pantheon-ai/agentic-context
Analysis toolkit for agentic context management research. Triage papers and tools into structured reference summaries and REVIEWED.md entries following the agentic-context repo conventions.
93
93%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
reference-tool-template.mdtriage-tool/references/
- title:
- <Tool name>
- author:
- <Author / org>
- date:
- YYYY-MM-DD
- type:
- reference
- tags:
- tool, context-management, <compression|tiered-loading|token-budgeting|injection|cli|daemon>
- source:
- <primary landing page (GitHub / docs / npm / PyPI)>
- source_repo:
- <GitHub URL>
- local_clone:
- ../tools/<repo-name>
- version:
- <vX.Y.Z / commit SHA>
- context:
- <why we care; what synthesis bucket this supports>
- related:
- ../ANALYSIS-<tool-analysis-file>.md
TL;DR (3–8 bullets)
What's novel / different
What does this do that adjacent tools do not?
Architecture overview
Context representation
How does the tool model the context window? (flat token stream, markdown vault, structured slots, etc.)
Injection mechanism
How does content get into the context? (system prompt prepend, tool result injection, retrieval, etc.)
Compression / summarization
Any lossy or lossless reduction applied before injection?
Eviction / overflow handling
What happens when context budget is exceeded?
Session lifecycle
How is context state managed across turns / sessions?
Deployment model
- Runtime: (CLI / daemon / library / MCP server / etc.)
- Language:
- Dependencies:
- Storage:
Benchmarks / self-reported metrics
Quote numbers with source; mark "as reported" for unverified claims.
Open questions / risks / missing details
Notes
Corrections, updates, follow-up pointers.