Why • Features • Results • Quick start • How it works • Docs • Paper • العربية

💡 Why MIHAD

Coding agents start every session like a new hire on day one. They forget yesterday's corrections and repeat the same mistakes. Saving everything they "learn" is not the answer either: an agent that remembers its own wrong conclusions repeats them with confidence.

MIHAD gives an agent a memory and a body of experience that grow with use, and it adopts nothing without independent evidence. It is built on a research programme with pinned protocols, real repository history and a blind quality judge. Its measured gains are largest for cheaper models: with a strong model as the agent, success was the same with and without it (51/63 vs 50/63; on the sound tasks of that benchmark, 45/45 either way). See the paper.

✨ What it does

📊 What the experiments showed

_{Exploratory results. The later experiments have 3 repetitions per arm and include repositories the designs had not seen; the earlier ones one run per cell. Methods and limits are in the paper.}

🚀 Quick start

Requirements: Python 3.12+, git, and at least one of OMP, Claude Code or Codex.

1 · Install the tool

git clone https://github.com/abdullahbalabel/mihad.git
cd mihad
python -m pip install -e .

2 · Add it to a project. Preview first, then install for your agent:

mihad-install D:/path/to/project --agent claude --dry-run
mihad-install D:/path/to/project --agent claude      # or: --agent codex, --agent omp, or several

3 · Work as usual. Open the project in your agent. To see what was learned and kept:

mihad report          # lessons, verifier scripts, skills, competence, memory, dream cycles
mihad-memory list     # what the memory holds

[!TIP] Codex loads a project's hooks only after you trust the project once in Codex. Claude Code needs no approval: the installer pre-approves the memory server for the project. See docs/AGENTS.md.

🤖 Agents

_{Verification status for each check is in docs/AGENTS.md.}

🧠 How it works

flowchart LR
    U([You]) -->|request| A[Coding agent]
    A -->|brief| B[(Memory and lessons)]
    A -->|each tool result| D{Live detection}
    D -->|warning| A
    A -->|wants to finish| R{Review}
    R -->|findings: one more turn| A
    A -->|session log| E[Episodes]
    U -->|your commit| E
    E --> M[Mining: pitfalls, corrections, skills]
    M --> V[Verifier scripts]
    V --> P[Dreaming: practice with and without lessons]
    P -->|A/B result| B

Every lesson has a gate before it is used:

📚 Documentation

📄 Research

Better Coding Agents Without Retraining the Model: Verified Memory, Operational Experience and a Conscience. What Learns Is the System Around the Model, version 2.7 (Markdown · Word).

@techreport{balabel2026mihad,
  author  = {Balabel, Abdullah Mohammed},
  title   = {Adopting Reasoning Outputs Only After Verification: The {MIHAD} Architecture,
             a Verified Memory and an Experience Engine for Coding Agents},
  year    = {2026},
  month   = {10},
  note    = {Version 2.1},
  url     = {https://github.com/abdullahbalabel/mihad}
}

🌙 بالعربية

⚖️ License

Copyright © 2026 Abdullah Mohammed Balabel.

Licensed under the PolyForm Noncommercial License 1.0.0. It is free for research, personal and other non-commercial use. Commercial use requires a separate licence from the author.