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  "title": "ZizkaDB",
  "description": "AGPL-3.0 audit-trail database for AI agents (Python) that records every agent decision and session as tamper-evident, checksum-backed logs, tracks the causal lineage between them, and lets you replay a session to see how the agent reached a decision. Each entry carries a checksum that changes if the record is altered, so the trail is verifiable for debugging and compliance. Self-hostable via Docker for free; a managed cloud tier is a separate paid option.",
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  "one_liner": "ZizkaDB is a self-hosted, tamper-evident audit database for agents: checksum-backed decision logs with causal lineage, replayable to see how an agent decided.",
  "open_source": "yes",
  "self_hostable": "yes",
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  "who_its_for": "A team running agents against real systems that needs to answer 'why did the agent do that' after the fact, and wants a verifiable, self-hosted record of every decision for debugging or compliance rather than scrollback in a chat log.",
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    "note": "AGPL-3.0, Python, ~81 stars but ~527 commits, the tamper-evident checksum-backed decision/session logs, causal lineage and replay, and Docker self-host confirmed on the repo (WebFetch 2026-09-20, ZIZKA-AI-SL org). A managed cloud tier (roughly EUR 29-69/month) is separate; the repo notes a split between the public self-host stack and a private zizkadb-cloud."
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  "caveats": "AGPL-3.0 is copyleft: you can self-host, modify and use it internally freely, but serving a modified version to outside users obliges you to publish your changes, so read the license against how you deploy it. Young by stars (~81) though heavily committed (~527). Tamper-evidence detects alteration of the record; it is an audit trail, not an access-control or policy layer.",
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      "est_tokens": 116,
      "text": "AGPL-3.0 audit-trail database for AI agents (Python) that records every agent decision and session as tamper-evident, checksum-backed logs, tracks the causal lineage between them, and lets you replay a session to see how the agent reached a decision. Each entry carries a checksum that changes if the record is altered, so the trail is verifiable for debugging and compliance. It self-hosts via Docker for free, with a managed cloud tier as a separate paid option."
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      "text": "lets you replay a session to see how the agent reached a decision. Each entry carries a checksum that changes if the record is altered, so the trail is verifiable for debugging and compliance. It self-hosts via Docker for free, with a managed cloud tier as a separate paid option.\n\n- AGPL-3.0, Python, ~81 stars, ~527 commits, and the checksum-backed tamper-evident logs with causal lineage and replay independently WebFetch-verified on the repo 2026-09-20 (github.com/ZIZKA-AI-SL/ZizkaDB).\n- Surfaced via the 2026-09-20 daily pull (Show HN: \"tamper-evident, checksum-backed decision and session replay\").\n- Curated from the GTM Stacker signal registry (2026-09-20 pass); license independently verified 2026-09-20."
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      "text": "~81 stars, ~527 commits, and the checksum-backed tamper-evident logs with causal lineage and replay independently WebFetch-verified on the repo 2026-09-20 (github.com/ZIZKA-AI-SL/ZizkaDB). - Surfaced via the 2026-09-20 daily pull (Show HN: \"tamper-evident, checksum-backed decision and session replay\"). - Curated from the GTM Stacker signal registry (2026-09-20 pass); license independently verified 2026-09-20.\n\nOnce an agent is touching the CRM, the sending domain or billing, \"why did it do that\" stops being a curiosity and becomes a question you have to answer, to a customer, a teammate or an auditor. ZizkaDB is the record for that: every decision and session logged in a way that shows if it was altered, linked by cause, and replayable after the fact. For a GTM or RevOps team that has moved past demos into agents doing real work, that verifiable trail is the difference between an incident you can reconstruct and one you can only guess at. Two honest notes: it is AGPL, so read the copyleft terms against how you deploy, and it is an audit layer, not a gate, so it tells you what happened rather than stopping it. As the memory that makes agent actions accountable, it fills a real slot."
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