bddl
Decision gist · record as of 2026-08-14
No—the repository is archived and abandoned; no active maintenance since the latest commit. The unclear license, high install friction (6 dependencies), and lack of Python version specification create friction. Install only if you are actively contributing to or maintaining a fork, or integrating with an existing BEHAVIOR simulator project that requires this exact version.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a simulator backend (e.g., OmniGibson) or a stub implementation like TrivialBackend to evaluate activity conditions; cannot be used standalone for simulation.
- High install friction due to 6 runtime dependencies including pytest, numpy, networkx, jupytext, future, and nltk.
- Repository is archived and abandoned as of the latest commit; no active maintenance.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify licensing terms before use in proprietary or commercial contexts.
last release 2025-06-23 (417 days) · last repo commit 2025-06-23 · 125 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 100,016 downloads/mo, #13,005 on PyPI
Alternatives
Verify before relying
pip install bddl
from bddl.activity import Conditions
behavior_activity = "storing_the_groceries"
activity_definition = 0
simulator = "omnigibson"
conds = Conditions(behavior_activity, activity_definition, simulator)- Whether the package works with current Python versions (requires_python is unspecified)
- Current status of the BEHAVIOR-1K vs BEHAVIOR-100 version split and which is recommended
- Whether the archived repository will accept bug reports or security patches
- Full list of supported predicates and their simulator-specific implementations
What it is and what it does
BDDL is a predicate logic language for defining household activity benchmarks in the BEHAVIOR framework. It represents each activity as a planning problem with three components: a categorized object list, an initial state with ground literals, and a goal condition expressed as a logical formula. The language is simulator-agnostic—it specifies only the state that must be reached for success, not how an agent achieves it.
To use BDDL, you either work with it standalone (parsing activity definitions and evaluating conditions via stub backends) or integrate it with a simulator like OmniGibson by implementing a BDDLBackend subclass and object-state predicates. The package includes logic evaluation and a basic solver for ground goal solutions. It depends on pytest, numpy, networkx, jupytext, future, and nltk, making installation heavyweight.
Use it for
- Define benchmark household tasks (e.g., cleaning, organizing) as formal planning problems for embodied AI research.
- Evaluate whether a simulated agent has satisfied activity success conditions by checking goal predicates.
- Integrate activity definitions with a physics simulator to sample initial scenes and validate agent behavior.
- Generate ground solutions to compositional goal conditions with quantification for task planning.
- Extend BDDL to a new simulator by implementing predicate checks and object-state instantiation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No—the repository is archived and abandoned; no active maintenance since the latest commit.
The unclear license, high install friction (6 dependencies), and lack of Python version specification create friction. Install only if you are actively contributing to or maintaining a fork, or integrating with an existing BEHAVIOR simulator project that requires this exact version.
Install
bddl on PyPI
Before you install
High install friction due to 6 runtime dependencies including pytest, numpy, networkx, jupytext, future, and nltk. Repository is archived and abandoned as of the latest commit; no active maintenance.
Requires a simulator backend (e.g., OmniGibson) or a stub implementation like TrivialBackend to evaluate activity conditions; cannot be used standalone for simulation.
License in practice
License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify licensing terms before use in proprietary or commercial contexts.
Quickstart
pip install bddl
from bddl.activity import Conditions
behavior_activity = "storing_the_groceries"
activity_definition = 0
simulator = "omnigibson"
conds = Conditions(behavior_activity, activity_definition, simulator)Verify before relying
- Whether the package works with current Python versions (requires_python is unspecified)
- Current status of the BEHAVIOR-1K vs BEHAVIOR-100 version split and which is recommended
- Whether the archived repository will accept bug reports or security patches
- Full list of supported predicates and their simulator-specific implementations
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | 6 packagespytestnumpynetworkxjupytextfuturenltk |
| Maintenance | Abandoned 417 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 100,016 / month, #13,005 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: bddl-3.6.0.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “domain-specific language planning problems”
- bddlBDDL is a domain-specific language for defining household activity…
- unified-planningUnified Planning provides a Python library for formulating automated…
- pyperplanPyperplan is a lightweight STRIPS planner that solves classical…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also unified-planning · pyperplan · rules · policyengine-us · leval · ax-platform · waiting · atdd · boolean.py · business-rules