Agentic Architectures

What does this agent do, and when should you use it?

Agentic Architectures is a LangGraph-based Python library and runnable textbook with executed Jupyter notebooks. It packages 35 patterns as Architecture classes spanning reflection, search, RAG, memory, tool use, multi-agent coordination, and safety routing. Every architecture uses the same .run(task) interface and returns an ArchitectureResult; the example reads output and final_score metadata. It supports nine providers: Nebius, OpenAI, Anthropic, Groq, Ollama, Together, Fireworks, Mistral, and Google, selected through environment configuration. The repository also includes a 17-task comparative benchmark, 35 executed notebooks, and tests for library and notebook integrity.

After installation, get_llm() creates an LLM client and a class such as Reflection(llm=get_llm(), max_iterations=2, target_score=8) can be instantiated. Calling arch.run("Write a haiku about a glacier.") runs the selected architecture and produces result.output plus result.metadata["final_score"]. Depending on the class, the flow can generate, critique, and refine; vote across samples; search trees; retrieve and grade documents; store memory; invoke tools; or coordinate multiple agents. Thirteen architectures use the deterministic-picker pattern: the LLM commits to boolean or enum features and Python composes the decision signal; nine more are described as immune to this scorer issue by design. BrowserAgent uses real Playwright with a safety gate, SWE-Agent is a sandboxed filesystem agent, and Voyager executes reusable Python skills through real subprocesses.

1. A Python team wants to compare Reflection, Self-Consistency, and Tree of Thoughts while preserving one .run(task) integration point.
2. An engineer building retrieval QA needs to test Agentic RAG, CRAG, Self-RAG, Adaptive RAG, and GraphRAG as alternative retrieval shapes.
3. An automation team evaluating browser-operation risk wants to study BrowserAgent’s safety gate; production Computer-Use requires Anthropic.
4. A developer designing coordinated research or article workflows wants runnable examples of Multi-Agent, Blackboard, Debate, STORM, and Meta-Controller.
5. A technical lead choosing an architecture for a task wants to inspect the included 17-task benchmark and per-task answer excerpts.

How do you install or deploy this agent?

From a fresh clone:

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

Fill .env with NEBIUS_API_KEY and the relevant credentials for the selected provider. The runnable-example extras are also documented as: pip install "agentic-architectures[nebius,faiss,tavily]".

How do you use this agent?

Set LLM_PROVIDER and the matching provider key, then run:

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")

Swap the Architecture class while keeping the downstream .run(task) call and ArchitectureResult shape. Run pytest -q for tests; set RUN_INTEGRATION=1 to enable real-LLM integration tests.

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