Everstaff
C+ 71 completed
Other
cli / python · small
368
Files
44,523
LOC
4
Frameworks
9
Languages
Pipeline State
completedRun ID
#395673Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
56.60Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:17Deduplication group #50473
Member of a group with 8 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Frontend
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AI Prompt
Create a full-stack platform for running autonomous AI agents, similar to Everstaff. I need the core functionality to support multi-LLM integration using providers like OpenAI, Anthropic, and Gemini, ideally via a library like LiteLLM. The system must feature a web UI that supports real-time WebSocket streaming for observability. Crucially, implement a Human-in-the-Loop (HITL) mechanism where agents pause and request human approval before critical actions. Also, include support for defining custom Python tools and allowing agents to delegate subtasks to child agents using a DAG-based workflow. The setup should be containerizable using a Dockerfile.
python fastapi react ai-agents llm websocket docker multi-agent cli web-app
Generated by gemma4:latest
Catalog Information
AI agents that know when to act and when to ask — autonomous by default, human-supervised when it counts.
Description
AI agents that know when to act and when to ask — autonomous by default, human-supervised when it counts.
Novelty
3/10Tags
python fastapi react ai-agents llm websocket docker multi-agent cli web-app
Technologies
fastapi pydantic
Claude Models
claude-opus-4-6
Quality Score
C+
70.9/100
Structure
78
Code Quality
71
Documentation
68
Testing
85
Practices
59
Security
57
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (96% test-to-source ratio)
- Code linting configured (eslint, ruff (possible))
- Consistent naming conventions (snake_case)
- Containerized deployment (Docker)
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- 9 files with critical complexity need refactoring
- Potential hardcoded secrets in 1 files
- 1987 duplicate lines detected \u2014 consider DRY refactoring
- 4 'god files' with >500 LOC need decomposition
Recommendations
- Add a LICENSE file (MIT recommended for open source)
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
22.1h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
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MIT
License
5.4%
Duplication
Languages
Frameworks
FastAPI React pytest Vite
Concepts (2)
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| auto_description | Project Description | AI agents that know when to act and when to ask — autonomous by default, human-supervised when it counts. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Frontend | web-frontend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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