Hello everyone! We are excited to announce AgentBridge, but you might wonder what it actually does? It creates an intermediate layer between raw API specifications and LLMs. It transforms OpenAPI specs and documentation into a structured format that makes sense to large language models.
The system uses a pipeline architecture with clear data transformations: input parsing → normalized interim format → semantic enhancement → final output generation.
The design focuses on what LLMs struggle with - understanding how API endpoints relate to accomplish real tasks. Instead of just repackaging endpoint information, we detect multi-step workflows and explicitly maps data dependencies between operations. Our CLI automatically generates integrations from OpenAPI specifications, it uses two different LLMs based on complexity requirements (Claude 3.7 Sonnet for complex pattern detection, Claude 3.5 Haiku for basic descriptions), saving compute where it makes sense.
If you're tired of writing endless prompt engineering to get LLMs to use APIs correctly, this might be worth checking out.
hanna_v_dev•7mo ago
The system uses a pipeline architecture with clear data transformations: input parsing → normalized interim format → semantic enhancement → final output generation.
The design focuses on what LLMs struggle with - understanding how API endpoints relate to accomplish real tasks. Instead of just repackaging endpoint information, we detect multi-step workflows and explicitly maps data dependencies between operations. Our CLI automatically generates integrations from OpenAPI specifications, it uses two different LLMs based on complexity requirements (Claude 3.7 Sonnet for complex pattern detection, Claude 3.5 Haiku for basic descriptions), saving compute where it makes sense.
If you're tired of writing endless prompt engineering to get LLMs to use APIs correctly, this might be worth checking out.