FareedKhan-dev/all-agentic-architectures

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Overview

A single Python library that packages every major agentic AI pattern from the literature as a runnable Architecture class with a uniform contract. Each pattern ships with a fully executed Jupyter notebook whose theory is written against the captured run — not synthetic examples. The library is multi-provider (Nebius, OpenAI, Anthropic, Groq, Ollama, Together, Fireworks, Mistral, Google) and built on top of LangGraph state machines.

The central technical discipline of the repository is the deterministic-picker pattern — every LLM-as-Scorer surface has the LLM commit to categorical features (booleans, enums) and lets Python compose the deciding signal. This is the universal escape from the LLM-as-Scorer flat-band pathology, applied in 13 of 35 architectures; 9 more are architecturally immune by design.

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Quickstart

pip install "agentic-architectures[nebius,faiss,tavily]"

from agentic_architectures import get_llm
from agentic_architectures.architectures import Reflection
arch = Reflection(llm=get_llm(), max_iterations=2, target_score=8)
result = arch.run("Write a haiku about a glacier.")
print(result.output)
print("score:", result.metadata["final_score"], "/ 10")

Same .run(task) interface across all 35 architectures. Same ArchitectureResult return shape. Swap the class, swap the pattern — your downstream code does not change.

git clone https://github.com/FareedKhan-dev/all-agentic-architectures
cd all-agentic-architectures
python -m venv .venv
.venv\Scripts\activate              # Windows
source .venv/bin/activate           # macOS / Linux
pip install -e ".[dev,test,docs,nebius,faiss,tavily,networkx]"
cp .env.example .env                # then fill in NEBIUS_API_KEY etc.
pytest -q                           # 283 tests pass in ~30s

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Architecture families

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The 35 architectures

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Provider compatibility

Switch via LLM_PROVIDER + the corresponding key in .env. No code changes.

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Benchmarks

A 17-task suite runs every architecture and scores results. Most recent run, real Nebius Llama-3.3-70B, ~25 min, ~$1.50 in tokens:

_{Full leaderboard with per-task answer excerpts: docs/benchmarks.md}

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Learning paths

Four curated reading orders, depending on what you're trying to do.

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Star history

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Tested

pytest -q
283 passed, 37 skipped (env-gated integration), 1 warning in ~30s

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Documentation

| | | |---|---| | Full docs site | Dark-mode site with embedded notebooks  _{(live after first deploy)} | | Quickstart | One-command install, 8-line example | | Switching providers | Capability matrix; one env var to swap | | Add your own architecture | 5-step contributor recipe | | Deterministic-picker pattern | The central technical pattern, explained once | | Memory variants | Comparison of all 7 memory shapes | | API reference | mkdocstrings auto-gen from docstrings  _{(live after first deploy)} | | Benchmarks | Full per-task leaderboard with answer excerpts |

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Contributing

Contributions welcome. Two paths:

1. Add a new architecture — follow the 5-step recipe. The PR template includes a deterministic-picker checklist. 2. Improve an existing one — bug fix, prompt tuning, performance, scoring rubric. Open an issue first to discuss scope.

See CONTRIBUTING.md for the dev setup, code style, and commit-message convention (Conventional Commits — release-please auto-generates the CHANGELOG).

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Citation

@misc{khan2026agentic,
  title         = {Agentic Architectures: A Library of 35 Production-Grade Agentic AI Patterns},
  author        = {Khan, Fareed},
  year          = {2026},
  howpublished  = {\url{https://github.com/FareedKhan-dev/all-agentic-architectures}},
  note          = {MIT licensed Python library and runnable textbook}
}

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License

MIT — © 2026 Fareed Khan.