ProAgent Workflow Automation
Turns human instructions into n8n workflows and coordinates specialized agents for workflow decisions.
- Source repo
- OpenBMB/ProAgent
- Stars
- ★ 865
- Last updated
- 2y ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- 28/100 · Major gaps
At a glance
- Works with
- Platform-specificOpenAI API (Partial support)
- You'll need
- Typical use
- An operations team with self-hosted n8n that wants to construct cross-application workflows from natural-language requests.
- Main limitation
- Core operation depends on OpenAI configuration and is explicitly based on GPT4-0613 plus an older OpenAI interface version, creating version-compatibility risk.
- Source review
- 28/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
ProAgent is the official code for the Agentic Process Automation (APA) paper, intended to turn human instructions into automation workflows. It operates around self-hosted n8n and expects n8n-exported workflow and credential files to be supplied to the codebase. Runtime behavior is configured in ProAgent/config.py: development builds workflows as described in the paper, refine modifies an existing workflow, and production loads an existing run for reproduction. Each run creates a readable record under ./records that refine and production can load later. Development requires OpenAI API configuration; the repository states that the implementation is based on GPT4-0613 and an older OpenAI interface version.
After a runtime mode is selected in ProAgent/config.py, the entry point is python main.py. In development mode, ProAgent builds workflows from human instructions, coordinates specialized agents for complex decisions, and can ask a human for help through a function call; n8n must be started first. The code consumes c.json and w.json exported through n8n export:credentials and n8n export:workflow, then writes run records to ./records. Refine loads an existing workflow and changes it for a new request with test-on-change enabled; production loads an existing run to reproduce it and tests APA-code only once at the end.
- An operations team with self-hosted n8n that wants to construct cross-application workflows from natural-language requests.
- A researcher reproducing the reported paper cases by loading an existing run from ./apa_case in production mode.
- An automation maintainer who has an existing n8n workflow and needs to refine it for a new request.
- A team building and testing workflows with human intervention when the system needs help understanding a request.
- A developer who has registered applications and credentials in n8n and needs the code to load workflow and credential IDs.
How do you install or deploy this agent?
Install Python packages:
pip install -r requirements.txtTo connect to real application services, install and run self-hosted n8n:
npm install n8n -g
export WEBHOOK_URL=https://n8n.x-agent.net/redirect/http%3A%2F%2Flocalhost%3A5678/
n8nAfter connecting or registering apps in n8n, export credentials and workflows:
n8n export:credentials --all --decrypted --output=./ProAgent/n8n_tester/credentials/c.json
n8n export:workflow --all --output=./ProAgent/n8n_tester/credentials/w.jsonDevelopment mode additionally requires OPENAI_API_KEY and OPENAI_API_BASE.
How do you use this agent?
Select development, refine, or production in ProAgent/config.py. For development, start n8n and set OPENAI_API_KEY plus OPENAI_API_BASE, then run:
python main.pyTo reproduce a saved case, use production mode to load an existing run from ./apa_case or ./records; this reproduction path does not require n8n. Use refine mode to load an existing workflow and improve it for a new request.
What are this agent's strengths and limitations?
- Combines workflow construction and execution-time decisions in one APA process instead of only running pre-authored RPA steps.
- Provides distinct development, refine, and production modes for construction, iterative changes, and run reproduction.
- Writes readable records under ./records that can be reloaded by refine or production.
- Includes a function-call path for proactively requesting human help when the request has been misunderstood.
- Core operation depends on OpenAI configuration and is explicitly based on GPT4-0613 plus an older OpenAI interface version, creating version-compatibility risk.
- Connecting real applications requires self-hosted n8n, pre-registered apps, and exported decrypted credentials and workflows.
- The repository states that its n8n compiler targets an older n8n version and may not work with newer releases.
- Self-hosted n8n setup and third-party account connections can encounter unaddressed problems; the repository also notes possible network restrictions in China.
How does this agent compare with similar options?
Against traditional RPA, ProAgent is positioned for workflow design and dynamic execution decisions that need human-like intelligence. Rather than only following fixed automation steps, it attempts to construct workflows from human instructions and coordinate specialized agents for decisions.
Key facts side by side with the most closely related agents.
| Agent | Source review | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|
| ProAgent Workflow Automation This agent | 28 · Major gaps | ★ 865 | 2y ago | Python | — |
| superglue Enterprise Integration Agents | 36 · Major gaps | ★ 2.1k | 1mo ago | TypeScript | OpenAI API · Claude API |
| LocalAI – Open-Source AI Engine | 53 · Major gaps | ★ 49k | 4d ago | Go | OpenAI API · Claude API |
| Hacker Podcast – AI-generated Chinese Hacker News Daily | 40 · Major gaps | ★ 2.6k | 21d ago | TypeScript | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: The repository provides no explicit mechanisms for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. README mentions using n8n and OpenAI API but does not describe permission minimization or user confirmation. Therefore all trust criteria score 0.
Evidence: README describes three running modes (development, refine, production) but lacks details on error handling or failure messages. Dependencies are pinned but availability is not discussed. Thus self_consistency scores 1, dependency_availability scores 1, failure_messages scores 0.
Evidence: README identifies target audience (researchers and developers) and use cases (APA workflow construction). However, capability boundaries are unclear, trigger conditions (e.g., user instructions) are not detailed, and environment fit (e.g., n8n version compatibility) is mentioned but limited. Therefore audience_and_scenarios scores 2, capability_boundaries scores 1, trigger_precision scores 1, environment_fit scores 1.
Evidence: README provides installation and running instructions but lacks FAQ, changelog, and versioning. License is Apache-2.0, but maintenance responsibility is unclear. Thus information_architecture scores 1, install_notes scores 2, naming_stability scores 1, examples_and_faq scores 1, known_limitations scores 2, license scores 3, versioning_changelog scores 0, maintenance_responsibility scores 1.
Evidence: README claims ProAgent can construct and execute workflows but does not provide output format or quality assessment. Marginal value is described (automating complex tasks), but cost-benefit is not quantified. Therefore output_usability scores 1, marginal_value scores 2, cost_benefit scores 1.
Evidence: README cites a paper but does not provide reproducible experimental details. No independent sources verify claims. Facts and inferences are mixed. Thus claim_traceability scores 1, cross_source_corroboration scores 1, fact_inference_separation scores 1.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository lacks permission management or user confirmation mechanisms; assess risks before use.
- It depends on n8n and OpenAI, but data flow and sensitive information handling are not described; proceed with caution.
- README mentions using an external redirect service that may be unstable and is not open-sourced.
FAQ
Does it require OpenAI?
OPENAI_API_KEY and OPENAI_API_BASE. The repository identifies GPT4-0613 and an older OpenAI interface version, and does not document support for other model providers.Is n8n always required?
How does it obtain access to third-party apps?
./ProAgent/n8n_tester/credentials/c.json, alongside the exported workflow file.