Skip to main content

SGR Agent Core - Schema-Guided Reasoning for building agent

Project description

SGR Agent Core — the first SGR open-source agentic framework for Schema-Guided Reasoning

Description

SGR Concept Architecture Open-source agentic framework for building intelligent research agents using Schema-Guided Reasoning. The project provides a core library with a extendable BaseAgent interface implementing a two-phase architecture and multiple ready-to-use research agent implementations built on top of it.

The library includes extensible tools for search, reasoning, and clarification, real-time streaming responses, OpenAI-compatible REST API. Works with any OpenAI-compatible LLM, including local models for fully private research.


Documentation

Get started quickly with our documentation:


Quick Start

Installation

pip install sgr-agent-core

Running Research Agents

The project includes example research agent configurations in the examples/ directory. To get started with deep research agents:

  1. Copy and configure the config file:
cp examples/sgr_deep_research/config.yaml my_config.yaml
# Edit my_config.yaml and set your API keys:
# - llm.api_key: Your OpenAI API key
# - search.tavily_api_key: Your Tavily API key (optional)
  1. Run the API server using the sgr utility:
sgr --config-file examples/sgr_deep_research/config.yaml

The server will start on http://localhost:8010 with OpenAI-compatible API endpoints.

Note: You can also run the server directly with Python:

python -m sgr_agent_core.server --config-file examples/sgr_deep_research/config.yaml

For more examples and detailed usage instructions, see the examples/ directory.


Benchmarking

SimpleQA Benchmark Comparison

Performance Metrics on gpt-4.1-mini:

  • Accuracy: 86.08%
  • Correct: 3,724 answers
  • Incorrect: 554 answers
  • Not Attempted: 48 answers

More detailed benchmark results are available here.


Open-Source Development Team

All development is driven by pure enthusiasm and open-source community collaboration. We welcome contributors of all skill levels!

If you have any questions - feel free to join our community chat↗️ or reach out Valerii Kovalskii↗️.

Special Thanks To:

This project is developed by the neuraldeep community. It is inspired by the Schema-Guided Reasoning (SGR) work and SGR Agent Demo↗️ delivered by "LLM Under the Hood" community and AI R&D Hub of TIMETOACT GROUP Österreich↗️

This project is supported by the AI R&D team at red_mad_robot, providing research capacity, engineering expertise, infrastructure, and operational support.

Learn more about red_mad_robot: redmadrobot.ai↗️ habr↗️ telegram↗️

Star History

Star History Chart

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sgr_agent_core-0.5.0.tar.gz (59.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sgr_agent_core-0.5.0-py3-none-any.whl (46.3 kB view details)

Uploaded Python 3

File details

Details for the file sgr_agent_core-0.5.0.tar.gz.

File metadata

  • Download URL: sgr_agent_core-0.5.0.tar.gz
  • Upload date:
  • Size: 59.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for sgr_agent_core-0.5.0.tar.gz
Algorithm Hash digest
SHA256 c18b38b17e1a3d36b1512aa297e0b0bd7ef28770eed88de7562df7fa2cea6130
MD5 7ab916d2e30f4966a93af584badcaa44
BLAKE2b-256 e068bc4fd03f17797d44de715df05c73356b9e7b222d06148faebd88ffebd86a

See more details on using hashes here.

File details

Details for the file sgr_agent_core-0.5.0-py3-none-any.whl.

File metadata

  • Download URL: sgr_agent_core-0.5.0-py3-none-any.whl
  • Upload date:
  • Size: 46.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for sgr_agent_core-0.5.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3df76908ebd270de9548a265973b6d71f88a4a8751387a9b8766d8322572eada
MD5 2303f2c7c0c48da1875317301c98d5c7
BLAKE2b-256 964b0bbaff1221d210e4eb176dc00c2c678552ba59d79f0a2425ee54c6af8510

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page