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Agno

Name: Agno
Brand: Agno
Availability: InStock

High-performance Python framework for multi-modal agents and teams, formerly known as Phidata.

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In Plain English

High-performance Python framework for multi-modal agents and teams, formerly known as Phidata.

Overview

Agno (the rebrand of Phidata) is a Python framework for building agents that combine LLMs, tools, memory, knowledge bases, and reasoning into production systems. The library positions itself as the 'fastest' agent framework — agents instantiate in microseconds, run with minimal overhead, and stream tokens with no buffering — and it ships first-class support for multi-modal inputs (text, image, audio, video) and reasoning models. Agents can be composed into teams with route, coordinate, or collaborate modes, mirroring patterns from CrewAI and AutoGen but with a smaller API surface. Agno includes built-in vector store integrations (PgVector, LanceDB, Qdrant, ChromaDB), a 'Knowledge' abstraction for RAG over PDFs, websites, and docs, and Memory v2 with summarization and forgetting. The included Agent UI (free, MIT-licensed) provides a chat surface for prototyping any agent without writing a frontend. Agno Cloud extends this with monitoring, multi-tenant deployments, hosted memory and knowledge stores, and a managed API gateway. The framework supports OpenAI, Anthropic, Google, Groq, Cerebras, DeepSeek, Mistral, AWS Bedrock, Azure, Ollama, vLLM, and any OpenAI-compatible endpoint, making it model-agnostic by default. Adoption has grown quickly inside companies that need a leaner alternative to LangChain.

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Editorial Review

Agno (formerly Phidata) bundles agent memory, knowledge bases, tools, and multi-agent orchestration into a single Python framework. Faster and simpler than LangChain for most agent use cases, with a production runtime (AgentOS) for teams that need managed hosting. The open-source tier covers most needs.

Key Features

Agno delivers exceptional performance with 529x faster agent instantiation compared to LangGraph, 57x faster than PydanticAI, and 70x faster than CrewAI. The framework maintains a 24x lower memory footprint than competing solutions, enabling rapid scaling and reduced infrastructure costs in production environments.+

Transform agents into production-ready APIs with the AgentOS runtime system. Deploy agents, teams, and workflows as scalable services with built-in monitoring, session management, conversation history, and enterprise-grade infrastructure that handles production workloads from day one.+

Built-in security architecture featuring JWT authentication, role-based access control (RBAC), request-level isolation, and complete data sovereignty. All data processing occurs within your infrastructure, ensuring zero data egress, unlimited retention, and compliance with strict privacy regulations.+

Manage your entire agent ecosystem through a secure web interface providing real-time chat capabilities, distributed tracing, performance monitoring, memory management, knowledge base administration, and system evaluations—all while maintaining complete data privacy.+

Create sophisticated agent teams and workflows with intelligent routing, collaboration protocols, automated task distribution, and shared knowledge contexts. Agents work together while maintaining individual memory contexts and specialized capabilities.+

Pricing Plans

Open Source

Agno Cloud Free

Agno Cloud Pro

Custom

See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with Agno

1Install Agno framework via pip with 'pip install agno' and import necessary modules for building your first agent
2Create a basic agent using the Agent() class with your preferred language model (Claude, GPT, etc.) and configure tools, instructions, and memory settings
3Set up AgentOS runtime to deploy your agent as a production API using the AgentOS() class and get_app() method for FastAPI integration
4Configure the built-in control plane for monitoring and management by accessing the web interface at your deployment URL with proper authentication
5Integrate memory and knowledge systems using built-in database connectors for persistent agent learning, conversation history, and knowledge base management

Ready to start? Try Agno →

Best Use Cases

🎯

Production agents that need low overhead and high throughput

⚡

Multi-modal applications combining text, vision, and audio

🔧

RAG systems with built-in knowledge and memory abstractions

🚀

Teams that want a leaner alternative to LangChain

Integration Ecosystem

18 integrations

Agno works with these platforms and services:

🧠 LLM Providers

OpenAIAnthropicgroqollama

📊 Vector Databases

PineconeQdrantWeaviate

☁️ Cloud Platforms

AWSGCPAzureRailway

💬 Communication

Slacktelegramwhatsapp

🗄️ Databases

PostgreSQLMySQLsqlite

📈 Monitoring

built-in-control-plane

View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Agno doesn't handle well:

⚠Python-only framework restricts adoption for teams working in JavaScript, Go, or other programming languages
⚠Newer market presence results in fewer community resources, tutorials, and third-party extensions compared to established frameworks like LangChain
⚠Enterprise security features and self-hosted control plane capabilities are only available in higher-tier paid plans
⚠Significant learning curve for teams transitioning from traditional development approaches to agent-based architectures and multi-agent paradigms
⚠Limited integration ecosystem compared to mature platforms, requiring custom development for specialized tools and enterprise systems
⚠Performance benchmarks may vary significantly based on specific use cases, infrastructure configuration, and agent complexity patterns

Pros & Cons

✓ Pros

✓Significantly lighter API than LangChain — fewer abstractions to learn
✓First-class multi-modal support (text, image, audio, video) in the base Agent
✓Free, polished Agent UI ships in the repo for instant prototyping
✓Microsecond agent cold-start makes per-request agent instantiation viable
✓Model-agnostic with 20+ providers including Groq, Cerebras, Ollama, and vLLM

✗ Cons

✗Smaller ecosystem and community than LangChain or CrewAI
✗Documentation gaps in advanced team-mode behaviors and edge cases
✗Agno Cloud pricing is opaque ('contact us') — no public tier for serious use
✗Recent rebrand from Phidata means older blog posts and tutorials reference outdated APIs

Frequently Asked Questions

How does Agno achieve 529x faster agent instantiation than LangGraph?+

Agno's performance advantage comes from its optimized architecture that minimizes computational overhead during agent creation and execution. The framework uses efficient memory management, streamlined initialization processes, and optimized data structures that significantly reduce the time and resources required for agent instantiation compared to other frameworks.

Can I deploy Agno agents in my own cloud infrastructure?+

Yes, Agno is designed for complete data sovereignty. All agents run entirely within your own cloud infrastructure (AWS, GCP, Railway, or any other provider), ensuring no data leaves your environment. This eliminates data egress costs, provides unlimited retention, and maintains full compliance control while ensuring complete privacy.

What's the difference between the Agno SDK and AgentOS?+

The Agno SDK is the development framework for building agents, teams, and workflows using Python code. AgentOS is the production runtime that transforms your agents into scalable APIs with built-in monitoring, security, and management capabilities. The SDK handles development, while AgentOS handles production deployment and operations.

Does Agno support multi-agent collaboration and workflows?+

Yes, Agno provides sophisticated multi-agent orchestration through Teams and Workflows. Agents can collaborate with shared context, specialized roles, and intelligent routing. The framework handles complex coordination patterns including parallel processing, conditional routing, and hierarchical agent structures with persistent memory and knowledge sharing.

How does Agno pricing work for additional seats and connections?+

The Pro plan includes 4 seats and 1 live connection for $150/month. Additional seats cost $30/month each, and additional live connections cost $95/month each. The free tier supports unlimited local development but doesn't include the live control plane or production support features.

What security measures does Agno implement for enterprise use?+

Agno implements full enterprise security with JWT authentication, role-based access control (RBAC), request-level isolation, and complete data sovereignty. All processing occurs within your infrastructure, eliminating external dependencies and ensuring compliance with strict data privacy and security regulations.

🔒 Security & Compliance

❌

SOC2

✅

GDPR

Yes

❌

HIPAA

✅

SSO

Yes

✅

Self-Hosted

Yes

✅

On-Prem

Yes

✅

RBAC

Yes

✅

Audit Log

Yes

✅

API Key Auth

Yes

✅

Open Source

Yes

✅

Encryption at Rest

Yes

✅

Encryption in Transit

Yes

Data Retention: configurable

🦞

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What's New in 2026

As of 2026, Agno is being marketed around the pairing of its open-source Python framework with AgentOS, positioned as the first enterprise-ready agentic operating system. The emphasis is on production readiness: fast agent instantiation, low memory overhead, scalable runtime behavior, and private-by-default deployment inside the customer's own cloud. The product is accumulating significant community signal, with the site citing 36,000+ aggregate ratings, reflecting growing adoption as teams move agent projects from prototype to production in 2026.

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Overview

Editorial Review

Key Features

Getting Started with Agno

1Install Agno framework via pip with 'pip install agno' and import necessary modules for building your first agent

2Create a basic agent using the Agent() class with your preferred language model (Claude, GPT, etc.) and configure tools, instructions, and memory settings

3Set up AgentOS runtime to deploy your agent as a production API using the AgentOS() class and get_app() method for FastAPI integration

4Configure the built-in control plane for monitoring and management by accessing the web interface at your deployment URL with proper authentication

5Integrate memory and knowledge systems using built-in database connectors for persistent agent learning, conversation history, and knowledge base management

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Agno doesn't handle well:

⚠Python-only framework restricts adoption for teams working in JavaScript, Go, or other programming languages

⚠Newer market presence results in fewer community resources, tutorials, and third-party extensions compared to established frameworks like LangChain

⚠Enterprise security features and self-hosted control plane capabilities are only available in higher-tier paid plans

⚠Significant learning curve for teams transitioning from traditional development approaches to agent-based architectures and multi-agent paradigms

⚠Limited integration ecosystem compared to mature platforms, requiring custom development for specialized tools and enterprise systems

⚠Performance benchmarks may vary significantly based on specific use cases, infrastructure configuration, and agent complexity patterns

Pros & Cons

✓ Pros

✓Significantly lighter API than LangChain — fewer abstractions to learn
✓First-class multi-modal support (text, image, audio, video) in the base Agent
✓Free, polished Agent UI ships in the repo for instant prototyping
✓Microsecond agent cold-start makes per-request agent instantiation viable
✓Model-agnostic with 20+ providers including Groq, Cerebras, Ollama, and vLLM

✗ Cons

✗Smaller ecosystem and community than LangChain or CrewAI
✗Documentation gaps in advanced team-mode behaviors and edge cases
✗Agno Cloud pricing is opaque ('contact us') — no public tier for serious use
✗Recent rebrand from Phidata means older blog posts and tutorials reference outdated APIs