Agents

Treats agent systems as trainable computational graphs where prompts and tools function as tunable parameters, enabling systematic optimization through language-based gradients. Implements a neural network-inspired training loop: forward pass (agent execution) → trajectory storage → loss evaluation via language models → backpropagation (language gradient generation) → symbolic component updates. This approach allows agents to improve performance through experience without parameter retraining.

Structures agent systems as directed acyclic computational graphs where each node represents a processing step (LLM call, tool invocation, data transformation) with explicit input/output contracts. Nodes are connected via edges defining information flow, enabling modular composition of complex multi-step reasoning. The framework tracks execution state, intermediate outputs, and tool usage across the entire pipeline for later analysis and optimization.

Enables creation of specialized agents optimized for specific task types or domains through targeted training on task-relevant datasets. Implements transfer learning where agents trained on general tasks can be fine-tuned on specialized tasks with smaller datasets. Supports domain-specific prompt templates, tool selections, and evaluation metrics that are automatically applied during training.

Maintains version history of agent configurations (prompts, tools, pipeline structure) and tracks experiments with different configurations. Records hyperparameters, training datasets, evaluation metrics, and results for each experiment. Enables comparison of different agent versions and rollback to previous configurations. Integrates with experiment tracking tools for reproducibility and collaboration.

Automatically captures complete execution traces including inputs, outputs, prompts used, tool invocations, and intermediate results at each pipeline node during agent execution. Stores trajectories in structured format enabling post-hoc analysis, loss evaluation, and gradient generation. Supports querying and filtering trajectories by node, execution path, or performance metrics for targeted optimization.

Uses language models to evaluate agent performance by analyzing execution trajectories and generating natural language feedback (gradients) for each pipeline node. Prompts the LLM to reflect on node outputs, identify failure modes, and suggest improvements to prompts or tool selections. Converts qualitative LLM feedback into structured gradient signals that guide symbolic component updates.

Automatically refines agent prompts and tool selections based on language gradients generated from trajectory analysis. Updates prompt text to address identified failure modes, adjusts tool availability based on usage patterns, and modifies tool invocation logic. Implements iterative refinement where each training step produces new prompt versions and tool configurations that are tested in subsequent agent executions.

Enables composition of multiple specialized agents into coordinated systems where agents communicate, delegate tasks, and share context. Implements message-passing protocols between agents, manages shared state and memory, and coordinates execution order. Supports hierarchical agent structures where higher-level agents delegate to specialized sub-agents and aggregate results.

Provides unified abstraction for integrating multiple LLM providers (OpenAI, Anthropic, local models) and vector databases (Pinecone, Weaviate, FAISS) into agent pipelines. Handles API authentication, request formatting, response parsing, and error handling across different providers. Supports switching between providers without code changes through configuration-based provider selection.

Enables automatic modification of agent pipeline structure (adding/removing nodes, changing edge connections, restructuring sub-pipelines) based on optimization feedback. Analyzes execution patterns to identify bottlenecks or redundant nodes, suggests architectural changes, and implements modifications while maintaining backward compatibility with existing trajectories. Supports A/B testing of different pipeline structures.

Implements the complete training loop: forward pass (agent execution on task distribution) → trajectory collection → loss evaluation → gradient generation → component updates → evaluation on held-out test set. Manages training state, tracks convergence metrics, implements early stopping, and generates training reports. Supports distributed training across multiple workers for parallel trajectory collection.

Provides tools for analyzing agent decision patterns, identifying failure modes, and understanding agent reasoning. Generates visualizations of pipeline execution, produces natural language summaries of agent behavior, and identifies common error patterns across trajectories. Supports counterfactual analysis (what if this node output was different?) and attention-style analysis of which components most influence final outputs.