ME

messaging-agents

Enables inter-agent communication and query delegation on a Letta server.

Install

mkdir -p .claude/skills/messaging-agents && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8073" && unzip -o skill.zip -d .claude/skills/messaging-agents && rm skill.zip

Installs to .claude/skills/messaging-agents

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Send messages to other agents on your server. Use when you need to communicate with, query, or delegate tasks to another agent.
127 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Query secondary agents
  • Coordinate task handoffs
  • Retrieve specialized memory blocks
  • Identify agent capabilities via tags

How it works

Uses a thread-safe conversations API to route messages between isolated agent environments on the same server.

Inputs & outputs

You give it
Target agent ID or query topic
You get back
Agent response or identification list

When to use messaging-agents

  • Query another agent
  • Coordinate tasks
  • Retrieve information from a specialist agent

About this skill

Messaging Agents

This skill enables you to send messages to other agents on the same Letta server using the thread-safe conversations API.

When to Use This Skill

  • You need to ask another agent a question
  • You want to query an agent that has specialized knowledge
  • You need information that another agent has in their memory
  • You want to coordinate with another agent on a task

What the Target Agent Can and Cannot Do

The target agent CANNOT:

  • Access your local environment (read/write files in your codebase)
  • Execute shell commands on your machine
  • Use your tools (Bash, Read, Write, Edit, etc.)

The target agent CAN:

  • Use their own tools (whatever they have configured)
  • Access their own memory blocks
  • Make API calls if they have web/API tools
  • Search the web if they have web search tools
  • Respond with information from their knowledge/memory

Important: This skill is for communication with other agents, not delegation of local work. The target agent runs in their own environment and cannot interact with your codebase.

Need local access? If you need the target agent to access your local environment (read/write files, run commands), use the Agent tool instead to deploy them as a subagent:

Agent({
  agent_id: "agent-xxx",            // Deploy this existing agent
  subagent_type: "general-purpose", // read-write access to your local tools
  prompt: "Look at the code in src/ and tell me about the architecture"
})

This gives the agent access to your codebase while running as a subagent.

Finding an Agent to Message

If you don't have a specific agent ID, use these skills to find one:

By Name or Tags

Load the finding-agents skill to search for agents:

letta agents list --query "agent-name"
letta agents list --tags "origin:letta-code"

By Topic They Discussed

Load the searching-messages skill to find which agent worked on something:

letta messages search --query "topic" --all-agents

Results include agent_id for each matching message.

CLI Usage (agent-to-agent)

Starting a New Conversation

letta -p --from-agent $LETTA_AGENT_ID --agent <id> "message text"

When no --environment is specified, the target agent will run in the same environment as the caller agent.

To route the target agent turn through a specific remote/local environment:

letta -p --from-agent $LETTA_AGENT_ID \
  --agent <id> \
  --environment <name-or-device-id-or-connection-id> \
  "message text"

Arguments:

ArgRequiredDescription
--agent <id>YesTarget agent ID to message
--from-agent <id>YesSender agent ID (injects agent-to-agent system reminder)
--environment <selector>NoRoute through an online environment by connection name, device ID, or connection ID
"message text"YesMessage body (positional after flags)

Example:

letta -p --from-agent $LETTA_AGENT_ID \
  --agent agent-abc123 \
  "What do you know about the authentication system?"

Response:

{
  "conversation_id": "conversation-xyz789",
  "response": "The authentication system uses JWT tokens...",
  "agent_id": "agent-abc123",
  "agent_name": "BackendExpert"
}

Continuing a Conversation

letta -p --from-agent $LETTA_AGENT_ID --conversation <id> "message text"

Add --environment <selector> to continue the conversation on a specific environment.

Discovering Environments

letta environments list --online-only
# alias:
letta envs list --online-only

Use connectionName, deviceId, or connectionId from the JSON output as the --environment selector. If a name is ambiguous, prefer deviceId or connectionId. In environments list, the current local runtime is marked with "isCurrent": true.

To force the target agent onto the current registered Letta Code environment, resolve the current environment and pass its connectionId:

CURRENT_ENV=$(letta environments current | jq -r .connectionId)
letta -p --from-agent $LETTA_AGENT_ID \
  --agent agent-abc123 \
  --environment "$CURRENT_ENV" \
  "Run on my same machine/environment."

Omit --environment when you want the target agent to run in the same environment as the caller agent.

Arguments:

ArgRequiredDescription
--conversation <id>YesExisting conversation ID
--from-agent <id>YesSender agent ID (injects agent-to-agent system reminder)
"message text"YesFollow-up message (positional after flags)

Example:

letta -p --from-agent $LETTA_AGENT_ID \
  --conversation conversation-xyz789 \
  "Can you explain more about the token refresh flow?"

Understanding the Response

  • Text-mode scripts return only the final assistant message (not tool calls, reasoning, or metadata)
  • JSON and stream-json responses include agent_id, conversation_id, and environment.source so you can continue the same conversation/runtime. Environment-routed turns also include environment.id, connection_id, device_id, and name.
  • The target agent may use tools, think, and reason - but you only see their final response
  • To see the full conversation transcript (including tool calls), use the searching-messages skill with letta messages list --agent <id> targeting the other agent

How It Works

When you send a message, the target agent receives it with a system reminder:

<system-reminder>
This message is from "YourAgentName" (agent ID: agent-xxx), an agent currently running inside the Letta Code CLI (docs.letta.com/letta-code).
The sender will only see the final message you generate (not tool calls or reasoning).
If you need to share detailed information, include it in your response text.
</system-reminder>

This helps the target agent understand the context and format their response appropriately.

Hidden Conversations

Agent-to-agent conversations (started via --from-agent) are created hidden on the target agent. They don't appear in the target's default conversation list in the ADE, so automated inter-agent chatter doesn't clutter the UI.

To inspect them:

  • List hidden conversations via the API with archive_status=archived (or all)
  • Pull the transcript directly with letta messages transcript --conversation <id>
  • The conversation_id returned when you sent the message is the handle you need

Continuing a hidden conversation with --conversation <id> keeps it hidden — only archive status is affected, messaging still works normally.

Related Skills

  • finding-agents: Find agents by name, tags, or fuzzy search
  • searching-messages: Search past messages across agents, or view full conversation transcripts

When not to use it

  • Local file system operations
  • Direct manipulation of own codebase
  • High-latency communication requirements

Prerequisites

Letta server access

Limitations

  • Target agents cannot access your local files
  • Requires knowledge of agent IDs or tags

How it compares

Facilitates inter-agent communication without cross-pollinating their local execution environments.

Compared to similar skills

messaging-agents side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
messaging-agents (this skill)03moReviewIntermediate
agent-protocol12moNo flagsIntermediate
agent-collective-intelligence-coordinator06moNo flagsAdvanced
agent-mesh-coordinator06moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

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