Llm Bawt
D 57 completed
Other
cli / python · small
196
Files
44,673
LOC
3
Frameworks
6
Languages
Pipeline State
completedRun ID
#396332Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
54.20Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:28Deduplication 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
3/10Tags
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
Languages
Frameworks
FastAPI pytest SQLAlchemy
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Repobility (the analyzer behind this table) · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project 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. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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