Agentic Frameworks

Agentic AI will introduce a goal-driven digital workforce that autonomously makes plans and takes actions — an extension of the workforce that doesn't need vacations or other benefits.

-- Gartner

Framework	Language(s)	License	Key features (short)	Suitable for (pros)	Main cons	GH stars / Deployment model
LangChain	Python, TypeScript	MIT	De-facto standard, broad vendor & vector DB integrations, large ecosystem	Enterprise GenAI building blocks; wide community & vendor compatibility	Complex architecture, steep learning curve, maintenance overhead	~98K / Self-hosted
LangGraph	Python, TypeScript	MIT	Graph-based stateful workflows, multi-agent, native LangChain integration, observability (LangSmith)	Enterprise multi-agent orchestration; LangChain ecosystem apps	Commercial features behind paid tier; dependency complexity	~8K / SaaS & Self-hosted
CrewAI	Python	MIT	Multi-agent workflows, wide LLM support, cloud integration, user-friendly UI	Fast time-to-market; lightweight agent creation for business use cases	Limited enterprise testing; vendor/dependency risks	~25K / SaaS or Self-hosted
Autogen	Python (multi-language)	MIT	Microsoft multi-agent conversational framework, async messaging, observability	Microsoft ecosystem alignment; prototyping with Autogen Studio	Experimental; limited production maturity	~38K / Self-hosted
Semantic Kernel	Python, C#, Java	MIT	Production-ready SDK, multi-language, built-in agent/process frameworks	Enterprise production agents; Azure integration; multi-language SDKs	Vendor dependency; evolving agent features	– / SDK (enterprise)
LlamaIndex	Python, TypeScript	MIT	Data-centric indexing framework, connectors, document parsing, modular design	Data-intensive LLM apps, knowledge-heavy systems, rapid TTM	Less focus on agent decision-making; evolving agentic features	~38K / SaaS & Self-hosted
AutoGPT	Python	MIT	Low-code/no-code autonomous agents, multi-LLM support, continuous agents	Quick prototyping, simple autonomous workflows	Waitlists/cloud limits, community fragmentation	~171K / SaaS & Self-hosted
PydanticAI	Python	MIT	Type-safe FastAPI-style framework, strong typing, DI, observability	Type-safe FastAPI-aligned projects and quick prototypes	Early stage, limited production usage	– / Early-stage framework
Spring AI	Java / Spring	MIT	LangChain-inspired; native Spring integration, async processing, RAG & Advisors API	Java/Spring enterprise applications, system integration	Newer framework; limited complex-scenario testing	– / Self-hosted (Spring)
Haystack	Python	MIT	Production-ready RAG/search pipelines, modular, Hugging Face & Elasticsearch integration	Cross-cloud RAG pipelines, production deployments, LLMOps	Limited multi-agent testing; complex setup	– / Self-hosted & deepsetCloud
Agno	Python	MPL-2.0	Full-stack multi-agent system: memory, knowledge, reasoning, UI; ~2μs agent creation, ~3.75 KiB per agent	Privacy-first and scalable; agent teams & composable workflows; high-performance high-volume operations	Newer ecosystem; limited production proof	~34K / Self-hosted
Mastra	TypeScript	Apache-2.0	TypeScript-first multi-agent framework; graph-based state machines; integrates with existing REST APIs and web services	Web applications and TypeScript projects; workflow-centric hybrid architectures	Newer framework; TypeScript-only	– / Self-hosted
Microsoft Agent Framework	.NET, Python	MIT	Unified SDK for orchestrating AI agents & workflows across .NET/Python	Enterprise production-ready; Microsoft ecosystem integration	Still early-stage; ecosystem tied to Microsoft stack	– / Self-hosted & Azure
Strands Agents	Python	Apache-2.0	Lightweight agent SDK, model-driven, supports workflows & tools	Simple to use; good for orchestration & agent collaboration	Smaller community; limited advanced tooling	– / Self-hosted
Google ADK (Agent Development Kit)	Python, Java	Open Source	Modular, model-agnostic agent framework optimized for multi-agent systems	Platform-agnostic; ideal for enterprise or academic adaptation	GCP-heavy; evolving documentation	– / Self-hosted & Cloud
OpenAI AgentKit	Various (JS/Python via API)	Proprietary / Mixed	Visual builder, agent orchestration, evaluation, UI embedding	Rapid prototyping; integrated with OpenAI ecosystem	Vendor lock-in; limited low-level control	– / SaaS & Self-hosted
Flue	TypeScript	Apache-2.0	Harness-first agent framework; built-in virtual sandbox (just-bash) + container sandbox (Daytona); sessions, skills, MCP, OpenTelemetry	Headless programmable agents; runtime-agnostic deploy (Node, Cloudflare, CI); harness-centric design	TypeScript-only; experimental; smaller ecosystem	~3.8K / Self-hosted
Eve	Filesystem-defined (markdown + tools)	Open Source	Filesystem-first agent framework; `instructions.md` defines the agent, tools auto-registered from `tools/`; built-in durable execution, sandboxed compute, approvals, channels, tracing, evals	Teams wanting file-based, diffable agent definitions; durable/resumable runs and HITL approvals out of the box	Public preview; API surface may change; strongest tooling fit is Vercel-hosted deployments	– / Public preview

LangChain

High-level Architecture

Key Features

De facto standard for building AI Apps with 1M+ builders and ~100K GitHub Stars
Comprehensive vendor integration and cloud-vendor support
Extensive third-party libraries integration
Support for diverse vector databases
Wide community knowledge and developer awareness

Suitable for (Pros)

Enterprise development with wider adoption as standard
Building foundational blocks of enterprise GenAI applications
Creating enterprise-specific frameworks
Projects requiring third-party vendor compatibility
Applications needing extensive community support

Where other frameworks flare better (Cons)

Complex architecture with steep learning curve
Too many integrations leading to code complexity
Continuous features/changes requiring frequent updates
Possibility of breaking changes and incompatible libraries
Maintenance overhead due to extensive ecosystem

LangGraph

High-level Architecture

Source: LangGraph Platform Architecture

Key Features

Open-source framework from LangChain team
Commercial solution for production deployment
Stateful design with graph-based workflow
Multi-agent capabilities
Native integration with LangChain
Enhanced observability with LangSmith
IDE support and community backing

Suitable for (Pros)

Enterprise multi-agent framework development
Projects requiring wide compatibility
Integration with different solutions/products
LangChain ecosystem applications
Complex workflow orchestration

Where other frameworks flare better (Cons)

Enterprise features limited to commercial version
Complex dependency management
Framework complexity challenges
Steep learning curve
Limited features in open-source version

CrewAI

High-level Architecture

Source: CrewAI Documentation

Key Features

Multi-agent framework capabilities
Workflow-based applications
Wide LLM support
Cloud provider integration
Structured workflow design
User-friendly interface
Strong community backing

Suitable for (Pros)

Fast-growing AI ecosystem
Quick time-to-market needs
Lightweight agent creation
Marketing agent development
Business-friendly implementations

Where other frameworks flare better (Cons)

Limited enterprise testing
Complex scenario handling
Vendor dependency concerns
Potential acquisition risks
Data integration challenges

Autogen

High-level Architecture

Source: Microsoft Research Documentation

Key Features

Microsoft-developed programming framework
Multi-agent conversation framework
Asynchronous messaging capabilities
Modular and extensible architecture
Built-in observability and debugging
Cross-language support
Full-type support system

Suitable for (Pros)

Microsoft ecosystem alignment
Open-source development projects
Wide range of application domains
Prototyping with Autogen studio UI
Complex agent interactions research

Where other frameworks flare better (Cons)

Still in experimental phase
Not fully production-ready
Microsoft solution dependency
Complex enterprise setup
Limited production use cases

Semantic Kernel

High-level Architecture

Source: Microsoft Documentation

Key Features

Production-ready SDK for LLM integration
Multi-language support (C#, Python, Java)
Built-in Agent and Process Frameworks
Strong enterprise integration capabilities
AI agent creation platform
Business process optimization tools

Suitable for (Pros)

Production environment AI agents
Multi-language SDK requirements
Microsoft Azure environment integration
Enterprise-level support needs
Structured development approaches

Where other frameworks flare better (Cons)

Limited to SDK functionality
Evolving agent framework
Microsoft vendor dependency
Java agent limitations
Complex setup requirements

LlamaIndex

High-level Architecture

Source: LlamaIndex Framework

Key Features

Advanced data framework capabilities
AI agents and document parsing
Workflow and connector-based integration
Modular and extensible architecture
LlamaCloud SaaS offering
LlamaParse for data transformation
Centralized LlamaHub for resources

Suitable for (Pros)

Data-intensive LLM applications
Complex document parsing needs
Quick time-to-market requirements
Knowledge-intensive AI systems
Chatbots and QA systems

Where other frameworks flare better (Cons)

Limited complex agent behaviors
Focus mainly on data indexing
Less decision-making capabilities
Evolving agentic features
Limited enterprise integration

AutoGPT

High-level Architecture

AutoGPT Platform Components

Key Features

Multiple LLM support (OpenAI, Anthropic, Groq, Llama)
Seamless integration capabilities
Low-code workflows
Autonomous operation
Continuous agents
Intelligent automation
Reliable performance metrics

Suitable for (Pros)

No-code/low-code development
Cloud-based agent building
Automated workflow creation
Quick prototype development
Simple agent deployments

Where other frameworks flare better (Cons)

Vendor lock-in concerns
Complex licensing structure
Limited LLM support
Waitlist-based cloud hosting
Community support challenges

PydanticAI

High-level Architecture

PydanticAI Components

Key Features

FastAPI-style development
Pydantic Logfire integration
Multiple LLM support
Real-time observability
Type-safety features
Graph support
Dependency injection

Suitable for (Pros)

Pydantic/FastAPI aligned projects
Simple framework requirements
Type-safe development needs
Basic scenario implementation
Quick prototyping

Where other frameworks flare better (Cons)

Beta stage development
Frequent framework changes
Limited production testing
Early stage ecosystem
Complex integration challenges

Spring AI

High-level Architecture

Source: Spring AI Documentation

Key Features

LangChain-inspired architecture
Spring ecosystem integration
Multiple LLM support
Built-in observability
Model evaluation tools
Advisors API for patterns
RAG capabilities
Chat conversation support

Suitable for (Pros)

Spring ecosystem projects
Java-based enterprise applications
Seamless Spring integration
System integration needs
Asynchronous processing requirements
Data connectivity projects

Where other frameworks flare better (Cons)

Relatively new framework
Limited complex scenario testing
Early stage development
Feature comparison gaps
Limited community resources

Haystack

High-level Architecture

Source: Haystack Documentation

Key Features

Production-ready LLM framework
RAG pipeline support
Complex search capabilities
Modular architecture
OpenAI/Chroma integration
Hugging Face compatibility
Elasticsearch support
deepsetCloud platform

Suitable for (Pros)

Cross-cloud LLM applications
Custom RAG pipelines
Jinja template integration
Free development environment
LLMOps capabilities
Production deployments

Where other frameworks flare better (Cons)

Limited multi-agent testing
Unclear roadmap visibility
Battle-testing needed
Complex setup requirements
Integration challenges

Other Frameworks/Platforms

Strands: An open-source agent framework & SDK launched by AWS on July 25 promoting model-driven approach to building and running AI agents .
Google ADK: Launched by Google on Apr 25 (with a focus on multi-agent apps) as a modular framework for developing and deploying AI agents, particularly optimized for Gemini and the Google ecosystem.
OpenAI Agents SDK: The OpenAI Agents SDK to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
Dapr Agents
OpenAI Swarm: Educational framework for lightweight multi-agent orchestration
MetaGPT: Research-based multi-agent framework promoting meta-programming
Flowise: Open-source drag-and-drop UI for agent building
Langflow: DataStax-acquired framework for interactive GenAI apps
OpenAGI: Simple framework, built by AI Planet, for building human-like agents
Camel-AI.org: Research-inspired customizable multi-agent framework inspired by the CAMEL research paper
PraisonAI: a production-ready Multi AI Agents framework, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex tasks.
BroadAI: A Multi-Agent Framework for building powerful & intelligent AI Systems
Vellum: A platform with products for Orchestration, Evaluation, Prompting, Retrieval, and Deployment - as a GUI tool for building and testing complex workflows.
Rivet: a drag and drop GUI LLM workflow builder
Solace Agent Mesh: Event-driven multi-agent framework built on the Solace messaging platform
Akka Agent Platform: Distributed agent platform built on the Akka actor model
Swarms AI: Choose from multiple swarm architectures to build sophisticated Enterprise AI systems
IBM Granite BeeAI: Build production-ready AI agents in both Python and Typescript
MetaGPT: The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

Additional Resources

Large Foundation Models for Enterprises

Liquid AI: LFMs (Liquid Foundation Models) for Enterprises

Comparative Analysis

Key Attributes	LangChain	LangGraph	Autogen	Semantic Kernel	LlamaIndex	AutoGPT	CrewAI
License	MIT	MIT	MIT	MIT	MIT	MIT	MIT
Open-source	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Developed by	LangChain	LangChain	Microsoft	Microsoft	LlamaIndex	Significant Gravitas	CrewAI
GitHub Stars (Jan '25)	98K	8K	38K	-	38K	171K	25K
Used By (GH Public Repos)	170K	10K	3K	-	16K	N/A	7K
Language	Python, TypeScript	Python, TypeScript	Python, C#	Python, C#, Java	Python, TypeScript	Python	Python
Enterprise Support	Yes	Yes	Yes	-	Yes	Yes	Yes
Deployment Model	Self-hosted	SaaS & Self-hosted	Self-hosted	-	SaaS & Self-hosted	SaaS & Self-hosted	SaaS & Self-hosted