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fair-llm

PyPIInfrastructure🇺🇸

PulseGate's liveness check found it on 3 Oct 2026; it is registered on PyPI and has been in the index since 10 Aug 2026. How this is checked

fair-llm is an open-source modular framework for building multi-agent AI applications. It provides provider-agnostic LLM adapters, typed tools, ReAct planning, RAG-backed memory, and Model Context Protocol support for developers.

Inferred · not functionally tested

Open SourceMITCLISelf-hosted
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Overview

6 features

Purpose: Building multi-agent AI applications with interchangeable LLM providers, typed tools, planning, memory, and MCP support.

Inferred · not functionally tested

Audience: AI developers

Inferred · not functionally tested

Functions: agents, rag

Inferred · not functionally tested

Interfaces: API: unknown · MCP: indicated (inferred, not tested) · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)

Recorded constraints: pricing: open_source · license: MIT · platforms: CLI · deployment: cli, self_hosted

Constraint provenance is unknown; confirm requirements with the publisher.

Record sources: pypi.org. These links do not verify the individual claims.

In the Multi-agent & orchestration space, fair-llm takes a focused approach. Inferred · not functionally tested: It focuses on building multi-agent AI applications with interchangeable LLM providers, typed tools, planning, memory, and MCP support. Inferred · not functionally tested: fair-llm is an open-source project aimed at AI developers. Basis unknown · not verified: The project is open source (MIT). Basis unknown · not verified: It runs on the command line, and it can be self-hosted.

It is developed by USAFA FAIR Lab (United States), and it first shipped in 2025. Inferred · not functionally tested: Key capabilities include multi-agent workflows, LLM adapters, and typed tools. Basis unknown · not verified: Catalogued interfaces include an MCP server.

Summary written by a language model from the project’s public pages.

Tasks: Inferred · not functionally tested

  • Multi-agent workflows
  • LLM adapters
  • Typed tools
  • ReAct planning
  • RAG memory
  • MCP support

Topics: Inferred · not functionally tested

Tags
multi-agent-frameworkllm-adaptersreact-planningrag-memorymcp
AI capabilities
TextStructured
Inference: Cloud API

JSON profile · Text profile · Access guide

Built with & integrations

AI providers
multiple
Connectors
MCP
Runs on
CLISelf-hosted

Trust & compliance

License
MIT
Public signals
HTTPSOpen SourceFree tier

Indexing history

2

What PulseGate has recorded for this listing

  1. Indexed23 Sep · 21:21 UTC
    fair-llm seen via PyPI Fresh Feed
    Source: PyPI Fresh Feed · Open
  2. Indexed10 Aug · 18:39 UTC
    fair-llm seen via PyPI Fresh Feed
    Source: PyPI Fresh Feed · Open

Frequently asked questions about fair-llm

What is fair-llm?
Inferred · not functionally tested: Fair-llm focuses on building multi-agent AI applications with interchangeable LLM providers, typed tools, planning, memory, and MCP support. It is catalogued under Multi-agent & orchestration on PulseGate.
Who should use fair-llm?
Inferred · not functionally tested: fair-llm is an open-source project built for AI developers.
Is fair-llm free?
Basis unknown · not verified: Yes — fair-llm is open source under the MIT license and free to use.
What platforms does fair-llm run on?
Basis unknown · not verified: fair-llm runs on the command line. It can also be self-hosted.
Is fair-llm still active?
PulseGate's liveness check found it on 3 Oct 2026.
What are alternatives to fair-llm?
Similar projects tracked by PulseGate include Agent M, llm-gent, and agentos-framework.Agent Mllm-gentagentos-framework
Who develops fair-llm?
fair-llm is developed by USAFA FAIR Lab, based in the United States.
When did fair-llm launch?
fair-llm first shipped in 2025.

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