SuperAGI

Provides a drag-and-drop interface to compose multi-step agent workflows by connecting action nodes, decision branches, and tool integrations without code. Uses a directed acyclic graph (DAG) execution model where each node represents an agent action or tool call, with conditional routing based on LLM outputs or explicit branching logic. Workflows are serialized as JSON configuration and executed by a runtime engine that manages state, context passing, and error handling across steps.

Maintains a centralized registry of tools and actions that agents can invoke, with automatic schema generation and validation. Each tool is defined with input/output schemas (JSON Schema), descriptions, and execution handlers. The framework automatically converts tool definitions into function-calling payloads compatible with OpenAI, Anthropic, and other LLM APIs, handling parameter validation, type coercion, and error propagation back to the agent for retry logic.

Provides tools for iterating on agent prompts and configurations, including A/B testing to compare performance across prompt variants. Supports prompt templating with variable substitution, version control for prompt history, and automated evaluation metrics (correctness, latency, cost). Includes prompt optimization suggestions based on execution traces and failure analysis.

Implements safety mechanisms to prevent agents from taking harmful actions or generating unsafe content. Includes input validation (blocking malicious queries), output filtering (detecting unsafe responses), and action guardrails (preventing agents from calling dangerous tools). Uses rule-based filters, LLM-based classifiers, and external safety APIs to detect and block unsafe behavior. Supports custom safety policies tailored to specific domains.

Implements a pluggable memory system for agents to store and retrieve conversation history, task state, and learned facts across sessions. Supports multiple storage backends (in-memory, PostgreSQL, vector databases) with automatic context window management that summarizes or truncates old messages to fit LLM token limits. Memory is organized by agent instance, conversation thread, and optional user/organization scope, with retrieval strategies including recency-based, semantic similarity, and explicit tagging.

Abstracts away provider-specific API differences (OpenAI, Anthropic, Ollama, Azure, etc.) behind a unified interface for model invocation. Handles provider-specific prompt formatting, token counting, streaming response handling, and error recovery. Supports dynamic provider selection based on cost, latency, or capability requirements, with automatic fallback to alternative providers on failure. Manages API keys, rate limiting, and usage tracking across providers.

Provides a runtime environment for executing agents in production, with support for containerized deployment (Docker), environment isolation, and resource management. Agents run as isolated processes or containers with configurable CPU/memory limits, automatic scaling based on workload, and health monitoring. Supports both synchronous (request-response) and asynchronous (background job) execution modes, with job queuing and result persistence for long-running tasks.

Implements structured reasoning patterns that decompose complex agent tasks into intermediate steps, with explicit reasoning traces visible to developers. Uses chain-of-thought prompting to encourage LLMs to explain their reasoning before taking actions, with support for multi-step planning where agents break down goals into sub-tasks. Includes built-in patterns for reflection (agent evaluates its own outputs), re-planning (agent adjusts strategy if initial plan fails), and hierarchical task decomposition (breaking large goals into smaller, manageable steps).

Provides comprehensive logging and tracing of agent execution, capturing every LLM call, tool invocation, decision point, and state change. Integrates with observability platforms (Datadog, New Relic, etc.) to export traces and metrics in standard formats (OpenTelemetry). Includes built-in dashboards for visualizing agent behavior, identifying bottlenecks, and tracking performance metrics (latency, cost, success rate). Supports custom event logging for domain-specific metrics.

Provides tools for testing agent behavior, including unit tests for individual tools, integration tests for workflows, and end-to-end tests for complete agent scenarios. Supports synthetic test case generation using LLMs to create diverse inputs and expected outputs, with assertion frameworks for validating agent responses against criteria (correctness, safety, latency). Includes regression testing to detect behavior changes across agent versions.

Enables agents to augment their reasoning with external knowledge sources through retrieval-augmented generation (RAG). Supports multiple knowledge base backends (vector databases, document stores, web search) with semantic search to find relevant context. Automatically chunks documents, generates embeddings, and retrieves top-k relevant passages to include in agent prompts. Includes citation tracking to attribute agent responses to source documents.

Enables multiple agents to work together on complex tasks through message passing, shared state, and coordination protocols. Agents can delegate sub-tasks to specialized agents, aggregate results, and make decisions based on multiple perspectives. Supports hierarchical agent structures (manager agents coordinating worker agents) and peer-to-peer collaboration (agents negotiating solutions). Includes conflict resolution mechanisms for when agents disagree.