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

Search messages across all agents 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 --computer is specified, the target agent will run on the same computer as the caller agent.

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

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

Use --computer cloud to route through the target agent's cloud sandbox:

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

Arguments:

Example:

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

Response (JSON format with --output json):

{
  "type": "result",
  "subtype": "success",
  "is_error": false,
  "result": "The authentication system uses JWT tokens...",
  "agent_id": "agent-abc123",
  "conversation_id": "conversation-xyz789",
  "environment": { "source": "same-environment" },
  "usage": { "prompt_tokens": 120, "completion_tokens": 80, "total_tokens": 200, "step_count": 1 }
}

Continuing a Conversation

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

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

Discovering Computers

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

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

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

CURRENT_COMPUTER=$(letta computers current | jq -r .connectionId)
letta -p --from-agent $LETTA_AGENT_ID \
  --agent agent-abc123 \
  --computer "$CURRENT_COMPUTER" \
  "Run on my same computer."

Omit --computer when you want the target agent to run on the same computer as the caller agent.

Arguments:

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 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