ZettelForge MCP server

ZettelForge

<!-- mcp-name: io.github.rolandpg/zettelforge -->

The only agentic memory system built for cyber threat intelligence.

When a senior analyst leaves, two or three years of context walks out with them — customer environments, prior investigations, actor TTPs, false-positive patterns, every hard-won "wait, we've seen this before." ZettelForge is an agentic memory system built so that context stays with the team.

It extracts CVEs, threat actors, IOCs, and ATT&CK techniques from analyst notes and threat reports, resolves aliases (APT28 = Fancy Bear = STRONTIUM = Sofacy), builds a STIX 2.1 knowledge graph, and serves every past investigation back to your analysts — and to Claude Code via MCP — in natural language. Runs entirely in-process. No API keys. No cloud. No data leaves the host.

Star · pip install zettelforge · Docs · ThreatRecall (hosted) · Changelog

v2.6.2 (2026-04-27): Config web editor ships with working dropdowns for all enum fields (LLM/embedding provider, log level, PII action, synthesis format) and a working Apply button. New [crewai] extra exposes ZettelForge as CrewAI tools -- pip install zettelforge[crewai]. Full changelog

If ZettelForge fits a CTI workflow you run, a star is the fastest signal that this category is worth continuing to invest in.

The problem

Every SOC loses analysts. When they leave, investigation context, actor attribution, and environment-specific false-positive patterns go with them. Their replacements re-open the same tickets, re-read the same reports, and re-build the same mental models from scratch.

General-purpose AI memory systems don't fix this for security teams. They can't tell APT28 from Fancy Bear, don't know that CVE-2024-3094 is the XZ Utils backdoor, can't parse Sigma or YARA, and have no concept of MITRE ATT&CK technique IDs. When a CTI analyst gives them a year of intel reports, they get back fuzzy semantic search over chat history.

ZettelForge was built for analysts who think in threat graphs. It extracts CVEs, threat actors, IOCs, and ATT&CK techniques automatically, resolves aliases across naming conventions, builds a knowledge graph with causal relationships, and retrieves memories using intent-aware blended search -- all in-process, with no external API dependency.

Shortened. The full README is on GitHub.