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AG2 alternatives

15 tools we tested head to head against AG2, ranked — and what each one actually does differently.

last reviewed 23 jul 2026 · from our best 16 ai agent frameworks ·list curated by Onur Ozcanxin

first — what you'd be leaving

AG2 ranks #16 of 16 in our ai agent frameworks testing. the community autogen fork — active but overshadowed.

63/100

preserves autogen's conversational multi-agent patterns under community governance and is actively developed — but at under 5,000 stars it's directly overshadowed by microsoft's own successor, so momentum is the real risk.

why people look for an alternative
  • under 5,000 stars
  • overshadowed by microsoft agent framework
  • longevity and momentum uncertain

stay with AG2 if preserves familiar autogen 0.2 conversation patterns is the thing you care about most — nothing below beats it on that.

the short version
best alternativeLangGraphteams that need durable state, human-in-the-loop and fine-grained control92/100
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  1. 1

    LangGraph

    #1 in ai agent frameworks · the most production-proven stateful-agent runtime

    92/100

    verdictthe thing serious teams build on: a low-level graph runtime with real durability and human-in-the-loop, 1.0-stable, and proven at production scale. steeper to learn, but it earns it.

    LangGraph vs AG2
     AG2LangGraph
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython + js/ts
    core abstractionconversational multi-agentgraph / state machine
    licenseApache-2.0MIT
    durable executionnoyes (checkpointers)
    provider lock-incross-providercross-provider

    switch forteams that need durable state, human-in-the-loop and fine-grained control

    pros
    • +genuine durable state, hitl and retries via checkpointers
    • +1.0 stable with a no-breaking-changes commitment
    • +most production-proven option in the category
    cons
    • steeper learning curve than the minimal sdks
    • surrounding langchain ecosystem feels heavy
    • best observability/evals live in paid langsmith
  2. 2

    OpenAI Agents SDK

    #2 in ai agent frameworks · minimal, clean and production-ready — despite the name

    89/100

    verdictthe productionised successor to swarm: a tight event-loop with handoffs, guardrails and built-in tracing that ships fast and reads cleanly. thinner on durability than langgraph, but you can bolt temporal on.

    OpenAI Agents SDK vs AG2
     AG2OpenAI Agents SDK
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython + typescript
    core abstractionconversational multi-agentevent loop + handoffs
    licenseApache-2.0MIT
    durable executionnovia temporal integration
    provider lock-incross-providercross-provider

    switch forteams that want a small, readable agent sdk with built-in tracing

    pros
    • +minimal, readable, fast to ship
    • +built-in tracing, guardrails and sessions
    • +cross-provider despite the openai name
    cons
    • no native durable execution (pair with temporal)
    • leans toward openai's own apis
    • lighter state story than langgraph
  3. 3

    Pydantic AI

    #3 in ai agent frameworks · type-safe agents from the team that validates half of python ai

    88/100

    verdictthe type-safety pick: v1-stable, native temporal durable execution and opentelemetry built in, from the team whose library underpins most python ai. younger multi-agent story than the leaders.

    Pydantic AI vs AG2
     AG2Pydantic AI
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython
    core abstractionconversational multi-agenttyped agent + graph
    licenseApache-2.0MIT
    durable executionnoyes (native temporal)
    provider lock-incross-providercross-provider

    switch forpython teams that want typed, validated agent outputs and clean observability

    pros
    • +best type-safety and structured outputs in python
    • +v1 stable with native temporal durable execution
    • +opentelemetry/logfire tracing and first-class mcp
    cons
    • younger multi-agent story than langgraph/crewai
    • smaller (if fast-growing) ecosystem
    • python only
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  4. 4

    Google ADK

    #4 in ai agent frameworks · code-first, ga, enterprise deploy — with gemini gravity

    86/100

    verdicta clean, ga code-first framework with strong multi-agent orchestration and a real enterprise deploy story on vertex — model-agnostic via litellm, but at its best on gemini and gcp.

    Google ADK vs AG2
     AG2Google ADK
    pricefree — Apache-2.0 (open source)free — Apache-2.0 (open source)
    free tieryesyes
    languagepythonpython + java
    core abstractionconversational multi-agentagent + workflow agents
    licenseApache-2.0Apache-2.0
    durable executionnovia vertex agent engine
    provider lock-incross-providermodel-agnostic (litellm)

    switch forteams building multi-agent systems that will deploy on google cloud

    pros
    • +clean code-first design with strong multi-agent
    • +built-in evals, tracing and a2a
    • +real enterprise deploy story on vertex
    cons
    • gravity toward google cloud and gemini
    • best features assume vertex
    • younger than langgraph as a runtime
  5. 5

    Claude Agent SDK

    #5 in ai agent frameworks · the exact harness that runs claude code, as a library

    85/100

    verdictthe best-in-class tool-using autonomous loop, because it's literally what runs claude code — with context management no one else matches. the trade-off is deliberate: claude-only.

    Claude Agent SDK vs AG2
     AG2Claude Agent SDK
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython + typescript
    core abstractionconversational multi-agentautonomous tool loop
    licenseApache-2.0MIT
    durable executionnocontext mgmt, not durable state
    provider lock-incross-providerclaude-only

    switch forautonomous agents that use files, commands and code with long-running context

    pros
    • +unmatched for file/command/code-using autonomous agents
    • +best-in-class context management and compaction
    • +subagents with isolated context, hooks and mcp
    cons
    • claude-only by design
    • not a multi-provider orchestrator
    • bring your own tracing/evals
  6. 6

    CrewAI

    #6 in ai agent frameworks · the fastest 'team of agents' mental model, now standalone

    83/100

    verdictthe most intuitive role-based framework, now free of its old langchain dependency, with flows for when you need determinism. great ergonomics; fine-grained control is the newer part.

    CrewAI vs AG2
     AG2CrewAI
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython
    core abstractionconversational multi-agentrole-based crews + flows
    licenseApache-2.0MIT
    durable executionnoflow persistence
    provider lock-incross-providercross-provider

    switch forteams that think naturally in roles and want agents collaborating on tasks

    pros
    • +fastest role-based 'team of agents' model
    • +now standalone — dropped the langchain dependency
    • +large community; flows add deterministic control
    cons
    • autonomous crews hard to make deterministic at scale
    • fine-grained control lives in newer flows
    • durability weaker than langgraph/temporal-backed tools
  7. 7

    Microsoft Agent Framework

    #7 in ai agent frameworks · the ga successor that merged autogen and semantic kernel

    81/100

    verdictthe official convergence of autogen and semantic kernel — 1.0 ga, the only serious first-class .net agent framework — but young as a merged product and heavy on azure gravity.

    Microsoft Agent Framework vs AG2
     AG2Microsoft Agent Framework
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython + .net
    core abstractionconversational multi-agentagent + workflow orchestration
    licenseApache-2.0MIT
    durable executionnoazure-backed
    provider lock-incross-providermulti-provider (azure-deepest)

    switch for.net shops and azure-centric enterprises building orchestrated agent fleets

    pros
    • +only serious first-class .net agent framework
    • +official 1.0 ga successor to autogen + semantic kernel
    • +a2a, mcp interop and azure deploy
    cons
    • young as a merged product
    • heavy microsoft/azure gravity
    • predecessors' churn muddies the migration story
  8. 8

    AWS Strands Agents

    #8 in ai agent frameworks · model-driven, ga, and already running inside aws services

    80/100

    verdicta clean, model-driven framework that's genuinely production-used inside aws, ga at 1.0 with four multi-agent primitives — model-agnostic, but with the expected aws pull.

    AWS Strands Agents vs AG2
     AG2AWS Strands Agents
    pricefree — Apache-2.0 (open source)free — Apache-2.0 (open source)
    free tieryesyes
    languagepythonpython + typescript
    core abstractionconversational multi-agentmodel-driven loop
    licenseApache-2.0Apache-2.0
    durable executionnoremote session manager
    provider lock-incross-providermodel-agnostic

    switch foraws-centric teams that want a model-first loop with native multi-agent primitives

    pros
    • +genuinely production-used inside aws services
    • +ga 1.0 with four native multi-agent primitives
    • +model-agnostic — swap backends without code changes
    cons
    • smaller community than the leaders
    • aws-ecosystem pull; deepest on bedrock
    • younger than langgraph/openai sdk
  9. 9

    LlamaIndex Workflows

    #9 in ai agent frameworks · the right pick when the agent is rag- and data-heavy

    78/100

    verdictan event-driven, 1.0-stable workflow runtime sitting on the strongest rag stack — the natural choice when retrieval, not orchestration, is the hard part of your agent.

    LlamaIndex Workflows vs AG2
     AG2LlamaIndex Workflows
    pricefree — Apache-2.0 (open source)free — MIT (open source)
    free tieryesyes
    languagepythonpython + typescript
    core abstractionconversational multi-agentevent-driven steps
    licenseApache-2.0MIT
    durable executionnoworkflow persistence
    provider lock-incross-providercross-provider

    switch foragents whose core job is retrieval over your own data

    pros
    • +best fit for rag- and data-heavy agents
    • +clean event-driven model, 1.0 stable
    • +sits on a strong retrieval stack
    cons
    • broader llamaindex package sprawl
    • more data-framework than pure agent runtime
    • orchestration lighter than langgraph
  10. 10

    Agno

    #10 in ai agent frameworks · fast, full-stack, now framework-agnostic — after a churny v2

    76/100

    verdicta high-performance, batteries-included library with a genuinely novel framework-agnostic runtime — but the breaking v2 rewrite and the phidata rename hurt its stability story.

    Agno vs AG2
     AG2Agno
    pricefree — Apache-2.0 (open source)free — Apache-2.0 (open source)
    free tieryesyes
    languagepythonpython
    core abstractionconversational multi-agentagent + team + agentos
    licenseApache-2.0Apache-2.0
    durable executionnoagentos runtime
    provider lock-incross-providercross-provider

    switch forteams wanting a fast, batteries-included runtime that can host other frameworks' agents

    pros
    • +very fast, batteries-included
    • +novel framework-agnostic agentos runtime
    • +teams and workflows for multi-agent
    cons
    • v2 was a breaking rewrite requiring migration
    • phidata rename plus v2 churn hurt stability perception
    • smaller track record than the leaders
+ 5 more tested, not detailed here
we ranked 16 ai agent frameworks in total. the 5 that didn't make this page are written up in the full ranking →

how these were compared

every tool on this page went through the same test as AG2 — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.

the ai agent frameworks test in full →
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