letta-ai/letta-code is an agent harness for building assistants that retain memory, identity, and experience across interactions instead of treating each task as isolated. Developers use it through local, desktop, browser, or messaging interfaces for interactive or continuously running agents, and its catalogue entries configure the agents' skills, instructions, rules, and behavior.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/letta-ai/letta-code/messaging-agentsnpx skills add letta-ai/letta-code --skill messaging-agentsgit clone --depth 1 https://github.com/letta-ai/letta-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/letta-ai/letta-code/messaging-agents)<a href="https://agentmods.dev/skills/letta-ai/letta-code/messaging-agents"><img src="https://agentmods.dev/badge/skills/letta-ai/letta-code/messaging-agents.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00031 | $0.01589 |
| Opus 5 | $0.00015 | $0.00794 |
| Sonnet 5 | $0.00006 | $0.00318 |
| Haiku 4.5 | $0.00003 | $0.00159 |
Grade A, and why
messaging-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed a51e60c5df8a
- 5d ago First seen · 189 lines · 31 tokens per session scan A 1ab46ec9b12b
messaging-agents is a skill published in the GitHub repository letta-ai/letta-code (3,207 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 1,589 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
writing
将共享历史中的已验证事实和计算结果整理成符合受众、格式与长度约束的成稿。.
kayba-stage-7-fixer
Implement the approved fixes from the action plan and log all changes. Trigger when the user says "run stage 7", "implement fixes", "apply action plan", or when invoked by the kayba-pipeline orchestrator. Requires eval/actionplan.md to exist.
regex-mastery
Use this skill when writing regular expressions, debugging pattern matching,optimizing regex performance, or implementing text validation. Triggers on regex, regular expressions, pattern matching, lookahead, lookbehind, named groups, capture groups, backreferences, and any task requiring text pattern matching.
ws-ckpt
工作区快照管理。用户说"保存一下"、"存个快照"时创建 checkpoint,仅限 Linux; 说"回滚"、"撤销"、"恢复到之前"时 rollback;说"删掉快照"时 delete; 说"对比快照"、"快照改了什么"时 diff; 说"看看快照"、"有哪些快照"时 list;说"查看快照状态"、"查看快照剩余空间"时 status。.
memory-recall
Search and recall relevant memories from past sessions via memsearch. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge -- especially questions like 'what did I decide about X', 'why did we do Y', or 'have I seen this…
food-order
Reorder previous Foodora orders, preview cart contents, and track delivery ETA/status with ordercli. Use when the user wants to reorder food, check delivery status, or browse recent Foodora order history. Never confirm an order without explicit user approval.