embabel/embabel-agent
Agent framework for the JVM. Pronounced Em-BAY-bel /ɛmˈbeɪbəl/
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest today, with 72 contributors on GitHub.
Peaked at #17 in the weekly ranking the week of Aug 9, its best in the past year.
Use Cases & Benefits
- Provides a Kotlin/Java framework for authoring intelligent agentic flows on the JVM that combine LLM interactions with domain models and code.
- Enables dynamic AI planning with strong typing and extensibility, allowing agents to adapt and replan actions without modifying existing code.
- Use for building complex, testable AI agents that integrate LLMs with enterprise JVM applications leveraging Spring and existing infrastructure.
- Use for creating multi-agent systems that perform goal-oriented or utility-based planning with dynamic decision-making and runtime behavior adaptation.
- Use for developing AI-powered workflows that mix local and cloud LLMs for cost-effective, privacy-conscious, and extensible agent orchestration.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Code of conduct: present
Watch-outs
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From the README
Embabel (Em-BAY-bel) is a framework for authoring agentic flows on the JVM that seamlessly mix LLM-prompted interactions with code and domain models. Supports intelligent path finding towards goals. Written in Kotlin but offers a natural usage model from Java. From the creator of Spring.
Have questions? Talk to the docs via the Embabel-powered hub — an Embabel agent that answers your questions about the framework in natural language.
Application developers don't usually have to deal with these concepts directly, as most conditions result from data flow defined in code, allowing the system to infer pre and post conditions.
These concepts underpin these differentiators versus other agent frameworks:
Flows can be authored in one of two ways:
Either way, flows are backed by a domain model of objects that can have rich behavior.
We are working toward allowing natural language actions and goals to be deployed.
The planning step is pluggable.
The default planning approach is Goal Oriented Action Planning. GOAP is a popular AI planning algorithm used in gaming. It allows for dynamic decision-making and action selection based on the current state of the world and the goals of the agent.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+563 stars over 84 days across 12 snapshot days; best day +66 on Aug 13.
Forks
445+47
Open issues
96+30
Compared with peers in AI Agents
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| vercel-labs/agent-desktop | 17.0K | +13.3K | 33 | today |
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| stablyai/orca | 116.8K | +6.6K | 40 | today |
| milind-soni/OpenMausBot | 13.5K | +6.0K | 45 | today |
| HKUDS/DeepTutor | 55.5K | +5.5K | 65 | 5 days ago |
| embabel/embabel-agent | 4.5K | — | 40 | today |
| Category median | 55.5K | +6.6K | 40 | — |
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