Firebase for AI agents — serverless, stateful workflow execution at scale with built-in memory
Key Features
Multi-Agent Single Agent Human-in-the-Loop Streaming Async Support Short-Term Memory Long-Term Memory Shared Memory Plugin System Custom Tools DAG Workflows API Server Self-Hosted
Community Feedback
Strengths
- Production-ready with 99.99% success rate
- YAML config simplifies workflow definition
- Infinite scalability
- Easy LLM provider switching at runtime
- First-class persistent memory
Weaknesses
- Learning curve for agent/session/YAML concepts
- Smaller community
- HOSTED BACKEND SHUT DOWN Dec 2025
Julep AI Details
| Organization | Julep AI |
| Organization Type | Company |
| Funding | Seed |
| Category | Framework |
| Subcategory | Orchestration |
| Deployment | SDK/Framework |
| Primary Language | Python |
| Runtime | Python 3.10+ |
| License | Apache-2.0 |
| Commercial Use | Permissive |
| Install Command | pip install julep |
| GitHub Stars | 6,605 |
| GitHub Forks | 974 |
| Release Cadence | Monthly |
| Maturity | Beta |
| Pricing Model | Free |
| Free Tier | Self-hosted is free (Apache-2.0) |
| Self-Hosted Free | Yes |
| Cost Model | free + LLM costs |
| Community Size | Small-medium |
| Community Activity | Moderate |
| Sentiment | Mixed |
| GPU Required | No |
| Research Date | 2026-03-24 |
| LLM Providers | OpenAI, Anthropic, Google |
| API Keys Required | LLM provider API key |
Use Cases
- Stateful multi-step agent workflows
- YAML-defined agent pipelines
- Long-running agent tasks with persistent memory
- Scalable agent workflow execution
When to Use
Best for: Self-hosted stateful AI agent workflows with YAML configuration
Avoid when: Need managed cloud hosting — hosted backend is shut down
Original data from HuggingFace, OpenCompass and various public git repos.
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Release v20260328a