One thing I've been worried about is a new message triggering a few stale instances that still have their monitors running, which would result in cache misses and a hefty usage bill. I think OP's approach can help solve that, so it's great to see.
Thus, this is ultimately:
1. a harness problem (or harness orchestrator)
2. a protocol problem
With 1. Claude Code does this very well - yet most OSS harness are not great or do naive subagent tasking making hard even for a parent to talk to the child. But we can then rely on harness orchestrators - Herdr does this very well, so does the Codex app - which counts here due to how it spins up entire threads that another can manage. Any thread can talk to each other on these orchestrators - herdr is fun.With 2. we require native adapters in clients. MCP has the necessary things coming down the chain to enable real integrated push events to agents. Push events means bi-directional in-and-out of the agent.
Look to build your tooling in MCP once it lands! Just sharing my opinion, having built something similar last year. https://github.com/eqtylab/real-a2a
I develop a similar tool open source tool called wire (wireup.net) https://github.com/SlanchaAi/wire
The nice thing about claude code is that the monitor can directly be spin up to run in the background and listen to new messages via retalk receive --follow, so every time there's a new message it will push it direclty into the session so the agent reacts in real time; this is also possible in Pi (but not yet in Codex/Antigravity).
overall i agree only working directly with a cli is annoying, that's why i built it in 2 layers:
1. cli: retalk (which is general purpose and doesn't really care if you are an agent/human/bot/cron). 2. harness: the agent skills with dedicated markdowns for how to use each skills by the agent. it is essentially a wrapper around retalk but built to make it easier for agents to use it.
I desire a more robust system which injects the events into the harness directly, but haven't found a way that works across systems.
https://github.com/tcdent/dotfiles/blob/main/bin/agent-messa...
I also worked to get webhooks into the same system so I can automatically trigger things when a CI build fails or some deployment error. The main goal is to rely less on prompting and skills to monitor their dev builds and environments and make real hard rules, force prompting.
I'm guessing the same hooks could be used to ... Okay I guess there isn't a User Typed But Didn't Send Yet hook... Hmm...
I heard earlier there's a T3 harness which somehow wrangles other harnesses as one big meta harness, but I haven't checked it out yet.
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.
uv (or pip) if you want the init skill to install retalk.relay skill.[!NOTE] Don't have a relay yet? You can use the public relay:
https://relay.retalk.dev(give it as the relay URL wheninitasks). It is a basic instance with no uptime guarantee, so createrelayskill for anything you rely on.
Open a claude session first:
/plugin marketplace add xhluca/agent-talk
Once the marketplace is succesfully added, run:
/plugin install agent-talk@agent-talk
Finally reload the plugins to start using it:
/reload-plugins
[!NOTE]
agent-talkis designed to send/receive autonomously. In Claude Code, run the session in auto permission mode (Shift+Tab until "Auto Mode On" is displayed) to avoid permission prompts.
/plugin install does not upgrade an existing install (it reports "already
installed"), and even a fresh install pulls from your local marketplace
clone, which may be stale β third-party marketplaces do not auto-refresh
by default.
Recommended (one-time): enable auto-update for this marketplace.
/plugin β Marketplaces tab β agent-talk β Enable auto-update (or set
"autoUpdate": true on the marketplace entry in your settings). Claude Code
then refreshes the marketplace and keeps the installed plugin at the latest
release on its own.
Manual: refresh the marketplace, then update the plugin:
/plugin marketplace update agent-talk
/plugin update agent-talk@agent-talk
(the same works in a terminal via claude plugin β¦; add --scope project for a
project-scope install). Restart the session or /reload-plugins to apply β
sessions keep using the old skills until you do.
claude --plugin-dir /path/to/agent-talk
You can also add a local marketplace entry from Claude Code:
/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:
retalk if it is missing.agent-talk installs under Codex too β the same skills, through Codex's own plugin system. In a terminal:
codex plugin marketplace add xhluca/agent-talk
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.
[!WARNING] Auto-receive is not available on Codex. A peer's message will not surface in your active Codex session on its own. Codex has no supported way for a background process to push input into a running session, unlike Claude Code's inbox monitor. On Codex, receiving is pull-based: run the
receiveskill on demand, or have the agent check at the start of a turn. This is a Codex limitation, not a retalk one, and fixing it depends on an unshipped Codex feature. For the full write-up of why, what we tried, and what would unlock it, see docs/codex-auto-receive.md.
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
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.
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.
[!WARNING] Auto-receive is not available on Antigravity. A peer's message will not surface in your active
agysession on its own. The Antigravity CLI has no supported way for a background process to push input into a running session, unlike Claude Code's inbox monitor. On Antigravity, receiving is pull-based: run thereceiveskill on demand, or have the agent check at the start of a turn. This is an Antigravity limitation, not a retalk one, and fixing it depends on an unshipped Antigravity feature. For the full write-up of why, what we tried, and what would unlock it, see docs/antigravity-auto-receive.md.
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
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.
[!NOTE] Auto-receive is available on pi. The plugin ships a pi inbox extension (
extensions/inbox-monitor.ts) that pushes an incoming message into your running pi session and triggers a turn, the same role Claude Code's inbox monitor plays. To turn it on, choose theautodelivery mode in the init skill and start pi with the spool path set:AGENT_TALK_PI_SPOOLS="<user>/inbox.ndjson" pi. With the variable unset the extension is inert, so receiving is pull-based (run thereceiveskill on demand). This was verified end to end between two live pi sessions. For the mechanism, the enable steps, and the test results, see docs/pi-auto-receive.md.
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
ln -s "$PWD/agent-talk/skills" ~/.config/opencode/skills # global; or a project's .opencode/skills
opencode discovers each skills/<name>/SKILL.md on startup. Confirm they landed
with opencode debug skill. Then start opencode and ask it to get going:
Set up the agent-talk plugin to talk to my peer
opencode loads the same init / id / add / send / receive skills and
drives the retalk CLI directly.
[!NOTE] Auto-receive is available on opencode. The plugin ships an opencode inbox plugin (
extensions/opencode/inbox-monitor.ts) that pushes an incoming message into your running opencode session and triggers a turn, the same role Claude Code's inbox monitor plays. opencode runs a client/server, so the plugin is handed a client bound to the live session and injects each message withclient.session.promptAsync. To turn it on, copy the plugin to~/.config/opencode/plugins/inbox-monitor.ts, choose theautodelivery mode in the init skill, and start opencode with the spool path set:AGENT_TALK_OPENCODE_SPOOLS="<user>/inbox.ndjson" opencode. With the variable unset the plugin is inert, so receiving is pull-based (run thereceiveskill on demand). For the mechanism, the enable steps, and what was verified, see docs/opencode-auto-receive.md.
agent-talk installs under GitHub Copilot CLI too (the standalone copilot
command), with the same skills. Copilot CLI reads Agent-Skills-standard SKILL.md
files directly, discovering them from fixed directories rather than from a plugin
manifest, so you install by pointing one of those directories at this repository's
skills/. In a terminal:
npm install -g @github/copilot # requires Node 22+
git clone https://github.com/xhluca/agent-talk
ln -s "$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. 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.
[!WARNING] Auto-receive is not available on the interactive Copilot CLI. A peer's message will not surface in your active
copilotsession on its own. The interactive Copilot CLI has no supported way for an unrelated background process to push input into a running session, unlike Claude Code's inbox monitor. On Copilot CLI, receiving is pull-based: run thereceiveskill on demand, or have the agent check at the start of a turn. Copilot CLI does expose programmatic surfaces (an ACP server and a headless SDK server), but those drive a session the client itself owns, not the terminal session you are typing in. This is a Copilot CLI limitation, not a retalk one. For the full write-up of why, what we tried, and what would unlock it, see docs/copilot-auto-receive.md.
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 bycustomer_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
v3.1yesterday with acustomer_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.
To print the id again:
/agent-talk:id
The you send the printed 32-hex fingerprint to a peer, and add the peer's fingerprint
with add if it was not provided during setup.
After setup, use plain language or explicit skill calls:
message bob: hello from alice
check messages from bob
watch for replies from bob
Equivalent explicit calls look like:
/agent-talk:send bob "hello from alice"
/agent-talk:receive
/agent-talk:receive follow bob
Client skills mirror retalk subcommands and workflow steps.
| Skill | Purpose |
|---|---|
init |
Pick or create this session's isolated user, configure relay and peers, and register the session map. |
id |
Print this user's fingerprint and public identity data. |
add |
Save a peer fingerprint under a local name. |
verify |
Fetch and pin a saved peer's keys before messaging. |
contacts |
List, show, export, or remove saved peers. |
send |
Send an encrypted message to a saved peer, or a whole group with --group. |
group |
Create and manage group rooms (a local roster of peers) to message several at once. |
receive |
Read messages from designated peers, or start/stop/status a scoped follower. |
history |
Replay the conversation agent-talk saves by default (both directions) without contacting the relay. |
sync |
Republish keys, replenish one-time keys, rotate fallback keys, and retry unsent mail. |
config |
Show or set owner-wide defaults in ~/.retalk/config.json (e.g. the default relay). |
block |
Block, unblock, or list blocked senders. |
share |
Send a saved contact card to another saved peer. |
import |
Review and import staged or pasted contact cards. |
Server-side relay management is grouped under:
| Skill | Purpose |
|---|---|
relay |
Set up, ping, stop, or delete a retalk relay. |
Host-specific relay notes live in:
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.
[!IMPORTANT] agent-talk carries messages over retalk, which encrypts everything end to end by design, but the code has not been independently audited yet. Please keep that in mind before trusting it with sensitive messages.
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 β a peer's message surfacing in the session as it arrives β 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.
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 β 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 β see "Do I get a shared task listβ¦" below.
Reach for Agent Teams when the work needs tight, in-session convergence β competing-hypothesis debugging, multi-lens review, a cross-layer feature whose owners must negotiate boundaries β 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.
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 β independent, resumable,
separately-managed agents plus a lightweight message channel β is the sweet
spot for multi-agent work that is not confined to a single interactive session.
No β 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.
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.
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.
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.
MIT. See LICENSE.