Environment:Diagram of thought Diagram of thought Python Graph Libraries
| Knowledge Sources | |
|---|---|
| Domains | Infrastructure, Graph_Processing |
| Last Updated | 2026-02-14 04:30 GMT |
Overview
Python environment with graph processing libraries (NetworkX or equivalent) for DAG reconstruction and analysis of DoT reasoning traces.
Description
This environment provides the software dependencies required for building, validating, and analyzing Directed Acyclic Graphs (DAGs) extracted from DoT reasoning output. The primary library is NetworkX for graph construction (adjacency lists, node metadata) and standard graph algorithms (topological sort, path finding). The Python standard library (`typing`, `dataclasses`, `collections.deque`) covers the remaining requirements. NetworkX is optional if a custom adjacency list implementation is used.
Usage
Use this environment when running the Adjacency_List_Builder implementation, which reconstructs the reasoning DAG from parsed `@node` and `@edge` records. Also required for the DAG_Traversal_Algorithms implementation if the NetworkX-based variant is used for topological sorting and critical path extraction.
System Requirements
| Category | Requirement | Notes |
|---|---|---|
| OS | Any (Linux, macOS, Windows) | Cross-platform Python |
| Hardware | Standard CPU | No GPU required |
| Python | Python 3.8+ | For typing, dataclasses support |
Dependencies
System Packages
- No OS-level system packages required
Python Packages
- `networkx` >= 2.6 (graph construction, topological sort, path algorithms)
Note: NetworkX is optional. A pure-Python adjacency list implementation using `dict` and `collections.deque` can substitute if minimal dependencies are preferred.
Credentials
No credentials required for this environment.
Quick Install
# Install NetworkX for graph processing
pip install networkx>=2.6
Code Evidence
DAG reconstruction requirement from `README.md:L116-124` describing the structural view:
@node id=3 role=critic
@edge src=2 dst=3 kind=critique
@status target=2 mark=validated
This protocol guarantees the graph is acyclic (since @edge sources must
have smaller IDs than destinations) and enables deterministic extraction
of the reasoning structure for analysis.
Acyclicity constraint from `README.md:L70` defining the edge format:
@edge src=<i> dst=<n> kind={use|critique|refine} (must have i < n)
Graph traversal requirements from `README.md:L126-130` describing semantic analysis:
Validated propositions are interpreted as subobjects in a mathematical space.
The final summary corresponds to a colimit—a universal construction that
optimally "glues together" all validated pieces of evidence.
Common Errors
| Error Message | Cause | Solution |
|---|---|---|
| `ModuleNotFoundError: No module named 'networkx'` | NetworkX not installed | `pip install networkx` |
| `networkx.NetworkXUnfeasible: Graph contains a cycle` | Invalid DAG with cyclic edges | Validate that all edges satisfy `src < dst` before graph construction |
| `KeyError: node_id` | Referencing a node not yet added to graph | Ensure nodes are added before edges that reference them; process `@node` records before `@edge` records |
Compatibility Notes
- NetworkX 2.x vs 3.x: The API is largely compatible. NetworkX 3.0+ returns views instead of lists for some methods; use `list()` wrapper if needed.
- Pure Python Alternative: For environments where installing NetworkX is undesirable, the adjacency list can be implemented as a `Dict[int, List[int]]` with manual topological sort using `collections.deque`.
- Large Graphs: For DoT traces with hundreds of nodes, NetworkX performs well. For extreme cases (10,000+ nodes), consider `igraph` or `graph-tool` for performance.