Llm Bawt

D 57 completed
Other
cli / python · small
196
Files
44,673
LOC
3
Frameworks
6
Languages

Pipeline State

completed
Run ID
#396332
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
54.20
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #49620
Member of a group with 19 similar repo(s) — canonical #100296 view group →
Top concepts (2)
Project DescriptionWeb Backend
Source: Repobility analyzer · https://repobility.com

AI Prompt

Create a model-agnostic LLM platform CLI tool using Python. I need it to provide a unified, OpenAI-compatible API for running chatbots and multi-agent systems. Key features must include multi-provider support (OpenAI, Ollama, GGUF), persistent semantic memory using PostgreSQL with pgvector, and a robust tool system supporting both OpenAI function calling and ReAct format. The CLI should also support configuring bot personalities via YAML, integrating web search from providers like Tavily, and running as a FastAPI-based background service. Please structure it to be Docker-first.
python cli llm fastapi openai postgresql docker ai multi-agent
Generated by gemma4:latest

Catalog Information

A model-agnostic LLM platform that provides a unified, OpenAI-compatible API for running configurable chatbots and multi-agent systems across cloud and local models.

Description

A model-agnostic LLM platform that provides a unified, OpenAI-compatible API for running configurable chatbots and multi-agent systems across cloud and local models.

Novelty

3/10

Tags

python cli llm fastapi openai postgresql docker ai multi-agent

Technologies

fastapi openai pydantic sqlalchemy

Claude Models

claude-opus-4-6

Quality Score

D
56.7/100
Structure
64
Code Quality
53
Documentation
79
Testing
50
Practices
44
Security
45
Dependencies
60

Strengths

  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 2 bare except/catch blocks swallowing errors
  • Potential hardcoded secrets in 1 files
  • 5744 duplicate lines detected \u2014 consider DRY refactoring
  • 22 'god files' with >500 LOC need decomposition

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)
  • Replace bare except/catch blocks with specific exception types
  • Move hardcoded secrets to environment variables or a secrets manager

Security & Health

11.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
Unknown
License
5.5%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
91.5%
markdown
6.3%
shell
1.4%
yaml
0.6%
toml
0.2%
text
0.0%

Frameworks

FastAPI pytest SQLAlchemy

Concepts (2)

Scored by Repobility's multi-pass pipeline · https://repobility.com
CategoryNameDescriptionConfidence
Repobility (the analyzer behind this table) · https://repobility.com
auto_descriptionProject DescriptionA model-agnostic LLM platform that provides a unified, OpenAI-compatible API for running configurable chatbots and multi-agent systems across cloud and local models.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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