Agent Squad Python Guide
2FastLabs/agent-squad
Map of the agent-squad Python framework for async multi-agent orchestration: which agent, classifier, storage and tool provider to pick, and the pitfalls to avoid.
A skill your agent uses for LiteLLM Python SDK, AI Gateway proxy, model routing, provider endpoint mapping, MCP/A2A agent tooling, pass-through routes, guardrails, virtual keys, spend tracking, and…
Install Litellm
$ npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill litellm --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/litellm .claude/skills/litellm && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "litellm" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellm into .claude/skills/litellm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "litellm", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellmType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill litellm --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/litellm .agents/skills/litellm && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "litellm" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellm into .agents/skills/litellm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "litellm", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill litellm --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/litellm .cursor/skills/litellm && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "litellm" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellm into .cursor/skills/litellm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "litellm", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/litellm--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill litellm --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/litellm .gemini/skills/litellm && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "litellm" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellm into .gemini/skills/litellm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "litellm", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install VectorSpaceLab/AREX-Skill litellmInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/litellm .github/skills/litellm && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "litellm" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellm into .github/skills/litellm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "litellm", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill litellm --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/litellm .opencode/skills/litellm && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "litellm" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/litellm into .opencode/skills/litellm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "litellm", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
litellm- GitHub stars
- 330
- Token cost
- ~1.1k tokens
- SKILL.md length
- 463 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
A skill your agent uses for LiteLLM Python SDK, AI Gateway proxy, model routing, provider endpoint mapping, MCP/A2A agent tooling, pass-through routes, guardrails, virtual keys, spend tracking, and…
- Works in 5 steps: If the user asks for direct Python… → If the user asks to run or configure the… → If the user asks about Router, model… → …
- LiteLLM Python SDK
- SKILL.md covers Start Here, Installation Patterns, Minimal Checks and Shared References, plus 2 more sections
- Runs Python scripts from its folder; calls python, pip and uv
What it does
Litellm is an agent skill from VectorSpaceLab/AREX-Skill. Use for LiteLLM Python SDK, AI Gateway proxy, model routing, provider endpoint mapping, MCP/A2A agent tooling, pass-through routes, guardrails, virtual keys, spend tracking, and troubleshooting across OpenAI-compatible LLM providers.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/model-and-config-primer.md`, `references/repo-provenance.md` and `references/repo-routing-metadata.json`).
It sits in AI & LLM Engineering, covering Model routing and gateways. It works with Model Context Protocol, OpenAI and Python. The repository describes itself as: A Skill Library for Automated Machine Learning.
When your agent uses it
- LiteLLM Python SDK
- AI Gateway proxy
- Provider endpoint mapping
- MCP/A2A agent tooling
Example prompts
- “/litellm”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- If the user asks for direct Python calls, imports, streaming, embeddings, structured outputs, callbacks, caching, token/cost checks, or…
- If the user asks to run or configure the AI Gateway proxy, write YAML config, manage keys/teams/spend, configure guardrails, or check…
- If the user asks about Router, model groups, fallbacks, retries, cooldowns, routing strategies, health-check routing, tags, aliases, or…
- If the user asks which provider prefix, endpoint helper, OpenAI-compatible route, pass-through target, or endpoint family to use, use…
- If the user asks about MCP, A2A, Claude Code/Cursor agent gateway setup, tool approval, MCP auth/OAuth, or agent routes, use…
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonpipuv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip and uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Litellm loads about 1.1k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 60 tokens; SKILL.md has 463 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 463 words (~1,101 tokens).
“Use this skill when a task involves LiteLLM as a Python SDK or AI Gateway. LiteLLM provides OpenAI-format calls across many providers, a proxy server for centralized model access, routing and fallback policy, provider endpoint translation, and agent-tool integrations such…”
- name
- litellm
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- NOASSERTION
Files
SKILL.md and 6 other files (scripts, references) in skills/repositories/repo-skills/litellm of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/model-and-config-primer.md
- references/repo-provenance.md
- references/repo-routing-metadata.json
- references/troubleshooting.md
- scripts/check_litellm_environment.py
- sub-skills
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Litellm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Litellm this skillVectorSpaceLab/AREX-Skill | 330 | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Agent Squad Python Guide2FastLabs/agent-squad | 7.8k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Using Ccproxy Inspectorstarbaser/ccproxy | 350 | — | ~2.7k | Automated safety check: Pass | Custom licence | |
| Evaluating Bitrouter Routesbitrouter/bitrouter | 235 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Opikcomet-ml/opik-mcp | 220 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Tool Designagentailor/fullstack-langgraph-nextjs-agent | 132 | — | ~3.2k | Automated safety check: Pass | MIT |
Similar skills
Using Ccproxy Inspector
starbaser/ccproxy
Operates the ccproxy inspector MITM system for intercepting, inspecting, and transforming LLM API traffic.
Evaluating Bitrouter Routes
bitrouter/bitrouter
A skill your agent uses when evaluating BitRouter route decisions or Eval Exchange subjects with task-native verifiers, human reviewers, private enterprise evaluators, agentic judges, or genuinely…
Opik
comet-ml/opik-mcp
Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST).
Tool Design
agentailor/fullstack-langgraph-nextjs-agent
Design and verify tools that AI agents can actually use — for any framework or language (MCP servers, LangChain/LangGraph, function-calling, raw JSON schema; TypeScript, Python, or otherwise).
Run Bitrouter Benchmark
bitrouter/bitrouter
A skill your agent uses when a user wants to run, compare, resume, audit, share, or submit a Harbor benchmark through BitRouter, including choosing a Harbor dataset and agent, confirming routed…
More from VectorSpaceLab/AREX-Skill
All 159 skills in this repoAgent Lightning
VectorSpaceLab/AREX-Skill
Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and…
Agent Tools
VectorSpaceLab/AREX-Skill
A skill your agent uses when configuring LiteLLM for MCP tools, A2A agents, Claude Code/Cursor agent gateway traffic, MCP auth/OAuth, tool permissions, semantic filtering, or agent-specific proxy…
Agents And Awel
VectorSpaceLab/AREX-Skill
Build and debug DB-GPT agents, tools, skills, teams, and AWEL workflows, including deterministic local DAG runs and HTTP-trigger topology without assuming an LLM, credential, or external service.
Agents And Middleware
VectorSpaceLab/AREX-Skill
Work on the actively maintained LangChain v1 agent package: initchatmodel, createagent, structured output, tools, middleware, embeddings initialization, provider routing, and agent runtime…
Agents Workflows
VectorSpaceLab/AREX-Skill
A skill your agent uses for giskard.agents async chat workflows, tools, prompt templates, structured outputs, retries, rate limiting, embeddings, and optional LiteLLM backend.
Alphafold3
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Related
Works with
Categories
Questions about Litellm
What does Litellm do?
A skill your agent uses for LiteLLM Python SDK, AI Gateway proxy, model routing, provider endpoint mapping, MCP/A2A agent tooling, pass-through routes, guardrails, virtual keys, spend tracking, and…. Litellm is an agent skill from VectorSpaceLab/AREX-Skill. Use for LiteLLM Python SDK, AI Gateway proxy, model routing, provider endpoint mapping, MCP/A2A agent tooling, pass-through routes, guardrails, virtual keys, spend tracking, and troubleshooting across OpenAI-compatible LLM providers.
When should I use Litellm?
Litellm fits situations like: liteLLM Python SDK; AI Gateway proxy; provider endpoint mapping; MCP/A2A agent tooling.
How do I install Litellm in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/litellm in VectorSpaceLab/AREX-Skill) into .claude/skills/litellm in your project. Claude Code loads it when a task matches its description.
How do I install Litellm in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a codex`. Or copy the skill folder (skills/repositories/repo-skills/litellm in VectorSpaceLab/AREX-Skill) into .agents/skills/litellm in your project. Codex loads it when a task matches its description.
Can I use Litellm in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill litellm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/litellm, .gemini/skills/litellm, .github/skills/litellm and .opencode/skills/litellm in your project.
What does Litellm need to run?
Going by SKILL.md and its folder, Litellm needs Python for the scripts in its folder and the command-line tools its instructions call (python, pip and uv). Our summary lists: Python 3.
Does Litellm access the network?
SKILL.md contains no URLs. Its commands use pip and uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Litellm safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Litellm use?
Litellm has a licence file (declared in SKILL.md) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Litellm use?
About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.
What are the alternatives to Litellm?
Skills that share tags, products or a category with Litellm: Agent Squad Python Guide (2FastLabs/agent-squad, 7.8k stars), Using Ccproxy Inspector (starbaser/ccproxy, 350 stars), Evaluating Bitrouter Routes (bitrouter/bitrouter, 235 stars) and Opik (comet-ml/opik-mcp, 220 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Litellm?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.