GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
LongCite is designed to enhance large language models by enabling them to generate fine-grained citations when answering queries with long context.
Good fit when
- When you require your LLM to provide detailed, well-cited responses in long-context scenarios.
- If your application demands accurate and granular attribution for information derived from extensive text bodies.
Avoid when
- If your use case involves short queries or contexts that do not need extensive citations.
- When the primary focus is on speed rather than detailed citation accuracy in responses.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (627d 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 LongCite PyPISimilar 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
LongCite focuses on enabling large language models to generate fine-grained citations in responses to queries with long context.
Capability facts
- Languages
- python
Source: github.language · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
⚙️ LongCite Deployment Environmental Setup : We recommend using to successfully deploy our models. We open source two models: LongCite glm4 9b and LongCite llama3.1 8b, which are trained based on GLM 4 9B and Meta Llama 3.1 8B, respectively, and support up to 128K context. These...
For agents
This page has a .md twin and JSON over the API.