Superagent

Abstracts multiple LLM providers (OpenAI, Anthropic, Cohere, local models) behind a single agent interface, routing requests to the optimal provider based on task requirements and cost/latency tradeoffs. Uses a provider-agnostic prompt templating system and response normalization layer to handle differences in API schemas, token limits, and output formats across vendors.

Enables agents to invoke external tools and APIs by registering function schemas (OpenAPI, JSON Schema) and automatically generating tool-calling prompts compatible with each LLM provider's function-calling format (OpenAI tools, Anthropic tool_use, etc.). Handles schema validation, parameter binding, and response marshaling between agent outputs and tool inputs.

Extends agents to process and reason over images, PDFs, and other document formats using vision-capable LLMs and document parsing. Handles image encoding, document chunking, and OCR to extract text from images and scanned documents, enabling agents to understand visual content and structured documents in addition to text.

Provides mechanisms to persist agent execution state (conversation history, tool call logs, decision trees) across sessions using configurable backends (database, vector store, file system). Implements context windowing strategies to manage token limits by selectively retrieving relevant historical context based on semantic similarity or recency, preventing context overflow in long-running agents.

Provides a declarative framework for composing multi-step agent workflows where agents can be chained, parallelized, or conditionally branched based on intermediate results. Uses a DAG-based execution model with support for error handling, retries, and state passing between workflow steps, enabling complex automation scenarios without manual orchestration code.

Integrates with vector databases and knowledge bases (Pinecone, Weaviate, Chroma, etc.) to enable agents to retrieve relevant documents or context using semantic search. Implements chunking strategies, embedding generation, and retrieval-augmented generation (RAG) patterns to ground agent responses in external knowledge without fine-tuning the underlying LLM.

Provides comprehensive logging and monitoring of agent execution including LLM calls, tool invocations, decision traces, and performance metrics. Integrates with observability platforms (Datadog, New Relic, custom webhooks) to surface agent behavior, latency bottlenecks, and error patterns in real-time, enabling debugging and optimization of agent workflows.

Provides a templating engine for constructing dynamic prompts that incorporate agent context, tool definitions, conversation history, and retrieved knowledge. Supports variable interpolation, conditional blocks, and provider-specific formatting (e.g., OpenAI system/user messages vs Anthropic message formats) to generate optimized prompts for each LLM provider without manual prompt engineering.

Provides tools for evaluating agent performance against test cases, benchmarks, and custom metrics. Supports running agents against predefined inputs, comparing outputs against expected results, and measuring metrics like accuracy, latency, cost, and tool usage patterns. Enables automated testing of agent behavior changes and regression detection.

Provides deployment options for agents including serverless functions (AWS Lambda, Google Cloud Functions), containerized environments (Docker, Kubernetes), and managed hosting. Handles scaling, load balancing, and request routing to support high-volume agent usage. Includes configuration for resource limits, concurrency controls, and auto-scaling policies.

Enables customization of agent behavior through configuration parameters, system prompts, and optional fine-tuning of underlying LLMs on domain-specific data. Supports adapter-based fine-tuning (LoRA) and prompt-based customization to adapt agents to specific use cases without full model retraining. Provides mechanisms to inject domain knowledge and behavioral constraints.