Best A2A Agents for Code Generation in 2026

A2A gives you something new in the code generation space: agents that can discover each other, talk over HTTP, and slot into a pipeline without glue code. You can have one agent write code, another review it, and a third open the PR. None of them need to know about each other's internals.

Here are the five agents worth evaluating right now.

Quick Comparison

Agent	Framework	Language	Streaming	Best For
Github Agent	A2A Python SDK	Python	No	PR reviews, issue triage
Code Agent	Pydantic AI + Modal	Python	No	General code generation (hosted)
Ag2 (AutoGen)	AutoGen	Python	No	Self-correcting code via multi-agent loops
Semantic Kernel A2A	Semantic Kernel	C#	No	.NET shops on Azure
Artinet SDK	LangChain	TypeScript	No	Building your own agent fast

Github Agent

Repo: a2aproject/a2a-samples/github-agent

The Github Agent is the one you put at the end of your pipeline. It reviews PR diffs, triages issues, searches code across repos, and queries repo metadata. It is an official A2A sample, so it tracks spec changes fast and the code is clean enough to fork.

from a2a.client import A2AClient

client = A2AClient(url="http://localhost:9000")
response = await client.send_message(
    message={
        "role": "user",
        "parts": [{"kind": "text", "text": "Review PR #42 in my-org/my-repo"}]
    }
)

The limitation is scope. It does repository operations -- it does not generate code. You need something upstream producing the code that this agent then reviews. It also does not support streaming, so long PR reviews block until complete.

Code Agent

Repo: prassanna-ravishankar/a2a-agent-bootstrapping

Code Agent is hosted on Modal, which means you can call it without running anything yourself. It generates code, reviews it, debugs error traces, and optimizes across Python, TypeScript, and Java.

The immediate advantage: it has a live Agent Card you can discover right now.

curl https://prassanna-ravishankar--code-agent-code-agent-app.modal.run/.well-known/agent.json

Caveats: you are sending your code to someone else's Modal deployment. For internal codebases, you will want to fork this and self-host. The agent also has no built-in verification loop -- it generates code and returns it, no self-review step. Pair it with the Ag2 agent below if you want that.

Ag2 (AutoGen)

Repo: a2aproject/a2a-samples/ag2

This is the agent to reach for when correctness matters more than speed. Under the hood, AutoGen runs a "coder" agent and a "reviewer" agent in a loop. The coder writes code, the reviewer pushes back, and they iterate until the reviewer is satisfied (or a turn limit is hit).

• Internal multi-agent debate catches bugs before results reach you
• Official A2A sample, Apache 2.0
• Built on AutoGen's mature orchestration layer

The tradeoff is latency. Multiple rounds of internal LLM calls mean this is noticeably slower than a single-pass generator. For a quick utility function, it is overkill. For a complex algorithm where a subtle off-by-one error would be expensive, it earns its keep.

Semantic Kernel A2A (.NET)

Repo: SiddyHub/a2a-semantic-kernel-dotnet

If your team is C#/.NET and already on Azure, this is really the only option. It wraps Semantic Kernel functions as A2A skills, integrates with Azure OpenAI, and fits into existing .NET microservice architectures.

The interesting bit is dynamic function discovery: the agent can pick up new capabilities at runtime via an Agent Card Repository. So as new agents come online in your network, this one adapts without redeployment.

The honest take: it is a community project, not an official sample. The A2A protocol support may lag behind spec changes. If you are not already using Semantic Kernel, there is no reason to start here -- the Python agents have better ecosystem support.

Artinet SDK

Repo: the-artinet-project/artinet-sdk

Artinet is not an agent. It is a TypeScript SDK for building agents. If none of the above do exactly what you need -- maybe you have proprietary code patterns, internal APIs, or company-specific linting rules -- Artinet gets you from zero to a working A2A-compliant agent with minimal boilerplate.

import { ArtinetAgent } from "artinet-sdk";

const agent = new ArtinetAgent({
  name: "my-code-agent",
  description: "Generates TypeScript code following our internal standards",
  skills: [{ id: "generate", name: "Generate Code", tags: ["typescript"] }],
});

It handles Agent Card generation, task routing, and response formatting. You write the business logic. LangChain under the hood gives you access to multiple LLM providers. The main downside is that you are building and maintaining your own agent, which is more work than using a pre-built one.

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These agents compose well. A realistic pipeline: Artinet-based custom agent receives the request and plans the implementation, Code Agent writes it, Ag2 runs a review loop, Github Agent opens the PR. Each runs independently over HTTP.

See the full Code Generation stack on StackA2A for all available agents.