Phidata
FreemiumOpen-source framework for building AI agents with memory, knowledge, and tools. Create production-ready agents that can search the web, analyze data, and take actions.
What does this tool do?
Agno (formerly Phidata) is an open-source framework for building production-ready AI agents with built-in memory, knowledge management, and tool integration. It positions itself as a faster and more lightweight alternative to LangGraph and CrewAI, offering a unified API for synchronous and asynchronous operations. The framework includes AgentOS, a production runtime that lets developers deploy agents as scalable APIs, plus a browser-based control plane for monitoring, tracing, and managing agent systems. Key differentiators include JWT/RBAC security, database-agnostic architecture (supports any database), multi-interface deployment (Slack, Telegram, WhatsApp), and claimed performance advantages: 529x faster agent instantiation than LangGraph and 24x lower memory footprint. The framework supports teams of agents, workflows with routing logic, and human-in-the-loop guardrails.
AI analysis from Feb 23, 2026
Key Features
- Agent framework with memory, knowledge, tools, guardrails, and human-in-the-loop controls
- AgentOS runtime for deploying agents, teams, and workflows as a single scalable API
- Built-in control plane UI for real-time chat, tracing, monitoring, and agent evaluation across accuracy, reliability, and performance dimensions
- Multi-agent teams with collaborative instructions and shared memory via persistent databases
- Workflow engine with routing logic to conditionally direct requests between specialized agents
- Multi-interface deployment: native support for web, Slack, WhatsApp, Telegram, and custom APIs
- Self-learning agents with persistent memory and knowledge management without data egress
- Security primitives: JWT authentication, role-based access control (RBAC), and request-level isolation
Use Cases
- 1Building internal knowledge base chatbots that search company documentation and maintain conversation history
- 2Creating multi-agent research teams that collaborate to gather and synthesize information from web and internal sources
- 3Developing social media automation workflows that route content to different platforms with channel-specific agents
- 4Enterprise support systems with memory-enabled agents that learn from past interactions and resolve tickets autonomously
- 5AI-powered content generation pipelines (video, text) that orchestrate multiple specialized agents in sequence
- 6Customer service bots deployed across multiple messaging platforms (Slack, WhatsApp, Telegram) with unified backend logic
- 7Real-time monitoring and decision-making systems with agents that access live data, take actions, and maintain contextual memory
Pros & Cons
Advantages
- Exceptional performance metrics: 529x faster instantiation than LangGraph and 24x lower memory footprint enable cost-effective scaling and rapid prototyping
- Secure-by-default architecture with JWT, RBAC, and request-level isolation built into the core rather than bolted on; data stays in your infrastructure with no egress costs
- Batteries-included framework includes memory management, knowledge integration, tool orchestration, and production runtime in one package, reducing dependency fragmentation
- Multi-interface deployment flexibility: same agent code deploys to web UI, Slack, WhatsApp, Telegram, and custom integrations without refactoring
- Intuitive developer experience with minimal boilerplate—users report 2-minute setup times and straightforward async/sync unification
Limitations
- Limited public documentation and pricing transparency on the website; no clear free tier specifications or pricing tiers displayed, making cost evaluation difficult
- Relatively young ecosystem compared to established alternatives like LangGraph, potentially fewer third-party tools and integrations available
- Performance benchmarks lack methodological transparency (no details on test conditions, hardware, or whether comparisons use identical agent complexity)
- Dependency on external databases (Postgres emphasized) for memory and knowledge; no lightweight local-first option mentioned for development or low-friction prototyping
- User testimonials are heavily cherry-picked and lack critical depth; no independent reviews, case studies, or failure scenarios discussed
Pricing Details
Pricing details not publicly available. The website mentions no free tier, paid plans, or usage-based pricing. No pricing calculator or commercial details are disclosed.
Who is this for?
Backend engineers and AI/ML developers building production agent systems; startups and enterprises seeking to deploy autonomous agents cost-efficiently at scale; teams requiring secure, on-premise-friendly agentic infrastructure; companies using Slack, WhatsApp, or Telegram that want unified agent backends; developers frustrated with high memory overhead or slow instantiation in competing frameworks.