Agno Pricing & Plans 2026

Name: Agno
Brand: Agno
Availability: InStock

Complete pricing guide for Agno. Compare all plans, analyze costs, and find the perfect tier for your needs.

Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Agno is worth it →

🆓Free Tier Available

💎2 Paid Plans

⚡No Setup Fees

Choose Your Plan

Open Source Framework

✓Full Agno Python framework
✓Agent, team, tool, memory, and knowledge base primitives
✓Model-agnostic LLM, vector store, and tool integrations
✓Community support via GitHub and public channels
✓Self-managed local and cloud deployment

Start Free Trial →

AgentOS (Enterprise Runtime)

Custom

✓Production agentic operating system for Agno agents
✓Private-by-default deployment inside the customer's own cloud
✓Enterprise security, access control, and observability
✓Scalable runtime for multi-agent workloads
✓Commercial support and SLAs

Start Free Trial →

Pricing sourced from Agno · Last verified March 2026

Feature Comparison

Features	Open Source Framework	AgentOS (Enterprise Runtime)
Full Agno Python framework	✓	✓
Agent, team, tool, memory, and knowledge base primitives	✓	✓
Model-agnostic LLM, vector store, and tool integrations	✓	✓
Community support via GitHub and public channels	✓	✓
Self-managed local and cloud deployment	✓	✓
Production agentic operating system for Agno agents	—	✓
Private-by-default deployment inside the customer's own cloud	—	✓
Enterprise security, access control, and observability	—	✓
Scalable runtime for multi-agent workloads	—	✓
Commercial support and SLAs	—	✓

Is Agno Worth It?

✅ Why Choose Agno

• Open-source Python framework means no licensing fees to adopt, and teams can read, fork, and audit the code rather than depending on a vendor-controlled black box
• Paired with AgentOS runtime so the same code that runs locally can be promoted to a production execution environment without rewriting orchestration, state, or observability layers
• Private-by-default deployment model runs agents inside the customer's own cloud, which materially simplifies security review for regulated industries handling PII or proprietary data
• Model-agnostic architecture lets teams swap LLM providers, vector stores, and tool backends without rewriting agent logic, reducing lock-in risk as the underlying model landscape shifts
• Performance-focused design with fast agent instantiation and low memory overhead makes it practical for high-throughput or latency-sensitive production workloads rather than only research prototypes
• First-class multi-agent coordination primitives for teams of specialist agents, memory, knowledge bases, and structured reasoning reduce the amount of scaffolding engineers need to hand-write

⚠️ Consider This

• Python-only framework, so teams working primarily in TypeScript, Go, Java, or other backend languages need a service boundary to integrate rather than using Agno natively
• AgentOS is the commercial differentiator and pricing is not fully transparent on the marketing site — larger deployments require a sales conversation to understand total cost
• The agent framework ecosystem is young and rapidly shifting, so patterns, APIs, and best practices are still maturing and may change between releases
• Enterprise features like advanced access controls, private cloud deployment, and premium support sit behind paid tiers, meaning the free open-source experience is not feature-equivalent to the production offering
• Operating multi-agent systems still requires non-trivial expertise in prompt engineering, evaluation, and cost monitoring — Agno streamlines the plumbing but does not remove the hard parts of building reliable agents

What Users Say About Agno

👍 What Users Love

✓Open-source Python framework means no licensing fees to adopt, and teams can read, fork, and audit the code rather than depending on a vendor-controlled black box
✓Paired with AgentOS runtime so the same code that runs locally can be promoted to a production execution environment without rewriting orchestration, state, or observability layers
✓Private-by-default deployment model runs agents inside the customer's own cloud, which materially simplifies security review for regulated industries handling PII or proprietary data
✓Model-agnostic architecture lets teams swap LLM providers, vector stores, and tool backends without rewriting agent logic, reducing lock-in risk as the underlying model landscape shifts
✓Performance-focused design with fast agent instantiation and low memory overhead makes it practical for high-throughput or latency-sensitive production workloads rather than only research prototypes
✓First-class multi-agent coordination primitives for teams of specialist agents, memory, knowledge bases, and structured reasoning reduce the amount of scaffolding engineers need to hand-write

👎 Common Concerns

⚠Python-only framework, so teams working primarily in TypeScript, Go, Java, or other backend languages need a service boundary to integrate rather than using Agno natively
⚠AgentOS is the commercial differentiator and pricing is not fully transparent on the marketing site — larger deployments require a sales conversation to understand total cost
⚠The agent framework ecosystem is young and rapidly shifting, so patterns, APIs, and best practices are still maturing and may change between releases
⚠Enterprise features like advanced access controls, private cloud deployment, and premium support sit behind paid tiers, meaning the free open-source experience is not feature-equivalent to the production offering
⚠Operating multi-agent systems still requires non-trivial expertise in prompt engineering, evaluation, and cost monitoring — Agno streamlines the plumbing but does not remove the hard parts of building reliable agents

Pricing FAQ

What is the difference between Agno and Phidata?

Agno is the successor to Phidata, rebuilt from the ground up with a production-first architecture. While Phidata focused primarily on the development framework, Agno adds the AgentOS runtime for serving agents as scalable production APIs and the Control Plane for monitoring and management. Existing Phidata users can migrate by updating their imports and dependencies.

How does Agno compare to LangChain and LangGraph in performance?

Agno significantly outperforms both. Benchmarks show 529x faster agent instantiation than LangGraph and 24x lower memory footprint. This translates to lower infrastructure costs and faster response times at scale. LangChain offers a broader ecosystem of integrations, but Agno's performance advantage makes it the better choice for production deployments where latency and cost matter.

Can I use Agno with any LLM provider?

Yes, Agno supports all major LLM providers including OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), Mistral, and local models via Ollama. You can switch providers by changing the model parameter in your agent configuration without modifying your application logic.

Is Agno free to use in production?

Yes, the core Agno framework and AgentOS runtime are fully open-source under the MPL-2.0 license with no usage restrictions. You can build, deploy, and run agents in production at any scale for free. The paid Pro ($150/month) and Enterprise tiers add managed Control Plane access, live monitoring, team collaboration, and dedicated support.

Does Agno support multi-agent systems?

Yes, Agno provides first-class support for multi-agent systems through its Teams primitive. Teams enable multiple specialized agents to collaborate with shared memory pools, dynamic routing, and coordinated decision-making. Reference implementations like the Investment Team demonstrate production-ready multi-agent coordination patterns.

Where is my data stored when using Agno?

All data remains in your own infrastructure. Agno stores sessions, memories, knowledge bases, and execution traces in your database (SQLite for development, PostgreSQL for production). No data is sent to Agno's servers. The Control Plane connects to your running AgentOS instance — it reads data from your infrastructure rather than storing it centrally.

Ready to Get Started?

AI builders and operators use Agno to streamline their workflow.

Try Agno Now →

More about Agno

Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Compare Agno Pricing with Alternatives

LangChain Pricing

The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.

Compare Pricing →

CrewAI Pricing

Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.

Compare Pricing →

Microsoft AutoGen Pricing

Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

Compare Pricing →

LangGraph Pricing

Graph-based workflow orchestration framework for building reliable, production-ready AI agents with deterministic state machines, human-in-the-loop capabilities, and comprehensive observability through LangSmith integration.

Compare Pricing →

Agno Pricing & Plans 2026

Complete pricing guide for Agno. Compare all plans, analyze costs, and find the perfect tier for your needs.

🆓Free Tier Available

💎2 Paid Plans

⚡No Setup Fees

Choose Your Plan

Open Source Framework

✓Full Agno Python framework
✓Agent, team, tool, memory, and knowledge base primitives
✓Model-agnostic LLM, vector store, and tool integrations
✓Community support via GitHub and public channels
✓Self-managed local and cloud deployment

Start Free Trial →

AgentOS (Enterprise Runtime)

Custom

✓Production agentic operating system for Agno agents
✓Private-by-default deployment inside the customer's own cloud
✓Enterprise security, access control, and observability
✓Scalable runtime for multi-agent workloads
✓Commercial support and SLAs

Start Free Trial →

Pricing sourced from Agno · Last verified March 2026

Feature Comparison

Features	Open Source Framework	AgentOS (Enterprise Runtime)
Full Agno Python framework	✓	✓
Agent, team, tool, memory, and knowledge base primitives	✓	✓
Model-agnostic LLM, vector store, and tool integrations	✓	✓
Community support via GitHub and public channels	✓	✓
Self-managed local and cloud deployment	✓	✓
Production agentic operating system for Agno agents	—	✓
Private-by-default deployment inside the customer's own cloud	—	✓
Enterprise security, access control, and observability	—	✓
Scalable runtime for multi-agent workloads	—	✓
Commercial support and SLAs	—	✓

Is Agno Worth It?

✅ Why Choose Agno

• Open-source Python framework means no licensing fees to adopt, and teams can read, fork, and audit the code rather than depending on a vendor-controlled black box
• Paired with AgentOS runtime so the same code that runs locally can be promoted to a production execution environment without rewriting orchestration, state, or observability layers
• Private-by-default deployment model runs agents inside the customer's own cloud, which materially simplifies security review for regulated industries handling PII or proprietary data
• Model-agnostic architecture lets teams swap LLM providers, vector stores, and tool backends without rewriting agent logic, reducing lock-in risk as the underlying model landscape shifts
• Performance-focused design with fast agent instantiation and low memory overhead makes it practical for high-throughput or latency-sensitive production workloads rather than only research prototypes
• First-class multi-agent coordination primitives for teams of specialist agents, memory, knowledge bases, and structured reasoning reduce the amount of scaffolding engineers need to hand-write

⚠️ Consider This

• Python-only framework, so teams working primarily in TypeScript, Go, Java, or other backend languages need a service boundary to integrate rather than using Agno natively
• AgentOS is the commercial differentiator and pricing is not fully transparent on the marketing site — larger deployments require a sales conversation to understand total cost
• The agent framework ecosystem is young and rapidly shifting, so patterns, APIs, and best practices are still maturing and may change between releases
• Enterprise features like advanced access controls, private cloud deployment, and premium support sit behind paid tiers, meaning the free open-source experience is not feature-equivalent to the production offering
• Operating multi-agent systems still requires non-trivial expertise in prompt engineering, evaluation, and cost monitoring — Agno streamlines the plumbing but does not remove the hard parts of building reliable agents

What Users Say About Agno

👍 What Users Love

✓Open-source Python framework means no licensing fees to adopt, and teams can read, fork, and audit the code rather than depending on a vendor-controlled black box
✓Paired with AgentOS runtime so the same code that runs locally can be promoted to a production execution environment without rewriting orchestration, state, or observability layers
✓Private-by-default deployment model runs agents inside the customer's own cloud, which materially simplifies security review for regulated industries handling PII or proprietary data
✓Model-agnostic architecture lets teams swap LLM providers, vector stores, and tool backends without rewriting agent logic, reducing lock-in risk as the underlying model landscape shifts
✓Performance-focused design with fast agent instantiation and low memory overhead makes it practical for high-throughput or latency-sensitive production workloads rather than only research prototypes
✓First-class multi-agent coordination primitives for teams of specialist agents, memory, knowledge bases, and structured reasoning reduce the amount of scaffolding engineers need to hand-write

👎 Common Concerns

⚠Python-only framework, so teams working primarily in TypeScript, Go, Java, or other backend languages need a service boundary to integrate rather than using Agno natively
⚠AgentOS is the commercial differentiator and pricing is not fully transparent on the marketing site — larger deployments require a sales conversation to understand total cost
⚠The agent framework ecosystem is young and rapidly shifting, so patterns, APIs, and best practices are still maturing and may change between releases
⚠Enterprise features like advanced access controls, private cloud deployment, and premium support sit behind paid tiers, meaning the free open-source experience is not feature-equivalent to the production offering
⚠Operating multi-agent systems still requires non-trivial expertise in prompt engineering, evaluation, and cost monitoring — Agno streamlines the plumbing but does not remove the hard parts of building reliable agents