# ed-donner/agents

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4,017 stars · 3,290 forks · Jupyter Notebook · mit

## Links

- GitHub: https://github.com/ed-donner/agents
- awesome-repositories: https://awesome-repositories.com/repository/ed-donner-agents.md

## Description

This project is an LLM autonomous agent framework and orchestration tool designed to build goal-driven agents that automate complex workflows. It functions as a system for converting high-level objectives into a series of autonomous actions and managing the coordination of multiple specialized agents to solve multi-step problems.

The framework features a tool integration layer that parses structured model outputs into executable functions and external API calls. It utilizes a non-blocking execution pipeline to manage task orchestration through recursive loops and asynchronous event handling.

The system covers the design and orchestration of multi-agent systems, enterprise task automation, and stateful interaction management to maintain context across execution cycles. It includes capabilities for goal-driven task decomposition and the management of internal agent states.

## Tags

### Artificial Intelligence & ML

- [AI Agent Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-orchestration-multi-agent/coordination-and-routing/ai-agent-orchestrators.md) — Provides a system for coordinating multiple specialized agents using structured workflows to solve complex, multi-step problems.
- [Autonomous AI Agent Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-ai-agent-frameworks.md) — Provides a functional framework for building self-directed agents that process inputs, execute tools, and manage planning. ([source](https://cdn.jsdelivr.net/gh/ed-donner/agents@main/README.md))
- [Stateful Execution Contexts](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/orchestration-engines/ai-agent/execution-environment-evaluation/stateful-execution-contexts.md) — Provides persistent memory mechanisms to track agent progress and intermediate data across multiple execution steps.
- [Agentic Execution Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-reasoning-loops/critic-agent-loops/agentic-execution-loops.md) — Implements agentic execution loops that continuously evaluate state against goals to trigger subsequent reasoning iterations.
- [AI Agent State Coordination](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-agent-state-coordination.md) — Manages the execution state and tool interactions for autonomous agents to ensure progress toward long-term goals.
- [AI Workflow Automation](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-workflow-automation.md) — Automates complex administrative and operational processes using intelligent agents and natural language orchestration.
- [Autonomous Agent Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-agent-frameworks.md) — Provides an environment for building agents that execute multi-step tasks using external tool integrations and orchestration.
- [Autonomous Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-agent-orchestration.md) — Orchestrates the deployment and lifecycle of modular agents with persistent memory to automate multi-step workflows.
- [Autonomous Workflow Automation](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-workflow-automation.md) — Enables the design of self-directed digital workers that execute multi-step processes to achieve high-level objectives.
- [LLM Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-orchestrators.md) — Functions as a framework managing the workflow and connection between LLM deployments and external tool sets.
- [LLM Workflow Orchestrations](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-workflow-orchestrations.md) — Chains language model calls and processing steps into automated, multi-step workflow sequences.
- [Multi-Agent Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestrators.md) — Coordinates teams of specialized AI agents to collaborate on complex tasks through a dedicated orchestration layer.
- [Agentic Goal Decomposition](https://awesome-repositories.com/f/artificial-intelligence-ml/task-decompositions/agentic-goal-decomposition.md) — Uses large language models to recursively decompose high-level objectives into a series of actionable sub-tasks.
- [LLM Tool Calling](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-tool-calling.md) — Maps natural language intentions from model outputs into executable functions and external API calls via a structured integration layer.
- [Multi-Agent Orchestration Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestration-systems.md) — Provides a platform for coordinating multiple autonomous agents to execute collaborative, complex workflows.
- [Multi-Agent Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-systems.md) — Coordinates multiple specialized AI agents into unified workflows to solve problems exceeding single-model capabilities.

### Development Tools & Productivity

- [LLM Tool Orchestration](https://awesome-repositories.com/f/development-tools-productivity/workflow-automation-tools/build-task-automation/llm-tool-orchestration.md) — Orchestrates the connection of language models to external APIs to execute real-world tasks based on dynamic tool selection.
- [Natural Language Function Executions](https://awesome-repositories.com/f/development-tools-productivity/local-function-execution/agent-integrated-functions/multi-caller-function-executions/natural-language-function-executions.md) — Translates natural language intentions from model outputs into structured, executable function calls.
- [Enterprise Workflow Automations](https://awesome-repositories.com/f/development-tools-productivity/task-automation-tools/enterprise-workflow-automations.md) — Automates professional operational workflows by integrating LLMs with external tools and corporate data sources.

### Software Engineering & Architecture

- [Asynchronous Action Handling](https://awesome-repositories.com/f/software-engineering-architecture/action-based-state-transitions/asynchronous-action-handling.md) — Implements architectural patterns for wrapping asynchronous operations to manage agent state and error handling during task execution.
- [Asynchronous Task Orchestrators](https://awesome-repositories.com/f/software-engineering-architecture/asynchronous-task-orchestrators.md) — Ships a non-blocking execution pipeline and event loop to manage goal decomposition and recursive agent logic.
- [Non-Blocking Event Loops](https://awesome-repositories.com/f/software-engineering-architecture/non-blocking-event-loops.md) — Employs a non-blocking event loop architecture to manage concurrent agent operations and environment responses.

### Web Development

- [Model Tool Calls](https://awesome-repositories.com/f/web-development/third-party-api-integrations/model-tool-calls.md) — Maps AI model outputs to executable third-party API requests through a dedicated integration layer.