LangGraph
Orchestrate resilient, stateful multi-agent systems with cyclic logic and human-in-the-loop control.

Phidata is a sophisticated open-source framework and cloud platform designed for building AI Agents with persistent memory, specialized knowledge, and functional tool-use capabilities. Unlike many abstraction-heavy frameworks, Phidata utilizes a 'Python-first' approach, allowing developers to define agents as standard Python objects that can be easily integrated into existing application stacks. Technically, it excels by providing built-in support for PostgreSQL-based state management, enabling agents to retain context across sessions without complex external wiring. In the 2026 market, Phidata has emerged as the leading alternative to LangChain for engineers who prioritize production stability and observability. It bridges the gap between raw LLM API calls and autonomous systems by offering a pre-configured middleware layer for RAG (Retrieval-Augmented Generation) and tool-calling. The Phidata Cloud provides a centralized dashboard for monitoring agent performance, tracing execution paths, and managing collaborative multi-agent teams, making it a critical piece of the enterprise AI infrastructure stack.
Uses PgVector and relational tables to store agent state and conversation history, allowing for complex SQL-based memory retrieval.
Orchestrate resilient, stateful multi-agent systems with cyclic logic and human-in-the-loop control.
Enterprise-grade Python framework for building secure, modular AI agents and multi-step workflows.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Enables the creation of 'Teams' where different agents (e.g., a Researcher and a Writer) share a unified session context.
Pre-built Python functions for DuckDuckGo search, SQL execution, shell commands, and YFinance integration.
End-to-end support for document chunking, embedding generation, and vector store upserts within the agent definition.
OpenTelemetry-compatible tracing for LLM calls, tool execution times, and retrieval accuracy.
One-command deployment to convert an agent into a production-ready REST API.
Automated tracking of vector database updates to ensure agents use the most recent information.
Manual collection and synthesis of financial data from multiple sources (YFinance, SEC filings) is time-consuming.
Registry Updated:2/7/2026
LLMs lack access to real-time user data and ticket history.
Documentation is often scattered across Git repos and Wikis.