aipack

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Run, Build, Share your AI Packs

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21
Dependents
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Stars
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Forks
30
Contributors
5
Commits
1,980
Latest release
0.8.37 ·

About

From the project README ·

AIPack - Run, Build, and Share AI Packs

An open-source agentic runtime to run, build, and share AI Packs.

  • Simple & Powerful, 1 Agent = 1 multi-stage Markdown file with built-in concurrency, Map-Reduce, and all APIs on a single aip-doc page.

  • Light & Lean, No bloat, < 20MB, single executable, ZERO dependencies.

  • Efficient, Engine written in Rust with a lightweight and efficient embedded Lua script. All aip functions are implemented in Rust.

  • Multi-AI, Supports all major AI providers and models (OpenAI, Google, Anthropic, xAI, Ollama, Groq, Fireworks.ai, ...) at the native layer for the major ones. For example, it can use Gemini models with zero thinking budget.

  • Local or Cloud, Runs locally, is completely IDE-agnostic, or runs in the cloud, on a server or serverless.

BIG UPDATE 0.8.x WITH NEW TUI

Check out:
Read the full README

Quick Start

Install From Binaries

Mac, Linux, and Windows on ARM and x86 platforms are supported. See below for binary installation instructions.

(More info at aipack.ai/doc/install)

Mac
# Mac ARM / Apple Silicon
curl -O https://repo.aipack.ai/aip-dist/stable/latest/aarch64-apple-darwin/aip.tar.gz && \
        tar -xvf aip.tar.gz && \
        ./aip self setup

# Mac x86
curl -O https://repo.aipack.ai/aip-dist/stable/latest/x86_64-apple-darwin/aip.tar.gz && \
        tar -xvf aip.tar.gz && \
        ./aip self setup        
Linux
# Linux x86
curl -O https://repo.aipack.ai/aip-dist/stable/latest/x86_64-unknown-linux-gnu/aip.tar.gz && \
        tar -xvf aip.tar.gz && \
        ./aip self setup

# Linux ARM
curl -O https://repo.aipack.ai/aip-dist/stable/latest/aarch64-unknown-linux-gnu/aip.tar.gz && \
        tar -xvf aip.tar.gz && \
        ./aip self setup
Windows
# Windows x86
Invoke-WebRequest -Uri "https://repo.aipack.ai/aip-dist/stable/latest/x86_64-pc-windows-msvc/aip.tar.gz" -OutFile "aip.tar.gz"
tar -xvf aip.tar.gz
.\aip.exe self setup
     

# Windows ARM
Invoke-WebRequest -Uri "https://repo.aipack.ai/aip-dist/stable/latest/aarch64-pc-windows-msvc/aip.tar.gz" -OutFile "aip.tar.gz"
tar -xvf aip.tar.gz
.\aip.exe self setup
Install from source

For now, installation requires building directly from source with Rust. It works on all major OSes.

  • Install Rust: https://www.rust-lang.org/tools/install
  • For now, install with cargo install aipack
Run
# In the terminal, navigate to your project
cd /path/to/my/project/

# Initialize workspace .aipack/ and ~/.aipack-base
aip init

# Make sure to export the desired API key (no spaces around `=`; Unix convention)
export OPENAI_API_KEY="sk...."
export ANTHROPIC_API_KEY="...."
export GEMINI_API_KEY="..."
# For more keys, see below

# Check the keys you've set up
aip check-keys

# To proofread your README.md (namespace: demo, pack_name: proof):
aip run demo@proof -f ./README.md

# You can just use @pack_name if there is no other pack with that name
aip run @proof -f ./README.md

Install From Git

Install a pack directly from a Git repository. Use an explicit # selector when the pack is located in a repository subdirectory:

aip install git://git.example.com/team/example-pack.git

# with path to the pack dir
aip install git://git.example.com/team/example-pack.git#path/to/pack_dir

# with auth
aip install git+ssh://git@github.com/owner/example-pack.git#path/to/pack_dir

# or with the SCP way (the copy/paste from github for example)
aip install git@github.com:owner/example-pack.git

The repository portion before # is cloned. Without a selector, the cloned repository root must contain pack.toml. With a selector, the selected directory must contain pack.toml. The manifest's namespace and name fields determine the installed path under ~/.aipack-base/pack/installed/<namespace>/<name>.

Pack paths are selected only with an explicit # selector. A path after .git without # is not inferred as a pack path. Ordinary http:// and https:// links continue to be treated as downloadable .aipack archives.

Git installations resolve the checked-out commit and record its full hash as [installed].commit in the copied pack.toml. When a selected subpath is used, [installed].source retains the original Git URL and selector.

Thanks to
pro@coder
  • You can install pro@coder with aip install pro@coder, and then
  • Run it with aip run pro@coder or aip run @coder if you don't have any other @coder pack in a different namespace.

This is the agent I use every day for my production coding.

IMPORTANT 1: Make sure everything is committed before use, especially while you are learning AIPack.

IMPORTANT 2: Make sure you have your API_KEY set as an environment variable. On macOS, there is experimental keychain support.

OPENAI_API_KEY
ANTHROPIC_API_KEY
GEMINI_API_KEY
DEEPSEEK_API_KEY
KIMI_API_KEY
ZAI_API_KEY
FIREWORKS_API_KEY
TOGETHER_API_KEY
NEBIUS_API_KEY
GROQ_API_KEY
COHERE_API_KEY
XAI_API_KEY

On mac, you can also use OMLX with for example

OMLX_ENDPOINT=http://127.0.0.1:8000/v1/

More Info

How it works

  • One Agent == One Markdown
    • An .aip agent file is just a Markdown file with sections for each stage of the agent's processing.
    • See below for all the possible stages.
  • aip run demo@proof -f "./*.md"
    • will run the installed agent file main.aip in the
    • pack named proof
    • namespace demo
    • agent file main.aip
    • Full path ~/.aipack-base/pack/installed/demo/proof/main.aip
    • You can pass input to your agent using:
      • -f "path/with/optional/**/glob.*" -f "README.md" (the Lua code will receive a {path = .., name =..} FileInfo-like structure as input)
      • -i "some string" -i "another input" (the Lua code will receive these strings as input)
    • Each input triggers one run of the agent.
  • aip run some/path/to/agent
    • If the path ends with .aip, it's treated as a direct file run.
    • If there is no .aip extension, then:
      • .../agent.aip will be executed if it exists.
      • or .../agent/main.aip will be executed if it exists.
  • AIPack agents are simple .aip files that can be placed anywhere on disk.
    • e.g., aipack run ./my-path/to/my-agent.aip ...
  • Multi AI Provider / Models - AIPack uses genai and therefore supports providers like OpenAI, Anthropic, Gemini, Groq, Ollama, Cohere, and more.
  • Lua is used for all scripting (thanks to the great mlua crate).
  • Handlebars is used for all prompt templating (thanks to the great Rust native handlebars crate).
Multi Stage

A single AIPack file may comprise any of the following stages.

StageLanguageDescription
# Before AllLuaReshape/generate inputs and add global data to the command scope (the "map" part of map/reduce).
# DataLuaGather additional data per input and return it for the next stages.
# SystemHandlebarsCustomize the system prompt using data from # Before All and # Data.
# InstructionHandlebarsCustomize the instruction prompt using data from # Before All and # Data.
# AssistantHandlebarsOptional for special customizations, such as the "Jedi Mind Trick."
# OutputLuaProcesses the ai_response from the LLM. If not defined, ai_response.content is output to the terminal.
# After AllLuaCalled with inputs and outputs for post-processing after all input processing is complete (the "reduce" part of map/reduce).
  • # Before All and # After All act like the map and reduce steps, running before and after the main input processing loop, respectively.

more info on stages

aipack doc

See the AIPack documentation at core/doc/README.md (with the Lua modules doc).

You can also run the ask-aipack agent.

# IMPORTANT: Make sure you have the `OPENAI_API_KEY` (or the key for your desired model) set in your environment.
aip run core@ask-aipack
# The prompt file will be at `.aipack/.prompt/core@ask-aipack/ask-prompt.md`

This Repo

Runs on
GPU support unknown
What shows it
  • no dependency or feature matches a backend row
Native code
Required
  • Binds native code: libsqlite3-sys, mlua-sys
Python bindings
No Python binding crate found
License
Apache-2.0 OR MIT

Compare with similar projects

Order: strongest tier first, then most downloads, then name, with unmeasured values last. A library counts downloads over 30 days. An app counts binary downloads per release. An app with none counts its crate downloads over 30 days. ML Rust runs this catalogue, so its closest project always gets a row.

aipack beside the projects that share a topic with it
ProjectDownloadsDependentsStarsLatest releaseRuns on (GPU)Native codeLicense
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Not measured Not applicable 3 Not measured Not measured Not measured Apache-2.0
Why ListedNo archive, New or Featured rule places it, so it is Listed.

What it lacks for Featured

  • The check holds no value for Homebrew installs on request over 365 days, Repology repository families, Docker pull growth over 90 days. A gate that lacks its signal does not pass.

What else the check found

  • The last activity is on 4 Nov 2026, 5 days ago. The rules expect an update within 365 days. Dormancy rule

Not measured: Homebrew installs on request over 365 days, Repology repository families, Docker pull growth over 90 days.

Exact rules: Listed, Featured app. The check on applied thresholds version 9.

Every rule outcome, as the check records it
  • expected-interval: app: E = 365 days
  • dormancy: last activity 2026-11-04, 5 days ago, E = 365: active
  • featured: not featured: unknown homebrew_on_request_365d, repology_families, docker_pull_growth_90d
  • listed: listed: no archive, new or featured rule applies
Crates2 crates

Measurements and sources

Each signal row shows the raw value, its source and the fetch date. The Sources list gathers every distinct source on this page. Movement needs more history.

Sources6 sources
AdoptionNot measured
Adoption measurements
SignalValueSourceFetchedCounts
Active dependentsNot measuredYes
New dependents (365 days)Not measuredYes
Peak active dependentsNot measuredYes
Active dependents as a share of the peakNot measuredYes
Binary downloads per releaseNot measuredYes
MaintenanceLatest human commit: 5 days ago4 not measured
Maintenance measurements
SignalValueSourceFetchedCounts
Latest human commit2026-11-04GitHub repositoryYes
Latest release2026-10-28crates.io database dumpYes
First release2024-09-16crates.io database dumpYes
Latest version0.8.37crates.io database dumpNo
Days the oldest outside issue waits for a reply728GitHub repositoryYes
Maintainer replies over 365 days6GitHub repositoryYes
Repository archivedfalseGitHub repositoryYes
Repository gonefalseGitHub repositoryYes
Release commit or manifest in the repotruecrates.io packageNo
Published docsNot measuredYes
Latest release fails cargo checkNot measuredYes
Build check dateNot measuredYes
Build check toolchainNot measuredYes
SecurityNot measured
Security measurements
SignalValueSourceFetchedCounts
Unpatched vulnerabilities past the grace windowNot measuredYes
RustSec unmaintained advisoryNot measuredYes
Deprecation noticeNot measuredYes
Deprecated requirementNot measuredYes
Distribution78 releases, 21 downloads over 30 days6 not measured
Distribution measurements
SignalValueSourceFetchedCounts
Releases78crates.io database dumpNo
Downloads over 30 days21crates.io database dumpNo
Binary downloads over the last 3 releases0GitHub releasesYes
Homebrew installs on request over 365 daysNot measuredYes
Repology repository familiesNot measuredYes
Docker pull growth over 90 daysNot measuredYes
Docker pulls at the growth baselineNot measuredYes
Every version a prereleasefalsecrates.io database dumpYes
Every version 0.0.xfalsecrates.io database dumpYes
Every version yankedNot measuredYes
Crate deleted from crates.ioNot measuredYes
Repository224 stars, 30 forks, 5 contributors, 1,980 commits. Stars alone never qualify a project.
Repository measurements
SignalValueSourceFetchedCounts
GitHub stars224GitHub repositoryYes — one part of the repository route
GitHub forks30GitHub repositoryYes — one part of the repository route
Contributors with a GitHub account5GitHub contributorsYes — one part of the repository route
Commits on the default branch1980GitHub repositoryNo
Repository's main crate left out of the entrynonecrates.io database dumpYes
Facts that never count
Facts that never count measurements
SignalValueSourceFetchedCounts
LicenseApache-2.0 OR MITcrates.io database dumpNo
Runs on, from listed crates and their direct dependenciesGPU support unknowncrates.io database dumpNo
What shows each backendno dependency or feature matches a backend rowcrates.io database dumpNo
Native code, from listed crates and their dependenciesBinds native code: libsqlite3-sys, mlua-syscrates.io database dumpNo
Python bindings, from direct dependencies of listed cratesNo Python binding crate foundcrates.io database dumpNo
Crates that depend on a listed crate directlyNot measuredNo

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Judgments

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