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Use when you need to communicate with, query, or delegate tasks to another agent.\n---\n\n# Messaging Agents\n\nThis skill enables you to send messages to other agents on the same Letta server using the thread-safe conversations API.\n\n## When to Use This Skill\n\n- You need to ask another agent a question\n- You want to query an agent that has specialized knowledge\n- You need information that another agent has in their memory\n- You want to coordinate with another agent on a task\n\n## What the Target Agent Can and Cannot Do\n\n**The target agent CANNOT:**\n- Access your local environment (read\u002Fwrite files in your codebase)\n- Execute shell commands on your machine\n- Use your tools (Bash, Read, Write, Edit, etc.)\n\n**The target agent CAN:**\n- Use their own tools (whatever they have configured)\n- Access their own memory blocks\n- Make API calls if they have web\u002FAPI tools\n- Search the web if they have web search tools\n- Respond with information from their knowledge\u002Fmemory\n\n**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.\n\n**Need local access?** If you need the target agent to access your local environment (read\u002Fwrite files, run commands), use the Agent tool instead to deploy them as a subagent:\n```typescript\nAgent({\n  agent_id: \"agent-xxx\",            \u002F\u002F Deploy this existing agent\n  subagent_type: \"general-purpose\", \u002F\u002F read-write access to your local tools\n  prompt: \"Look at the code in src\u002F and tell me about the architecture\"\n})\n```\nThis gives the agent access to your codebase while running as a subagent.\n\n## Finding an Agent to Message\n\nIf you don't have a specific agent ID, use these skills to find one:\n\n### By Name or Tags\nLoad the `finding-agents` skill to search for agents:\n```bash\nletta agents list --query \"agent-name\"\nletta agents list --tags \"origin:letta-code\"\n```\n\n### By Topic They Discussed\nLoad the `searching-messages` skill to find which agent worked on something:\n```bash\nletta messages search --query \"topic\" --all-agents\n```\nResults include `agent_id` for each matching message.\n\n## CLI Usage (agent-to-agent)\n\n### Starting a New Conversation\n\n```bash\nletta -p --from-agent $LETTA_AGENT_ID --agent \u003Cid> \"message text\"\n```\n\nWhen no `--environment` is specified, the target agent will run in the same\nenvironment as the caller agent.\n\nTo route the target agent turn through a specific remote\u002Flocal environment:\n\n```bash\nletta -p --from-agent $LETTA_AGENT_ID \\\n  --agent \u003Cid> \\\n  --environment \u003Cname-or-device-id-or-connection-id> \\\n  \"message text\"\n```\n\n**Arguments:**\n| Arg | Required | Description |\n|-----|----------|-------------|\n| `--agent \u003Cid>` | Yes | Target agent ID to message |\n| `--from-agent \u003Cid>` | Yes | Sender agent ID (injects agent-to-agent system reminder) |\n| `--environment \u003Cselector>` | No | Route through an online environment by connection name, device ID, or connection ID |\n| `\"message text\"` | Yes | Message body (positional after flags) |\n\n**Example:**\n```bash\nletta -p --from-agent $LETTA_AGENT_ID \\\n  --agent agent-abc123 \\\n  \"What do you know about the authentication system?\"\n```\n\n**Response:**\n```json\n{\n  \"conversation_id\": \"conversation-xyz789\",\n  \"response\": \"The authentication system uses JWT tokens...\",\n  \"agent_id\": \"agent-abc123\",\n  \"agent_name\": \"BackendExpert\"\n}\n```\n\n### Continuing a Conversation\n\n```bash\nletta -p --from-agent $LETTA_AGENT_ID --conversation \u003Cid> \"message text\"\n```\n\nAdd `--environment \u003Cselector>` to continue the conversation on a specific environment.\n\n### Discovering Environments\n\n```bash\nletta environments list --online-only\n# alias:\nletta envs list --online-only\n```\n\nUse `connectionName`, `deviceId`, or `connectionId` from the JSON output as the\n`--environment` selector. If a name is ambiguous, prefer `deviceId` or\n`connectionId`. In `environments list`, the current local runtime is marked with\n`\"isCurrent\": true`.\n\nTo force the target agent onto the current registered Letta Code environment,\nresolve the current environment and pass its `connectionId`:\n\n```bash\nCURRENT_ENV=$(letta environments current | jq -r .connectionId)\nletta -p --from-agent $LETTA_AGENT_ID \\\n  --agent agent-abc123 \\\n  --environment \"$CURRENT_ENV\" \\\n  \"Run on my same machine\u002Fenvironment.\"\n```\n\nOmit `--environment` when you want the target agent to run in the same\nenvironment as the caller agent.\n\n**Arguments:**\n| Arg | Required | Description |\n|-----|----------|-------------|\n| `--conversation \u003Cid>` | Yes | Existing conversation ID |\n| `--from-agent \u003Cid>` | Yes | Sender agent ID (injects agent-to-agent system reminder) |\n| `\"message text\"` | Yes | Follow-up message (positional after flags) |\n\n**Example:**\n```bash\nletta -p --from-agent $LETTA_AGENT_ID \\\n  --conversation conversation-xyz789 \\\n  \"Can you explain more about the token refresh flow?\"\n```\n\n## Understanding the Response\n\n- Text-mode scripts return only the **final assistant message** (not tool calls, reasoning, or metadata)\n- JSON and stream-json responses include `agent_id`, `conversation_id`, and `environment.source` so you can continue the same conversation\u002Fruntime. Environment-routed turns also include `environment.id`, `connection_id`, `device_id`, and `name`.\n- The target agent may use tools, think, and reason - but you only see their final response\n- To see the full conversation transcript (including tool calls), use the `searching-messages` skill with `letta messages list --agent \u003Cid>` targeting the other agent\n\n## How It Works\n\nWhen you send a message, the target agent receives it with a system reminder:\n```\n\u003Csystem-reminder>\nThis message is from \"YourAgentName\" (agent ID: agent-xxx), an agent currently running inside the Letta Code CLI (docs.letta.com\u002Fletta-code).\nThe sender will only see the final message you generate (not tool calls or reasoning).\nIf you need to share detailed information, include it in your response text.\n\u003C\u002Fsystem-reminder>\n```\n\nThis helps the target agent understand the context and format their response appropriately.\n\n## Hidden Conversations\n\nAgent-to-agent conversations (started via `--from-agent`) are created **hidden** on the target agent. 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Use when the user asks to add a custom \u002Fcommand, slash command, command shortcut, scoped conversation-backed command, or command-driven panel behavior.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1950,1951,1952],{"name":14,"slug":15,"type":16},{"name":1885,"slug":1886,"type":16},{"name":1953,"slug":1954,"type":16},"CLI","cli","2026-07-13T06:23:18.266798",{"slug":1957,"name":1957,"fn":1958,"description":1959,"org":1960,"tags":1961,"stars":23,"repoUrl":24,"updatedAt":1966},"customizing-statusline","customize Letta Code statusline mods","Creates, edits, and migrates Letta Code statusline mods. Use when handling the \u002Fstatusline command or continuing work started by \u002Fstatusline.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1962,1963],{"name":1953,"slug":1954,"type":16},{"name":1964,"slug":1965,"type":16},"Engineering","engineering","2026-07-13T06:23:27.465985",19,{"items":1969,"total":2074},[1970,1976,1982,1988,1994,2000,2006,2011,2021,2037,2047,2059],{"slug":1878,"name":1878,"fn":1879,"description":1880,"org":1971,"tags":1972,"stars":23,"repoUrl":24,"updatedAt":1890},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1973,1974,1975],{"name":14,"slug":15,"type":16},{"name":1885,"slug":1886,"type":16},{"name":1888,"slug":1889,"type":16},{"slug":1892,"name":1893,"fn":1894,"description":1895,"org":1977,"tags":1978,"stars":23,"repoUrl":24,"updatedAt":1905},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1979,1980,1981],{"name":14,"slug":15,"type":16},{"name":1900,"slug":1901,"type":16},{"name":1903,"slug":1904,"type":16},{"slug":1907,"name":1907,"fn":1908,"description":1909,"org":1983,"tags":1984,"stars":23,"repoUrl":24,"updatedAt":1917},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1985,1986,1987],{"name":14,"slug":15,"type":16},{"name":1885,"slug":1886,"type":16},{"name":1915,"slug":1916,"type":16},{"slug":1919,"name":1919,"fn":1920,"description":1921,"org":1989,"tags":1990,"stars":23,"repoUrl":24,"updatedAt":1929},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1991,1992,1993],{"name":14,"slug":15,"type":16},{"name":1885,"slug":1886,"type":16},{"name":1927,"slug":1928,"type":16},{"slug":1931,"name":1931,"fn":1932,"description":1933,"org":1995,"tags":1996,"stars":23,"repoUrl":24,"updatedAt":1943},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1997,1998,1999],{"name":14,"slug":15,"type":16},{"name":1938,"slug":1939,"type":16},{"name":1941,"slug":1942,"type":16},{"slug":1945,"name":1945,"fn":1946,"description":1947,"org":2001,"tags":2002,"stars":23,"repoUrl":24,"updatedAt":1955},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2003,2004,2005],{"name":14,"slug":15,"type":16},{"name":1885,"slug":1886,"type":16},{"name":1953,"slug":1954,"type":16},{"slug":1957,"name":1957,"fn":1958,"description":1959,"org":2007,"tags":2008,"stars":23,"repoUrl":24,"updatedAt":1966},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2009,2010],{"name":1953,"slug":1954,"type":16},{"name":1964,"slug":1965,"type":16},{"slug":2012,"name":2012,"fn":2013,"description":2014,"org":2015,"tags":2016,"stars":23,"repoUrl":24,"updatedAt":2020},"dispatching-coding-agents","dispatch stateless coding agents","Dispatch stateless coding agents (Claude Code or Codex) via Bash. Use when you're stuck, need a second opinion, or need parallel research on a hard problem. They have no memory — you must provide all context.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2017,2018,2019],{"name":14,"slug":15,"type":16},{"name":1927,"slug":1928,"type":16},{"name":21,"slug":22,"type":16},"2026-07-26T05:46:56.388845",{"slug":2022,"name":2022,"fn":2023,"description":2024,"org":2025,"tags":2026,"stars":23,"repoUrl":24,"updatedAt":2036},"editing-letta-code-desktop-preferences","edit Letta Code Desktop preferences","Edits Letta Code Desktop (LCD) preferences by safely reading and updating ~\u002F.letta\u002Fdesktop_preferences.json. Use only when the user asks to change current Desktop\u002FLCD settings such as theme, default working directory, remote access preference, or remote environment name via the preferences JSON.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2027,2030,2033],{"name":2028,"slug":2029,"type":16},"Configuration","configuration",{"name":2031,"slug":2032,"type":16},"Desktop","desktop",{"name":2034,"slug":2035,"type":16},"Themes","themes","2026-07-13T06:23:41.407811",{"slug":383,"name":383,"fn":2038,"description":2039,"org":2040,"tags":2041,"stars":23,"repoUrl":24,"updatedAt":2046},"locate and manage agents on server","Find other agents on the same server. Use when the user asks about other agents, wants to migrate memory from another agent, or needs to find an agent by name or tags.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2042,2043],{"name":14,"slug":15,"type":16},{"name":2044,"slug":2045,"type":16},"Management","management","2026-07-16T06:02:17.297841",{"slug":2048,"name":2048,"fn":2049,"description":2050,"org":2051,"tags":2052,"stars":23,"repoUrl":24,"updatedAt":2058},"generating-mod-envs","generate Letta mod learning environments","Generates and reviews mod learning env JSON files for Letta Code local mods. Use when asked to teach, learn, or optimize a mod behavior; create, draft, validate, improve, or explain envs for `\u002Fmods learn --env`; or design evaluation scenarios, memory fixtures, requiredResultMarkers, requiredTraceMarkers, negative controls, and candidate diversity hints.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2053,2054,2057],{"name":14,"slug":15,"type":16},{"name":2055,"slug":2056,"type":16},"AI Infrastructure","ai-infrastructure",{"name":2028,"slug":2029,"type":16},"2026-07-13T06:23:08.838181",{"slug":2060,"name":2060,"fn":2061,"description":2062,"org":2063,"tags":2064,"stars":23,"repoUrl":24,"updatedAt":2073},"image-generation","generate images from text prompts","Generate images from text prompts (and optionally edit\u002Fremix input images). Use when the user asks to create, generate, draw, render, or edit an image, illustration, logo, icon, diagram, or photo.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2065,2068,2071],{"name":2066,"slug":2067,"type":16},"Creative","creative",{"name":2069,"slug":2070,"type":16},"Graphics","graphics",{"name":2072,"slug":2060,"type":16},"Image Generation","2026-07-13T06:23:06.189403",69]