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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 ads, 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 LICENSEfile, 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.