--- name: agno-e47a28e327d8 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 --- Source attribution: Abhishek Sharma Original project: https://github.com/NeverSight/skills_feed/blob/8db54595422f40924a0e2c3365b2f40e9aba0ad4/data/skills-md/abhisheksharma-17/skills-graph/agno/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Abhishek Sharma Original project: https://github.com/NeverSight/skills_feed/blob/8db54595422f40924a0e2c3365b2f40e9aba0ad4/data/skills-md/abhisheksharma-17/skills-graph/agno/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management, Data & Analytics Roles: ML / AI Engineer, Product Manager, Growth Product Manager, Data Scientist, Backend Developer Specialties: API, Database, Backend Tools: Supabase, MongoDB, PostgreSQL, Redis, Firebase Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 56d4e6ae772b1c58ed1529c72ba10fb139b39f8bacbb9e010de91a8523b4e3fc