abhisheksharma-17/agno

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

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

# 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