoping is not enforced — agents can access variables outside their scope if not careful","Change notifications are synchronous — slow handlers can block other agents","No built-in versioning or history — state changes are not logged by default"],"requires":["Python 3.9+","Group chat or multi-agent system","ContextVariables configuration","ag2 package with context management modules"],"input_types":["variable names and values","variable scope specifications","change notification handlers"],"output_types":["variable values","change notifications","state snapshots"],"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+","LLM provider API key (OpenAI, Anthropic, or compatible)","ag2 package installed from PyPI","Multiple agent instances (minimum 3 for meaningful group chat)","Eligibility policy definitions (can be custom predicates or built-in policies)","logging module (built-in)","OpenTelemetry libraries (optional, for distributed tracing)","Log aggregation system (optional, for production use)","ag2 package with logging modules","MCP server (local or remote)"],"failure_modes":["Reply function ordering matters — first matching handler wins, no priority system for conflicting handlers","Conversation history stored in-memory by default — no built-in persistence layer for long-running agents","Synchronous message passing can create blocking bottlenecks in large agent networks (10+ agents)","Speaker selection policies are evaluated sequentially — complex eligibility rules can add 50-200ms per turn","No built-in deadlock detection — misconfigured policies can cause infinite loops or agent starvation","Shared context grows linearly with conversation length — no automatic context pruning or summarization","Max agent count in a single group chat not formally specified but performance degrades significantly beyond 10 agents","Verbose logging can impact performance — logging overhead can be 5-10% of execution time","OpenTelemetry integration requires additional dependencies and configuration","Log volume can be very large for long-running agents — requires log aggregation and filtering","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":"2026-06-17T09:51:05.295Z","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-autogen","compare_url":"https://unfragile.ai/compare?artifact=pypi-autogen"}},"signature":"1H19kIxqYbkqQL23okizZGWGTwEqJh1Vlspi1cAJmG2KbXRwjJKNJqbVPaLNlVGGiUafEKDSqVq8eyIPjtvvCQ==","signedAt":"2026-06-22T07:00:40.971Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-autogen","artifact":"https://unfragile.ai/pypi-autogen","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-autogen","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"}}