--- name: agno description: "Build AI agents, multi-agent teams, and agentic workflows using the Agno framework. MANDATORY TRIGGERS: Agno, agno-agi, AgentOS, any mention of the Agno framework. Also trigger when the user wants to build AI agents with tools/memory/knowledge, create multi-agent systems, RAG pipelines, reasoning agents, agentic workflows, or deploy agents to production. Trigger even if the user just says 'build me an agent', 'create an AI assistant', or 'make a chatbot' — if Agno is anywhere in their stack or project dependencies. When in doubt about whether to use this skill for agent-building tasks, use it." license: MIT metadata: version: "1.2.0" author: Abhishek Sharma tags: ["agno", "ai-agents", "multi-agent", "agentos", "rag", "workflows", "mcp"] --- # Agno Framework — Skill Router Agno is an open-source framework for building, deploying, and managing multi-agent systems. This skill is organized into focused reference files. Read only what the current task requires. ## Reference Files | Reference | File | Read When | |-----------|------|-----------| | **Agents** | `references/agents.md` | Creating agents, tools, structured output, storage, memory, knowledge, state, streaming | | **Teams** | `references/teams.md` | Multi-agent coordination, team modes (coordinate, route, broadcast, tasks), delegation | | **Workflows** | `references/workflows.md` | Orchestrating agents/teams/functions as repeatable pipelines with sequential, parallel, conditional, loop, and router patterns | | **Workflow Patterns** | `references/workflow-patterns.md` | Full code examples for every workflow pattern (sequential, parallel, conditional, loop, router, mixed, background execution, conversational) | | **Input / Output** | `references/input-output.md` | Structured input (Pydantic validation), structured output (typed responses), multimodal (images, audio, video, files), streaming, output/parser models, expected output | | **Models** | `references/models.md` | Model providers (40+ supported), model-as-string syntax ("provider:model_id"), error handling & retries, response caching, multimodal compatibility matrix, OpenAI-compatible models (OpenAILike, OpenResponses) | | **Database** | `references/database.md` | All storage backends (Postgres sync/async, MongoDB, Redis, Supabase, SQLite, DynamoDB, MySQL), chat history, session management, connection strings | | **Memory** | `references/memory.md` | Automatic vs agentic memory, MemoryManager, MemoryTools, memory optimization, multi-user isolation, agents sharing memory, teams with memory, best practices | | **Knowledge** | `references/knowledge.md` | RAG pipelines, vector databases (PgVector, Chroma, LanceDB, Pinecone, Qdrant, 20+ options), embedders, readers (PDF, CSV, web, YouTube, etc.), chunking strategies, search types (vector/keyword/hybrid), filtering, reranking, custom retrievers, contents DB | | **Learning** | `references/learning.md` | Learning Machines, 6 learning stores (user profile, user memory, session context, entity memory, learned knowledge, decision log), learning modes (Always/Agentic/Propose), custom schemas, namespaces, curator maintenance | | **Skills & Tools** | `references/agno-skills.md` | Agno Skills (SKILL.md packages, scripts, references, progressive loading), quick tool overview | | **Tools (Deep Dive)** | `references/tools.md` | Comprehensive tools reference — creating tools, @tool decorator, custom Toolkits, hooks, exceptions, caching, RunContext, MCP, and all 120+ pre-built toolkits organized by category (search, data, web, dev, comms, media, productivity) | | **Reasoning** | `references/reasoning.md` | Three reasoning approaches: Reasoning Models (GPT-5, DeepSeek-R1, Claude extended thinking), ReasoningTools (think/analyze), Reasoning Agents (reasoning=True), split reasoning+response models, KnowledgeTools, MemoryTools, WorkflowTools, streaming events | | **Multimodal** | `references/multimodal.md` | Image input/generation (DALL-E, Gemini), audio input/output (transcription, speech, voice config), video analysis (Gemini), file/PDF processing, media classes (Image, Audio, Video, File), cross-modal pipelines, model compatibility | | **Context & Sessions** | `references/context.md` | Sessions, chat history (3 patterns), session summaries, context engineering (system/user message building, few-shot), workflow sessions, persistence (database backends, schema) | | **State Management** | `references/state.md` | Session state across agents/teams/workflows — basic state with tools, agentic state (auto), team shared state, workflow step state, multi-user isolation, overwrite vs merge, state hooks, cross-session search | | **Context Management** | `references/context-mgmt.md` | System message construction, context enrichment flags, chat history controls, context compression (BETA), dependency injection, few-shot learning, prompt caching, token tracking, debug mode | | **Guardrails** | `references/guardrails.md` | Input validation and safety — PII detection/masking, prompt injection defense, OpenAI content moderation, custom guardrails (BaseGuardrail), hooks integration, exceptions (InputCheckError, CheckTrigger), agent + team usage | | **Human-in-the-Loop** | `references/hitl.md` | Human oversight of agent execution — user confirmation (approve/reject tools), user input (collect field values), dynamic user input (UserControlFlowTools, agent-driven), external tool execution (sandboxed), async/streaming, while-loop pattern | | **Evals** | `references/evals.md` | Evaluation framework — accuracy (LLM-as-a-judge), performance (latency/memory), reliability (tool call verification), agent-as-judge (custom criteria scoring), AgentOS integration, database persistence | | **Hooks** | `references/hooks.md` | Pre-hooks and post-hooks — execute custom logic before/after Agent/Team runs, input validation/transformation, output validation/transformation, @hook decorator, background execution, exceptions (InputCheckError, OutputCheckError, CheckTrigger) | | **Tracing** | `references/tracing.md` | OpenTelemetry-based observability — setup_tracing(), traces & spans, agent/team/workflow tracing, batch processing, DB query functions (get_trace, get_traces, get_span, get_spans), AgentOS tracing, performance monitoring | | **Run Cancellation** | `references/run-cancellation.md` | Cancel running agent/team/workflow executions — cancel_run(run_id), streaming cancellation events (RunEvent.run_cancelled, TeamRunEvent.run_cancelled, WorkflowRunEvent.workflow_cancelled), RunStatus.cancelled, API endpoints | | **AgentOS** | `references/agentos.md` | Production runtime — AgentOS class, 50+ API endpoints, SSE streaming, control plane (os.agno.com), configuration (YAML/AgentOSConfig), security (Basic Auth, RBAC/JWT), background hooks, custom lifespan, Registry for visual builder | | **Culture** | `references/culture.md` | Experimental shared knowledge layer — universal principles, best practices, 3 management modes (automatic, agentic, manual), CultureManager, CulturalKnowledge data model, seeding organizational standards | | **Custom Logging** | `references/custom-logging.md` | Custom loggers — configure_agno_logging(), per-component loggers (agent/team/workflow), file logging, named loggers (agno, agno-team, agno-workflow convention) | | **Observability** | `references/observability.md` | Third-party monitoring platforms — AgentOps, Arize Phoenix, Atla, LangDB, Langfuse, LangSmith, Langtrace, LangWatch, Maxim, OpenLIT, Traceloop, Weave (WandB), OpenInference instrumentation, OTLP export | | **Integrations** | `references/integrations.md` | Platform integrations — Discord bot (DiscordClient, thread creation, media support), Memori (open-source memory layer, fact extraction, entity search) | | **Migrations** | `references/migrations.md` | Database migrations (MigrationManager, AgentOS endpoints, upgrade/downgrade, v1→v2), Workflows 2.0 migration (class-based → step-based, state management, streaming) | | **Deploy** | `references/deploy.md` | Deployment templates (Docker, Railway, AWS ECS), pre-built solutions (Dash, Scout, Gcode), apps (10 agent apps, team apps, workflow apps), interfaces (Slack, Discord, WhatsApp, Telegram, MCP, AG-UI) | | **Database Providers** | `references/database-providers.md` | All 18 database backends — PostgreSQL/MySQL/SQLite (sync+async), MongoDB, Redis, DynamoDB, Firestore, SurrealDB, Neon, Supabase, SingleStore, GCS, JSON, In-Memory — classes, imports, connection strings, Docker commands | | **Vector Store Providers** | `references/vector-store-providers.md` | All 14+ vector databases — PgVector, ChromaDB, LanceDB, Pinecone, Qdrant, Weaviate, Milvus, MongoDB Atlas, SingleStore, Cassandra, ClickHouse, Upstash, AstraDB — classes, imports, search types | | **Embedder Providers** | `references/embedder-providers.md` | All 12+ embedding providers — OpenAI, Azure OpenAI, Google, Voyage, Cohere, Mistral, Ollama, HuggingFace, Together, Fireworks, SentenceTransformer, FastEmbed — classes, imports, default models | | **FAQs** | `references/faqs.md` | Common troubleshooting — env vars setup, Workflow vs Team decision guide, structured outputs vs JSON mode, TPM rate limiting, model switching, AgentOS connection issues, Docker errors, JWT auth, TablePlus | ## Install Agno ```bash uv pip install -U agno # Core uv pip install -U agno openai # + OpenAI uv pip install -U agno anthropic # + Anthropic uv pip install -U 'agno[os]' # + AgentOS runtime ``` ## Install This Skill ```bash # Via Smithery (any platform) smithery install agno # Manual — copy this folder to your platform's skill directory: # Claude Code: .claude/skills/agno/ or ~/.claude/skills/agno/ # Antigravity: .agent/skills/agno/ or ~/.gemini/antigravity/skills/agno/ # Gemini CLI: .gemini/skills/agno/ or ~/.gemini/skills/agno/ # Cursor: .cursor/skills/agno/ or ~/.cursor/skills/agno/ # Codex: .codex/skills/agno/ or ~/.codex/skills/agno/ # Windsurf: .windsurf/skills/agno/ or ~/.codeium/windsurf/skills/agno/ # Trae: .trae/skills/agno/ or ~/.trae/skills/agno/ # Agno native (load from code) # from agno.skills import Skills, LocalSkills # agent = Agent(skills=Skills(loaders=[LocalSkills("/path/to/agno-skill")])) ``` ## Version Tracking - **Skill version:** 1.2.0 | **Agno tracked:** 2.5.3 | **Snapshot:** 2026-02-21 - Version metadata: `VERSION.json` - Update checker: `python scripts/check-updates.py` (checks PyPI, docs sitemap, stale files, integrity) - Changelog: `CHANGELOG.md` ## Docs - https://docs.agno.com/introduction - https://docs.agno.com/examples/introduction (2000+ examples) - https://github.com/agno-agi/agno