{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"agno","slug":"agno","name":"Agno","type":"framework","url":"https://github.com/agno-agi/agno","page_url":"https://unfragile.ai/agno","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"agno__cap_0","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 class coordinates multiple specialized agents through a hierarchical orchestration layer that manages message routing, state synchronization, and execution order across agents. Teams use a registry-based agent discovery pattern where each agent maintains its own context and tools, with the Team runtime handling inter-agent communication via a message queue and shared session state. The framework supports both sequential and parallel agent execution patterns with automatic dependency resolution.","intents":["I need to build a multi-agent system where different agents handle different domains (e.g., research agent + analysis agent + writing agent)","I want agents to collaborate on complex tasks by passing results between each other without manual orchestration","I need to scale from single-agent to multi-agent without rewriting core agent logic"],"best_for":["teams building complex AI systems requiring task decomposition across specialized agents","developers migrating from single-agent to multi-agent architectures","enterprises needing coordinated AI workflows with audit trails"],"limitations":["Team execution is synchronous by default — parallel agent execution requires explicit async configuration","No built-in load balancing across agent instances — requires external orchestration for horizontal scaling","Agent communication overhead increases with team size; teams >10 agents may require custom routing logic"],"requires":["Python 3.9+","Agno core framework installed","At least 2 Agent instances configured with distinct roles/tools"],"input_types":["text prompts","structured messages with metadata","multimodal content (text + images/files)"],"output_types":["structured RunResponse objects","event streams with agent execution traces","session-persisted conversation history"],"categories":["planning-reasoning","multi-agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_1","uri":"capability://memory.knowledge.agentic.rag.with.knowledge.base.integration.and.semantic.search","name":"agentic rag with knowledge base integration and semantic search","description":"Agno's Knowledge class implements a retrieval-augmented generation system that combines vector database backends (Qdrant, Pinecone, LanceDB) with semantic search strategies and content processing pipelines. When an agent queries the knowledge base, the framework performs hybrid search (semantic + keyword), chunks documents using configurable strategies, and injects retrieved context into the agent's prompt with source attribution. The system supports remote content integration (URLs, PDFs, web scraping) with automatic chunking and embedding generation via the model's embedding API.","intents":["I want my agent to answer questions grounded in my company's documentation without hallucinating","I need to ingest multiple document types (PDFs, web pages, markdown) and make them searchable by my agents","I want to control how much context is injected into the agent's prompt to manage token usage and latency"],"best_for":["developers building domain-specific agents (customer support, internal knowledge assistants)","teams with large document repositories needing semantic search without building custom pipelines","enterprises requiring source attribution and audit trails for AI-generated answers"],"limitations":["Embedding generation is synchronous and blocks agent execution — large document ingestion (>10k documents) requires async preprocessing","Vector database must be pre-configured and accessible; no built-in local vector store (requires Qdrant/Pinecone/LanceDB)","Chunking strategy is fixed per Knowledge instance — dynamic chunk sizing based on query complexity not supported","Search relevance depends on embedding model quality; poor embeddings lead to irrelevant context injection"],"requires":["Python 3.9+","Vector database instance (Qdrant, Pinecone, or LanceDB) with network access","API key for embedding model (OpenAI, Anthropic, or local model)","Documents pre-processed into supported formats (PDF, markdown, plain text, URLs)"],"input_types":["text queries from agents","document URLs and file paths","structured metadata (tags, categories)"],"output_types":["ranked list of relevant document chunks with similarity scores","formatted context strings with source attribution","search metadata (retrieval strategy used, number of results)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_10","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation.and.type.safety","name":"structured output generation with schema validation and type safety","description":"Agno supports structured output generation where agents return data conforming to a predefined JSON schema or Python dataclass. The framework passes the schema to the model's structured output API (OpenAI's JSON mode, Claude's tool_choice, Gemini's schema validation) and validates the response against the schema before returning to the agent. Type hints on dataclasses are automatically converted to JSON schemas compatible with each provider. Validation failures trigger automatic retries with corrected prompts.","intents":["I want my agent to return structured data (JSON, dataclass) instead of free-form text","I need type-safe agent responses that I can directly deserialize into Python objects","I want automatic validation and retry when the agent's output doesn't match the expected schema"],"best_for":["developers building agents that feed into downstream systems requiring structured data","teams needing type-safe agent responses for integration with typed codebases","applications where agent output must conform to strict schemas (API responses, database records)"],"limitations":["Schema complexity is limited — deeply nested or recursive schemas may exceed model context or cause validation failures","Structured output support varies by provider — some providers have limited schema expressiveness","Validation failures trigger retries, adding latency — no configurable retry limits or fallback strategies","Schema changes require agent redeployment — no dynamic schema evolution"],"requires":["Python 3.9+","Model with structured output support (OpenAI, Claude, Gemini)","JSON schema or Python dataclass defining the output structure"],"input_types":["output schema (JSON schema or dataclass)","agent prompt and context"],"output_types":["validated structured output (dict or dataclass instance)","validation error details on failure"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_11","uri":"capability://planning.reasoning.evaluation.framework.for.agent.performance.measurement.and.benchmarking","name":"evaluation framework for agent performance measurement and benchmarking","description":"Agno's evaluation framework provides tools for measuring agent performance against predefined test cases with metrics like accuracy, latency, token usage, and cost. Evaluators can be defined as Python functions that compare agent outputs against expected results or human judgments. The framework supports batch evaluation across multiple test cases and generates reports with aggregated metrics. Integration with observability platforms enables tracking evaluation metrics over time to detect performance regressions.","intents":["I want to measure my agent's accuracy on a set of test cases before deploying to production","I need to compare agent performance across different models or configurations","I want to track agent performance over time to detect regressions or improvements"],"best_for":["teams with rigorous testing requirements before agent deployment","researchers benchmarking agent performance across models","enterprises monitoring agent quality in production"],"limitations":["Evaluation metrics are custom-defined — no built-in metrics for common use cases (QA, summarization, translation)","Batch evaluation is synchronous and slow — evaluating 1000 test cases may take hours","Human judgment evaluation requires manual annotation — no built-in crowdsourcing or annotation tools","Evaluation results are not automatically compared across runs — trend analysis requires external tools"],"requires":["Python 3.9+","Test cases with expected outputs or evaluation criteria","Evaluator functions (Python callables)"],"input_types":["test cases (input + expected output)","agent instance to evaluate","evaluator functions"],"output_types":["evaluation results (per-test-case metrics)","aggregated metrics (accuracy, latency, cost)","evaluation reports"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_12","uri":"capability://automation.workflow.scheduling.system.for.periodic.agent.execution.and.task.automation","name":"scheduling system for periodic agent execution and task automation","description":"Agno's scheduling system enables agents to be executed on a schedule (cron-like expressions, intervals) without manual triggering. Scheduled tasks are persisted in the database and executed by a background scheduler. Each scheduled execution creates a new session with its own context and memory. The framework supports task dependencies (execute task B after task A completes) and conditional scheduling (execute only if previous execution succeeded). Execution history and logs are persisted for audit trails.","intents":["I want my agent to run daily reports or periodic data processing without manual triggering","I need to chain multiple agent tasks together (e.g., scrape data → analyze → send report)","I want to monitor scheduled task execution and debug failures"],"best_for":["developers building autonomous agents that perform periodic tasks","teams with scheduled data processing or reporting workflows","enterprises needing audit trails for automated tasks"],"limitations":["Scheduling is single-instance — no distributed scheduling across multiple servers","Task dependencies are limited to sequential execution — complex DAGs require custom orchestration","Conditional scheduling is basic (success/failure) — no support for data-driven conditions","Execution history is not automatically cleaned up — long-running systems may accumulate large logs"],"requires":["Python 3.9+","Database for persisting scheduled tasks and execution history","Background scheduler process (separate from agent execution)"],"input_types":["schedule definition (cron expression or interval)","agent to execute","optional task dependencies"],"output_types":["execution history with timestamps and results","task status (pending, running, completed, failed)","execution logs and error details"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_13","uri":"capability://tool.use.integration.registry.system.for.agent.and.tool.discovery.with.dynamic.configuration","name":"registry system for agent and tool discovery with dynamic configuration","description":"Agno's registry system provides a centralized catalog of agents, tools, and models that can be discovered and instantiated at runtime. Agents and tools can be registered with metadata (description, tags, version) and retrieved by name or tag. The registry supports dynamic configuration where agent parameters (model, tools, knowledge base) can be overridden at runtime without code changes. Registry entries can be persisted in a database or loaded from configuration files.","intents":["I want to manage multiple agent configurations and switch between them without code changes","I need to discover available agents and tools at runtime for dynamic agent composition","I want to version agents and tools and manage multiple versions simultaneously"],"best_for":["teams with multiple agent configurations or variants","enterprises needing dynamic agent composition based on runtime conditions","developers building agent marketplaces or plugin systems"],"limitations":["Registry is single-instance — no distributed registry for multi-server deployments","Configuration overrides are shallow — nested configuration changes require full object replacement","Version management is manual — no automatic version resolution or compatibility checking","Registry discovery is by name or tag — no semantic search or capability-based discovery"],"requires":["Python 3.9+","Registry backend (in-memory, database, or configuration file)"],"input_types":["agent/tool definitions with metadata","configuration overrides"],"output_types":["agent/tool instances with applied configuration","registry metadata (description, tags, version)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_14","uri":"capability://planning.reasoning.evaluation.framework.with.metrics.and.tracing","name":"evaluation framework with metrics and tracing","description":"Provides an evaluation framework for assessing agent performance through custom metrics, execution tracing, and integration with observability platforms. The framework captures execution traces (inputs, outputs, tool calls, latencies), enables custom metric definitions, and exports traces to external observability systems (LangSmith, Datadog, etc.), enabling quantitative agent evaluation and performance monitoring.","intents":["I want to measure agent accuracy, latency, and cost across multiple runs","I need to debug agent failures by examining detailed execution traces","I want to integrate agent metrics with my observability platform"],"best_for":["teams evaluating agent performance in production","developers debugging complex agent behaviors","builders implementing agent monitoring and alerting"],"limitations":["Metrics are custom-defined; no built-in metrics for common scenarios","Trace export requires external observability platform integration; no built-in trace storage","Evaluation is offline (post-execution); no real-time metric computation","Trace sampling is not supported; all traces are captured (can be expensive at scale)"],"requires":["Python 3.9+","Custom metric definitions","Optional: observability platform (LangSmith, Datadog, etc.)"],"input_types":["execution traces","agent inputs and outputs","custom metric definitions"],"output_types":["metric values","execution traces","performance reports"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_15","uri":"capability://automation.workflow.scheduling.and.background.task.execution","name":"scheduling and background task execution","description":"Enables agents to schedule background tasks and periodic executions through a scheduling system that manages task queues, execution timing, and result persistence. The framework supports cron-like scheduling, one-time tasks, and task dependencies, with automatic retry logic and failure handling, enabling agents to perform long-running operations without blocking user requests.","intents":["I want my agent to run periodic tasks (e.g., daily reports, data sync)","I need to schedule one-time tasks for later execution","I want background tasks to retry on failure with exponential backoff"],"best_for":["developers building agents with background processing needs","teams implementing periodic agent tasks","builders needing task scheduling without external job queues"],"limitations":["Scheduling is in-process; no distributed scheduling across multiple servers","Task persistence requires external database; no built-in task queue","Task dependencies are limited; no complex DAG scheduling","Retry logic is fixed; no custom retry strategies"],"requires":["Python 3.9+","Database for task persistence","AgentOS runtime or custom scheduler integration"],"input_types":["task definitions (agent, parameters)","schedule specifications (cron, one-time)","task metadata"],"output_types":["task execution results","task status and history","failure notifications"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_2","uri":"capability://tool.use.integration.tool.calling.with.schema.based.function.registry.and.execution.controls","name":"tool calling with schema-based function registry and execution controls","description":"Agno's tool system uses a @tool decorator pattern to register Python functions as callable tools, automatically generating JSON schemas compatible with OpenAI, Anthropic, and Google function-calling APIs. When an agent decides to use a tool, the framework validates the function signature, marshals arguments from the model's function-calling response, executes the function with timeout/retry controls, and injects the result back into the agent's context. The system supports both synchronous and asynchronous tool execution with built-in error handling and human-in-the-loop approval gates.","intents":["I want my agent to call external APIs and Python functions without writing boilerplate schema definitions","I need to control tool execution (timeouts, retries, approval gates) to prevent runaway agent behavior","I want to add new tools to an agent without modifying the core agent code"],"best_for":["developers building agents that interact with external systems (APIs, databases, file systems)","teams needing fine-grained control over agent actions for safety/compliance","builders prototyping agents and iterating on tool definitions"],"limitations":["Tool execution is blocking — async tools require explicit async/await syntax, not automatically parallelized","Schema generation from Python type hints has edge cases with complex nested types (Union, Optional with multiple types)","No built-in tool versioning — updating a tool signature may break agents expecting the old signature","Human-in-the-loop approval adds latency; approval gates are synchronous and block agent execution"],"requires":["Python 3.9+","Type hints on all tool functions (required for schema generation)","Model with function-calling support (OpenAI, Anthropic, Google Gemini, Ollama)"],"input_types":["Python functions with type annotations","function arguments as JSON from model's function-calling response"],"output_types":["function return values (any Python type)","error messages with stack traces on execution failure","tool execution metadata (latency, retry count)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_3","uri":"capability://memory.knowledge.session.scoped.agent.memory.with.persistence.and.learning","name":"session-scoped agent memory with persistence and learning","description":"Agno's session management system maintains conversation history, agent state, and learned patterns within a session scope that persists across multiple agent runs. The framework uses a Session object that stores messages, tool execution history, and metadata in a configurable backend (in-memory, SQLite, PostgreSQL). The LearningMachine component analyzes agent behavior within sessions, extracts patterns from successful interactions, and stores them in a learning store for future retrieval. Memory is automatically injected into the agent's context window, with configurable retention policies (sliding window, summarization) to manage token usage.","intents":["I want my agent to remember previous interactions with a user across multiple conversations","I need to analyze what my agent learned from successful interactions and reuse those patterns","I want to manage memory efficiently so the agent doesn't exceed token limits on long conversations"],"best_for":["developers building conversational agents that need persistent user context","teams analyzing agent behavior to improve performance over time","applications with long-running sessions (customer support, tutoring systems)"],"limitations":["Session persistence requires external database — no built-in local session store for development","Memory injection is naive (full history prepended to prompt) — no intelligent summarization or compression by default","LearningMachine analysis is post-hoc; patterns are extracted after session completion, not during execution","Session isolation is strict — cross-session learning requires explicit pattern export/import"],"requires":["Python 3.9+","Database backend (SQLite for local development, PostgreSQL for production)","Session ID management in application layer"],"input_types":["agent messages and responses","tool execution results","user feedback/annotations"],"output_types":["conversation history with metadata","learned patterns and insights","session summaries for memory injection"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_4","uri":"capability://automation.workflow.workflow.orchestration.with.multi.step.task.decomposition.and.human.in.the.loop","name":"workflow orchestration with multi-step task decomposition and human-in-the-loop","description":"Agno's Workflow system decomposes complex tasks into discrete steps (Agent steps, Tool steps, Conditional steps, Human approval steps) that execute sequentially or conditionally based on previous results. Each step has its own context, input/output schema, and execution controls. The framework supports human-in-the-loop (HITL) workflows where execution pauses at designated steps, waits for human approval or input, and resumes with the human's decision injected into the context. Workflows generate execution traces with step-level granularity, enabling debugging and audit trails.","intents":["I need to break down a complex task (e.g., research → analysis → approval → publication) into steps that can be monitored and controlled","I want to pause agent execution at critical points to get human feedback before proceeding","I need detailed execution traces to debug workflow failures and understand agent decision-making"],"best_for":["enterprises building AI-assisted workflows requiring human oversight","teams with complex multi-step processes (content creation, data analysis, compliance workflows)","developers needing fine-grained control over agent execution flow"],"limitations":["Workflow execution is synchronous and blocking — parallel step execution not supported","Human approval gates require external UI/API for human interaction — no built-in approval interface","Conditional logic is limited to simple branching — complex decision trees require custom step implementations","Workflow state is not automatically persisted — long-running workflows require external state management"],"requires":["Python 3.9+","Workflow definition with explicit step ordering","External system for human approval (UI, email, Slack integration)"],"input_types":["workflow definition (steps, conditions, schemas)","initial input data","human approval/feedback at HITL steps"],"output_types":["step execution results with metadata","workflow completion status","execution trace with step-level details"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_5","uri":"capability://image.visual.multimodal.message.handling.with.media.type.support.and.streaming","name":"multimodal message handling with media type support and streaming","description":"Agno's message system supports multimodal content (text, images, audio, files) through a Media class that encapsulates different content types and their metadata. Messages can contain multiple media items with source attribution (URL, file path, base64 encoding). The framework handles media serialization/deserialization for different model providers (OpenAI Vision, Claude, Gemini) with automatic format conversion. Streaming responses are processed incrementally with event-based callbacks, enabling real-time response rendering without buffering the entire response.","intents":["I want my agent to process images, PDFs, and other files alongside text without manual format conversion","I need to stream agent responses in real-time to provide immediate feedback to users","I want to preserve media source attribution for audit trails and debugging"],"best_for":["developers building multimodal agents (vision, document processing, audio analysis)","teams needing real-time response streaming for user-facing applications","applications requiring media provenance tracking"],"limitations":["Media format conversion is provider-specific — not all models support all media types (e.g., Ollama has limited vision support)","Streaming requires WebSocket or Server-Sent Events (SSE) — not compatible with simple request/response APIs","Large media files (>100MB) may exceed model context limits — no automatic media compression or summarization","Media caching is not built-in — repeated references to the same media require re-encoding"],"requires":["Python 3.9+","Model with multimodal support (OpenAI Vision, Claude 3, Gemini, Llama 2 Vision)","WebSocket or SSE infrastructure for streaming responses"],"input_types":["text strings","image files (PNG, JPEG, WebP, GIF)","PDF documents","audio files","URLs to remote media"],"output_types":["structured Message objects with media metadata","streaming event objects with incremental response chunks","media source attribution metadata"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_6","uri":"capability://tool.use.integration.model.provider.abstraction.with.unified.interface.and.provider.specific.optimizations","name":"model provider abstraction with unified interface and provider-specific optimizations","description":"Agno's Model class provides a unified interface for multiple LLM providers (OpenAI, Anthropic, Google Gemini, Ollama, custom providers) with provider-specific optimizations for function calling, structured outputs, and streaming. The framework abstracts away provider differences in API signatures, response formats, and capability support. Each provider implementation handles retry logic, timeout management, and client lifecycle (connection pooling, rate limiting). The system supports model switching at runtime without changing agent code.","intents":["I want to switch between different LLM providers (OpenAI → Claude → Gemini) without rewriting my agent","I need to use provider-specific features (OpenAI's parallel function calling, Claude's extended thinking) while maintaining a consistent agent interface","I want built-in retry logic and timeout handling without implementing it myself"],"best_for":["developers building provider-agnostic agents","teams evaluating multiple LLM providers for cost/performance","enterprises with multi-provider strategies (primary + fallback)"],"limitations":["Provider-specific features (e.g., Claude's extended thinking, OpenAI's vision) are not fully abstracted — using them requires provider-specific code paths","Model capability detection is static — no runtime capability negotiation for dynamic feature selection","Retry logic is provider-agnostic and may not respect provider-specific rate limits or quota policies","Structured output support varies by provider — some providers require JSON schema validation, others don't"],"requires":["Python 3.9+","API keys for desired providers (OpenAI, Anthropic, Google, etc.)","Model name/ID for the specific model variant"],"input_types":["model configuration (provider, model name, API key)","messages with text and optional media","tool definitions and structured output schemas"],"output_types":["unified Response objects with model output","streaming event objects","provider-specific metadata (token usage, finish reason)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_7","uri":"capability://automation.workflow.event.streaming.system.with.real.time.execution.tracing.and.observability","name":"event streaming system with real-time execution tracing and observability","description":"Agno's event streaming system emits granular events during agent execution (agent_start, tool_call, tool_result, agent_response, error) that can be consumed via WebSocket, Server-Sent Events (SSE), or callback functions. Each event includes execution context (step ID, timestamp, duration, tokens used) and structured data (input, output, error details). The framework integrates with observability platforms (OpenTelemetry, custom tracing) to export spans and traces for distributed tracing and performance analysis. Event filtering allows consumers to subscribe to specific event types without receiving the full event stream.","intents":["I want to monitor agent execution in real-time and display progress to users","I need detailed execution traces for debugging agent failures and understanding decision-making","I want to export agent execution data to observability platforms (Datadog, New Relic, etc.) for monitoring and alerting"],"best_for":["developers building user-facing agent applications requiring real-time feedback","teams with observability/monitoring requirements","enterprises needing audit trails and execution tracing"],"limitations":["Event streaming adds latency (~50-100ms per event) — high-frequency event emission may impact agent performance","Event consumers must handle backpressure — slow consumers can block agent execution if using synchronous callbacks","Event filtering is client-side — no server-side filtering to reduce event volume","Event retention is not built-in — long-running sessions may accumulate large event logs"],"requires":["Python 3.9+","WebSocket or SSE infrastructure for streaming (or callback function for synchronous consumption)","Optional: observability platform SDK (OpenTelemetry, Datadog, etc.)"],"input_types":["agent execution events (internal to framework)","event filter configuration (event types to subscribe to)"],"output_types":["structured event objects with execution context","OpenTelemetry spans for distributed tracing","custom observability platform events"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_8","uri":"capability://automation.workflow.agentos.runtime.with.rest.api.and.stateless.deployment","name":"agentos runtime with rest api and stateless deployment","description":"Agno's AgentOS is a production runtime that wraps agents and teams in a stateless REST API with automatic endpoint generation, session management, and database auto-discovery. The runtime handles request routing, session persistence, authentication, and scaling without requiring manual API definition. It supports WebSocket connections for streaming responses and real-time event delivery. The framework auto-discovers database backends (PostgreSQL, SQLite) and configures session/knowledge base storage automatically. A Control Plane UI provides monitoring, session management, and agent configuration.","intents":["I want to deploy my agent as a production API without writing Flask/FastAPI boilerplate","I need session management, authentication, and monitoring built-in to my agent deployment","I want to scale agents horizontally with stateless execution and external session storage"],"best_for":["teams deploying agents to production with minimal DevOps overhead","enterprises needing built-in monitoring and session management","developers prototyping agent APIs quickly"],"limitations":["AgentOS is opinionated about API structure — custom endpoint logic requires extending the runtime","Authentication is basic (API keys) — no built-in OAuth2 or SAML support","Database auto-discovery works only for PostgreSQL and SQLite — other databases require manual configuration","Horizontal scaling requires external load balancer — no built-in service mesh or auto-scaling"],"requires":["Python 3.9+","PostgreSQL or SQLite for session/knowledge base storage","Agent or Team instance to wrap","Optional: Docker for containerized deployment"],"input_types":["HTTP requests (JSON body with user message)","WebSocket connections for streaming","session ID for session-scoped execution"],"output_types":["HTTP responses with agent output","WebSocket event stream with real-time updates","session metadata and execution traces"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__cap_9","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.standardized.tool.ecosystems","name":"model context protocol (mcp) server integration for standardized tool ecosystems","description":"Agno integrates with the Model Context Protocol (MCP) standard, allowing agents to discover and use tools from MCP servers without custom integration code. The framework handles MCP client initialization, tool discovery, schema translation to the agent's function-calling format, and result marshaling. Agents can connect to multiple MCP servers simultaneously, with tool namespacing to prevent conflicts. The system supports both local MCP servers (stdio transport) and remote servers (HTTP/WebSocket).","intents":["I want my agent to use tools from MCP servers (e.g., Anthropic's official tools) without writing custom integrations","I need to connect my agent to multiple tool ecosystems (MCP servers) and coordinate their use","I want to standardize tool integration across my organization using MCP"],"best_for":["developers building agents that need access to standardized tool ecosystems","teams adopting MCP as a standard for tool integration","enterprises with multiple tool providers needing a unified integration layer"],"limitations":["MCP server discovery is manual — no built-in registry or auto-discovery mechanism","Tool schema translation may lose provider-specific features (e.g., Claude's tool_choice parameter)","MCP server availability is not monitored — failed servers cause agent execution failures without graceful degradation","Tool namespacing is manual — conflicts between tools from different servers require explicit resolution"],"requires":["Python 3.9+","MCP server instance (local or remote)","MCP client library compatible with Agno"],"input_types":["MCP server configuration (transport type, endpoint)","tool discovery requests"],"output_types":["discovered tools with translated schemas","tool execution results from MCP servers"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agno__headline","uri":"capability://ai.agents.multimodal.ai.agent.framework","name":"multimodal ai agent framework","description":"Agno is a lightweight framework designed for building and managing multimodal AI agents, enabling both single and multi-agent systems with minimal configuration, ideal for developers looking to create intelligent agent platforms.","intents":["best multimodal AI agent framework","multimodal AI agents for team collaboration","how to build AI agents with memory","lightweight framework for AI agents","AI agent systems for production"],"best_for":["developers building AI agents","teams requiring multi-agent orchestration"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["ai-agents"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Agno core framework installed","At least 2 Agent instances configured with distinct roles/tools","Vector database instance (Qdrant, Pinecone, or LanceDB) with network access","API key for embedding model (OpenAI, Anthropic, or local model)","Documents pre-processed into supported formats (PDF, markdown, plain text, URLs)","Model with structured output support (OpenAI, Claude, Gemini)","JSON schema or Python dataclass defining the output structure","Test cases with expected outputs or evaluation criteria","Evaluator functions (Python callables)"],"failure_modes":["Team execution is synchronous by default — parallel agent execution requires explicit async configuration","No built-in load balancing across agent instances — requires external orchestration for horizontal scaling","Agent communication overhead increases with team size; 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