tom-swe
Theory of Mind modeling for Software Engineering assistants
Decision gist · record as of 2026-08-14
No. The repository is archived and marked abandoned, indicating no active maintenance or support. While the package itself has low install friction and a permissive MIT license, the lack of ongoing development creates risk for bug fixes, security updates, and compatibility with evolving dependencies. Use only if you are willing to maintain a fork or accept the possibility of unresolved issues.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Requires LLM API credentials (litellm key and endpoint) configured via .env file to function.
- Low friction installation as a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it freely usable in most contexts.
last release 2025-11-26 (261 days) · last repo commit 2026-08-03 · 118 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 9,189,321 downloads/mo, #1,555 on PyPI
Alternatives
Verify before relying
pip install tom-swe
from tom_swe.tom_module import TomModule
import asyncio
async def demo():
tom = TomModule()
consultation = await tom.consult(
user_id="demo_user",
current_context="User wants to implement a new feature"
)
print(consultation)
asyncio.run(demo())- Whether the archived repository status means the package is no longer maintained or supported.
- How the three-tier memory system (sessions → analyses → profiles) performs with real user data at scale.
- Compatibility details with specific OpenHands versions or other SWE agent frameworks beyond OpenHands.
What it is and what it does
TOM-SWE is a Theory of Mind package that augments software engineering agents with user understanding capabilities. It analyzes user behavior, preferences, and working styles through LLM-powered consultation, building a three-tier memory system from session data to user profiles. The package integrates with OpenHands and similar SWE agent frameworks, allowing agents to provide personalized guidance and adapt their behavior based on learned user intent.
The package depends on jinja2, json-repair, litellm, pydantic, python-dotenv, tiktoken, and tqdm to handle templating, LLM communication, configuration, and progress tracking. It requires an external LLM API (configured via litellm credentials) to perform the psychological analysis and consultation generation that drives its core functionality.
Use it for
- Enhance OpenHands or other SWE agents with automatic user preference learning and personalized task guidance.
- Analyze user session data to extract behavioral patterns and working style insights for agent adaptation.
- Provide consultation recommendations to agents based on learned user psychology and preferences.
- Build user profiles from cleaned session data to enable long-term personalization across multiple interactions.
- Integrate Theory of Mind reasoning into autonomous coding agents for improved task performance.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The repository is archived and marked abandoned, indicating no active maintenance or support. While the package itself has low install friction and a permissive MIT license, the lack of ongoing development creates risk for bug fixes, security updates, and compatibility with evolving dependencies. Use only if you are willing to maintain a fork or accept the possibility of unresolved issues.
Install
tom-swe on PyPI
Before you install
Low friction installation as a pure Python wheel. Maintenance is a concern: the repository is archived and marked abandoned as of the fact sheet date, with no active development despite recent release dates in the metadata.
Requires Python 3.10 or later. Requires LLM API credentials (litellm key and endpoint) configured via .env file to function.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it freely usable in most contexts.
Quickstart
pip install tom-swe
from tom_swe.tom_module import TomModule
import asyncio
async def demo():
tom = TomModule()
consultation = await tom.consult(
user_id="demo_user",
current_context="User wants to implement a new feature"
)
print(consultation)
asyncio.run(demo())Verify before relying
- Whether the archived repository status means the package is no longer maintained or supported.
- How the three-tier memory system (sessions → analyses → profiles) performs with real user data at scale.
- Compatibility details with specific OpenHands versions or other SWE agent frameworks beyond OpenHands.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesjinja2json-repairlitellmpydanticpython-dotenvtiktokentqdm |
| Maintenance | Abandoned 261 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 9,189,321 / month, #1,555 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tom_swe-1.0.3-py3-none-any.whl
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