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AI Agent Framework Comparison

Compare 9 LLM agent frameworks, LangGraph, LlamaIndex, CrewAI, AutoGen, OpenAI Agents SDK, Google ADK, PydanticAI, Semantic Kernel and Strands, side by side. Filter by language, license, memory, RAG and MCP, then pick your stack in one screen. Every cell is source-cited. No signup, no paywall, nothing leaves your browser.

By Induwara AshinsanaUpdated Jun 25, 2026
Compare AI agent frameworks9 frameworks
Data verified · Jun 25, 2026
Language
License
Capabilities

9 of 9 frameworks match

Microsoft AutoGen

Python · C#

MIT

Conversational, event-driven multi-agent runtime (v0.4) with OpenTelemetry tracing built in.

Multi-agent:
Conversational / group-chat multi-agent
Providers:
Provider-agnostic (OpenAI, Azure, local)
Released:
2023
Docs

CrewAI

Python

MIT

Role-based 'crews' of agents with built-in memory and a process model (sequential or hierarchical).

Multi-agent:
Role-based crews + sequential/hierarchical flows
Providers:
Provider-agnostic (via LiteLLM)
Released:
2024
Docs

Google ADK

Python · Java

Apache-2.0

Google's Agent Development Kit: agent hierarchies, bidirectional streaming and native MCP, Gemini-first but model-agnostic.

Multi-agent:
Agent hierarchies + workflow agents
Providers:
Gemini first-class; model-agnostic via LiteLLM
Released:
2025
Docs

LangGraph

Python · TypeScript / JS

MIT

Graph-based orchestration on the LangChain stack: explicit state, branching and checkpointed memory for stateful multi-agent workflows.

Multi-agent:
Graph of nodes/edges with shared state
Providers:
Provider-agnostic (LangChain integrations)
Released:
2024
Docs

LlamaIndex

Python · TypeScript / JS

MIT

RAG-first data framework: indexing, retrievers and query engines, with an AgentWorkflow layer for agentic flows on top.

Multi-agent:
AgentWorkflow (event-driven workflows)
Providers:
Provider-agnostic (LlamaIndex integrations)
Released:
2022
Docs

OpenAI Agents SDK

Python · TypeScript / JS

MIT

Lightweight handoffs-and-guardrails SDK with built-in tracing and session memory; OpenAI-first but model-portable.

Multi-agent:
Handoffs between agents + guardrails
Providers:
OpenAI first-class; any Chat-Completions provider via LiteLLM
Released:
2025
Docs

PydanticAI

Python

MIT

Type-safe, model-agnostic agents from the Pydantic team, with structured outputs and first-class Logfire/OpenTelemetry tracing.

Multi-agent:
Agent delegation + pydantic-graph
Providers:
Model-agnostic (OpenAI, Anthropic, Gemini, …)
Released:
2024
Docs

Semantic Kernel

C# · Python · Java

MIT

Microsoft's enterprise-oriented SDK spanning C#, Python and Java, with plugins, agent orchestration and OpenTelemetry.

Multi-agent:
Agent framework + orchestration patterns
Providers:
Provider-agnostic (OpenAI, Azure, local)
Released:
2023
Docs

Strands Agents

Python

Apache-2.0

AWS's model-driven, MCP-native SDK with agents-as-tools, swarm and graph multi-agent patterns and OpenTelemetry tracing.

Multi-agent:
Agents-as-tools, swarms and graphs
Providers:
Amazon Bedrock first-class; model-agnostic
Released:
2025
Docs

Every cell maps to a documented fact from the framework's own docs, repository LICENSE file, and published SDK packages, links are on each card. License and language facts are taken verbatim from the source; subjective "which is best" verdicts are deliberately excluded. Data last verified Jun 25, 2026.

How it works

This is a reference tool, not a numeric calculator, so the methodology is about how the data is sourced and how filtering is computed, both fully in your browser, with zero network requests at runtime.

  1. Field definitions. Each capability column has a precise, documented meaning, for example, MCP / tools means the framework ships first-party support for the Model Context Protocol or a documented tool-calling abstraction. The exact definitions appear in the tooltip on each capability chip.
  2. Tri-state cells. Every capability is Yes (documented first-party), Partial (community/experimental or via an add-on) or No (not documented). Each Yes and Partial links to the page that documents it.
  3. License and language. The license is taken verbatim from each repository's LICENSE file / SPDX id. Only officially published SDK languages count toward the language column.
  4. Filtering logic. Language and License chips are OR within their group, a framework matches if it offers any one of the selected languages or licenses. Capability chips are AND, a framework must support every selected capability, because if you require Memory and MCP you want frameworks that have both, not either. The three groups are AND-ed together, then an optional name search narrows further. In set terms a framework matches iff langOk && licenseOk && everyCapOk && nameOk.
  5. No scoring, no winner. Results are shown in alphabetical order. The tool never emits a single "best" framework, because that would be opinion rather than a citable fact.

The filter predicate is implemented twice, a generic group loop and an independent Set-based re-derivation, and a self-check confirms the two agree on every framework for any query. This is the same cross-check discipline the site's calculators use, applied to boolean logic instead of arithmetic.

Worked examples

TypeScript framework with RAG

Language = TypeScript/JS · Capability = RAG

  1. TypeScript SDKs: LangGraph, LlamaIndex, OpenAI Agents SDK.
  2. Of those, all three have RAG support (LlamaIndex Yes; LangGraph and OpenAI Agents SDK Partial, counted as supported).
  3. CrewAI is Python-only, so it never appears in a TypeScript result.
  4. Result = LangGraph, LlamaIndex, OpenAI Agents SDK (3 matches).

Apache-2.0 licensed, with a Python SDK

Language = Python · License = Apache-2.0

  1. Apache-2.0 frameworks in the dataset: Google ADK and Strands Agents.
  2. Both ship a Python SDK, so both pass the language group.
  3. Every MIT-licensed framework is excluded by the license filter.
  4. Result = Google ADK, Strands Agents (2 matches).

Edge case: a filter that matches nothing

Language = Go

  1. No framework in this dataset publishes a Go SDK.
  2. The language group fails for every row.
  3. Result = 0 matches → the tool shows an explicit 'no frameworks match' state with a one-tap clear-filters action, never a blank table.

Frequently asked questions

Sources & references

Each framework's own documentation is the authoritative source for its features; license and language facts come from the repository itself. Primary sources, one per framework:

Every cell on this page was last cross-checked against these sources on Jun 25, 2026. The dataset is re-verified on major framework releases. Spotted something out of date? Email me with a source link and I'll fix it.

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Comments & feedback

Spotted a bug or want an improvement? Tell us, our team reviews every comment, and good ideas get built. Comments are public and anonymous.

Found a cell that's out of date, or a framework worth adding?

Email me at [email protected] with a source link, corrections usually ship within 24 hours.