Agno — Build, run, and manage your own agent platform with Agno SDK and AgentOS runtime. Best for Product teams building in-product copilots or chat agents, ML teams needing data labeling and classification tools, AI teams automating document processing and synthetic data generation. Free to use.
Product teams building in-product copilots or chat agentsML teams needing data labeling and classification toolsAI teams automating document processing and synthetic data generationData science teams doing data enrichment and segmentationData engineering teams automating quality audits and reports
Not ideal for
Teams looking for a fully open-source agent framework with no paid tierDevelopers needing extensive pre-built integrations (not listed yet)Projects requiring a large ecosystem of community pluginsTeams wanting a simple chatbot without multi-user or RBAC featuresBudget-constrained teams without clarity on pricing
If you need to productionize multi-agent systems with full control over data and infrastructure, Agno is a strong contender. Its AgentOS runtime and unified control plane stand out, but the documentation is still maturing.
Agno positions itself as a complete agent platform, covering both development (SDK) and production (AgentOS runtime). This dual nature could be appealing for teams that want to avoid stitching together separate tools. The emphasis on owning your data—session, memory, traces—and running in your own cloud is a major selling point for privacy-conscious enterprises. AgentOS claims to work with any framework, model, and cloud, which reduces vendor lock-in. Use cases like data labeling, document processing, and in-product copilots are concrete and suggest real-world traction.
However, the documentation is still sparse. Only a high-level overview is available, with no detailed API references, pricing, or integration list. The website mentions a "beautiful UI" but provides no screenshots. Teams evaluating Agno for complex multi-agent workflows will likely need to dig deeper via the GitHub repository or community. Compared to alternatives like LangChain or CrewAI, Agno seems more focused on the operational side (runtime, RBAC, scheduling) than on agent orchestration patterns.
The lack of explicit pricing or open-source license details is a red flag for budget-conscious teams. The website's only CTAs are to build an agent or try AgentOS, but without clear cost structures. For now, Agno appears aimed at teams that already have a clear need and are willing to engage early.
Skip Agno if Skip Agno if you prefer a fully managed SaaS with no infrastructure setup and want to avoid running your own database and cloud resources.
How likely is Agno to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
60
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Agno
Agno is an SDK and runtime for building your own agent platform. It enables you to build agents, multi-agent teams, and step-based agentic workflows using the Agno SDK. You can run agents as a service using the AgentOS runtime, where every agent becomes an API that runs multi-user, isolated sessions with tracing, scheduling, RBAC, and audit logs. Teams use Agno across a wide range of work, from AI-native software like in-product copilots and agentic widgets, to data labeling, document processing, and employee assistants. AgentOS productionizes agents built with any framework, any model, on any cloud. Your platform runs in your cloud, and your data is stored in your database. You own your session, memory, and trace data, and use it to auto-improve your agents. Compared to other agent frameworks, Agno emphasizes a unified control plane and full ownership of data and infrastructure.
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Key Features
Build agents with Agno SDK
Multi-agent teams and workflows
AgentOS runtime for production
Multi-user isolated sessions
Tracing and audit logs
Role-based access control (RBAC)
Scheduling for agent runs
Agent API (agents become endpoints)
Built-in agent storage
Agent observability
Security and authentication
Data labeling capability
Real-world workflow fit
Concrete scenarios for the personas Agno actually fits — and what changes day-one when you adopt it.
Product engineer building a customer support copilot
You use Agno SDK to create an agent with OpenAI GPT-5.2, connect it to your PostgreSQL database for session memory, and add a human-in-the-loop hook for escalations.
Outcome: Agent handles 80% of queries autonomously, with full traceability and rollback capability. You deploy via AgentOS and monitor with Langfuse.
Data scientist automating weekly data quality reports
You define a workflow that queries your data warehouse, runs validation checks, and generates a report. You schedule it to run every Monday via Agno's scheduler.
Outcome: Reports are automatically produced and stored; you can add new checks by editing a Python component and promoting the new version.
AI researcher building a multi-agent research team
You create a team with a leader agent and three specialist sub-agents (web search, code analysis, document retrieval). Each sub-agent uses a different model provider.
Outcome: Team coordinates to answer complex questions, with the leader routing tasks and aggregating results. All interactions are traced for debugging.
Use Cases
Build a customer support agent that remembers preferences across sessions using Memori.
Deploy a multi-modal document extraction agent that processes images and text.
Create a product copilot that answers questions by navigating Slack, Drive, and codebase in real time.
GPT-5.2GPT-5.5Claude Opus 4.7Gemini 2.5 ProAnthropic ClaudeBedrock modelsxAI GrokLlama 3.3 70B
Limitations
Smaller community than LangChain — fewer third-party examples and integrations to pick from. Renamed from Phidata in 2024, so some search results point to stale docs. Hosted platform is newer and less proven than self-hosted usage patterns. Documentation can be sparse for advanced use cases.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
—
—
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Agno tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
Free
Ideal for
Development teams and hobbyists who want full control and have their own infrastructure to self-host
What this tier adds
Free entry point — you get the full SDK, all integrations, and self-hosted deployment with no usage limits
Agno Cloud
Usage-based
Ideal for
Teams that want managed hosting with observability and collaboration without DevOps overhead
What this tier adds
Adds hosted deployment, team workspaces, and built-in observability; pricing is usage-based
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Usage-based pricing on Agno Cloud can scale unpredictably for high-volume deployments
•Self-hosted requires operational overhead for database and cloud infrastructure
•Advanced observability integrations may incur costs from third-party providers like Langfuse
Where the pricing makes sense
The company stage and team size where Agno's pricing actually pencils out — and where peers do it cheaper.
Agno's open-source offering is free to self-host, making it cost-effective for teams with existing infrastructure. The usage-based cloud pricing is flexible but can surprise heavy users. Compared to LangChain's free tier or Vertex AI's pay-per-use, Agno's value peaks for teams that need production controls without per-seat licenses.
Setup time & first value
How long it actually takes to get something useful out of Agno — broken out by persona, not the marketing-page minute.
For a single agent with default storage (filesystem): under 10 minutes if you have Python installed. For a full production setup with PostgreSQL, tracing, and scheduling: 1-2 hours for a team familiar with their cloud environment. The Scout context agent takes about 30 minutes to run locally.
Switching to or from Agno
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From LangChain: rewrite agent logic using Agno SDK — can reuse existing tool functions by wrapping them as Agno tools
→From CrewAI: migrate team definitions to Agno's Team primitive with minimal changes to prompts and tool lists
→From Phidata (older name): update imports to the new agno package — API is mostly compatible
Migrating out
↗To LangChain: export agent logic and rewrite tool wrappers; loss of Agno's versioned component system
↗To Vertex AI Agent Builder: use the generated agent code as reference for building a hosted agent on Google Cloud
↗To custom SDK: export the agent configuration (model, tools, prompt) as JSON and reimplement in any language
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-05-15: Memori SDK announced — an open-source, backend-agnostic memory layer for agents across multiple databases (HN)
•2025: Renamed from Phidata to Agno — some docs and URLs still redirect
•2026: Added versioned components and rollback capabilities to AgentOS
