Agentic Framework Solutions

Overview

This page consolidates all agentic AI development frameworks — open-source libraries, managed cloud SDKs, and specialized platforms — and maps them to a Technology Radar adapted from the Thoughtworks Radar methodology. Each framework is assessed across five dimensions: research backing, industry adoption, GitHub community signal, production readiness, and open-source availability.

The four radar rings:

Technology Radar

Solutions are split across two charts to avoid label clutter. Both share the same x-axis (ring position: Caution → Adopt). The y-axis differs per chart.

How to read: - Right side (x > 0.5) = Trial or Adopt — production-ready, recommended for evaluation - Left side (x < 0.5) = Assess or Caution — early-stage or narrowly scoped

Chart 1 — Open Source & Community Frameworks

Y-axis: specialized/focused use case (bottom) → general-purpose multi-paradigm (top)

Chart 2 — Vendor-Backed & Managed SDKs

Y-axis: cloud/platform-specific (bottom) → cross-platform / ecosystem-agnostic (top)

🟢 Adopt

These frameworks have demonstrated production readiness, strong community adoption, and clear fit for building agentic AI systems.

Radar signal (Thoughtworks Vol. 34, Apr 2026 — opinionated source): Thoughtworks moved LangGraph from Adopt to Trial, citing that the graph+global-shared-state pattern is not always the best fit and that simpler agent architectures (e.g. Pydantic AI) often produce leaner, more debuggable systems. This wiki retains LangGraph at Adopt based on broad industry production adoption and its continued strength for stateful, durable workflows. PydanticAI has moved from Assess to Adopt in this wiki based on the same Vol. 34 signal. See the Thoughtworks Vol. 34 Agentic AI Digest for full context.

LangChain

Type: Open-source framework (MIT) Language(s): Python, TypeScript GitHub: langchain-ai/langchain — ~100K stars, 1M+ builders Deployment: Self-hosted

The de facto standard for building LLM-powered applications. LangChain provides a unified interface for chains, agents, tools, memory, and vector store integrations. Its breadth of vendor integrations and the size of its community make it the most commonly referenced framework in enterprise GenAI projects.

Why Adopt: Largest ecosystem in the space. Extensive third-party integrations (vector DBs, LLM providers, tools). Proven in production across thousands of enterprise deployments. Comprehensive documentation and community knowledge.

Best for: Enterprise GenAI applications requiring broad vendor compatibility, teams building foundational agentic infrastructure, projects where community support and ecosystem breadth matter.

Limitations: Complex architecture with steep learning curve. Heavy dependency tree. Frequent breaking changes. Abstraction layers can introduce latency overhead.

LangGraph

Type: Open-source graph-based workflow framework (MIT) + commercial platform Language(s): Python, TypeScript GitHub: langchain-ai/langgraph — ~8K stars Deployment: SaaS (LangGraph Platform) + Self-hosted

LangGraph extends LangChain with a graph-based state machine model for building stateful, multi-actor agent workflows. Nodes represent agent steps; edges encode conditional routing. The commercial LangGraph Platform adds persistence, streaming, human-in-the-loop controls, and deployment infrastructure.

Why Adopt: The most principled open-source approach to stateful multi-agent orchestration. Native LangChain integration. LangSmith observability built in. Growing enterprise adoption for complex workflow orchestration. AWS Bedrock AgentCore recommends retaining LangGraph for orchestration logic while delegating runtime concerns to the platform.

Note (Thoughtworks Radar Vol. 34, Apr 2026): Thoughtworks moved LangGraph to Trial, noting that simpler agent architectures (agents communicating through code execution, graph structures added only when needed) often produce leaner, more debuggable systems. This wiki retains LangGraph at Adopt based on broad industry production adoption and its continued strength for stateful, durable workflows. Teams should evaluate whether the graph pattern is warranted for their specific use case.

Best for: Multi-agent systems requiring complex state management, conditional branching, and cycle detection. Long-running workflows requiring durable execution (persistence, HITL, checkpointing). Teams already on the LangChain stack.

Limitations: Graph + global-shared-state model can be over-engineered for simpler agent tasks. Enterprise features (persistence, deployment, HITL) require the commercial platform. Steep learning curve. Dependency complexity inherited from LangChain.

CrewAI

Type: Open-source multi-agent framework (MIT) + SaaS platform Language(s): Python GitHub: crewAIInc/crewAI — ~25K stars Funding: $18M (Oct 2024) Deployment: SaaS + Self-hosted

CrewAI is a role-based multi-agent framework where agents are assigned specific roles, goals, and tools to collaborate on tasks. Its business-friendly abstraction and fast time-to-market have made it the fastest-growing framework in the agentic space. Supports a wide array of LLMs and cloud providers.

Why Adopt: Fastest-growing community in the multi-agent space. Simplest path to building collaborative agent teams. Strong business-use-case alignment. Active development and growing enterprise adoption.

Best for: Business-oriented multi-agent workflows, marketing and content agents, teams that need quick time-to-market with minimal framework complexity.

Limitations: Less proven for large-scale enterprise data integration scenarios. Vendor dependency and potential acquisition risk. Limited complex scenario testing compared to LangGraph.

AutoGen (Microsoft)

Type: Open-source multi-agent conversational framework (MIT) Language(s): Python (multi-language support) GitHub: microsoft/autogen — ~38K stars Deployment: Self-hosted

Microsoft Research's framework for building multi-agent systems through asynchronous message passing. AutoGen agents communicate via structured conversations, enabling complex multi-agent collaboration patterns. AutoGen Studio provides a no-code UI for prototyping. The v0.4 rewrite introduced a fully async, event-driven architecture.

Why Adopt: Strong research pedigree (Microsoft Research). Large community. Async-first architecture well-suited for complex agent interactions. AutoGen Studio lowers the barrier for prototyping. Active development with significant Microsoft investment.

Best for: Research and prototyping of multi-agent systems, Microsoft ecosystem alignment, teams exploring conversational multi-agent patterns.

Limitations: Production maturity lags behind LangChain/LangGraph. Microsoft ecosystem dependency. Complex enterprise setup. The v0.4 rewrite introduced breaking changes from earlier versions.

LlamaIndex

Type: Open-source data framework for LLM applications (MIT) + SaaS (LlamaCloud) Language(s): Python, TypeScript GitHub: run-llama/llama_index — ~38K stars Deployment: SaaS (LlamaCloud) + Self-hosted

LlamaIndex is the leading data-centric framework for LLM applications. It provides connectors, parsers, indexing, and retrieval primitives that form the foundation of RAG pipelines and knowledge-intensive agents. LlamaParse handles complex document parsing; LlamaHub centralizes community integrations.

Why Adopt: The strongest framework for data-intensive LLM applications and RAG pipelines. Modular architecture. Extensive connector ecosystem. LlamaCloud provides managed parsing and indexing at scale.

Best for: Knowledge-intensive agents, RAG systems, document-heavy applications, chatbots and QA systems requiring sophisticated retrieval.

Limitations: Less focused on agent decision-making and orchestration compared to LangGraph or CrewAI. Agentic features are evolving. Less suited for pure orchestration use cases.

🔵 Trial

These frameworks are worth using on projects that can tolerate some risk. Actively growing, with real production usage, but not yet the default choice.

Semantic Kernel (Microsoft)

Type: Production-ready SDK (MIT) — enterprise-grade Language(s): Python, C#, Java GitHub: microsoft/semantic-kernel Deployment: SDK (enterprise) + Azure integration

Microsoft's production-ready SDK for integrating LLMs into enterprise applications. Semantic Kernel provides built-in Agent and Process Frameworks, plugin architecture, and deep Azure OpenAI integration. Multi-language support (Python, C#, Java) makes it uniquely suited for polyglot enterprise environments.

Why Trial: The only framework with first-class C# and Java support alongside Python. Strong enterprise integration story. Built-in process orchestration. Actively maintained by Microsoft with enterprise SLAs via Azure.

Best for: Enterprise teams on the Microsoft stack, polyglot environments requiring C# or Java agents, Azure OpenAI deployments.

Limitations: Microsoft vendor dependency. Evolving agent framework — features are still maturing. Java agent support is more limited than Python/C#. Less community visibility than LangChain.

AWS Strands Agents

Type: Open-source agent SDK (Apache 2.0) Language(s): Python GitHub: strands-agents/sdk-python Deployment: Self-hosted + AWS ecosystem

AWS's open-source agent SDK, launched July 2025. Strands takes a model-driven approach — the LLM itself drives the agentic loop rather than hard-coded orchestration logic. Lightweight and composable, with native integration into the AWS ecosystem (Bedrock, AgentCore, Lambda).

Why Trial: Model-driven approach reduces orchestration boilerplate. Native AWS integration for teams already on Bedrock. Apache 2.0 license. AWS backing provides long-term maintenance confidence.

Best for: AWS-native teams building agents on Bedrock, teams that prefer minimal framework abstraction, orchestration and agent collaboration use cases.

Limitations: Smaller community than LangChain/CrewAI. AWS ecosystem dependency for full feature set. Newer framework with limited production case studies outside AWS.

Google ADK (Agent Development Kit)

Type: Open-source agent framework Language(s): Python, Java GitHub: google/adk-python Deployment: Self-hosted + Google Cloud (Vertex AI Agent Engine)

Google's modular, model-agnostic agent framework, launched April 2025. ADK is optimized for multi-agent systems and integrates natively with Gemini and Vertex AI Agent Engine. Supports both Python and Java, with a focus on composable agent pipelines and tool use.

Why Trial: Strong multi-agent architecture. Platform-agnostic design despite GCP optimization. Python + Java support. Native Vertex AI integration for GCP teams. Active development with Google backing.

Best for: GCP-native teams, multi-agent systems requiring composable pipelines, enterprise or academic adaptation of Google's agent patterns.

Limitations: GCP-heavy for full feature set. Evolving documentation. Smaller community than LangChain. Less proven outside the Google ecosystem.

Haystack (deepset)

Type: Open-source production RAG/search pipeline framework (MIT) + deepsetCloud Language(s): Python GitHub: deepset-ai/haystack Deployment: Self-hosted + deepsetCloud

Haystack is a production-ready framework for building RAG pipelines and search-augmented LLM applications. Its modular pipeline architecture supports Hugging Face models, Elasticsearch, OpenAI, and Chroma. deepsetCloud provides a managed deployment option.

Why Trial: Strong production track record for RAG and search use cases. Modular architecture enables cross-cloud deployments. LLMOps capabilities built in. Hugging Face and Elasticsearch integration are differentiators.

Best for: Cross-cloud RAG pipelines, production search-augmented LLM applications, teams needing LLMOps capabilities.

Limitations: Limited multi-agent testing. Complex setup for non-RAG use cases. Roadmap visibility has been a community concern. Less suited for pure orchestration scenarios.

Agno

Type: Open-source full-stack multi-agent framework (MPL-2.0) Language(s): Python GitHub: agno-agi/agno — ~34K stars Deployment: Self-hosted

Agno is a high-performance multi-agent framework with a full-stack design: built-in memory, knowledge, reasoning, and UI components. Claims ~2μs agent instantiation time and ~3.75 KiB memory per agent — making it suitable for high-volume, latency-sensitive deployments. Supports agent teams and composable workflows.

Why Trial: Exceptional performance characteristics for high-volume use cases. Full-stack design reduces the need to bolt together separate memory and knowledge components. Privacy-first (self-hosted). Growing community (~34K stars).

Best for: High-performance, high-volume agent deployments, privacy-first self-hosted environments, teams that want an integrated memory + knowledge + reasoning stack.

Limitations: Newer ecosystem with limited production proof outside early adopters. MPL-2.0 license has copyleft implications for some commercial use cases. Less community knowledge than LangChain.

OpenAI Agents SDK

Type: Proprietary SDK (OpenAI) — lightweight Language(s): Python, JavaScript Docs: platform.openai.com/docs/guides/agents Deployment: SaaS + Self-hosted

OpenAI's production-ready upgrade of the experimental Swarm framework. The Agents SDK provides a lightweight, low-abstraction path to building agentic applications on OpenAI models. Includes built-in tool use, handoffs between agents, and tracing. Designed for simplicity over configurability.

Why Trial: Simplest path to production agents on OpenAI models. Minimal abstractions reduce complexity. Official OpenAI support. Good fit for teams already committed to the OpenAI ecosystem.

Best for: Teams building on OpenAI models who want minimal framework overhead, rapid prototyping, simple agentic workflows.

Limitations: OpenAI vendor lock-in. Limited low-level control. Less flexible than open-source alternatives for multi-provider or self-hosted deployments.

🟡 Assess

Interesting frameworks worth understanding and monitoring. Not yet proven at scale or too narrowly scoped for general recommendation.

PydanticAI

Type: Open-source type-safe agent framework (MIT) Language(s): Python GitHub: pydantic/pydantic-ai Deployment: Self-hosted

A FastAPI-style agent framework built on Pydantic's type system. PydanticAI brings dependency injection, structured output validation, and Pydantic Logfire observability to agent development. Supports multiple LLM providers and graph-based workflows.

(Updated: Moved from Assess to Adopt in Thoughtworks Technology Radar Vol. 34, April 2026. The framework's architecture — favoring simple agents communicating through code execution over rigid graphs with massive shared state — has proven itself in production. This approach often produces leaner, more debuggable systems than LangGraph for many use cases.)

Why Adopt: Production-validated simpler agent architecture pattern. Built-in Logfire observability. Strong type-safety for teams using Pydantic/FastAPI. Instructor library pairs with it as the stable structured-output abstraction. Both SDKs now natively on OpenTelemetry.

Best for: Type-safe agent development in FastAPI-aligned projects. Teams that want simpler agent architecture without graph overhead. Structured output pipelines requiring validation and retries.

Limitations: Python-only. Teams deeply invested in LangGraph patterns may find migration non-trivial. Non-Pydantic stacks require integration effort.

Spring AI

Type: Open-source LangChain-inspired framework for Java/Spring (MIT) Language(s): Java / Spring GitHub: spring-projects/spring-ai Deployment: Self-hosted (Spring ecosystem)

Spring AI brings LangChain-inspired patterns to the Java/Spring ecosystem. Provides LLM integration, RAG capabilities, an Advisors API for reusable patterns, and async processing. Native Spring Boot integration makes it the natural choice for Java enterprise teams.

Why Assess: The only mature option for Java/Spring enterprise teams building LLM applications. Spring ecosystem integration is a genuine differentiator. But it is a newer framework with limited complex-scenario testing.

Best for: Java/Spring enterprise applications, system integration projects requiring async processing, teams that cannot adopt Python-based frameworks.

Limitations: Newer framework with limited production proof. Feature gaps compared to Python-based alternatives. Limited community resources relative to LangChain.

Mastra

Type: Open-source TypeScript-first multi-agent framework (Apache 2.0) Language(s): TypeScript Docs: mastra.ai Deployment: Self-hosted

Mastra is a TypeScript-first framework for building multi-agent systems with graph-based state machines. Designed to integrate with existing REST APIs and web services, making it a natural fit for web application teams. Supports workflow-centric hybrid architectures.

Why Assess: The strongest TypeScript-native option for web teams building agentic features. Graph-based state machines provide principled workflow control. But it is a newer framework with limited production proof and TypeScript-only scope.

Best for: Web applications and TypeScript projects, workflow-centric hybrid architectures, teams that cannot adopt Python-based frameworks.

Limitations: TypeScript-only — not suitable for Python-first teams. Newer framework with limited production case studies. Smaller community than LangChain or CrewAI.

Microsoft Agent Framework

Type: Open-source unified SDK for .NET and Python (MIT) Language(s): .NET, Python GitHub: microsoft/agent-framework Deployment: Self-hosted + Azure

Microsoft's unified SDK for orchestrating AI agents and workflows across .NET and Python. Designed to complement Semantic Kernel with higher-level orchestration primitives. Still early-stage with ecosystem tied to the Microsoft stack.

Why Assess: Potential to become the standard for .NET agent development. Microsoft backing provides long-term maintenance confidence. But it is still early-stage with limited community signal.

Best for: Enterprise teams on .NET who need agent orchestration beyond what Semantic Kernel provides.

Limitations: Early-stage — limited production proof. Ecosystem tied to Microsoft stack. Overlapping scope with Semantic Kernel creates confusion about which to use.

🔴 Caution

Use with care. These frameworks are either early-stage/experimental, narrowly scoped, or have characteristics that limit general applicability.

AutoGPT

Type: Open-source autonomous agent platform (MIT) + SaaS Language(s): Python GitHub: Significant-Gravitas/AutoGPT — ~171K stars Deployment: SaaS + Self-hosted

AutoGPT was one of the earliest autonomous agent projects and remains the most-starred agent repository on GitHub. It supports low-code/no-code agent building, multiple LLMs, and continuous autonomous operation. However, the project has experienced community fragmentation, waitlist-based cloud hosting, and limited production maturity relative to its star count.

Why Caution: GitHub stars reflect historical interest rather than current production adoption. Community fragmentation and complex licensing structure. Waitlist-based cloud hosting limits accessibility. More mature alternatives (LangGraph, CrewAI) have overtaken it for production use cases.

Best for: Quick prototyping and simple autonomous workflows where production maturity is not required.

Limitations: Community fragmentation. Complex licensing. Waitlist-based cloud. Limited production case studies relative to star count. More mature alternatives available.

Radar Summary Table

Selection Guide

Use this to narrow down options based on your constraints.

Radar Assessment Criteria

Each framework was assessed on the following dimensions. Ratings are as of May 2026.

Radar positions are reassessed periodically. GitHub star counts and adoption signals are point-in-time as of the assessment date.

See Also

• Agent Development Frameworks Overview
• AI Coding Agents
• LangChain
• LangGraph
• CrewAI
• AutoGen
• LlamaIndex
• AWS Strands
• Google ADK
• Multi-Agent Systems
• Agent Memory Solutions
• Observability Solutions
• Thoughtworks Tech Radar Vol. 34 — Agentic AI Digest

References

• LangChain GitHub — de facto standard framework
• LangGraph GitHub — graph-based stateful workflows
• CrewAI GitHub — role-based multi-agent framework
• CrewAI $18M funding — Oct 2024
• AutoGen GitHub — Microsoft multi-agent framework
• LlamaIndex GitHub — data-centric LLM framework
• Semantic Kernel GitHub — Microsoft enterprise SDK
• AWS Strands launch blog — July 2025
• Google ADK launch blog — April 2025
• Haystack GitHub — deepset RAG framework
• Agno GitHub — high-performance multi-agent framework
• OpenAI Agents SDK docs — OpenAI agent platform
• PydanticAI GitHub — type-safe agent framework
• Spring AI GitHub — Java/Spring LLM framework
• Mastra — TypeScript multi-agent framework
• Microsoft Agent Framework GitHub — .NET/Python SDK
• AutoGPT GitHub — autonomous agent platform
• Thoughtworks Technology Radar Vol. 34 — April 2026 edition; source of LangGraph→Trial and PydanticAI→Adopt moves
• Thoughtworks Technology Radar — radar methodology reference