semble is an agent orchestration repository at MinishLab/semble; maintainers describe it as: Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read. Its recorded primary language is Python. License metadata lists MIT. GitHub metadata shows about 3,376 stars. The project homepage is https://minish.ai/packages/semble/introduction/.
License
MIT
Stars
5,503
Features
- Recorded summary for semble: Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
- semble uses Python as its recorded primary language, which helps with stack-fit review.
- semble helps evaluate coordination, planning, or task-decomposition patterns in agent systems.
- semble supports investigation of retrieval, embedding, or knowledge-grounded application flows.
- semble fits engineering teams assessing code, CLI, SDK, runtime, or developer-tooling workflows.
- semble lists MIT license metadata; review obligations before redistribution or hosted use.
Use Cases
- Evaluate semble when the need is agent orchestration and the repo summary matches: Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
- Compare the Python implementation in semble before choosing a similar internal architecture.
- Use semble to test agent coordination patterns with a concrete open-source codebase.
- Use semble to prototype retrieval-backed knowledge features using the repository direction.
- Use semble to study developer-tooling implementation details before building internal workflows.
- Complete a MIT license review before packaging semble into a commercial or hosted workflow.
FAQ
Start from the repository summary (Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read), then verify maintenance status, integration boundaries, and whether its agent orchestration, retrieval and knowledge workflows, developer engineering workflows focus matches the intended workflow. Repository: https://github.com/MinishLab/semble. Stars: about 3,376. License: MIT. Language: Python.
semble is best treated as a repository-level component or reference implementation for agent orchestration, retrieval and knowledge workflows, developer engineering workflows. Good evaluation scenarios include: Evaluate semble when the need is agent orchestration and the repo summary matches: Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read Compare the Python implementation in semble before choosing a similar internal architecture. Use semble to test agent coordination patterns with a concrete open-source codebase.
