beamcore/tools

TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.3BQuant:BF16Context Size:32kPublished:Jun 8, 2026License:gemmaArchitecture:Transformer0.0K Featherless Exclusive Cold

The beamcore/tools model is a 0.3 billion parameter function-calling model developed by beamcore, fine-tuned from google/functiongemma-270m-it. It is specifically designed for on-device, local execution as a pre-flight search and routing assistant for the Elixir-native Beamcore agent harness. This lightweight model excels at analyzing user intent to determine if search/traversal tools are needed, emitting structured tool calls for local file system operations, and minimizing token usage for main reasoning models by providing relevant context.

Loading preview...

Overview

beamcore/tools is a highly optimized, lightweight (0.3 billion parameters) function-calling model, fine-tuned from google/functiongemma-270m-it. Its primary purpose is to act as a pre-flight search and routing assistant for the Elixir-native Beamcore agent harness, designed for efficient on-device execution.

Key Capabilities

  • Local Execution: Optimized for running locally to reduce latency and cost.
  • Pre-flight Search Routing: Analyzes user requests to determine if codebase context is needed before engaging a larger reasoning model.
  • Structured Tool Calls: Emits specific tool calls for workspace inspection, including:
    • glob: Finds files matching a pattern.
    • grep: Searches file contents by regex.
    • tree: Shows a compact directory tree.
    • read: Reads file contents with offset/limit.
  • Token Minimization: By gathering necessary context locally, it drastically reduces token overhead and billing for subsequent calls to larger, more expensive frontier models.

Good For

  • Agentic Software Engineering Workflows: Ideal for scenarios where large workspace structures and extensive tool schemas would otherwise lead to slow, expensive, and context-polluting interactions with large language models.
  • Efficient Context Gathering: Automating the process of fetching relevant local files or code snippets based on user intent.
  • Cost-Effective AI Agents: Reducing operational costs by minimizing the tokens sent to main reasoning models.