Astron Agent
An enterprise workflow platform for agent orchestration, MCP-enabled tools, and RPA automation.
- Source repo
- iflytek/astron-agent
- Stars
- ★ 8.9k
- Last updated
- today
- License
- Apache-2.0
- Primary language
- Java
- FA score
- 21/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changes
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An enterprise automation team needs an agent workflow that connects internal and external business systems and uses RPA for cross-system actions.
- Not a fit if
- Teams that need a ready Kubernetes/Helm production install today
- Teams that need an English-only UI and documentation
- Source review
- 21/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
Astron Agent is an enterprise-oriented agentic workflow development platform covering AI workflow orchestration, model management, AI and MCP tool integration, RPA automation, and team collaboration. Its documented runtime boundary is a deployed application stack: Docker Compose starts services including Casdoor, while an nginx proxy exposes the AstronAgent frontend locally. The platform is positioned to connect agent decisions with cross-system execution and to support high-availability deployments. The README describes API-based model access and deployment of enterprise MaaS clusters, but it does not name model providers, workflow nodes, or a public API specification. It fits teams evaluating a self-hosted enterprise workflow platform and prepared to validate their required integrations and configuration.
For deployment, users clone the repository, enter docker/astronAgent, copy .env.example to .env, configure environment variables, and run docker compose -f docker-compose-with-auth.yaml up -d. That command starts all services, including Casdoor; its admin interface is at http://localhost:8000, and the AstronAgent frontend is served through an nginx proxy at http://localhost/. The platform combines AI workflow orchestration with model management, AI/MCP tools, and intelligent RPA to build agent applications that can connect to enterprise systems. The supplied material does not define a workflow schema, node catalog, tool-call API, or application output protocol.
- An enterprise automation team needs an agent workflow that connects internal and external business systems and uses RPA for cross-system actions.
- A platform engineering team wants to deploy an agent-application development stack with an authentication service through Docker Compose.
- An organization with existing model services wants to evaluate model access and validation in workflows through APIs or an enterprise MaaS cluster path.
- A business technology team is building process-oriented agent applications that combine AI capabilities, MCP tools, and automated execution.
- A team piloting a highly available enterprise agent workflow platform needs to validate its integration model and tool ecosystem in its own environment.
How do you install or deploy this agent?
Docker and Docker Compose are required, along with access to the repository. Run:
git clone https://github.com/iflytek/astron-agent.git
cd astron-agent/docker/astronAgent
cp .env.example .env
vim .env
docker compose -f docker-compose-with-auth.yaml up -dAfter startup, the Casdoor admin interface is http://localhost:8000 with default credentials admin / 123, and the application frontend is http://localhost/. The README requires .env configuration, but the supplied material does not list the variables or their values.
How do you use this agent?
Start the stack using the deployment steps, then open http://localhost/ for the AstronAgent frontend. To administer the authentication service, open http://localhost:8000 and use the documented default Casdoor credentials, admin / 123. The README positions the product for combining AI workflows, models, AI/MCP tools, and RPA, but it supplies no UI walkthrough, CLI command, or API example for creating a first workflow, configuring a model, or invoking an MCP tool. The first documented verification point is that the services and frontend are reachable; creating an agent workflow requires usage material not included here.
What are this agent's strengths and limitations?
- It brings AI workflow orchestration, model management, MCP tool integration, and intelligent RPA into one platform scope, supporting workflows that extend from decisions to cross-system execution.
- It provides a concrete docker-compose-with-auth.yaml startup path, including Casdoor authentication and documented local access endpoints.
- The README explicitly positions the platform for high-availability deployment and scalable, production-ready agent applications.
- Its Apache 2.0 license is explicitly described as permitting commercial use, modification, and distribution.
- Helm charts are still under development, so the supplied material provides no current executable Kubernetes installation path.
- No specific model providers, MCP servers, RPA connectors, workflow nodes, or public APIs are listed; critical integrations need validation before adoption.
- Deployment requires configuring .env, but the supplied material does not provide the variable inventory, defaults, or production examples.
- The plug-and-play ecosystem claim is tied to iFLYTEK Open Platform capabilities and tools; the supplied material does not specify access conditions or costs for that ecosystem.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Astron Agent This agent | 21 · Major gaps | Self-hosted serviceFree + model costs | ★ 8.9k | today | Java | — |
| Agentspan | 62 · Some gaps | CLIFree + model costs | ★ 498 | 1mo ago | TypeScript | OpenAI API · Claude API |
| BISHENG Enterprise LLM DevOps Platform | Insufficient evidence | Self-hosted serviceFree | ★ 12k | 2d ago | Python | — |
| Conductor Workflow Engine | 68 · Some gaps | CLIFree + model costs | ★ 32k | 1d ago | Java | Claude Code · OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: Repository includes authentication tests (test_app_auth.py) and CI workflows, but no documentation on permission model, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Deductions: These aspects are not addressed in the provided files, so cannot be assessed.
Evidence: CI workflows include quality checks and tests, but no dependency availability guarantees (e.g., lock files, image digests) or detailed failure messages. Deductions: Dependency availability and failure messages lack evidence; self-consistency is only partially shown (CI config aligns with README deployment instructions).
Evidence: README specifies target audience (enterprise) and scenarios (workflow orchestration, RPA integration), and provides Docker Compose and Helm (in development) deployment options. Deductions: Capability boundaries are incomplete, trigger precision (e.g., workflow triggers) not specified, environment fit only partially covered (no OS, resource requirements).
Evidence: README provides clear architecture info, installation steps, examples and FAQ links, and Apache-2.0 license. Deductions: Naming stability not documented, known limitations not listed, versioning/changelog missing, maintenance responsibility only partially via contributing guide.
Evidence: README describes outputs (deployable Agent platform) and marginal value (enterprise features), but no cost-benefit analysis or performance data. Deductions: Output usability based on description only, marginal value and cost-benefit lack quantitative evidence.
Evidence: Claims in README (e.g., enterprise-grade reliability) lack specific evidence, CI workflows exist but not verified. Deductions: Claim traceability insufficient, cross-source corroboration absent, facts vs inferences not clearly separated.
- 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.
- No documentation on permission model, user confirmation, or data flow transparency; assess security controls before deployment.
- Dependency security not audited; check lock files and known vulnerabilities.
- Helm charts are under development; use with caution in production.
- Default Casdoor credentials (admin/123) pose a security risk; change immediately.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v1.1.2 Astron Agent v1.1.2 — Security Release
Release notes excerpt
Security Release Astron Agent v1.1.2 is a security release. Operators running v1.1.1 or earlier should upgrade promptly, especially those running multi-tenant deployments. Workflow code execution and tenant isolation This release addresses unsafe workflow code-node execution that could allow an authenticated user with workflow creation permissions to execute code in the workflow service container and access or modify other tenants' data in affected deployments. …View on GitHub ↗ -
Release notes excerpt
- payee: "@whyiug" reward: CNY RMB: 500 accounts: - issue: "#1575" payer: |- @FenjuFu --- currency: CNY RMB reward: 500 source: ""View on GitHub ↗ -
v1.1.1 Astron Agent v1.1.1
Release notes excerpt
What's Changed Based on PRs merged into main since v1.1.0. New Features - feat(console): Integrate workflow functionality into the Agent ([#1487](https://github.com/iflytek/astron-agent/pull/1487)) - feat(mcp): add Streamable HTTP with SSE fallback ([#1552](https://github.com/iflytek/astron-agent/pull/1552)) Fixes - fix(knowledge): load platform config from persistent storage ([#1558](https://github.com/iflytek/astron-agent/pull/1558)) - fix(workflow): allow zero LLM temperature ([#1562](https://github.com/iflytek/astron-agent/pull/1562)) …View on GitHub ↗