Agentic Architectures

Thirty-five production-grade agentic AI patterns. End to end.

A library and a living textbook — real LLM outputs, provider-agnostic, deterministic-picker discipline throughout, and a comparative benchmark leaderboard that ranks every architecture against every relevant task.

35   ARCHITECTURES

283   PASSING TESTS

17   BENCHMARK TASKS

9   LLM PROVIDERS

0   MOCKED RUNS

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.

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

Architecture families

The 35 architectures

Provider compatibility

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

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}

Learning paths

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

Star history

Tested

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

Documentation

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).

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}
}

License

MIT — © 2026 Fareed Khan.