cactus-computeAI apps

needle

A small local-model Python package for tool calling and structured extraction, with offline inference, confidence thresholds, LoRA fine-tuning, and model export.

  • Backend
  • Data
  • Automation
  • Data analysis
  • Library/framework
  • Web
  • macOS
  • Linux
  • Browser
  • Runs locally
needle screenshot
Popularity
12.9k Stars
GitHub stars
Recent activity
10/1/2026
Updated in the last 30 days
License
APACHE-2.0
Permissive

Why it matters

We look beyond stars: what problem it solves, whether it creates real utility, and what makes its approach worth noticing.

Last 90 days

Problem

Addresses the challenge of deploying language models on severely resource-constrained edge devices by offering a 45M-parameter model consuming about 28MB of RAM for tool calling and extraction.

Practical value

Provides practical utility through a self-contained binary and Python package supporting offline inference, confidence gating, tool retrieval, and LoRA fine-tuning via straightforward APIs.

Innovation / differentiation

Innovates with a Simple Attention Network architecture combining Hadamard MLPs, engram key-value memory, and byte-level grammar-constrained decoding for ultra-compact models.

Leverage potential

Enables high leverage through low-cost LoRA fine-tuning, synthetic data generation, and straightforward model export into single-file binaries.

Why now

Arrives at an opportune time as local AI and edge deployment demands surge, aligning well with the industry push for privacy-first, offline-capable small language models.

Community activity

In the last 90 days there were 58 new issues and 71 pull requests; the bounded issue/PR samples include 50 issue authors and 36 PR contributors, with 0 releases.

Maintainer responsiveness

The 58-issue window sample had a 90% close rate, and the 71-pull-request sample had a 58% merge rate. Maintainer-response observations covered 74% of that issue sample, with a 0% response rate and median first response of not enough data.

36 contributors in PR sample0 releasesIssue response rate 0% · sample 43 (74% coverage)PR merge rate 58% · 71 sampled in window

Key highlights

  • Declare Python tools with decorators and receive structured tool-call arguments
  • Extract typed structured data from text with Pydantic schemas
  • Run core inference locally after the model is cached without network access

Quick start

How it is installed, how hard it is, and where to start.

Where it runs

Runs locally

Difficulty

Medium — some setup needed

Runs locally
  1. 01Install with pip install cactus-needle; the first run fetches and caches the required inference engine or model resources.
  2. 02Describe allowed Python functions with @needle.tool, or define a Pydantic model and use extract() for structured extraction.
  3. 03Set confidence thresholds and validate arguments in real automation; use the project’s LoRA fine-tuning and export workflow only when adaptation to your own tools is needed.

Best for

  • Developers who want to try it on their machine

More about it

Needle 2 combines a small tool-calling model, its inference engine, and a Python API in one package. Python functions can be declared as tools with decorators so the model selects a function and produces structured arguments, while Pydantic models can be used for structured extraction from text. After the model and engine are fetched and cached, core inference does not require network access. The project also includes response confidence scores, tool retrieval, a local playground, and LoRA fine-tuning and export workflows. Optional synthetic-data generation uses an external model API, but that is not required for core inference.

Sources

Each field shows its status and source — expand to review.

13 · Expand
  • capability tags

    Verified

    automation, data_analysis

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • editor note

    Verified

    {"en":"Its natural fit is not general chat but bounded tool selection, argument filling, and structured extraction in local or device-oriented applications. A small model reduces resource demands, but real business actions should still be protected by confidence thresholds and application-side validation.","zh":"它的优势场景不是通用聊天,而是设备端或本地应用中范围明确的工具选择、参数填写和结构化提取。小模型能降低资源占用,但真正接入业务动作前仍应利用置信度阈值和应用侧校验兜底。"}

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • how to use

    Verified

    {"steps":[{"en":"Install with `pip install cactus-needle`; the first run fetches and caches the required inference engine or model resources.","zh":"用 `pip install cactus-needle` 安装包;首次使用会获取并缓存所需推理引擎或模型资源。"},{"en":"Describe allowed Python functions with `@needle.tool`, or define a Pydantic model and use `extract()` for structured extraction.","zh":"用 `@needle.tool` 描述允许调用的 Python 函数,或定义 Pydantic 模型后调用 `extract()` 做结构化提取。"},{"en":"Set confidence thresholds and validate arguments in real automation; use the project’s LoRA fine-tuning and export workflow only when adaptation to your own tools is needed.","zh":"在真实自动化中设置置信度阈值和参数校验;需要针对自有工具优化时,再使用项目的 LoRA 微调与导出流程。"}],"installAt":"local","difficulty":"medium"}

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • intro

    Verified

    {"en":"Needle 2 combines a small tool-calling model, its inference engine, and a Python API in one package. Python functions can be declared as tools with decorators so the model selects a function and produces structured arguments, while Pydantic models can be used for structured extraction from text. After the model and engine are fetched and cached, core inference does not require network access. The project also includes response confidence scores, tool retrieval, a local playground, and LoRA fine-tuning and export workflows. Optional synthetic-data generation uses an external model API, but that is not required for core inference.","zh":"Needle 2 把小型工具调用模型、推理引擎和 Python API 放在同一套包中。安装后可以用装饰器把 Python 函数声明成工具,让模型选择函数并生成结构化参数;也可以用 Pydantic 模型从文本中提取结构化数据。模型和引擎首次获取并缓存后,核心推理不依赖网络。项目还提供置信度分数、工具检索、本地 Playground,以及 LoRA 微调和导出流程。可选的数据合成功能需要额外的模型 API,但这不是核心推理的必需条件。"}

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • License

    Verified

    Apache-2.0

    Source: GitHub API · license.spdx_id=Apache-2.0 · 10/1/2026

  • needs api key

    Verified

    No

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • One-liner

    Verified

    {"en":"A small local-model Python package for tool calling and structured extraction, with offline inference, confidence thresholds, LoRA fine-tuning, and model export.","zh":"面向本地工具调用和结构化提取的小型模型 Python 包,支持离线推理、置信度阈值、LoRA 微调与模型导出。"}

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • Platforms

    Verified

    macos, linux, browser

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • Category hint

    Verified

    Edge AI model

    Source: Project README · Description: '14MB foundation model for tiny devices' and 'tool calling for tiny devices', indicating edge deployment. · 8/14/2026

  • product forms

    Verified

    library_framework, web

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • role tags

    Verified

    backend, data

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • supports local

    Verified

    Yes

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

  • supports self host

    Verified

    No

    Source: manual_curated · Manually curated from the verified project README; no in-app AI draft. · 8/17/2026

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