Skip to main content

Generate environment maps (services, artifacts, env vars) for LLM decision-making

Project description

env2llm

AI Cost Tracking

PyPI Version Python License AI Cost Human Time Model

  • 🤖 LLM usage: $1.6232 (5 commits)
  • 👤 Human dev: ~$337 (3.4h @ $100/h, 30min dedup)

Generated on 2026-06-07 using openrouter/qwen/qwen3-coder-next


Generate environment maps for LLM agents: available services, commands, artifacts, masked environment variables, and runtime policies.

Default output is DOQL-flavored LESS (environment.doql.less), compatible with nlp2dsl examples:

environment[name="03-report-and-notify"] {
  NLP2DSL_BACKEND_URL: "http://localhost:8010";
  LLM_MODEL: "openrouter/...";
}

runtimes[0] { id: "orchestrator:nlp-service"; kind: "orchestrator"; ... }
commands[3] { name: "generate_report"; required: "report_type"; ... }

Install

cd env2llm
pip install -e ".[dev]"

CLI

# From an nlp2dsl example directory
env2llm /path/to/nlp2dsl/examples/03-report-and-notify

# Other formats
env2llm . --format yaml
env2llm . --format json
env2llm . --format markdown

# Optional: attach live Linux desktop snapshot (GNOME/X11 via wmctrl/xdotool)
env2llm . --probe-desktop
# or: ENV2LLM_DESKTOP_PROBE=1 env2llm .

# Koru projects: auto-attach MCP tool catalog (koru_list_tickets, …) when koruapi installed
env2llm . --probe-mcp
# or auto when cwd is a Koru repo with koruapi on PYTHONPATH

Writes to .nlp2dsl/registry/environment.<ext> (and mirrors legacy paths for DOQL).

Desktop / GUI probe (optional)

When --probe-desktop or ENV2LLM_DESKTOP_PROBE=1 is set, env2llm adds:

  • desktop { canvas_width, canvas_height, compositor, display_server, ... }
  • desktop_displays[N] { id, output, left, top, width, height, is_primary, index } — full multi-monitor layout (xrandr or mss)
  • desktop_pointer { x, y, display_id, display_x, display_y, ... } — mouse position in global and display-local coords (xdotool)
  • desktop_ide_calibrations[N] { ide, chat_x, chat_y, config_path, display_id, ... } — Koru OS-injector chat anchors from ~/.koru/ide-os-injector.json and <project>/.koru/ide-os-injector.json
  • desktop_windows[N] { title, x, y, width, height, ... } — top-level windows (wmctrl)
  • runtime probe:desktop (desktop://session) for GUI automation via nlp2uri
  • commands desktop_focus_window, desktop_move_window, desktop_screenshot_*, desktop_open_app
  • summary keys in data (desktop.displays, desktop.pointer, desktop.pointer_display, desktop.window_titles, …)

Requires (Linux): xrandr (preferred) or Python mss for monitor layout; xdotool for pointer; wmctrl for window titles/geometry. Headless hosts get status: unknown without failing workflow maps.

Browser window titles are detected heuristically (Firefox, Chrome, Edge, …). Page DOM/content is not scraped — use testql / Playwright for that layer.

Python API

from env2llm import RegistryService, ensure_environment_map, render_format, generate_system_map

path = ensure_environment_map("examples/03-report-and-notify")
ir = generate_system_map("examples/03-report-and-notify", example_id="03-report-and-notify")
yaml_text = render_format(ir, "yaml")

# Live registry service (load / refresh / render / MQTT)
service = RegistryService(".", project_id="my-app")
registry_json = service.render("json")

REST API (env2llm-serve)

env2llm-serve --project . --port 8770
# optional MQTT fan-out:
ENV2LLM_MQTT_ENABLED=1 env2llm-serve --project . --mqtt
Method Path Opis
GET /health status
GET /v1/registry?format=json|yaml|doql|md&refresh=1 registry
POST /v1/registry/refresh regenerate + persist (+ MQTT)
GET /v1/registry/desktop desktop probe slice
GET /v1/registry/commands command schemas
GET /v1/registry/uris nlp2uri URI index (needs nlp2uri[envmap])
GET /v1/registry/mqtt MQTT bridge status

MCP (env2llm-mcp)

{
  "mcpServers": {
    "env2llm": {
      "command": "env2llm-mcp",
      "args": ["--project", "/path/to/project"],
      "env": { "ENV2LLM_MQTT_ENABLED": "1" }
    }
  }
}

Tools: env2llm_get_registry, env2llm_render_registry, env2llm_refresh_registry, env2llm_get_desktop, env2llm_list_commands, env2llm_list_uris, env2llm_mqtt_status.

MQTT (env2llm-mqtt)

Requires pip install 'env2llm[mqtt]' (paho-mqtt).

# Standalone bridge: listen for refresh, publish retained snapshots
ENV2LLM_MQTT_ENABLED=1 env2llm-mqtt bridge --project .

# One-shot publish
env2llm-mqtt publish --project . --probe-desktop

Topics (prefix env2llm):

Topic Retain Zawartość
{prefix}/{project}/registry yes full SystemMapIR JSON
{prefix}/{project}/registry/desktop yes desktop probe slice
{prefix}/{project}/events no refresh/metadata events
{prefix}/{project}/registry/refresh subscribe: trigger refresh
Variable Default
ENV2LLM_MQTT_ENABLED off
ENV2LLM_MQTT_HOST 127.0.0.1
ENV2LLM_MQTT_PORT 1883
ENV2LLM_MQTT_TOPIC_PREFIX env2llm
ENV2LLM_MQTT_USERNAME / PASSWORD optional

Output formats

Format File Use case
doql.less environment.doql.less LLM + nlp2dsl orchestrator (default)
yaml environment.yaml Tooling, human edit
json environment.json APIs, CI
markdown environment.md Prompt injection summary

Environment variables

Variable Purpose
NLP2DSL_BACKEND_URL Gateway service URL
NLP2DSL_NLP_SERVICE_URL NLP orchestrator URL
NLP2DSL_WORKER_URL Worker executor URL
LLM_MODEL LLM provider/model id
ENV2LLM_CONTEXT Path to generated map (set after bootstrap)
ENV2LLM_DESKTOP_PROBE 1 to attach live desktop snapshot (same as --probe-desktop)
NLP2DSL_DOQL_CONTEXT Alias for nlp2dsl compatibility

Secrets (*_KEY, *_TOKEN) are masked in output.

Extraction from nlp2dsl

This package contains the former nlp2dsl_sdk modules:

  • doql/ — parse/render runtime context
  • ir.pySystemMapIR schema (env2llm.system_map.v1)
  • generate.py, bridge.py, runtimes.py — introspection pipeline
  • registry.py — live registry refresh after workflow steps
  • policy/ — process and invoice policies from example-profiles.yaml

nlp2dsl can depend on env2llm and replace direct imports over time.

License

Licensed under Apache-2.0.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

env2llm-0.1.11.tar.gz (74.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

env2llm-0.1.11-py3-none-any.whl (84.9 kB view details)

Uploaded Python 3

File details

Details for the file env2llm-0.1.11.tar.gz.

File metadata

  • Download URL: env2llm-0.1.11.tar.gz
  • Upload date:
  • Size: 74.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for env2llm-0.1.11.tar.gz
Algorithm Hash digest
SHA256 06362c3ed218a2bf9eb3c637e846891282c28be801911fd9967742c362e2f954
MD5 5bd33419f99c6f3230ab515050294504
BLAKE2b-256 a61e49753c3082dfdc26bc35a20ddd739e0c7037cc3b53129764140537f20b7c

See more details on using hashes here.

File details

Details for the file env2llm-0.1.11-py3-none-any.whl.

File metadata

  • Download URL: env2llm-0.1.11-py3-none-any.whl
  • Upload date:
  • Size: 84.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for env2llm-0.1.11-py3-none-any.whl
Algorithm Hash digest
SHA256 830ca9edea9ee87e09cdb0713c7162d0f0ed1fb1e9069be707fb2b2d2af00938
MD5 2aef63ac8eae2228fadbf56f295e2bfd
BLAKE2b-256 cf070d721a7254b20d83222400e088a68ea8b58e8b50186617d85f98ad9b73a6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page