Skip to main content

Saddlefit

CI Coverage Release License

Saddlefit fits an agent's harness to one compact model, on your own tasks. It adds what fixes failures, drops what stops helping, and proves each change on tasks the tutor never saw.

The model's weights never change; only the files around it do: system prompt, skills, tool guidance, tool scripts, loop settings, reasoning mode and an EXPERIENCE.md of lessons.

Status: pre-alpha. Built milestone by milestone; see the milestones and the spec.

Three loops

Three loops: the fast loop during a task, the middle loop overnight, the slow loop that improves the tutor itself
  • Fast — during a task. When the task's own check fails, the student probes a few harness variants locally and keeps what passes; the finding goes on probation in a journal.
  • Middle — overnight. A tutor diagnoses training failures and proposes one change at a time; it is accepted only if it wins on repeated runs over held-out tasks, and pruned by ablation once it stops paying for its context.
  • Slow — every few weeks. Statistics on accepted changes become a ranked playbook, gated by an A/B test, until a compact local tutor can take over.
The fast loop in detail
Fast loop: a failed check triggers local probes, and a change that helps stays on probation in the journal
The middle loop in detail
Middle loop: a change is accepted only if it wins on the training batch and again on held-out tasks
The slow loop in detail
Slow loop: a new playbook must beat the old one in an A/B run before the tutor uses it, and a shadow tutor takes over once it is as good as the cloud one

What counts as a compact model

An open-weight model that runs on one consumer machine and meets all four conditions (saddlefit doctor checks them):

  1. Memory — at 4-bit, weights plus a 32k-token context fit in 24 GB or less.
  2. Active size — at most about 10B parameters active per token (a 30B-A3B MoE qualifies).
  3. Speed — roughly 20 tokens per second or more on the target machine.
  4. Tool use — reliable tool calls, natively or through constrained JSON output.
Tier What it is Typical hardware
S dense, 1–4B 8–16 GB laptops, CPU only
M dense, 4–10B, or a looped model 16–18 GB Macs, GPUs with 8–12 GB
L MoE, up to ~35B total and ~5B active 32–36 GB Macs, 24 GB GPUs

Quickstart

Requires Python 3.12+, Docker and a local model runner such as Ollama.

uv tool install saddlefit
cd your-python-project
saddlefit init --model qwen3:8b
saddlefit doctor
saddlefit tasks mine
saddlefit baseline
saddlefit fit
saddlefit report

Every command also accepts --json.

Where it comes from

The middle loop follows AutoSaddler; the fast loop and the improver that improves itself follow Harness². What Saddlefit adds is the focus on one compact local model, measured on your own tasks, with a harness that stays small.

Ideas from AutoSaddler and Harness², and what Saddlefit adds Map of related approaches by when the harness improves and who executes; Saddlefit learns ahead of time for a small local model

See the spec, Part D for sources and a fuller comparison.

Documentation

License

Apache-2.0

Metadata

Release files for saddlefit 0.1.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for saddlefit 0.1.3
File Size Uploaded Provenance
saddlefit-0.1.3.tar.gz 92.3 kB With provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for saddlefit 0.1.3
File Interpreter ABI Platform
saddlefit-0.1.3-py3-none-any.whl Python 3 none any Details

Total release size: 199.9 kB

Release files / saddlefit-0.1.3.tar.gz

Download URL saddlefit-0.1.3.tar.gz
Size 92.3 kB
Tags Source
SHA-256 checksum
How to use checksums
a57542acad31a06d477c6cc842b08aea688c0ba050f658fe1af4c700738206e1
BLAKE2b-256 checksum
How to use checksums
b7e6cdaf03fc789836cc5be0324c859097f903a8f5eebec5bec1fcf43733ab32
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 21, 2026.

Transparency log

Release files / saddlefit-0.1.3-py3-none-any.whl

Download URL saddlefit-0.1.3-py3-none-any.whl
Size 107.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
688778f3570d7d65698b0204f0e35fa1d57711b6a3da08a7606b59af4a1d8e74
BLAKE2b-256 checksum
How to use checksums
f58018bdc91d8f2651a326175454ad6b98493f4e5d3c6d699eb9694c8ae8d1a2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 21, 2026.

Transparency log

Security for saddlefit 0.1.3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for saddlefit-0.1.3.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 21, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page