Pass verifiable, tamper checkable findings between AI agents instead of relying on unstructured plain text handoffs.
Store per claim provenance and reliability labels so downstream agents do not have to blindly trust an earlier agent's output.
Generate derived HTML, SVG, and Mermaid views from the same integrity bound evidence packet for human review.
Verify a packet's integrity using either a Python or Node.js implementation and confirm both agree on the same hash.
| onepunchforall/aep | 0xhassaan/nn-from-scratch | 3ks/embedoc | |
|---|---|---|---|
| Stars | 0 | 0 | — |
| Language | Python | Python | Python |
| Last pushed | — | — | 2023-06-08 |
| Maintenance | — | — | Dormant |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 4/5 | 4/5 | 1/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires understanding of the packet schema and running both Python and Node verifiers to confirm cross-runtime integrity.
AEP, short for Agent Evidence Packet, is a file format built to solve a specific problem in AI agent pipelines. When one AI agent hands its findings off to another, those findings usually arrive as plain text with no way to check where the information came from or whether it has been changed along the way. AEP replaces that unstructured prose with a structured, schema validated, tamper detectable format, with tooling written in Python. Each AEP packet is a directory, using a .aepkg extension, containing JSONL files, meaning one JSON record per line, for sources, spans, claims, relations, events, reviews, and validation runs. A span records the exact quoted location a claim came from, along with a SHA-256 hash of that quote. A root manifest ties everything together with its own SHA-256 state hash, which any compliant validator can independently recompute. If anything in the packet changes after that hash is set, validation fails. Every claim in a packet carries a reliability label, an evidence basis pointing back to a specific quoted span, and reviewer receipts, so a claim can be checked without simply trusting the agent that produced it. The format can generate derived views from the same underlying data: an HTML table, an SVG diagram of the integrity tree, and a Mermaid dependency graph, all tied back to the same root hash. Separate verifier programs written in Python and in Node.js were tested against the same packets and produced identical hashes, showing that two independent implementations agree the data has not been tampered with. AEP is aimed at developers building AI agent systems where verifiable provenance matters more than file size or ease of hand editing. The README reports the format closes 11 documented attack classes and includes over 35 fail closed validation error codes, at the cost of packets being noticeably larger on disk than plain HTML or Markdown. The project is released under the Apache 2.0 license.
A schema validated, tamper detectable file format for AI agent memory that replaces plain text handoffs with structured, cryptographically verifiable claims.
Mainly Python. The stack also includes Python, Node.js, JSONL.
Use freely for any purpose, including commercial use, as long as you keep the copyright and license notices.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.