{"generated_at":"2026-04-30T14:57:10.988Z","count":17,"frameworks":[{"name":"LangGraph","slug":"langgraph","url":"https://langchain-ai.github.io/langgraph","githubUrl":"https://github.com/langchain-ai/langgraph","description":"Graph-based agent orchestration layer from the LangChain team. Models agent execution as a state machine — nodes are steps, edges are transitions, state is persisted. The consensus production framework for stateful agents in 2026.","license":"open-source","hostingModel":"both","languageSupport":["python","typescript"],"mcpSupport":true,"editorialScore":5,"verdict":"LangGraph is the production standard for stateful agent execution. If your agent handles real data, transactions, or workflows where state continuity matters, LangGraph is the framework to reach for.","bestFor":"Production agent workloads requiring stateful, deterministic execution with persistence, memory, and durable step logic","strengths":["Consensus production pick in 2026 across practitioner communities","37.8k+ dependent packages — highest adoption signal in this category","Deterministic execution via state machine graph model","Built-in persistence, memory, and durable execution","Streaming support throughout the execution graph","494+ releases — most active release cadence in this category"],"weaknesses":["Steeper learning curve — requires graph and state machine thinking","Tied to LangChain ecosystem as a dependency","LangGraph Cloud adds cost for managed execution at scale","MCP support is via LangChain integrations, not native-first"],"latestVersion":"1.1.7a1","lastChecked":"2026-04-13T16:24:00.371Z","featured":true},{"name":"Google ADK","slug":"google-adk","url":"https://google.github.io/adk-docs/","githubUrl":"https://github.com/google/adk-python","description":"Google's open-source, code-first framework for building, evaluating, and deploying AI agents. Optimised for Gemini but model-agnostic, with Python, Java, and Go SDKs. Designed for Cloud Run and Vertex AI Agent Engine deployment.","license":"open-source","hostingModel":"both","languageSupport":["python","java","go"],"mcpSupport":true,"editorialScore":4,"verdict":"The strongest framework for teams already in the Google / GCP ecosystem. Batteries-included tooling, multi-language SDKs, and native MCP support put it ahead of most alternatives for Gemini workloads. Model-agnostic in theory, but the Gemini-first design creates friction outside GCP.","bestFor":"Teams building on Gemini or deploying to GCP who want a batteries-included framework with built-in observability and Cloud Run / Vertex AI integration","strengths":["18.9k GitHub stars — strongest community signal of any dedicated agent framework","Multi-language: Python, Java, and Go SDKs all officially maintained by Google","Batteries included: built-in code execution sandbox, session management, and web UI for debugging","Native MCP support for tool integration","Callbacks provide clean, low-boilerplate hooks into agent execution for observability","Deploy anywhere: Cloud Run, Vertex AI Agent Engine, or fully self-hosted"],"weaknesses":["Optimised for Gemini — non-Gemini model workflows may encounter friction despite model-agnosticism claim","GCP / Vertex AI alignment creates ecosystem pressure toward Google Cloud deployment","More opinionated than LangGraph — custom architectures require more fighting the framework","Python SDK is most mature; Go and Java versions are feature-complete but see new capabilities later"],"latestVersion":"2.0.0a3","lastChecked":"2026-04-13T16:23:59.840Z","featured":true},{"name":"LangChain","slug":"langchain","url":"https://langchain.com","githubUrl":"https://github.com/langchain-ai/langchain","description":"The original agent engineering platform for building pipelines, chains, and tool-using agents with Python or TypeScript. The widest ecosystem of integrations with the most documented production gotchas.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python","typescript"],"mcpSupport":true,"editorialScore":4,"verdict":"Reach for LangChain when you need breadth of integrations and an established ecosystem, but expect to manage complexity and latency overhead in production. LangGraph is the better choice for stateful production work.","bestFor":"Teams that need maximum integration breadth and can afford to manage the complexity debt","strengths":["Largest ecosystem — 279k+ dependent packages","Widest integration library: 200+ LLM providers, vector stores, and tools","LCEL pipeline abstraction reduces boilerplate for chain composition","Both Python and TypeScript SDKs actively maintained","First-class MCP support with .mcp.json in the repo","133k GitHub stars — the reference point for all framework comparisons"],"weaknesses":["Hidden latency overhead (~1.3s per invocation reported by practitioners)","Testing and debugging described as difficult — heavy abstraction layers","Frequent breaking changes force pinning to old versions","Abstraction layers obscure what is actually happening in production","Rapid API churn — posts from mid-2025 routinely go stale within months"],"latestVersion":"1.3.0a2","lastChecked":"2026-04-13T16:24:00.953Z","featured":true},{"name":"Mastra","slug":"mastra","url":"https://mastra.ai","githubUrl":"https://github.com/mastra-ai/mastra","description":"TypeScript-native agent framework with built-in workflows, memory, RAG, and MCP support. Designed for full-stack TypeScript teams shipping agents as part of web applications.","license":"open-source","hostingModel":"both","languageSupport":["typescript"],"mcpSupport":true,"editorialScore":4,"verdict":"The strongest TypeScript-native option for full-stack teams. Still early but the only framework that treats TypeScript as the primary citizen rather than a port of Python patterns.","bestFor":"Full-stack TypeScript teams embedding agents inside Next.js or Node.js applications","strengths":["TypeScript-first — not a Python port, designed from the ground up for TS","Native MCP client and server support","Built-in workflow primitives with durable execution","Integrated RAG and vector storage abstractions","Designed to co-locate with Next.js applications"],"weaknesses":["Younger project — limited production case studies","Smaller community and ecosystem than Python frameworks","API surface still stabilising","Documentation coverage uneven across features"],"latestVersion":"0.13.0","lastChecked":"2026-04-13T16:23:59.846Z","featured":true},{"name":"AutoGen","slug":"autogen","url":"https://microsoft.github.io/autogen","githubUrl":"https://github.com/microsoft/autogen","description":"Microsoft's multi-agent programming framework, now production-ready as Microsoft Agent Framework (MAF) 1.0. Focused on multi-agent conversation patterns with enterprise-grade backing and cross-runtime interoperability via A2A and MCP.","license":"open-source","hostingModel":"both","languageSupport":["python","dotnet"],"mcpSupport":true,"editorialScore":4,"verdict":"AutoGen/MAF is the enterprise choice when you need Microsoft Azure integration, cross-language agent support, or are building conversational multi-agent systems where correctness matters more than speed.","bestFor":"Enterprise teams in Azure environments or building cross-language multi-agent conversational systems","strengths":["Microsoft backing with long-term commitment and enterprise support","Microsoft Agent Framework 1.0 is now production-ready with stable APIs","Cross-language support: Python and .NET/C#","A2A and MCP protocol support for cross-runtime interoperability","AutoGen Studio for visual agent composition","56.9k GitHub stars and active ecosystem"],"weaknesses":["Major breaking changes between v0.2 and v0.4 with a difficult migration path","AutoGen Studio human input mode is limited — stuck on NEVER in some configurations","Research-lab heritage persists in the developer experience","4.1k dependents — significantly lower adoption than LangGraph or LlamaIndex"],"latestVersion":"0.7.5","lastChecked":"2026-04-13T16:24:00.230Z","featured":false},{"name":"Haystack","slug":"haystack","url":"https://haystack.deepset.ai","githubUrl":"https://github.com/deepset-ai/haystack","description":"Open-source AI orchestration framework from deepset for building context-engineered, production-ready LLM applications. Pipeline-first architecture gives explicit control over retrieval, routing, memory, and generation.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python"],"mcpSupport":true,"editorialScore":4,"verdict":"Haystack is the production-grade RAG framework for teams that want explicit, composable control over every step of their retrieval and generation pipeline. Hayhooks makes it uniquely suited for serving agents as MCP infrastructure.","bestFor":"Enterprises that need explicit, auditable pipeline control for RAG and agents — especially where Apache 2.0 licensing matters or MCP server deployment is needed","strengths":["Explicit pipeline architecture — nothing is hidden, every step is composable and auditable","Production RAG gold standard with deep enterprise pedigree from deepset","Hayhooks: deploy pipelines and agents as REST APIs or MCP servers","SearchableToolset: dynamic tool discovery from large catalogs via BM25","Apache 2.0 license — the most permissive in this category"],"weaknesses":["1.3k dependents — lowest adoption count in this set","More niche positioning — requires pipeline thinking from day one","Smaller community than LangChain or LlamaIndex","Fewer off-the-shelf integrations than the broader LangChain ecosystem"],"latestVersion":"2.27.0","lastChecked":"2026-04-13T16:23:59.753Z","featured":false},{"name":"LlamaIndex","slug":"llamaindex","url":"https://llamaindex.ai","githubUrl":"https://github.com/run-llama/llama_index","description":"Originally a RAG library, now a full agent framework with the Workflows abstraction. The leading document agent and OCR platform — best when your agent's primary job is retrieval, document processing, or knowledge base operations.","license":"open-source","hostingModel":"both","languageSupport":["python","typescript"],"mcpSupport":true,"editorialScore":4,"verdict":"LlamaIndex is the right choice when document ingestion, OCR, or knowledge retrieval is the core of your agent's work. It leads the field on RAG tooling and LlamaParse makes complex document processing genuinely easier.","bestFor":"Agents whose primary job is retrieval, document processing, knowledge base operations, or multi-modal parsing","strengths":["LlamaParse: best-in-class document parsing and OCR for complex formats","Event-driven Workflows abstraction for multi-step async agent logic","48.4k GitHub stars and 23.9k dependents — strong adoption","Both Python and TypeScript support with LlamaCloud managed option","492+ releases with a long track record of production use"],"weaknesses":["Heavier than needed when retrieval is not the primary job","LlamaCloud costs can surprise teams at production scale","Agent orchestration is secondary to retrieval — not the right choice for pure orchestration work"],"latestVersion":"0.14.20","lastChecked":"2026-04-13T16:24:00.655Z","featured":false},{"name":"OpenAI Agents SDK","slug":"openai-agents-sdk","url":"https://openai.github.io/openai-agents-python/","githubUrl":"https://github.com/openai/openai-agents-python","description":"OpenAI's official Python SDK for building production agents. Provides a thin, ergonomic layer over the Responses API with native support for handoffs, tracing, guardrails, and MCP tool integration.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python","typescript"],"mcpSupport":true,"editorialScore":4,"verdict":"The most direct path to production agents on OpenAI models. Low abstraction overhead and first-class tracing make it suitable for teams that want control without a heavyweight framework.","bestFor":"Python teams building directly on OpenAI models who want minimal abstraction and first-class observability","strengths":["Official OpenAI SDK — model updates and new capabilities land here first","Native MCP tool support built in from release","First-class tracing and observability via OpenAI dashboard","Agent handoffs and guardrails as first-class primitives","Minimal abstraction — close to the metal, easy to debug","Python and TypeScript (JS) SDKs both available"],"weaknesses":["OpenAI-model-only by design — no native multi-provider support","TypeScript SDK (openai-agents-js) is less mature and has fewer features than the Python version","Abstraction overhead can make debugging complex agent loops harder than plain SDK","Younger ecosystem than LangChain or LlamaIndex"],"latestVersion":"0.13.6","lastChecked":"2026-04-13T16:24:00.113Z","featured":false},{"name":"PydanticAI","slug":"pydanticai","url":"https://ai.pydantic.dev","githubUrl":"https://github.com/pydantic/pydantic-ai","description":"Agent framework from the Pydantic team — built around type safety, dependency injection, and production observability. First-class MCP and A2A support with declarative agent definition via YAML/JSON.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python"],"mcpSupport":true,"editorialScore":4,"verdict":"PydanticAI is the production-oriented choice for Python teams that take type safety seriously. If you have already embraced Pydantic's philosophy, this is the natural next step for agent work.","bestFor":"Python teams who prioritise type safety, structured observability, and want MCP/A2A natively without bolting it on","strengths":["First-class type safety from the team that defined Python validation","Clean dependency injection pattern — no hidden state","Built-in observability via Logfire with structured traces and no config","Native MCP, A2A, and UI event stream standard support","Capabilities system for composing tools, hooks, and instructions into reusable units","Declarative agent definition: define agents in YAML/JSON without code"],"weaknesses":["Still v0.1.x — API surface not yet considered stable","16.2k GitHub stars and 3.8k dependents — smaller ecosystem than LangChain","Fewer tutorials and community resources than older frameworks","Logfire observability is commercial (free tier available, production tier paid)"],"latestVersion":"1.80.0","lastChecked":"2026-04-13T16:24:00.077Z","featured":false},{"name":"Semantic Kernel","slug":"semantic-kernel","url":"https://learn.microsoft.com/en-us/semantic-kernel/overview/","githubUrl":"https://github.com/microsoft/semantic-kernel","description":"Microsoft's open-source SDK for building AI agents and copilots. Supports Python, C#, and Java with enterprise-grade features including process framework, memory, and plug-in architecture.","license":"open-source","hostingModel":"both","languageSupport":["python","dotnet","java"],"mcpSupport":true,"editorialScore":4,"verdict":"The strongest choice for enterprise teams in Microsoft-stack environments. Its breadth of language support and deep Azure integration give it a moat in enterprise AI deployments.","bestFor":"Enterprise teams building copilots and agents on Azure or within Microsoft-stack environments","strengths":["Broadest language SDK coverage: Python, C#, and Java all maintained","Deep Azure OpenAI and Microsoft 365 integration","Process framework for durable, stateful multi-step agent workflows","Mature plug-in architecture with built-in memory abstractions","MCP support via plug-in layer"],"weaknesses":["Significant learning curve — the abstraction surface is large","Observability gaps: the plugin/kernel architecture creates a 'black magic' feeling; tracing internal state requires additional tooling compared to lighter frameworks","C# / .NET is by far the most polished SDK; Python and Java versions receive new features later and have rougher edges","Microsoft-ecosystem alignment creates friction for non-Azure deployments","Documentation quality varies notably across language SDKs","Slower community iteration than Python-native frameworks"],"latestVersion":"1.41.2","lastChecked":"2026-04-13T16:24:00.486Z","featured":false},{"name":"Agno","slug":"agno","url":"https://docs.agno.com","githubUrl":"https://github.com/agno-agi/agno","description":"Lightweight Python framework for building multi-modal, multi-agent systems with built-in memory, knowledge, and reasoning. Formerly PhiData, rebranded to Agno in 2025 with a focus on performance and model-agnostic design.","license":"open-source","hostingModel":"both","languageSupport":["python"],"mcpSupport":true,"editorialScore":3,"verdict":"Fast and model-agnostic with strong multi-modal primitives. A credible alternative to LangChain for teams who find LangChain too heavy, but the rebranding from PhiData means community and docs are still catching up.","bestFor":"Python teams wanting a lightweight, model-agnostic framework with built-in memory and multi-modal support without LangChain's abstraction weight","strengths":["Genuinely fast — benchmarks consistently show lower latency than heavier frameworks","Model-agnostic by design across OpenAI, Anthropic, Google, and local models","Built-in memory and knowledge base primitives","Multi-modal support for text, image, audio, and video","MCP support included"],"weaknesses":["Rebranded from PhiData — community continuity and ecosystem still resettling","The '10,000x faster than LangChain' benchmark is misleading — applies only to agent instantiation time (SSL context setup), not LLM execution. Negligible in real workloads.","Smaller ecosystem and fewer third-party integrations than LangChain","Documentation quality inconsistent post-rebranding","Less production battle-testing than LangGraph or LangChain"],"latestVersion":"2.5.16","lastChecked":"2026-04-13T16:24:00.307Z","featured":false},{"name":"AutoGen Studio","slug":"autogen-studio","url":"https://microsoft.github.io/autogen/docs/autogen-studio-overview","githubUrl":"https://github.com/microsoft/autogen","description":"Visual no-code interface for AutoGen, now part of Microsoft Agent Framework. Drag-and-drop agent composition with a web-based UI. Best used to prototype AutoGen/MAF agent teams without writing Python.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python"],"mcpSupport":true,"editorialScore":3,"verdict":"AutoGen Studio is a useful prototyping layer on top of AutoGen/MAF for teams that want visual agent composition, but it is not a production deployment target. Consider it a design tool, not a runtime.","bestFor":"Prototyping AutoGen/MAF agent teams without writing Python — visual composition only","strengths":["Visual drag-and-drop agent composition — accessible to non-engineers","Part of AutoGen/MAF ecosystem with Microsoft backing","Good for rapid proof-of-concept with stakeholders"],"weaknesses":["Not a production deployment target — visual layer only","Some fundamental features still missing (human input mode limited)","Dependent on AutoGen/MAF releases — tracks the parent framework"],"latestVersion":"0.7.5","lastChecked":"2026-04-13T16:24:00.246Z","featured":false},{"name":"CrewAI","slug":"crewai","url":"https://crewai.com","githubUrl":"https://github.com/crewAIInc/crewAI","description":"Multi-agent framework built around role-playing crews — each agent has a role, goal, and backstory. Best known for making multi-agent systems approachable with minimal boilerplate.","license":"open-source","hostingModel":"both","languageSupport":["python"],"mcpSupport":true,"editorialScore":3,"verdict":"CrewAI is the fastest path from idea to a working multi-agent prototype, but its sequential execution and production limitations become blockers when you need reliable orchestration under real load.","bestFor":"Rapid multi-agent prototyping and role-based crew designs where time-to-demo matters more than production hardening","strengths":["Intuitive role-based abstraction — role, goal, backstory per agent","Fast release cadence: weekly updates and very active community","48.4k GitHub stars with strong practitioner awareness","MCP support actively maintained with quick CVE response times","CrewAI Cloud available for managed deployments"],"weaknesses":["Sequential task execution despite appearing parallel — manager coordination documented as unreliable","Not optimised for open-source models due to LiteLLM dependency friction","Performance bottlenecks at scale documented across multiple independent reviews","Rapid release cadence can introduce breaking changes between minor versions"],"latestVersion":"1.14.2a3","lastChecked":"2026-04-13T16:23:59.739Z","featured":false},{"name":"DSPy","slug":"dspy","url":"https://dspy.ai","githubUrl":"https://github.com/stanfordnlp/dspy","description":"Stanford's framework for algorithmically optimising LLM prompts and pipelines. Rather than hand-writing prompts, DSPy compiles declarative programs into optimised prompt chains using automatic few-shot generation and bootstrapping.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python"],"mcpSupport":false,"editorialScore":3,"verdict":"A fundamentally different approach to agent and pipeline design — compiling programs rather than writing prompts. High ceiling for teams willing to invest in the paradigm shift; steep learning curve for those expecting a conventional framework.","bestFor":"Research teams and ML engineers who want to optimise prompt pipelines systematically rather than hand-tune prompts","strengths":["Automatic prompt optimisation — the compiler finds better prompts than hand-writing","Declarative pipeline definition separates logic from prompting","Strong academic backing and active Stanford research group","Multi-provider model support","Production-proven at Dropbox for LLM judge optimisation — measurable iteration speed gains","Enables model comparison with measurable evidence rather than manual trial and error"],"weaknesses":["Paradigm shift required — not intuitive for teams used to conventional agent frameworks","Python-only; multi-language teams must stand up a separate repo to use it","No native MCP support","Requires building an evaluation dataset upfront — high barrier before optimisation delivers value","Optimisation runs are compute-intensive and expensive at scale","Not suited to interactive or real-time agent tasks; optimised for offline pipeline tuning","Widespread confusion in practitioner communities about the core value proposition reduces adoption"],"latestVersion":"3.1.3","lastChecked":"2026-04-13T16:24:00.551Z","featured":false},{"name":"Letta","slug":"letta","url":"https://docs.letta.com","githubUrl":"https://github.com/letta-ai/letta","description":"Agent framework specialising in stateful, memory-augmented agents with persistent context across sessions. Formerly MemGPT, rebranded to Letta with an expanded framework focus beyond memory management.","license":"open-source","hostingModel":"both","languageSupport":["python"],"mcpSupport":true,"editorialScore":3,"verdict":"The most mature option for agents that need persistent memory across sessions. If your use case requires agents that remember context long-term, Letta is the specialist. For general-purpose agents, its memory focus is overkill.","bestFor":"Use cases requiring persistent agent memory across sessions — customer-facing agents, long-running assistants, and multi-session workflows","strengths":["Deepest persistent memory implementation of any open-source framework","Stateful agent server with REST API for production deployment","Multi-agent orchestration with shared and private memory spaces","MCP tool support","Strong academic provenance from the original MemGPT research"],"weaknesses":["Memory focus makes it heavier than necessary for stateless agent tasks","Rebranded from MemGPT — ecosystem and docs still in transition","Smaller community than LangChain or LlamaIndex","Server-based architecture adds operational overhead for simple deployments"],"latestVersion":"0.16.7","lastChecked":"2026-04-13T16:24:00.049Z","featured":false},{"name":"Smolagents","slug":"smolagents","url":"https://huggingface.co/docs/smolagents","githubUrl":"https://github.com/huggingface/smolagents","description":"HuggingFace's lightweight Python framework for code-first agents. Takes a minimal-abstraction approach where agents write and execute Python code as their primary action mechanism.","license":"open-source","hostingModel":"self-hosted","languageSupport":["python"],"mcpSupport":true,"editorialScore":3,"verdict":"A strong option for practitioners who want agents that reason through code rather than structured tool calls. Best suited for data science and research workflows close to the HuggingFace ecosystem.","bestFor":"Data science teams and researchers running open-source models who want code-execution as the primary agent action","strengths":["Code-agent approach — agents write Python to act, not just call tools","First-class support for HuggingFace models and Hub","Genuinely minimal — easy to read and modify the source","MCP support added in recent releases","Multi-provider: works with OpenAI, Anthropic, and local models"],"weaknesses":["Code execution as default action raises security considerations in production","Younger ecosystem with fewer production case studies than LangChain","HuggingFace-centric design creates friction outside that ecosystem","Limited enterprise tooling and support"],"latestVersion":"1.24.0","lastChecked":"2026-04-13T16:24:00.289Z","featured":false},{"name":"Strands Agents","slug":"strands-agents","url":"https://strandsagents.com/","githubUrl":"https://github.com/strands-agents/sdk-python","description":"AWS's open-source, model-driven SDK for building AI agents with minimal boilerplate. Defaults to Amazon Bedrock but supports Anthropic, Gemini, OpenAI, and others. Python and TypeScript SDKs maintained.","license":"open-source","hostingModel":"both","languageSupport":["python","typescript"],"mcpSupport":true,"editorialScore":3,"verdict":"A clean, minimal framework from AWS that delivers a simple agent loop with native MCP and multi-provider support. The Bedrock default creates AWS credential friction for non-AWS teams, and the community is still building. Worth evaluating for AWS-native workloads or teams wanting a lightweight alternative to heavier frameworks.","bestFor":"AWS-native teams or Python developers wanting a minimal, model-agnostic agent loop without the abstraction weight of LangChain","strengths":["Genuinely minimal — simple agent loop that is easy to understand and customise","Model-agnostic: supports Bedrock, Anthropic, Gemini, LiteLLM, OpenAI, Ollama, and more out of the box","Native MCP support built in from launch","Python and TypeScript SDKs both maintained","AWS-backed v1.0 production-ready release with stated production support commitment","Built-in multi-agent patterns (supervisor/worker) with clear documentation"],"weaknesses":["Default configuration requires AWS credentials and Bedrock model access — friction for non-AWS teams","AWS / Bedrock orientation despite model-agnosticism; ecosystem documentation is Bedrock-first","Younger project (launched May 2025) — smaller community and fewer production case studies than ADK or LangGraph","5.6k GitHub stars — notably lower community traction than comparable frameworks","TypeScript SDK less mature than Python version"],"latestVersion":"1.35.0","lastChecked":"2026-04-13T16:24:00.207Z","featured":false}]}