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 skills add letta-ai/letta-code --skill image-generationgit 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/image-generation)<a href="https://agentmods.dev/skills/letta-ai/letta-code/image-generation"><img src="https://agentmods.dev/badge/skills/letta-ai/letta-code/image-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/letta-ai/letta-code/image-generation"><img src="https://agentmods.dev/badge/skills/letta-ai/letta-code/image-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 19 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 5.1 | $0.00047 | $0.01353 |
| Opus 5.5 | $0.00019 | $0.00541 |
| Sonnet 5.5 | $0.00009 | $0.00271 |
| Haiku 4.5 | $0.00005 | $0.00135 |
Grade A, and why
image-generation scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sS -X POST "$base_url/v1/images/generations" \ How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation
Generate images via Letta's hosted endpoint POST /v1/images/generations. The API
usually returns base64 image bytes, but some providers return signed image URLs;
save either form to a local image file before replying.
Example
Generate the image, save it locally, then show it inline:
base_url="${LETTA_BASE_URL%/}"
curl -sS -X POST "$base_url/v1/images/generations" \
-H "Authorization: Bearer $LETTA_API_KEY" \
-H "Content-Type: application/json" \
-d '{"provider":"gemini","prompt":"a friendly robot mascot waving, flat vector logo, mint green background","n":1}' \
> image-response.json
python3 - <<'PY'
import base64, json, urllib.request
with open("image-response.json") as f:
response = json.load(f)
image = response["images"][0]
if image.get("b64_json"):
data = base64.b64decode(image["b64_json"])
else:
data = urllib.request.urlopen(image["url"]).read()
with open("robot-mascot.png", "wb") as f:
f.write(data)
print("saved robot-mascot.png")
PY
In Bash tools launched by Letta Code, use the runtime-provided
LETTA_BASE_URL and LETTA_API_KEY together for Letta API calls. Build URLs
relative to ${LETTA_BASE_URL%/} and send Authorization: Bearer $LETTA_API_KEY.
Do not hardcode https://api.letta.com: Desktop and remote runtimes may provide
a proxy base URL, and the credential may only be valid through that URL. If
either variable is missing, the user needs to authenticate with Letta Cloud (or
provide a Letta API key); do not ask for an OpenAI/Gemini provider key. This
endpoint also does not use /connect BYOK providers — the only provider values
supported here are flux, gemini, and openai.
Then show the image to the user by embedding the saved file in your reply:
Here's the mascot:

The Letta Code UI renders local file paths in markdown image tags, so the image
appears inline. Always display generated images this way — don't just report
the path, and never paste the raw base64 / a data: URI. The markdown path must
match where you saved the file. For n > 1, save each image to its own file and
embed each on its own line. Keep credit amounts and billing metadata out of
user-facing replies and captions unless the user asks about cost. When asked,
read billing.credits_charged from the saved response.
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.
- 1mo ago Changed · +1 lines a4f201d9a874
- 1mo ago First seen · 125 lines · 47 tokens per session scan A 76e394e5f1b2
image-generation is a skill published in the GitHub repository letta-ai/letta-code (3,625 stars, last pushed today), licensed Apache-2.0. It adds 47 tokens to every session and 1,353 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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memory-system
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A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.
pkf-writing
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memory-import
Bulk-import an external knowledge base into Prismer memory via the three-layer import pipeline (07 §3.1). Use when the user asks to import/migrate documents, a knowledge base, or notes into workspace memory. Layer 1 lands raw documents (zero LLM, instantly searchable); Layer 2 is YOUR distillation work — you read the…
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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.