{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-agno-agi--agno","slug":"agno-agi--agno","name":"agno","type":"agent","url":"https://docs.agno.com","page_url":"https://unfragile.ai/agno-agi--agno","categories":["ai-agents","deployment-infra"],"tags":["agents","ai","ai-agents","developer-tools","python"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-agno-agi--agno__cap_0","uri":"capability://tool.use.integration.multi.model.agent.orchestration.with.provider.abstraction","name":"multi-model agent orchestration with provider abstraction","description":"Agno abstracts multiple LLM providers (OpenAI, Anthropic Claude, Google Gemini, Ollama) through a unified Model interface with provider-specific client lifecycle management, retry logic, and streaming response handling. Each provider integration implements standardized interfaces for tool calling, structured outputs, and streaming while preserving provider-specific capabilities like Gemini's parallel grounding or Claude's extended thinking.","intents":["I want to build an agent that can switch between LLM providers without rewriting agent logic","I need provider-specific features like Gemini's grounding or Claude's extended thinking in my agent","I want automatic retry logic and client lifecycle management across different LLM APIs"],"best_for":["teams building multi-provider agent systems to avoid vendor lock-in","developers needing provider-specific optimizations (parallel grounding, extended thinking)","production systems requiring fallback providers and automatic retry strategies"],"limitations":["Provider-specific features (e.g., Gemini grounding, Claude thinking) require conditional code paths","Streaming response processing adds latency variance across providers due to different buffering strategies","No automatic cost optimization across providers — requires manual selection logic"],"requires":["Python 3.9+","API keys for at least one provider (OpenAI, Anthropic, Google, or local Ollama instance)","agno library installed via pip"],"input_types":["text prompts","structured messages with media (images, documents)","tool schemas for function calling"],"output_types":["text responses","structured JSON outputs","streaming event streams","tool invocation requests"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_1","uri":"capability://tool.use.integration.declarative.tool.calling.with.schema.based.function.registry","name":"declarative tool calling with schema-based function registry","description":"Agno provides a @tool decorator and Function class that converts Python functions into LLM-callable tools with automatic schema generation, type validation, and execution controls. Tools are registered in an agent's function registry and invoked through provider-native function calling APIs (OpenAI functions, Anthropic tool_use, Gemini function calling) with built-in error handling, timeout controls, and human-in-the-loop approval gates.","intents":["I want to expose Python functions to my agent without manually writing JSON schemas","I need to control which tools an agent can call and add approval gates for sensitive operations","I want automatic type validation and error handling for tool execution"],"best_for":["developers building agents with custom business logic integrations","teams requiring human-in-the-loop approval for tool execution","systems needing fine-grained tool execution controls (timeouts, retry policies)"],"limitations":["Schema generation from Python type hints may not capture all validation constraints (custom validators require manual schema override)","Tool execution is synchronous by default; async tools require explicit async/await patterns","No built-in tool versioning — schema changes require agent redeployment"],"requires":["Python 3.9+","Type hints on function parameters for automatic schema generation","agno library with tool module"],"input_types":["Python functions with type hints","Docstrings for tool descriptions","Custom validation logic (optional)"],"output_types":["JSON schemas for LLM function calling","Tool execution results","Error responses with context"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_10","uri":"capability://planning.reasoning.evaluation.framework.with.tracing.and.observability","name":"evaluation framework with tracing and observability","description":"Agno provides an Evaluation Framework for testing and validating agent behavior with built-in tracing that captures execution spans, tool calls, and decision points. The framework integrates with third-party observability platforms (LangSmith, Datadog, etc.) for centralized monitoring. Traces include full execution context, enabling debugging and performance analysis of agent systems.","intents":["I want to test and evaluate my agent's behavior systematically","I need detailed execution traces for debugging agent decisions","I want to integrate with observability platforms for centralized monitoring"],"best_for":["teams building production agents requiring systematic evaluation","systems needing detailed execution traces for debugging","applications requiring integration with observability platforms"],"limitations":["Tracing adds overhead; high-frequency tracing may impact performance","Third-party observability integration requires separate API keys and configuration","Trace retention policies are platform-specific; no built-in trace archival"],"requires":["Python 3.9+","agno.evaluation module","Optional: third-party observability platform API keys"],"input_types":["Agent execution context","Evaluation test cases","Observability platform credentials"],"output_types":["Execution traces","Evaluation results","Performance metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_11","uri":"capability://image.visual.media.handling.with.multimodal.message.support","name":"media handling with multimodal message support","description":"Agno's media system enables agents to process and generate multimodal content (images, documents, audio) through a unified Message abstraction. Messages can include text, images, documents, and other media types, with automatic encoding/decoding for different providers. The framework handles media storage, retrieval, and provider-specific formatting (e.g., base64 for OpenAI, URLs for Anthropic).","intents":["I want my agent to process images and documents in addition to text","I need to handle different media formats and provider-specific encoding requirements","I want to build multimodal agents without manual media format conversion"],"best_for":["teams building multimodal agents (vision, document processing)","systems requiring flexible media handling across providers","applications needing automatic media encoding/decoding"],"limitations":["Media storage is provider-specific; no unified media storage abstraction","Large media files may exceed provider token limits; chunking strategies are manual","Media encoding/decoding adds latency; no built-in caching for repeated media"],"requires":["Python 3.9+","Provider support for media types (OpenAI, Anthropic, Google Gemini)","agno.media module"],"input_types":["Images (JPEG, PNG, WebP, GIF)","Documents (PDF, text)","Audio (provider-dependent)","URLs to media"],"output_types":["Encoded media for provider APIs","Media metadata","Processed media results"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_12","uri":"capability://automation.workflow.scheduling.system.for.periodic.agent.execution","name":"scheduling system for periodic agent execution","description":"Agno's Scheduling system enables agents to execute on defined schedules (cron-style, interval-based) through a registry-based approach. Scheduled agents are managed by the AgentOS runtime and execute in isolated sessions, with results stored and accessible via API. The framework handles schedule persistence, execution history, and failure recovery.","intents":["I want my agent to run on a schedule (daily, hourly, etc.) without manual triggering","I need to track execution history and results for scheduled agent runs","I want to manage multiple scheduled agents from a central control plane"],"best_for":["teams building periodic agents (monitoring, reporting, data collection)","systems requiring scheduled task execution without external schedulers","applications needing centralized schedule management"],"limitations":["Schedule persistence requires external database; no in-memory scheduling for production","Execution history is not automatically pruned; requires manual cleanup","No built-in retry logic for failed scheduled executions"],"requires":["Python 3.9+","AgentOS runtime","External database for schedule persistence","agno.scheduling module"],"input_types":["Agent definitions","Schedule specifications (cron, interval)","Execution parameters"],"output_types":["Execution results","Execution history","Schedule status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_13","uri":"capability://data.processing.analysis.database.auto.discovery.and.schema.management","name":"database auto-discovery and schema management","description":"Agno's AgentOS runtime includes automatic database discovery that detects available databases and generates tool schemas for database operations. The framework introspects database schemas and creates tools for querying, inserting, and updating data without manual schema definition. Supports multiple database backends (PostgreSQL, MySQL, SQLite) with provider-specific optimizations.","intents":["I want my agent to query and modify databases without manual tool definition","I need automatic schema introspection to generate database tools","I want to support multiple database backends with a single agent configuration"],"best_for":["teams building database-aware agents (data analysis, reporting)","systems requiring flexible database access without manual tool coding","applications supporting multiple database backends"],"limitations":["Schema introspection may fail with complex database setups (views, custom types); manual schema definition required","Auto-generated tools may not capture business logic constraints; custom validation required","No built-in query optimization; complex queries may require manual tuning"],"requires":["Python 3.9+","Database connection details (PostgreSQL, MySQL, SQLite, etc.)","Database driver (psycopg2, mysql-connector, sqlite3, etc.)","agno.os module with database auto-discovery"],"input_types":["Database connection strings","Query specifications"],"output_types":["Database schemas","Query results","Modification confirmations"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_14","uri":"capability://automation.workflow.control.plane.ui.for.agent.management.and.monitoring","name":"control plane ui for agent management and monitoring","description":"Agno provides a Control Plane UI for managing deployed agents, monitoring execution, and viewing session history. The UI displays agent configurations, execution traces, message history, and performance metrics. It enables manual agent triggering, session inspection, and debugging without CLI or API access.","intents":["I want a visual interface to manage and monitor my deployed agents","I need to inspect agent execution traces and message history for debugging","I want to trigger agents manually and view results without CLI access"],"best_for":["teams managing multiple deployed agents requiring visual oversight","systems needing non-technical access to agent management","applications requiring execution debugging and inspection"],"limitations":["Control Plane UI is read-mostly; limited editing capabilities for agent configurations","Real-time updates depend on WebSocket support; polling fallback may lag","No built-in role-based access control; authentication is basic"],"requires":["AgentOS runtime deployed","Web browser access to Control Plane UI","Optional: authentication credentials"],"input_types":["Agent configurations","Execution parameters","Manual trigger requests"],"output_types":["Agent status","Execution traces","Message history","Performance metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_2","uri":"capability://planning.reasoning.multi.agent.team.orchestration.with.role.based.coordination","name":"multi-agent team orchestration with role-based coordination","description":"Agno's Team system coordinates multiple agents with distinct roles and responsibilities through a composition model where agents are added to a team with specific configurations. Teams manage agent communication, message routing, and execution order through a run context that tracks session state, message history, and execution events. The framework handles inter-agent message passing and coordination without requiring explicit message queue infrastructure.","intents":["I want to build a multi-agent system where agents have different roles (researcher, analyst, writer)","I need agents to communicate and coordinate on complex tasks without manual message routing","I want to manage team execution order and dependencies between agent actions"],"best_for":["teams building complex agentic workflows with specialized agent roles","systems requiring coordinated multi-agent reasoning (e.g., research + analysis + writing)","applications needing transparent agent communication and decision tracking"],"limitations":["Team coordination is sequential by default; parallel agent execution requires explicit workflow configuration","No built-in consensus mechanisms for conflicting agent outputs — requires custom resolution logic","Message routing between agents is implicit through shared run context; complex routing patterns require custom orchestration"],"requires":["Python 3.9+","Multiple Agent instances configured with distinct roles","agno.team.Team class"],"input_types":["Agent instances with tools and models","Team configuration (agent roles, execution order)","Initial user input/task"],"output_types":["Team execution results","Message history across all agents","Execution events and traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_3","uri":"capability://automation.workflow.workflow.orchestration.with.human.in.the.loop.step.execution","name":"workflow orchestration with human-in-the-loop step execution","description":"Agno's Workflow system defines multi-step processes as sequences of WorkflowStep instances that execute in order with support for conditional branching, human approval gates, and step-level error handling. Each step can be an agent execution, tool invocation, or custom Python function, with execution context passed between steps. The framework provides event streaming for step execution progress and supports pausing workflows for human input before proceeding.","intents":["I want to define multi-step processes where some steps require human approval before proceeding","I need to orchestrate complex workflows with conditional logic and error recovery","I want real-time visibility into workflow execution progress and step outcomes"],"best_for":["teams building approval-required workflows (content review, financial decisions)","systems needing complex multi-step orchestration with conditional branching","applications requiring audit trails and human decision tracking"],"limitations":["Workflows are stateless by default; long-running workflows require external session storage for resumption","Step dependencies are linear; complex DAG-style dependencies require custom orchestration logic","Human approval gates block workflow execution; no timeout mechanisms for stalled approvals"],"requires":["Python 3.9+","WorkflowStep definitions","agno.workflow module","Optional: external session storage for persistence"],"input_types":["WorkflowStep definitions (agents, tools, functions)","Step input data and context","Human approval decisions"],"output_types":["Workflow execution results","Step-by-step execution events","Approval decision records"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_4","uri":"capability://memory.knowledge.agentic.rag.with.knowledge.base.integration.and.vector.search","name":"agentic rag with knowledge base integration and vector search","description":"Agno's Knowledge system provides a Knowledge Base abstraction that integrates with vector databases (Chroma, Pinecone, Weaviate, etc.) for semantic search and retrieval-augmented generation. The framework includes a content processing pipeline that handles document ingestion, chunking, embedding, and storage, with support for multiple search strategies (semantic, keyword, hybrid). Agents can query knowledge bases through built-in tools, enabling context-aware reasoning over custom documents.","intents":["I want my agent to search and reason over custom documents without manual context management","I need to ingest documents, chunk them, embed them, and make them searchable by my agent","I want to use multiple search strategies (semantic, keyword, hybrid) depending on the query type"],"best_for":["teams building document-aware agents (customer support, research assistants)","systems requiring semantic search over large document collections","applications needing flexible search strategies for different query types"],"limitations":["Content processing pipeline requires external embedding models (OpenAI, local models); no built-in embeddings","Vector database selection is manual; no automatic optimization for query patterns","Chunking strategy is configurable but not adaptive; large documents may require manual chunk size tuning"],"requires":["Python 3.9+","Vector database (Chroma, Pinecone, Weaviate, etc.) or local embedding model","Embedding API key (OpenAI) or local embedding model","agno.knowledge module"],"input_types":["Documents (PDF, text, markdown, web content)","Search queries (text)","Chunking configuration"],"output_types":["Retrieved document chunks","Relevance scores","Metadata (source, chunk index)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_5","uri":"capability://memory.knowledge.learning.machine.with.persistent.memory.and.experience.replay","name":"learning machine with persistent memory and experience replay","description":"Agno's LearningMachine system provides persistent memory storage for agent interactions, enabling agents to learn from past experiences through a Learning Store abstraction. The framework tracks agent decisions, outcomes, and feedback, storing them in configurable backends (database, vector store) for later retrieval and analysis. Agents can query their learning history to improve decision-making, and the system supports experience replay patterns for continuous improvement.","intents":["I want my agent to learn from past interactions and improve its decision-making over time","I need to store and retrieve agent experiences for analysis and debugging","I want to implement feedback loops where agent outcomes inform future behavior"],"best_for":["teams building long-lived agents that improve through interaction","systems requiring audit trails and decision history for compliance","applications needing feedback loops for continuous agent improvement"],"limitations":["Learning Store requires external database or vector store; no built-in in-memory option for production","Experience replay patterns are not automated; requires custom logic to query and apply learnings","No built-in mechanisms for handling conflicting or contradictory learnings"],"requires":["Python 3.9+","External database or vector store for Learning Store backend","agno.learning module"],"input_types":["Agent interactions (prompts, responses, tool calls)","Feedback signals (success/failure, user ratings)","Learning queries"],"output_types":["Stored experiences","Retrieved learnings","Experience replay results"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_6","uri":"capability://automation.workflow.session.scoped.stateless.api.serving.with.agentos.runtime","name":"session-scoped stateless api serving with agentos runtime","description":"Agno's AgentOS runtime serves agents as stateless, session-scoped REST APIs and WebSocket endpoints through FastAPI integration. Each session maintains isolated execution context, message history, and state without server-side persistence, enabling horizontal scaling. The runtime handles database auto-discovery, authentication, registry management, and provides built-in monitoring endpoints for observability.","intents":["I want to deploy my agent as a scalable REST API without managing session state on the server","I need WebSocket support for real-time streaming responses from my agent","I want built-in authentication, monitoring, and observability for my deployed agent"],"best_for":["teams deploying agents to production with horizontal scaling requirements","systems needing real-time streaming responses via WebSocket","applications requiring built-in monitoring and observability"],"limitations":["Session state is ephemeral; long-running sessions require external storage for resumption","Database auto-discovery may fail with complex multi-database setups; manual configuration required","Authentication is basic (API key); no built-in OAuth or SAML support"],"requires":["Python 3.9+","FastAPI","Optional: external database for session persistence","agno.os module (AgentOS)"],"input_types":["HTTP POST requests with JSON payloads","WebSocket connections","Session identifiers"],"output_types":["JSON responses","Streaming event streams (WebSocket)","Monitoring metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_7","uri":"capability://automation.workflow.event.streaming.and.real.time.execution.monitoring","name":"event streaming and real-time execution monitoring","description":"Agno's event streaming system emits execution events (agent steps, tool calls, responses) as they occur, enabling real-time monitoring and client-side progress tracking. Events are structured with timestamps, execution context, and step-level details, and can be consumed via WebSocket connections or event listeners. The framework supports event filtering and selective streaming to reduce bandwidth for large-scale deployments.","intents":["I want real-time visibility into agent execution progress without polling","I need to stream agent responses to clients as they are generated","I want to monitor tool execution and step-level events for debugging"],"best_for":["teams building real-time agent UIs with live progress updates","systems requiring execution monitoring and debugging capabilities","applications needing event-driven architectures for agent orchestration"],"limitations":["Event streaming adds overhead; high-frequency events may impact performance","Event filtering is manual; no built-in sampling or rate-limiting","Event history is not persisted by default; requires external event store for audit trails"],"requires":["Python 3.9+","WebSocket support (for real-time streaming)","agno.utils.events module"],"input_types":["Agent execution context","Tool invocations","Step completions"],"output_types":["Structured event objects","JSON event streams","Event metadata (timestamps, context)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_8","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Agno supports structured output generation where agents can return JSON responses conforming to user-defined schemas. The framework uses provider-native structured output APIs (OpenAI JSON mode, Anthropic structured outputs, Gemini schema validation) to ensure responses match the specified schema. Validation occurs at the model level, reducing post-processing overhead and enabling type-safe agent responses.","intents":["I want my agent to return structured JSON responses that conform to a specific schema","I need type-safe agent outputs for downstream processing without manual validation","I want to leverage provider-native structured output APIs for better reliability"],"best_for":["teams building agents that feed into downstream systems requiring structured data","systems needing type-safe agent outputs for data pipelines","applications requiring schema validation at the model level"],"limitations":["Schema support varies by provider; some providers have limited schema expressiveness","Complex nested schemas may require flattening for some providers","Schema validation failures are provider-specific; error messages vary across providers"],"requires":["Python 3.9+","Provider support for structured outputs (OpenAI, Anthropic, Google Gemini)","JSON schema definition"],"input_types":["JSON schema definitions","Agent prompts with schema context"],"output_types":["JSON responses conforming to schema","Validation errors (if schema mismatch)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agno-agi--agno__cap_9","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration","name":"model context protocol (mcp) server integration","description":"Agno integrates with the Model Context Protocol (MCP) standard, allowing agents to discover and invoke tools exposed by MCP servers. The framework handles MCP client lifecycle, tool schema discovery, and invocation routing, enabling agents to access external tool ecosystems without manual integration. MCP servers can be local or remote, and Agno manages connection pooling and error handling.","intents":["I want my agent to access tools from MCP servers without manual integration","I need to compose agents with tools from multiple MCP server ecosystems","I want to leverage existing MCP tool libraries in my agent system"],"best_for":["teams building agents that need access to external tool ecosystems","systems integrating with MCP-compatible services (Claude, other agents)","applications requiring dynamic tool discovery and composition"],"limitations":["MCP server availability is required at runtime; no fallback for unavailable servers","Tool schema discovery adds startup latency; caching strategies are manual","Error handling for MCP tool failures requires custom logic"],"requires":["Python 3.9+","MCP server running and accessible (local or remote)","agno.tools.mcp module"],"input_types":["MCP server connection details","Tool invocation requests"],"output_types":["Tool schemas from MCP server","Tool execution results"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for at least one provider (OpenAI, Anthropic, Google, or local Ollama instance)","agno library installed via pip","Type hints on function parameters for automatic schema generation","agno library with tool module","agno.evaluation module","Optional: third-party observability platform API keys","Provider support for media types (OpenAI, Anthropic, Google Gemini)","agno.media module","AgentOS runtime"],"failure_modes":["Provider-specific features (e.g., Gemini grounding, Claude thinking) require conditional code paths","Streaming response processing adds latency variance across providers due to different buffering strategies","No automatic cost optimization across providers — requires manual selection logic","Schema generation from Python type hints may not capture all validation constraints (custom validators require manual schema override)","Tool execution is synchronous by default; async tools require explicit async/await patterns","No built-in tool versioning — schema changes require agent redeployment","Tracing adds overhead; high-frequency tracing may impact performance","Third-party observability integration requires separate API keys and configuration","Trace retention policies are platform-specific; no built-in trace archival","Media storage is provider-specific; no unified media storage abstraction","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8130978379984322,"quality":0.35,"ecosystem":0.65,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-01T16:28:43Z"},"community":{"stars":39883,"forks":5327,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agno-agi--agno","compare_url":"https://unfragile.ai/compare?artifact=agno-agi--agno"}},"signature":"qrDAYpS0rsbXWsseSMo9LkA21gb2eFcM2/4H4H1fJb1Gt2Xk087gqZ32n/JRQ4I7YHC46Te+WFs7jLXBhyYkDg==","signedAt":"2026-06-21T04:14:38.643Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agno-agi--agno","artifact":"https://unfragile.ai/agno-agi--agno","verify":"https://unfragile.ai/api/v1/verify?slug=agno-agi--agno","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"}}