Agentic Context Engine is an open-source engine that gives AI agents a persistent learning loop, helping them remember successful strategies and learn from failures across sessions. It is used to improve production agents, and also powers Kayba’s hosted service. Catalogue add-ons support workflows for operating and configuring the engine.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/kayba-ai/agentic-context-engine/stage-1-api-analysisnpx skills add kayba-ai/agentic-context-engine --skill stage-1-api-analysisgit clone --depth 1 https://github.com/kayba-ai/agentic-context-engineWrote 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/skills/kayba-ai/agentic-context-engine/stage-1-api-analysis)<a href="https://agentmods.dev/skills/kayba-ai/agentic-context-engine/stage-1-api-analysis"><img src="https://agentmods.dev/badge/skills/kayba-ai/agentic-context-engine/stage-1-api-analysis.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 | $0.00093 | $0.01004 |
| Opus 5 | $0.00046 | $0.00502 |
| Sonnet 5 | $0.00019 | $0.00201 |
| Haiku 4.5 | $0.00009 | $0.00100 |
Grade A, and why
kayba-stage-1-api-analysis 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 5d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stage 1: Kayba API Analysis (Fetch-Only Mode)
Fetch pre-computed insights from the Kayba API. Traces have already been uploaded and analyzed — this stage only pulls results.
Inputs
TRACES_FOLDER— passed by the orchestrator but ignored in this stage. Traces are already uploaded and analyzed on the Kayba side. Do NOT upload, validate, or read trace files.
Process
Step 1: Setup
Ensure eval/ directory exists at the project root.
Step 2: Fetch insights
kayba insights list --json > eval/insights.json
If kayba is not found in PATH, search common locations (.venv/bin/kayba, project virtualenvs). If found, use the full path. If not found anywhere, report the error and stop.
If KAYBA_API_KEY is not set, report the error and stop.
Step 3: Insight quality gate
Read eval/insights.json and run quality checks before building the summary:
- Empty check: if the insights array is empty (0 insights returned), report this as a warning. Write a minimal summary noting "0 insights generated" and stop — downstream stages cannot proceed without insights.
- Duplicate detection: compare insight
contentfields pairwise. If two insights cover substantially the same behavior (same section, overlapping evidence traces, similar corrective action), flag them as potential duplicates in the summary. Do not remove them — just annotate. - Evidence coverage: for each insight, check if the
evidencefield references specific traces (e.g., "task_7 turn 4"). Insights with no trace-specific evidence are lower quality — flag as "low-evidence" in the summary. - Vote signal: insights with
status: "accepted"andhelpful > 0have been human-validated. Insights withstatus: "new"andhelpful: 0, harmful: 0are unvalidated — note this distinction in the summary.
Log the quality gate result: "Insight quality: {total} insights, {accepted} accepted, {new_unvalidated} unvalidated, {duplicates} potential duplicate pairs, {low_evidence} low-evidence"
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.
- 5d ago First seen · 85 lines · 93 tokens per session scan A 838146a48a5f
kayba-stage-1-api-analysis is a skill published in the GitHub repository kayba-ai/agentic-context-engine (2,564 stars, last pushed 6d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,004 once invoked, about $0.0005 per session on Opus 5. 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 2026-08-30.
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