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mgl-gpr

Open Source
Common Lisp Updated Feb 15, 2026
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Compared with 4 other Common Lisp agents Open source Self-hostable Updated Feb 2026
💰 Open Source 🔄 Updated Feb 2026 🖥️ Self-hostable

💰 Pricing

Open Source

Pricing not publicly listed.

🎯 Use Cases

Optimizing hyperparameters for machine learning models Solving complex nonlinear regression problems Academic research in evolutionary algorithms Prototyping probabilistic models in Common Lisp

⚖️ Pros & Cons

✅ Pros

  • Open source and self-hostable
  • Specialized for Common Lisp environments
  • Combines evolutionary algorithms with Gaussian processes

❌ Cons

  • Deprecated (no longer actively maintained)
  • Limited to Common Lisp ecosystem
  • Lacks documentation due to deprecated status

Overview

mgl-gpr is a deprecated Common Lisp library implementing evolutionary algorithms, likely for optimization or machine learning tasks. It appears to focus on Gaussian Process Regression (GPR), a probabilistic model for nonlinear regression and classification.

Problem It Solves

Provides tools for evolutionary optimization and probabilistic modeling, enabling flexible experimentation with genetic algorithms and Gaussian processes in Common Lisp.

Target Audience: Developers and teams working with common lisp automation.

Inputs

  • • User configuration
  • • API credentials (if required)
  • • Task parameters

Outputs

  • • Automated task results
  • • Status reports
  • • Generated content or actions

Example Workflow

  1. 1 User configures the agent with required parameters
  2. 2 Agent receives input data or trigger
  3. 3 Agent processes the request using its core logic
  4. 4 Agent interacts with external services if needed
  5. 5 Results are returned to the user

Sample System Prompt 


              You are mgl-gpr, an AI assistant. Help the user accomplish their task efficiently.

Tools & Technologies

LLM APIs Python

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⚖️ Compare mgl-gpr

FAQs

Is this agent open-source?
Yes
Can this agent be self-hosted?
Yes
What skill level is required?
Intermediate

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