Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ag2ai/ag2-skills --skill ag2-a2agit clone --depth 1 https://github.com/ag2ai/ag2-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-a2a)<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-a2a"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-a2a/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-a2a"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-a2a.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00271 | $0.03888 |
| Opus 5 | $0.00135 | $0.01944 |
| Sonnet 5 | $0.00054 | $0.00778 |
| Haiku 4.5 | $0.00027 | $0.00389 |
Grade A, and why
ag2-a2a scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A2A (Agent-to-Agent) integration
Expose an AG2 Agent over the A2A protocol — a standard JSON-RPC / REST / gRPC contract plus a discoverable AgentCard — so other systems can call your agent over the network. The same module also lets an AG2 Agent consume a remote A2A agent by using A2AConfig as the agent's model config.
When to use
- You want an AG2 agent reachable as a standard networked service that non-AG2 A2A clients can call (interop), not just from Python.
- You need a discoverable agent: a published
AgentCardat/.well-known/agent-card.jsondescribing the agent's skills, transports, auth, and capabilities. - You want a choice of transport (JSON-RPC, REST, or gRPC) — possibly several at once from one server.
- You need declared auth schemes (bearer / API key / OAuth2 / OIDC / mTLS), push notifications, or multi-tenancy surfaced on the card.
- Conversely, you want one AG2 agent to call a remote A2A agent as if it were an LLM provider — use
A2AConfigas the calling agent'sconfig.
If you only need a web frontend (React/CopilotKit) in front of an agent, use the AG-UI skill instead. If two AG2 agents simply need to talk inside one process/cluster, the AG2 network is the standard multi-agent pattern.
Installation
pip install "ag2[a2a]"
Required. The
a2aextra pulls ina2a-sdk(with the HTTP server). gRPC needs thea2a-sdk[grpc]extra in addition. If you cannot run commands, state the exactpip installcommand.
Public API (from ag2.a2a import ...): A2AConfig, A2AServer, build_card.
60-second recipe — serve an agent over A2A
A2AServer.build_jsonrpc(...) returns a ready-to-serve Starlette ASGI app. Run it directly with uvicorn:
from ag2 import Agent
from ag2.a2a import A2AServer
from ag2.config import OpenAIConfig
agent = Agent(
name="weather_bot",
prompt="You answer questions about the weather.",
config=OpenAIConfig(model="gpt-4o-mini"),
)
server = A2AServer(agent)
# returns a Starlette ASGI app; `url` is the public URL written into the card
app = server.build_jsonrpc(url="http://localhost:8000")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 294 lines · 271 tokens per session scan A d90f088b5fc7
ag2-a2a is a skill published in the GitHub repository ag2ai/ag2-skills (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 271 tokens to every session and 3,888 once invoked, about $0.0014 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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