slm-mesh

name	slm-mesh
description	Cross-session peer coordination via the SLM mesh network. Lets multiple AI agent sessions on the same machine discover each other, send messages, share lightweight state, and lock files to avoid conflicts. Requires full, power, or mesh MCP profile. All 8 tools are MCP-only — there is no CLI fallback.
when_to_use	- Multiple agent sessions running simultaneously on the same machine - "Announce what I'm working on to other sessions" - "Check if another agent has locked a file before I edit it" - "Send a message to the other Claude session" - "Is anyone else working on this project?" - Parallel agent workflows needing coordination - Cross-session state sharing without persisting to the memory store
allowed-tools	mesh_summary, mesh_peers, mesh_send, mesh_inbox, mesh_state, mesh_lock, mesh_events, mesh_status, Bash

slm-mesh — Cross-Session Peer Coordination

The mesh network lets multiple AI agent sessions on the same machine discover each other and coordinate in real time — without writing to the persistent memory store. Mesh messages are transient (48-hour TTL); they complement memory (which is durable) rather than replacing it.

Mesh is local-only: it uses the SLM daemon as a local broker. No data leaves the machine.

---

Profile requirement

Mesh tools are available in the full, power, and mesh MCP profiles. Confirm the active profile with slm status before calling mesh tools. If the tools are not available, switch to full profile with switch_profile("full") (requires code or higher active profile). See slm-profile.

---

Tool reference

1. mesh_summary — announce what this session is doing

mesh_summary(summary: str = "") -> dict

Call at session start to register on the mesh and announce your purpose. Other sessions can see your summary via mesh_peers. The session stays alive via automatic heartbeat.

mesh_summary(summary="Refactoring auth module in api/src/auth/")

Response: {peer_id, summary, project_path, registered, heartbeat_active, broker_response}

Call this once at the start of any session that will participate in the mesh. The peer registration happens automatically at MCP startup, but calling mesh_summary sets the human-readable description that other agents see.

---

2. mesh_peers — list active sessions

mesh_peers() -> dict

Returns all active peer sessions on this machine.

mesh_peers()

Response: {peers: [{peer_id, summary, project_path, last_seen}], count, my_peer_id}

Use this to discover other sessions before sending a message or checking for conflicts.

---

3. mesh_send — send a message to another session

mesh_send(
  to: str,       # peer_id | "broadcast" | "project:/path/to/dir"
  message: str,  # max 4 KB — use file paths for large data
) -> dict

Send a targeted, broadcast, or project-wide message.

# Direct message to a specific peer
peers = await mesh_peers()
target_id = peers["peers"][0]["peer_id"]
mesh_send(to=target_id, message="I'm starting work on auth/handler.py — please hold off")

# Broadcast to all sessions
mesh_send(to="broadcast", message="Deploying to staging in 5 minutes")

# Message all sessions working in the same project
mesh_send(to="project:~/myproject", message="Tests are green on main")

4 KB message cap. For large payloads (diffs, file contents), write to a file and send the path instead. The circuit breaker opens automatically if the daemon is repeatedly unreachable — mesh_send returns ok: false in that case.

---

4. mesh_inbox — read messages sent to this session

mesh_inbox() -> dict

Returns unread messages (direct, broadcast, and project-targeted). Messages are automatically marked as read after retrieval.

inbox = await mesh_inbox()
for msg in inbox["messages"]:
    print(msg["from"], msg["content"])

Response: {messages: [{id, from, content, sent_at, read}], count, unread}

Messages auto-expire after 48 hours.

---

5. mesh_state — get or set shared coordination state

mesh_state(
  key: str = "",
  value: str = "",
  action: str = "get",   # "get" | "set"
) -> dict

Shared state is visible to all authenticated peers. Use it for non-secret coordination metadata: feature flags, task assignments, progress markers.

# Set state
mesh_state(key="deploy_in_progress", value="true", action="set")
mesh_state(key="current_reviewer", value=my_peer_id, action="set")

# Read one key
mesh_state(key="deploy_in_progress", action="get")

# Read all state
mesh_state(action="get")

Security constraint: Credentials, tokens, passwords, and API keys are rejected by the broker. Never store secrets in shared state.

---

6. mesh_lock — file lock coordination

mesh_lock(
  file_path: str,         # must be an absolute path
  action: str = "query",  # "query" | "acquire" | "release"
) -> dict

Check, acquire, or release a file lock before editing a shared file.

# Step 1: check if the file is already locked
lock = await mesh_lock(file_path="/abs/path/to/auth/handler.py", action="query")

if lock.get("locked"):
    print(f"File is locked by {lock['locked_by']} — wait")
else:
    # Step 2: acquire the lock
    mesh_lock(file_path="/abs/path/to/auth/handler.py", action="acquire")

    # ... edit the file ...

    # Step 3: release the lock when done
    mesh_lock(file_path="/abs/path/to/auth/handler.py", action="release")

file_path must be an absolute path (starts with / on Unix, drive letter on Windows). Relative paths are rejected.

---

7. mesh_events — recent mesh activity log

mesh_events() -> dict

Returns the activity log for the mesh network: peer joins, leaves, messages sent, and state changes. Use to understand what other sessions have been doing.

---

8. mesh_status — mesh broker health

mesh_status() -> dict

Returns broker uptime, peer count, and connection health. Use at session start to confirm the mesh is available before relying on coordination.

Response includes: broker_up, peer_count, uptime_seconds, my_peer_id, heartbeat_active.

---

Common workflow: parallel agents coordinating on a shared repo

# Both agents call at session start:
await mesh_summary(summary="Working on feature/auth-refactor")

# Agent A: check who else is active
peers = await mesh_peers()
# → sees Agent B working on the same project

# Agent A: before editing a shared file
lock = await mesh_lock("/repo/src/auth/handler.py", action="query")
if not lock.get("locked"):
    await mesh_lock("/repo/src/auth/handler.py", action="acquire")
    # ... edit handler.py ...
    await mesh_lock("/repo/src/auth/handler.py", action="release")

# Agent A: after finishing a phase
await mesh_send(to="project:/repo", message="Auth refactor complete — handler.py ready for review")

# Agent B: check inbox
inbox = await mesh_inbox()

---

Mesh vs memory: when to use which

Need	Use
Ephemeral coordination signal (< 48h)	mesh_send / mesh_state
Durable fact across sessions/days	remember
File conflict prevention	mesh_lock
Cross-profile fact sharing	scope="shared"/"global" on remember
Session announcement	mesh_summary
Finding parallel agents	mesh_peers

---

Error handling

All 8 mesh tools return structured errors — they never raise exceptions.

Error	Cause	Action
broker_up: false from mesh_status	Daemon not running or mesh not configured	Run slm status to check daemon health
ok: false from mesh_send with circuit-breaker message	Repeated daemon unreachability	Daemon unreachable; stop sending until broker is up
ok: false from mesh_lock	Lock operation failed	Check file_path is absolute; retry once
Empty peers from mesh_peers	No other sessions registered	You're the only active session

Mesh failures are non-fatal for the primary task. If mesh_status shows broker_up: false, proceed without mesh coordination — do not block work on mesh availability.

---

Related skills

• slm-profile — activate full/power/mesh profile to access mesh tools
• slm-scope — for durable cross-profile sharing (complement to transient mesh state)
• slm-remember — persist coordination decisions that should survive session end
• slm-governance — enterprise governance of mesh (who can send/receive)

---

SuperLocalMemory v4.1.11 · Qualixar · AGPL-3.0-or-later