Agno

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Overview

Agno is a platform for building, running, and managing AI agent platforms. It consists of a 3-layer architecture: SDK for building agents, multi-agent teams, and workflows; Runtime for running them as production services with session management, tracing, and RBAC; and Control Plane for platform management via AgentOS UI. Everything except the Control Plane is free and open-source. It targets developers creating production AI agent systems, supporting multimodal agents with memory, knowledge, tools, and reasoning.

Key Features

  • Production API - 50+ endpoints with SSE and websockets for building products on agent platforms.
  • Storage - Postgres for sessions/memory, Clickhouse for traces.
  • 100+ Integrations - Pre-built toolkits for integrating with external tools.
  • Context Providers - Access live data from Slack, Drive, wikis, MCP, and custom sources.
  • Observability - OpenTelemetry tracing, run history, and audit logs.
  • Security - JWT-based RBAC and multi-tenant isolation.
  • Interfaces - Expose agents via Slack, Telegram, WhatsApp, Discord, AG-UI, A2A.
  • Scheduling - Cron-based scheduling and background jobs.
  • User Profiles - Structured storage for user static facts and dynamic context.

Pricing

PlanPriceIncludes
Free TierFreeSDK, Runtime, open-source components, self-hosted storage.
Control PlanePaid (usage-based)AgentOS UI for managing platforms, hosted control plane.
EnterpriseCustomAdvanced RBAC, support, custom deployments.

Platforms & Requirements

Deploys as containerized images on any cloud platform supporting Docker. Requires Postgres for operational data and optionally Clickhouse for traces. No specific hardware minimums stated; performance optimized for low memory footprint.

Integrations & Ecosystem

  • Supermemory (memory API)
  • Tavily (web search)
  • OpenAI models
  • Slack, Telegram, WhatsApp, Discord
  • Postgres, Clickhouse
  • OpenTelemetry
  • 100+ toolkits for databases/APIs

Alternatives

AppDifference
LangChainMore modular LLM chaining library; less focus on production runtime and control plane.
CrewAIEmphasizes multi-agent collaboration; lighter on production deployment features.
AutoGenMicrosoft-backed conversational agents; requires more custom orchestration.
HaystackSpecialized in RAG pipelines; narrower scope than full agent platforms.

Reputation

Agno is recognized for its speed, low memory footprint, and model-agnostic design, making it suitable for production multi-agent systems. Users praise the simple Python-based setup and extensive integrations. Some note it's relatively new (released 2023), with community growth ongoing but smaller than established frameworks.

Sources (7)
  1. https://supermemory.ai/docs/integrations/agno
  2. https://docs.agno.com/examples/learning/user-profile/overview
  3. https://docs.agno.com
  4. https://www.digitalocean.com/community/conceptual-articles/agno-fast-scalable-multi-agent-framework
  5. https://www.youtube.com/watch?v=XN6dSSx6Ehg
  6. https://docs.agno.com/learning/stores/user-profile
  7. https://docs.tavily.com/documentation/integrations/agno