AI Agent Frameworks and Platforms in 2026: Features, GitHub, and Ecosystem Roles
Compare ten AI agent frameworks and platforms through official GitHub repositories, documented features, competing approaches, and the work each layer owns.
In this record09
- 01AI agent frameworks on GitHub: features and fit
- 02AI agent platforms: Dify and Langflow
- 03Which projects are competitors or alternatives?
- 04LangChain and LangGraph can be used together
- 05AutoGen requires a maintenance-status check
- 06Ecosystem roles: what belongs around the framework?
- 07An example: an agent that prepares a competitor brief
- 08Where Agent Search MCP fits
- 09How to use GitHub evidence before choosing
LangChain, LangGraph, OpenAI Agents SDK, CrewAI, Microsoft Agent Framework, Google ADK, PydanticAI, and Mastra provide different ways to build agents in code. Dify and Langflow add visual application-building surfaces. A useful comparison shows what you would build with each one, which alternatives address the same need, and which components you can use together.
The tables below link directly to the official GitHub repositories. I selected ten projects to cover application SDKs, stateful orchestration, several language ecosystems, and visual delivery. The feature descriptions come from their maintainers' documentation, checked on September 12, 2026. This is a documented-capability comparison; it does not report a benchmark of these frameworks.
AI agent frameworks on GitHub: features and fit
The final column is a question to investigate in your own application. It identifies work to plan for, rather than a feature the project necessarily lacks.
| Project and official GitHub | Main role | Documented features | Useful starting point / question to resolve |
|---|---|---|---|
| LangChain | Application framework | Model and tool integrations; agent-building abstractions | An application connecting several providers or data sources. How much control do you need below its agent abstraction? |
| LangGraph | Stateful orchestration | Graph-based control flow, persistence, durable execution, human intervention | A task that must pause and resume. Which state and side effects must survive interruption? |
| OpenAI Agents SDK | Agent SDK | Agent loop, tools, handoffs, sessions, guardrails, tracing | Embedding a tool-using agent in an application. Which model, tool, and tracing services will you configure? |
| CrewAI | Multi-agent and workflow framework | Role-based Crews plus event-driven Flows | Research or business tasks split across roles. Which transitions should a Flow control? |
| Microsoft Agent Framework | Agent and workflow framework | Python and .NET, multiple providers, graph workflows, checkpointing | Teams building within Python or .NET services. Which hosting and persistence components will you operate? |
| Google ADK | Agent development framework | Tools, multi-agent composition, workflow execution, development tooling | Teams considering Google's agent stack. Which capabilities exist in the language SDK you will use? |
| PydanticAI | Typed Python agent SDK | Typed tools and dependencies, validated structured output, model integrations | An agent feeding structured data into existing Python code. How will you handle invalid or incomplete results? |
| Mastra | TypeScript agent framework | Agents, workflows, memory, evaluation, suspend/resume | A TypeScript or Node.js product. Which storage and deployment setup will support its workflows? |
Language support can live in separate repositories: LangChain.js, LangGraph.js, and OpenAI Agents SDK for JavaScript/TypeScript have their own packages and documentation. Check the implementation you will install; a Python example does not establish equivalent behavior in another SDK.
AI agent platforms: Dify and Langflow
A platform can package workflow editing, model configuration, knowledge connections, and application delivery. Compare that operating surface as well as its agent features.
| Platform and official GitHub | Documented features | Useful starting point | What to inspect before adopting it |
|---|---|---|---|
| Dify | Visual workflows, RAG pipelines, agent tools, model management, observability; cloud and self-hosting options | A team building and maintaining AI applications through a shared interface | Workspace management, self-hosting operations, required integrations, and license conditions |
| Langflow | Visual composition, editable Python components, playground, API and MCP serving | A team assembling flows that will also be called from other applications | Custom-component maintenance, deployment, and access to the exposed endpoints |
Both provide visual authoring, so their features overlap. Their project descriptions emphasize different starting points: Dify packages an application workspace, while Langflow emphasizes composing flows and exposing them through APIs or MCP. Run the same small workflow in both before deciding whether that distinction matters for your team.
Repository visibility alone does not settle reuse rights. For example, Dify's license includes additional conditions, while Mastra's license mapping distinguishes its core from enterprise directories. Read the license for the code and use case you intend to adopt.
Which projects are competitors or alternatives?
Two projects become alternatives when you expect them to own the same work. This gives a more useful shortlist than putting every agent-related repository into one ranking.
| Your decision | Candidates to compare | A useful comparison task |
|---|---|---|
| Build a tool-using application in Python | LangChain, OpenAI Agents SDK, PydanticAI | Call the same tool, return the same data structure, and inspect validation and error handling |
| Coordinate a task with branches and pauses | LangGraph, CrewAI Flows, Microsoft Agent Framework, Google ADK | Pause for review, restart the process, and inspect where execution resumes |
| Build agents inside a TypeScript product | Mastra, LangChain.js / LangGraph.js, OpenAI Agents SDK JS | Connect the existing UI and backend; compare workflow storage and deployment work |
| Let a team edit and publish flows visually | Dify, Langflow | Build a document-backed assistant, update its data, and expose it to the intended users |
These are proposed evaluation tasks, not results from a comparative test. A candidate may cover several rows. Start with the row that describes your main requirement, then test the remaining requirements against that shortlist.
LangChain and LangGraph can be used together
LangChain's documentation describes its agents as built on LangGraph. LangChain supplies a higher-level interface and integrations; LangGraph gives more direct control over execution and state. You can begin at either level, and LangGraph can be used without LangChain. Their overlap reflects a shared stack as well as a choice of abstraction.
AutoGen requires a maintenance-status check
Older comparisons frequently list AutoGen as a starting point for new projects. Its current official README says it is in maintenance mode and directs new users to Microsoft Agent Framework. Existing AutoGen applications need a migration assessment; they do not become unusable merely because the recommendation changed. Microsoft provides an AutoGen migration guide.
Ecosystem roles: what belongs around the framework?
An agent application also needs a model, tools, a place to execute, and a way to inspect results. Some products bundle several of these responsibilities.
| Layer | Work it owns | Relationship to the shortlist |
|---|---|---|
| Model service | Generates responses and tool-call requests | A dependency selected through the framework's supported model interfaces |
| Agent framework / runtime | Runs the loop, routes work, and manages the state its APIs expose | The code-level choice compared above |
| Application platform | Gives people a way to configure, publish, and operate applications | May package a framework and runtime behind its interface |
| Execution environment / harness | Connects the model to tools, files, permissions, and a working environment | Must be evaluated where actions actually run; terminology and packaging vary |
| MCP | Defines a protocol for connecting applications to tools and other external context | Can connect a framework or platform to separately maintained services |
| Search, retrieval, and other tools | Fetch or change information in their own systems | Can remain separate when the main framework changes |
| Evaluation and observability | Record runs and assess outcomes against chosen criteria | Help compare candidate implementations and diagnose failures |
MCP's official introduction describes a connection standard. An MCP server and an orchestration framework therefore often work together. Adopting MCP still leaves your application to decide when a tool is appropriate, what access it receives, and how its result is used.
For the definitions in more detail, see framework vs platform vs harness vs MCP.
An example: an agent that prepares a competitor brief
Suppose the task is to compare three products using official documentation and return a brief with source links. The acceptance conditions could be simple: every feature claim has a source, missing evidence remains visible, and a person reviews the brief before it is sent.
The model helps extract and explain differences. A framework coordinates searches, page reads, and drafting. A search tool discovers sources; a reader fetches their contents. Persistent workflow state becomes relevant if review spans sessions. A visual platform may be useful if another team member needs to change the workflow regularly.
This task does not automatically need multiple agents. You can first try one agent with search and reading tools. If separate research and checking roles improve a measured problem, compare a role-based implementation with the simpler version using the same source set and acceptance conditions.
For this example, I would record which claims are supported, which sources failed, whether an interrupted run repeated an action, and how much each completed brief cost. Those observations would give me a reason to choose a framework.
Where Agent Search MCP fits
I maintain Agent Search MCP, a Chinese and English web-search tool service exposed through MCP. Its GitHub repository documents the available sources, setup, and limitations.
It belongs in the search-tool row of the map. If you have already chosen a framework and need web search, compare that service with the search API or provider integration you would otherwise use. Check the quality of sources for your queries, visible failure behavior, and the result format your agent receives. For a broader provider comparison, continue with the AI agent search API guide.
How to use GitHub evidence before choosing
Stars help you discover projects. A selection decision also needs the maintained package, its license, release history, and examples for your actual requirements. Open issues can reveal a relevant limitation, but their count alone does not establish reliability or adoption.
For each shortlisted project, follow its official repository to the documentation and release you intend to use. Run one representative task and one interruption case. Record the version, configuration, and observed result so another developer can reproduce your comparison.
This September revision replaces the article's June popularity tables and market predictions with a sourced comparison. It makes no current star-count or market-share claims. To turn a shortlist into an implementation decision, use the framework selection guide, which works through control flow, recovery, approvals, and deployment.