LN: Xie et al. (2023) — OpenAgents: An Open Platform for Language Agents in the Wild
Bibliographic Reference
Citation: Xie, T., Zhou, F., Cheng, Z., et al. (2023). OpenAgents: An open platform for language agents in the wild. arXiv:2310.10634. https://arxiv.org/abs/2310.10634
Pass 1 — Bird’s Eye View (5 Cs)
| C | Assessment |
|---|---|
| Category | System design + deployment paper |
| Context | Addresses the gap between academic agent benchmarks and real-world deployable agent platforms |
| Correctness | Deployed publicly with user evaluation; provides usage statistics from real users |
| Contributions | (1) Three specialized agents: Data Agent (pandas/SQL), Plugins Agent (200+ tools), Web Agent (browser automation); (2) Open-source implementation; (3) Natural language interface for non-technical users |
| Clarity | Good. Each agent is described independently with concrete examples. |
Relevance: ⭐⭐⭐⭐
The Data Agent architecture (NL → Python/SQL → execution) directly maps to PUMA’s pipeline for processing Jira datasets.
Pass 2 — Content
Three Agent Types
| Agent | Primary Tools | Use Case |
|---|---|---|
| Data Agent | Python, pandas, matplotlib, sklearn | Data analysis, visualization, ML pipelines |
| Plugins Agent | 200+ plugins (weather, maps, finance…) | Real-world task automation |
| Web Agent | Browser automation, web scraping | Information gathering, form filling |
Data Agent Architecture (most relevant to PUMA)
User Query (NL)
↓
Code Generator LLM → Python/pandas code
↓
Safe Execution Sandbox (subprocess isolation)
↓
Output + Error Handling (retry on exception)
↓
Response to User
The Data Agent generates Python/pandas code, executes it in a sandboxed subprocess, captures stdout/stderr, and feeds errors back to the LLM for self-correction — essentially implementing a code-based Reflexion loop.
Key Design Patterns
- Tool use through code generation: Instead of tool APIs, agents write code — more flexible but requires safe execution environment
- Error recovery: LLM receives the Python traceback and rewrites the code
- Conversation memory: Previous steps are included as context for multi-turn analysis
PUMA Integration
- Dataset processing: The Data Agent architecture (NL → pandas → results) is the pattern for PUMA’s data preprocessing pipeline for TAWOS and Jira SR
- Stage 2 automation: Automate data quality checks, stratified sampling, and feature extraction using a Data Agent pattern
- Open-source foundation: OpenAgents is MIT-licensed; PUMA could fork the Data Agent for interactive Jira dataset exploration
Related Notes
- PN-MultiAgent-ArchitecturePatterns — three-agent specialization pattern
- LN-Shinn-2023-Reflexion — error recovery via self-reflection
- LN-Datasets-JiraSR-TAWOS — target datasets for Data Agent
- SP-Architecture-v1 — pipeline architecture