GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding.
Good fit when
- If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3.
- When your work involves speculative decoding features which are a focal point within this framework.
Avoid when
- If the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project.
- In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (211d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install EAGLESimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository provides the official code for EAGLE-1, EAGLE-2 and EAGLE-3 models published in ICML'24, EMNLP'24, and NeurIPS'25. The project focuses on large language model development including inference capabilities and speculative decoding.
Capability facts
- Languages
- python
Source: github.language · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
python -m venv ~/venvs/ea_envSource link
Tags
README
Setup & Installation
For agents
This page has a .md twin and JSON over the API.