Astra Assistant API vs Ollama
Side-by-side comparison of two AI agent tools
Short answer
- Astra Assistant API has had no commit in 13 months; Ollama is actively maintained (299 commits in the last 90 days).
- Ollama is growing faster: +2,491 GitHub stars in the last 30 days vs +-0 for Astra Assistant API.
- Pick Astra Assistant API for: drop in replacement for the OpenAI Assistants API. Pick Ollama for: get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
From GitHub data refreshed daily.
Astra Assistant APIopen-source
Drop in replacement for the OpenAI Assistants API
Ollamaopen-source
Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
Metrics
| Astra Assistant API | Ollama | |
|---|---|---|
| Stars | 207 | 182.1k |
| Star velocity /mo | -0.15873015873015872 | 2.5k |
| Commits (90d) | 0 | 299 |
| Releases (6m) | 0 | 10 |
| Overall score | 0.12429558000960704 | 0.8430554752532844 |
Pros
- +与 OpenAI Assistants API v2 完全兼容，支持无缝迁移现有代码
- +支持数十种 LLM 提供商和本地模型，避免厂商锁定
- +基于 Apache Cassandra 的 AstraDB 后端提供企业级可扩展性和性能
- +完全本地运行，确保数据隐私和安全，无需将敏感信息发送到外部服务器
- +支持广泛的开源模型生态，包括最新的 Kimi-K2.5、GLM-5、DeepSeek 等前沿模型
- +丰富的集成生态系统，可与 Claude Code、OpenClaw 等工具连接，快速构建跨平台 AI 应用
Cons
- -需要配置和管理 AstraDB 实例，增加了基础设施复杂性
- -社区规模相对较小，生态系统和第三方集成不如 OpenAI 官方 API 丰富
- -自托管部署需要额外的运维和安全管理工作
- -依赖本地计算资源，运行大型模型需要较高的 CPU/GPU 和内存配置
- -模型推理速度受限于本地硬件性能，可能不如云端专用硬件快
- -需要手动管理模型版本更新和依赖关系
Use Cases
- •从 OpenAI Assistants API 迁移，同时保持代码兼容性和添加多提供商支持
- •构建需要数据主权和本地部署的企业级 AI 助手应用
- •开发多模型 AI 应用，需要在不同 LLM 提供商之间进行成本优化和性能比较
- •企业级私有部署，在内网环境中运行大语言模型，确保敏感数据不外泄
- •开发者工具集成，通过 Claude Code 等编码助手在本地环境中获得 AI 代码建议
- •多平台聊天机器人开发，使用 OpenClaw 将本地模型部署到 Slack、Discord 等通讯平台
FAQ
- Which is more popular, Astra Assistant API or Ollama?
- Ollama has more GitHub stars (182,082 vs 207).
- Which is more actively developed, Astra Assistant API or Ollama?
- Ollama had more commits in the last 90 days (299 vs 0).
- Should I use Astra Assistant API or Ollama?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.