Hl7tap

pip install cognis-hl7tap
hl7tap scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ hl7tap-emit --version
hl7tap 0.1.0

$ hl7tap-emit --help
usage: hl7tap [-h] [--version] [--format {table,json}] <command> ...

Parse, pretty-print, and diff HL7 v2 messages (curl for HL7 interfaces).

positional arguments:
  <command>
    parse               parse and pretty-print a message
    diff                diff two HL7 messages

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)

examples:
  # Pretty-print a message
  hl7tap parse demos/01-basic/adt_a01.hl7

  # Emit structured JSON for piping into jq / CI
  hl7tap parse message.hl7 --format json | jq .message_type

  # Diff two messages (exits non-zero when they differ -- CI gate)
  hl7tap diff old.hl7 new.hl7
  hl7tap diff old.hl7 new.hl7 --format json

  # Read from stdin
  cat message.hl7 | hl7tap parse -

Blocks above are real hl7tap output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"Findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Traffic",
        "description": "Network traffic from unknown IP address",
        "created_at": "2023-02-20T14:30:00Z",
        "updated_at": "2023-02-20T14:30:00Z",
        "labels": ["network", "suspicious"],
        "observables": [
            {
                "type": "ip",
                "value": "192.168.1.100"
            },
            {
                "type": "port",
                "value": 443
            }
        ]
    }
]
}

Usage — step by step

1. Install the tool:

   pip install cognis-hl7tap
   

2. Parse and pretty-print an HL7 v2 message (use - for stdin):

   hl7tap parse adt_a01.hl7
   

3. Emit structured JSON for piping into jq or assertions:

   hl7tap parse message.hl7 --format json | jq .message_type
   

4. Diff two messages. diff exits non-zero when they differ, so it doubles as a CI gate:

   hl7tap diff old.hl7 new.hl7
   

5. Automate / stream. Read from stdin and inspect segments in a pipeline:

   cat message.hl7 | hl7tap parse - --format json | jq .segments
   

Contents

• Why hl7tap? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why hl7tap?

The missing 'curl for HL7 interfaces' — integration engineers can finally test ADT/ORM/ORU feeds without spinning up Mirth.

hl7tap is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Parse Segment
• ✅ Parse Message
• ✅ Message To Dict
• ✅ Pretty Print
• ✅ Diff Messages
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-hl7tap
hl7tap --version
hl7tap scan .                       # scan current project
hl7tap scan . --format json         # machine-readable
hl7tap scan . --fail-on high        # CI gate (non-zero exit)

Example

$ hl7tap scan .
  [HIGH    ] HL7-001  example finding             (./src/app.py)
  [MEDIUM  ] HL7-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[hl7tap<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

hl7tap is interoperable with every popular way of using AI:

• MCP server — hl7tap mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe hl7tap scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis hl7tap	HAPI HL7v2 + tcpreplay
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of HAPI HL7v2 + tcpreplay, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (hl7tap mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/hl7tap.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/hl7tap.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/hl7tap.git" # uv
pip install cognis-hl7tap                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/hl7tap:latest --help        # Docker
brew install cognis-digital/tap/hl7tap                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/hl7tap/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/hl7tap	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• phiscrub — Stream-scan logs, CSVs, and free-text notes for PHI (names, MRNs, SSNs, dates, addresses) and redact or tokenize in place.
• dicomsweep — De-identify DICOM imaging studies per the DICOM PS3.15 Annex E profile, scrubbing tags and burned-in pixel text.
• fhirlint — Validate FHIR R4/R5 resources and bundles against profiles (US Core, etc.) with precise, line-level error reporting.
• consentledger — Maintain a tamper-evident, hash-chained audit log of patient-data access and consent events.
• synthcohort — Generate statistically realistic synthetic patient cohorts (FHIR/CSV) from a schema spec for dev and testing.
• trialwatch — Query, diff, and monitor ClinicalTrials.gov records, alerting on status, enrollment, or result changes.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If hl7tap saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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