Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/endogenai/dogma/aigne-afs-evaluation)<a href="https://agentmods.dev/agents/endogenai/dogma/aigne-afs-evaluation"><img src="https://agentmods.dev/badge/agents/endogenai/dogma/aigne-afs-evaluation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03069 |
| Opus 5 | $0.00000 | $0.01535 |
| Sonnet 5 | $0.00000 | $0.00614 |
| Haiku 4.5 | $0.00000 | $0.00307 |
Grade A, and why
aigne-afs-evaluation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AIGNE AFS Context Governance Layer Evaluation
Status: Final Research Question: Can the AIGNE Agent File System (AFS) context governance pipeline serve as the substrate for EndogenAI's token offloading strategy — replacing or augmenting the current scratchpad approach? Date: 2026-03-09
1. Executive Summary
The AIGNE AFS (Agentic File System) is a context governance layer backed by SQLite + a local vector store, exposing an MCP tool interface for agent integration. It implements a formal context engineering pipeline (Context Constructor → Context Updater → Context Evaluator) layered on top of the LangChain-attributed write→select→compress→isolate four-stage pattern (docs/research/agentic-research-flows.md §Attribution Correction).
Governing axioms: MANIFESTO.md §2 — Algorithms Before Tokens (a deterministic context manager reduces token burn per session) and MANIFESTO.md §3 — Local Compute-First (SQLite + local vector store, no cloud dependency).
Recommendation: MONITOR — do not adopt yet.
AIGNE AFS is architecturally the strongest candidate for a context governance layer among options surveyed. Its design directly addresses EndogenAI's primary context management gap: no semantic retrieval layer over episodic/experiential memory between sessions. However, it shares the same prerequisite as the episodic memory libraries evaluated in docs/research/episodic-memory-agents.md: the local compute baseline (OPEN_RESEARCH.md item 1) must be resolved before a local AFS deployment is viable. Premature adoption before that baseline is stable adds infrastructure complexity against an uncertain inference backend.
The current scratchpad-based approach remains the correct architecture for now. The evaluation below provides a concrete integration design that should be executed once the prerequisite is met.
2. Hypothesis Validation
H1 — The AIGNE AFS pipeline directly addresses the EndogenAI context management gap
Validated. The AIGNE paper enumerates seven memory types. The current EndogenAI architecture satisfies two of them:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 220 lines · 0 tokens per session scan A df2ac4a69639
aigne-afs-evaluation is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 12d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 3,069 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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