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Agentchain

AgentChain is an open-source Python framework that uses large language models to plan and orchestrate multiple agents or models for complex tasks, with text, image, audio and tabular input and output.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agentchain — OpenViST project profile in Multi-Agent Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when building a developer-controlled workflow that needs a language model to plan tasks and coordinate several agents or tools, especially across different data modalities.

Who it is for

  • Python developers building multi-agent task-planning workflows.
  • Teams prototyping applications that combine language-model reasoning with image, audio or tabular processing.

Documented uses

  • Turn natural-language instructions into tasks coordinated across multiple agents or models.
  • Process multimodal inputs and produce outputs across text, images, audio and tabular data.
  • Combine tools such as search, computation, communications and media processing in a workflow.
Before you choose

Requirements & limits

The public repository declares an MIT license. The README requires an OpenAI API key and lists additional credentials for selected agent functions.

  • The stated minimum is 29 GB of GPU memory; GPU devices must also be assigned in main.py.
  • Model checkpoints must be downloaded, and dependencies installed before the documented main-script step.
  • Additional credentials are task-dependent: SerpAPI for web search and AWS and Twilio credentials for phone-call workflows.
  • The README says video support is coming soon; its demonstrated examples cover audio, images and tabular data.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the documented AgentChain environment

Install the listed requirements and prepare checkpoints, credentials and the audio dependency before running the main script.

Before you start

  • Python environment and the repository files
  • OpenAI API key, identified as mandatory
  • At least 29 GB of GPU memory, with GPU devices assigned in main.py
  • ffmpeg for Whisper use

Steps

  1. Install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  2. Download model checkpoints with the command below.

    Documented command
    bash download.sh

  3. Export the environment variables for the agents you plan to use; add SerpAPI credentials for web search and AWS and Twilio credentials for phone-call workflows.

  4. For Whisper, install ffmpeg; the README gives Ubuntu commands sudo apt update && sudo apt install ffmpeg.

  5. When prepared, the README names the command below as the run command.

    Documented command
    python main.py

Expected outcome

The documented dependencies, checkpoints and selected agent credentials are prepared, with the main-script command identified as the next step.

Guide limitations

  • The README states a minimum of 29 GB GPU memory and requires GPU assignment in main.py.
  • The command sequence is documentation, not a verified execution result.
  • Commenting out tools and models can reduce GPU memory use but also reduces capabilities.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
AgentchainTurn natural-language instructions into tasks coordinated across multiple agents or models.CLI, ServerThe public repository declares an MIT license. The README requires an OpenAI API key and lists additional credentials for selected agent functions.
MassgenRun multiple AI agents against the same user task.CLIThe repository documents installation through pip or uv and requires Python 3.11 or later. Setup can configure API keys for OpenAI, Anthropic, Google and xAI; credentials are needed for the selected model backends.
Langchain Multi Agents BoilerplateRoute incoming football questions to model and agent workflows according to task complexity.WebThe repository is public and specifies an MIT license. Running the application requires OpenAI and Groq API keys and a Redis server.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
jina-ai/agentchain · Official homepage title · retained in its source language
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