🚀 TaskForce AI Agent Framework

TaskForce is a modular, open, and production-ready TypeScript framework for orchestrating LLM-powered autonomous AI agents, task pipelines, dynamic toolchains, RAG workflows, and memory/retrieval systems.

It is designed for real-world use-cases such as document analysis, automation, agent collaboration, enterprise search, and more.

> Docs:
> Agents ・ Tasks ・ Tools ・ Built-in Tools
> Memory & Retrieval ・ Fine-tuning ・ TaskForce Core

---

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✨ Features

- Multi-agent orchestration: Hierarchical, sequential or fully AI-driven
- Plug-and-play tools: Web, file, API, vision, code, custom
- Retrieval-Augmented Generation (RAG): Chroma, Pinecone, LangChain, JSON, etc.
- Extensible memory system: Short/long, per-agent, global, persistent
- Integrated fine-tuning: OpenAI, HuggingFace, custom models
- Advanced error handling, retry, delegation, logging
- Rich CLI, API, and UI integration ready
- Community-first, open source, production-ready

---

⚡️ Getting Started

$3

``bash
npm install taskforce-agent
`

---

$3

Create a .env file in your project root to enable all tool and memory features:

`dotenv


General

`typescript
import {
TaskForce,
Agent,
Task,
DirectoryReadTool,
BraveSearchTool,
ExecutionMode,
} from "taskforce-agent";

// 1. Define agents
const researcher = new Agent({
id: "researcher",
name: "Market Researcher",
goal: "Find up-to-date insights on AI trends",
tools: [new BraveSearchTool(), new DirectoryReadTool()],
memory: true,
});

const writer = new Agent({
id: "writer",
name: "Writer",
goal: "Write a summary blog post based on research",
tools: [],
});

// 2. Define tasks
const researchTask = new Task({
id: "t1",
description: "Research the latest AI trends",
expected_output: "A list of 3 trending developments in AI",
agent: "researcher",
});
const writeTask = new Task({
id: "t2",
description: "Write a markdown blog post summarizing research",
expected_output: "A 4-paragraph markdown blog post",
agent: "writer",
inputFromTask: "t1",
});

// 3. Create TaskForce
const tf = new TaskForce({
agents: [researcher, writer],
tasks: [researchTask, writeTask],
executionMode: ExecutionMode.AiDriven, // or "Hierarchical", "Parallel"
verbose: true,
});

// 4. Run the workflow
await tf.run({ topic: "AI in 2025" });
`

---

🧠 Conceptual Overview

| Component | Description |
| ------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| Agent | An LLM-powered persona with a role, tools, memory, and goals. (details) |
| Task | A unit of work, assigned to an agent, with input/output and dependencies. (details) |
| Tool | A function, API, or skill agents can invoke (search, code, web, RAG, vision, etc). (details) |
| Memory | Vector memory, chat history, and RAG—per-agent or global. (details) |
| TaskForce | The orchestrator managing agents, tasks, tools, execution, delegation, and coordination. (details) |

---

🛠️ Tooling

> Tools are plug-and-play. Assign them to agents dynamically!
> See built-in tools for all available options, advanced usage, and parameterized tool constructors.

Popular Built-ins:

- BraveSearchTool (web search)
- DirectoryReadTool (list files)
- PDFSearchTool (search in PDFs)
- CSVSearchTool, MDXSearchTool, JSONSearchTool, DOCXSearchTool, etc.
- ScrapeElementFromWebsiteTool (CSS selector-based web scraping)
- DALLETool, StableDiffusionTool (vision/generation)
- GithubSearchTool, YoutubeChannelSearchTool, WikipediaSearchTool
- RagTool (semantic/vector search via Chroma/Pinecone/JSON/...)
- _Custom tools_: Write your own in minutes!

`typescript
import { PDFSearchTool } from "taskforce-agent/tools";
const pdfTool = new PDFSearchTool();
`

---

🔗 Retrieval-Augmented Generation (RAG)

- Out-of-the-box support: Local/JSON, Chroma, Pinecone, LangChain, custom retrievers
- Integrates with agent memory, retrieval chains, and tools
- Multi-source and hybrid retrieval supported
- _Learn more in retrieval.md_

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🏗️ AI-Driven Planning & Orchestration

- executionMode: AiDriven lets the LLM break down tasks, assign agents, and choose execution mode
- SmartManagerAgent auto-created for planning and evaluation
- Advanced features: auto-chaining, context injection, result evaluation, replanning

---

🏛️ Memory System

- Short-term memory: Per-agent, in-memory vector store
- Long-term memory: JSON, ChromaDB, Pinecone, or custom backends
- Auto-memory attach: Use memory: true in agent/taskforce config
- Scope: Shared or per-agent for full flexibility
- _See memory.md_

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🎛️ Fine-Tuning Support

- Native support for OpenAI, HuggingFace, and custom models
- Simple API & CLI usage for fine-tuning and managing LLMs
- _See fine-tuning.md & openai-fine-tuning.md_

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🧩 Extending & Customizing

- Add custom agents, tools, memory providers, retrievers, evaluation logic
- All core classes designed for extension
- Plugin/registry APIs for community tool sharing

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📚 Documentation & Resources

- Agent Design
- Task Design
- Tool System
- Built-in Tool List
- Memory & Retrieval
- Fine-tuning
- TaskForce Core
- SmartManagerAgent

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⚡️ Best Practices

- Prefer AI-Driven execution for flexible, scalable workflows
- Assign only agents—tasks and tools can be minimal for simple flows
- Use TaskForce for all but the smallest projects
- Build tools as reusable classes, leverage the registry
- Use RAG/memory for context-rich, robust agents

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📝 Registering & Using Tools

`typescript
import { ToolRegistry, DirectoryReadTool } from "taskforce-agent";
ToolRegistry.register(new DirectoryReadTool(), "file", "built-in");
`

---

🤝 Collaborator

$3

We welcome contributions! If you'd like to add a new tool, suggest a feature, or report a bug:

- Fork this repository and submit a pull request (PR)
- Open an issue for feedback or feature requests

* Add new tools and help with tool registry extensibility.
* Write/expand test cases for tool and agent orchestration logic.
* Document edge-case behaviors (unexpected tool/agent inputs, LLM or API errors, etc.).
* Propose architecture enhancements (memory, planning, evaluation).
* Suggest and demo integrations (UI, REST/gRPC, notebook, SaaS, etc.).
* Review and improve docs to help new users and developers onboard smoothly.

> _Important note:_
> TaskForce is an open-source initiative developed independently. As with any evolving project, there might be areas to improve or refine. Your contributions, feedback, and ideas are highly encouraged to help ensure continuous improvement and growth.

> Have an idea or improvement? Open an issue or PR and help shape the future of TaskForce! > _Contributions are welcome!_ For bug reports or suggestions, please open an issue or pull request.

| GitHub | Role |
| -------------------------------------------------- | -------------------- |
| @marcoaras | Creator & Maintainer |
| @jeudomos | Creator & Maintainer |
| @pribrahimsari | Creator & Maintainer |

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🚦 Roadmap

The following priorities are open for contribution and community feedback. If you have suggestions, ideas, or solutions for any of these, please join as a contributor!

- [ ] Full end-to-end demo projects
Real-world demo workflows and scenario-based documentation (with code).

- [ ] UI/API layer and live orchestration
Integrated dashboards for live monitoring, dynamic agent/task management, and REST/gRPC API endpoints.

- [ ] Tool coverage & comprehensive testing

- Write/test cases for all built-in tools (unit, integration, edge cases).
- Ensure parameterized tool usage (e.g., constructor options like BraveSearchTool({ useScrapeLinksAfterSearch: true })`) is covered with examples and tests.
- Automated tool compatibility matrix: which tools work with which agents, models, and memory backends.

- [ ] Advanced Tool Registry and plugin ecosystem

- Allow easy registration and sharing of custom tools via the registry.
- Support for tool versioning and marketplace-style discovery.
- Encourage contributions of community-built tools.

- [ ] Hybrid/multi-source RAG and advanced memory demos

- Combine multiple retrievers in a single workflow (e.g., internal + web + database).
- Examples and patterns for hybrid agent memory strategies.

- [ ] Test coverage, edge-case management, and error handling

- Robust handling of tool errors, timeouts, and malformed inputs.
- Coverage reporting with badges (Codecov).

- [ ] Dynamic prompt chaining, AI-driven orchestration, and agent self-evaluation

- Let agents evaluate and replan their own task pipelines.
- Deep prompt composition and dynamic context injection.

- [ ] Contributor-driven improvements

- If you spot a gap in tool behavior, documentation, or testing, open an issue or PR!
- If you find an integration problem (API, vector DB, embedding, file parsing, etc.), propose a fix or example.

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📄 License

MIT © TaskForce Contributors
_Created by marcoaras_

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🗂️ Links

- Full documentation in /docs
- agent.md ・ tool.md ・ tool-list.md ・ taskForce.md
- task.md ・ memory.md ・ retrieval.md
- fine-tuning.md・ openai-fine-tuning.md ・ smartManagerAgent.md