8 Best A2A Alternatives in 2026 (Open Source)
A2A — Agent2Agent (A2A) is an open protocol enabling communication and interoperability between opaque agentic applications. vs MCP: enables agent-to-agent collaboration (agents as peers) while MCP connects agents to tools; vs custom APIs: standardized discovery via Agent Cards and built-in support for long-running tasks and streaming
Short answer
- Closest match to A2A: agent protocol.
- Most actively developed: DeerFlow (1,274 commits in the last 90 days).
- Fastest growing: DeerFlow (+5,271 GitHub stars in the last 30 days).
- No commit in 6+ months: agent protocol, Eidolon and GPTeam.
These 8 open-source tools do the same job. They are ordered by how closely they match A2A, with live GitHub data so you can see which projects are actively maintained.
By package downloads AgentScope is the most used here (296.7K in the last 30 days), even though DeerFlow has the most GitHub stars. See all agent tools by downloads.
| Tool | GitHub stars | Stars / 30d | Last commit | Downloads / 30d |
|---|---|---|---|---|
| A2A(original) | 26.0k | +494 | 2026-10-02 | — |
| agent protocol | 1.5k | 0 | 2025-04-08 | — |
| Eidolon | 492 | +1 | 2024-12-19 | — |
| Agency Swarm | 4.6k | +74 | 2026-10-02 | 3.3K |
| AutoGen | 61.2k | +782 | 2026-04-06 | — |
| CAMEL | 17.8k | +205 | 2026-09-30 | 42.8K |
| GPTeam | 1.7k | +1 | 2024-06-28 | — |
| DeerFlow | 83.3k | +5,271 | 2026-10-03 | — |
| AgentScope | 32.7k | +1,829 | 2026-09-30 | 296.7K |
1. agent protocol
Common interface for interacting with AI agents. The protocol is tech stack agnostic - you can use it with any framework for building agents.
What sets it apart: vs custom agent APIs: industry-standard interoperability protocol backed by AI Engineer Foundation — like OpenAPI but specifically for AI agents, eliminating per-agent integration work
Best for: Benchmarking and comparing different AI agents; Building cross-compatible agent developer tools; Reducing boilerplate API development for agents
2. Eidolon
The first AI Agent Server, Eidolon is a pluggable Agent SDK and enterprise ready, deployment server for Agentic applications
What sets it apart: vs LangChain/CrewAI: agents are deployed as HTTP services with built-in server, enabling true microservice agent architectures with dynamic inter-agent tool discovery
Best for: Deploying agents as production HTTP services; Multi-agent systems needing inter-agent communication
3. Agency Swarm
Reliable Multi-Agent Orchestration Framework
What sets it apart: Multi-agent framework modeling real-world organizational structures with directional communication flows — vs CrewAI (role-based but less control) or AutoGen (conversation-centric)
Best for: Building multi-agent systems modeled as organizational structures; Teams wanting full control over agent instructions and communication; Production multi-agent deployments with typed tools
4. AutoGen
A programming framework for agentic AI
What sets it apart: Microsoft's layered multi-agent framework (Core/AgentChat/Extensions) with no-code Studio, .NET support, and MCP integration — most enterprise-backed open-source agent framework
Best for: Building multi-agent AI systems with complex orchestration; Teams prototyping agent workflows with no-code Studio; Cross-language (Python/.NET) agent applications
5. CAMEL
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
What sets it apart: Purpose-built for studying agent scaling laws with million-agent simulation support — vs other frameworks focused on practical deployment
Best for: Research on multi-agent collaboration and emergent behaviors; Synthetic data generation for model training
6. GPTeam
GPTeam: An open-source multi-agent simulation
What sets it apart: GPTeam models a configurable world where memory-equipped agents move, communicate, and work in parallel on common goals.
Best for: Developers exploring multi-agent architectures; Researchers experimenting with agent memory and communication; Teams prototyping collaborative agent simulations
7. DeerFlow
Open-source agent harness for long-horizon research, coding, and content creation
What sets it apart: vs AutoGPT: purpose-built for deep research with sub-agent orchestration and sandbox; vs LangGraph: higher-level harness with built-in memory, sandbox, and skill system rather than bare graph framework
Best for: Deep research and exploration tasks; Building multi-agent systems with sub-agent orchestration; Teams wanting coding agent integration (Claude Code/Codex)
8. AgentScope
Build and run agents you can see, understand and trust.
What sets it apart: Unlike LangGraph (stateful graph orchestration) and CrewAI (role-based crews), AgentScope uniquely combines realtime voice agents, A2A protocol, agentic RL fine-tuning, and Kubernetes-native deployment — designed for the rising capability of agentic LLMs
Best for: Teams building production multi-agent systems with realtime voice and A2A interoperability; Chinese-market developers wanting first-class DashScope/Qwen integration
FAQ
- What are the best alternatives to A2A?
- The closest open-source alternatives to A2A are agent protocol, Eidolon and Agency Swarm, followed by AutoGen, CAMEL and GPTeam. They are ranked by how closely they match what A2A does.
- Which A2A alternative is the most popular?
- DeerFlow has the most GitHub stars among A2A alternatives, with 83,349 stars.
- Which A2A alternative is the most actively maintained?
- By recent activity, DeerFlow (1,274 commits in the last 90 days) is the most actively developed alternative.