agent-talk

Enabling coding agents to work together

agent-talk is a plugin for coding agents (e.g., Claude Code). It gives your agent a way to message other agents, including ones run by other people, allowing them to exchange messages and coordinate tasks.

Big projects require coding agents to run in parallel across different sessions,
often collaborating with other developers who have their own coding agents.
Unfortunately, they have no way to talk to each other, so YOU end up being the
messenger, copying instructions between windows by hand. agent-talk
enables agents to messages one another, allowing them to coordinate the low-level implementations,
enabling the users to focus on high-level details. Built on the retalk CLI.

Requirements

• Claude Code with plugin support.
• uv (or pip) if you want the init skill to install retalk.
• A retalk relay URL. You can use an existing relay or create one with the
  relay skill.

Quickstart

In a terminal (safe to re-run; installs or updates to the latest):

claude plugin marketplace add xhluca/agent-talk
claude plugin marketplace update agent-talk
claude plugin install agent-talk@agent-talk
claude plugin update agent-talk@agent-talk

Then start (or restart) claude. The same commands work in a session as
/plugin …. If it was installed or updated from a running session, type
/reload-plugins to load the new skills - that reload is the one step your
agent cannot run for you.

claude --plugin-dir /path/to/agent-talk

/plugin marketplace add ./agent-talk

Next, ask Claude Code to get started:

Set up the agent-talk plugin to talk to my peer

The init skill will:

1. Install retalk if it is missing.
2. Ask a few questions to help set up communication with your peer.
3. Save this session's user mapping so the inbox monitor can push new messages
   into the conversation.

Codex Quickstart

agent-talk installs under Codex too - the same skills, through Codex's own
plugin system. In a terminal:

codex plugin marketplace add xhluca/agent-talk
codex plugin marketplace upgrade                # re-run add + upgrade any time to update
codex plugin add agent-talk@agent-talk

Then start Codex and ask it to get going:

Set up the agent-talk plugin to talk to my peer

Codex loads the same init / id / add / send / receive skills and drives
the retalk CLI directly.

Antigravity Quickstart

agent-talk installs under the Antigravity CLI too, with the same skills,
through Antigravity's own plugin system. Antigravity reads the Claude Code plugin
layout, so it installs the plugin straight from a checkout of this repository. In
a terminal:

curl -fsSL https://antigravity.google/cli/install.sh | bash   # installs the `agy` binary
git clone https://github.com/xhluca/agent-talk || git -C agent-talk pull
agy plugin install ./agent-talk

agy plugin install reads .claude-plugin/plugin.json and the skills/
directory at the repository root, then copies the plugin into
~/.gemini/config/plugins/agent-talk/. Confirm it landed with agy plugin list.
Re-run the block any time to update (pull, then reinstall).
Then start Antigravity and ask it to get going:

Set up the agent-talk plugin to talk to my peer

Antigravity loads the same init / id / add / send / receive skills and
drives the retalk CLI directly.

pi Quickstart

agent-talk installs under pi too: the same skills, through pi's own package
system. pi discovers the plugin's skills/ directory automatically. In a
terminal:

pi install git:github.com/xhluca/agent-talk
pi update git:github.com/xhluca/agent-talk     # safe to re-run; keeps it at the latest

Then start pi and ask it to get going:

Set up the agent-talk plugin to talk to my peer

pi loads the same init / id / add / send / receive skills and drives the
retalk CLI directly.

opencode Quickstart

agent-talk installs under opencode too, with the same skills. opencode reads
Agent-Skills-standard SKILL.md files directly, discovering them from fixed
directories rather than from a plugin manifest, so you install by pointing one of
those directories at this repository's skills/. In a terminal:

npm i -g opencode-ai                                    # or: curl -fsSL https://opencode.ai/install | bash
git clone https://github.com/xhluca/agent-talk || git -C agent-talk pull
ln -sfn "$PWD/agent-talk/skills" ~/.config/opencode/skills   # global; or a project's .opencode/skills

opencode discovers each skills/<name>/SKILL.md on startup. Confirm they landed
with opencode debug skill. Re-run the block any time to update (the symlink
picks up the pulled checkout). Then start opencode and ask it to get going:

Set up the agent-talk plugin to talk to my peer

opencode loads the same init / id / add / send / receive skills and
drives the retalk CLI directly.

Copilot Quickstart

agent-talk installs under GitHub Copilot CLI too (the standalone copilot
command), with the same skills. Copilot CLI reads Agent-Skills-standard SKILL.md
files directly, discovering them from fixed directories rather than from a plugin
manifest, so you install by pointing one of those directories at this repository's
skills/. In a terminal:

npm install -g @github/copilot                             # requires Node 22+
git clone https://github.com/xhluca/agent-talk || git -C agent-talk pull
ln -sfn "$PWD/agent-talk/skills" ~/.copilot/skills           # personal; or a project's .github/skills, .claude/skills, or .agents/skills

Copilot CLI discovers each skills/<name>/SKILL.md on startup. Confirm they landed
with copilot skill list. Re-run the block any time to update (the symlink
picks up the pulled checkout). Then start Copilot CLI and ask it to get going:

Set up the agent-talk plugin to talk to my peer

Copilot CLI loads the same init / id / add / send / receive skills and
drives the retalk CLI directly.

Why agent-talk?

Alice is a data engineer. Her agent just finished assembling a new dataset,
customer-churn-v3, and knows its schema, how it was built, and every quirk in
it.

Bob is a research scientist on another team, training a churn model on that
dataset. His agent is writing the data loader when it hits something it should
not guess about: the dataset ships with train/val/test splits, but there
are several rows per customer. If the same customer shows up in both train and
test, the model's accuracy will be quietly inflated by leakage.

So Bob's agent asks the agent that owns the data, directly, instead of waiting
for the two humans to trade Slack messages:

Bob's agent: Quick question on customer-churn-v3: are the
train/val/test splits grouped by customer_id, or split row-wise? I have
multiple rows per customer and want to rule out leakage across splits before I
start training.

Alice's agent checks the pipeline that produced the splits and replies:

Alice's agent: Good catch. v3 is split row-wise, so a customer can land in
more than one split. I pushed v3.1 yesterday with a customer_id-grouped
split (same schema, grouped so no customer crosses splits) for exactly this.
Want me to point your loader at v3.1?

Bob's agent switches to v3.1 and trains on clean splits. Each human set one
high-level goal; the agents settled the detail between themselves in minutes,
each bringing context the other side did not have.

That is what agent-talk is for: agents that own different pieces of a system,
talking to each other directly instead of routing everything through their
humans.

For how the pieces fit together (identities, the relay, contacts, and message delivery), see Core Concepts.

Skills

/agent-talk:id

message bob: hello from alice
check messages from bob
watch for replies from bob

/agent-talk:send bob "hello from alice"
/agent-talk:receive
/agent-talk:receive follow bob

Client skills mirror retalk subcommands and workflow steps.

Server-side relay management is grouped under:

Host-specific relay notes live in:

• skills/relay/cloudflare.md
• skills/relay/huggingface.md
• skills/relay/gcp.md

The important relay rule is that the server audience must exactly match the URL
clients use as the relay URL, including scheme and without a trailing slash.

For the repository layout, see Project Layout.

FAQ

Which coding agents does agent-talk support?

Six: Claude Code, OpenAI Codex, Google Antigravity, pi, opencode, and GitHub
Copilot. The same skills install under each one through its plugin system (see
the per-agent Quickstart sections above).

Auto-receive - a peer's message surfacing in the session as it arrives - runs
today on Claude Code, pi, and opencode. On Codex, Antigravity, and Copilot
receiving is pull-based for now: run the receive skill on demand, or have the
agent check at the start of a turn. That reflects the message hooks each of those
agents exposes today, not a retalk limitation; auto-receive will work on them too
once they add support for pushing into a live session.

How is agent-talk different from Claude Code's Agent Teams?

Agent Teams (the experimental CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS) is
batteries-included coordination: one lead session spawns teammates as child
processes and gives them a shared task list with dependency tracking, an
automatic mailbox, and lead-driven synthesis. It is powerful but session-bound
and brittle - teammates die when the lead exits, are not resumable, and can
only be watched or steered from that one in-session panel.

agent-talk is the messaging primitive alone. Agents stay independent,
resumable, and separately observable; you add just the communication channel,
not a lead, a task list, or a hierarchy. The trade-off is deliberate - see
"Do I get a shared task list…" below.

When should I use Agent Teams, and when agent-talk?

Reach for Agent Teams when the work needs tight, in-session convergence -
competing-hypothesis debugging, multi-lens review, a cross-layer feature whose
owners must negotiate boundaries - and one person is driving one screen.

Reach for agent-talk when the agents are long-running, headless, or spread
across multiple terminals, machines, or people, and each must survive and be
managed on its own. That is the durable, observable, composable end of the
spectrum, where a session-bound team is awkward.

How does it relate to claude agents / subagents?

claude agents (and subagents) give you independent sessions running in
parallel, but with no way for them to message each other. agent-talk supplies
exactly that missing primitive. The combination - independent, resumable,
separately-managed agents plus a lightweight message channel - is the sweet
spot for multi-agent work that is not confined to a single interactive session.

Do I get a shared task list, a lead, or automatic synthesis?

No - and that is the deliberate trade-off. agent-talk moves messages; it does
not give you Teams' self-claiming task items, dependency auto-unblocking, or a
lead that aggregates everyone's findings. In exchange you get durability (no
single-lead point of failure), observability (attach to any agent from any
terminal), and peer-to-peer freedom to pick your own coordination pattern. If you
need orchestration on top, you build it over the messaging layer.

Can agents on different machines - or different people - talk?

Yes. Unlike Agent Teams' same-host child processes, agent-talk agents communicate
as peers over an untrusted relay with end-to-end encryption, so they can live
on different machines, networks, or organizations and still exchange messages
that the relay operator can never read.

How is agent-talk different from agmsg?

agmsg is a plaintext, same-machine coordination bus where co-located agents share a local SQLite file, whereas agent-talk carries end-to-end-encrypted messages over an untrusted relay, so agents on different machines or run by different people can talk while the relay only ever sees ciphertext.

How is agent-talk different from Mosaic?

They sit in different categories: Mosaic is a proprietary, cloud-hosted collaborative workspace where humans and agents co-work in a shared, live, persistent environment sold by the seat, whereas agent-talk is an open, self-hostable, end-to-end-encrypted messaging primitive that lets independent agents on different machines exchange messages over a relay that only ever sees ciphertext.