agentx-kit 0.2.0

An open-source, provider-agnostic agentic framework + interactive project scaffolder for LangChain and CrewAI. Pick your LLM provider, agents, RAG, memory, MCP tools and skills — generate a ready-to-run uv project.

🧬 AgentX-Kit

A provider-agnostic agentic framework + interactive project scaffolder for LangChain & CrewAI.

Pick your LLM provider (OpenAI, Azure, OpenRouter, Anthropic, Gemini, Vertex AI, Bedrock, Groq, Ollama), choose your framework, agents, RAG, memory, MCP tools and skills — and AgentX-Kit generates a ready-to-run project in its own uv virtual environment.

pip install "agentx-kit[all]"
agentx new                 # interactive wizard → scaffolds a uv project

The PyPI distribution is agentx-kit; the import name and CLI are agentx (pip install agentx-kit → import agentx / agentx --help).

📦 Installation

From PyPI (recommended)

pip install agentx-kit                 # core: CLI + scaffolder + base abstractions
pip install "agentx-kit[all]"          # everything

Each LLM provider is an optional extra so you only pull the SDKs you use:

pip install "agentx-kit[openai,langgraph]"        # OpenAI + LangGraph
pip install "agentx-kit[bedrock,crewai,rag,mcp]"  # Bedrock + CrewAI + RAG + MCP

Using uv

uv pip install "agentx-kit[all]"

From GitHub (latest, unreleased)

pip install "agentx-kit[all] @ git+https://github.com/muhammadyahiya/agentx-kit.git"

From a local clone (development)

git clone https://github.com/muhammadyahiya/agentx-kit.git
cd agentx-kit
uv venv && uv pip install -e ".[all,dev]"   # or: pip install -e ".[all,dev]"
pytest -q

Requires Python 3.10–3.13 and (for the scaffolder's .venv creation) uv.

Verify

agentx version
agentx providers     # lists every provider + the env vars it needs

Why

• One factory, every provider. get_chat_model("bedrock", ...) or get_chat_model("openrouter", ...) — same call, lazy imports, install only the extras you use.
• Two frameworks. LangChain/LangGraph and CrewAI from the same building blocks.
• Batteries included. RAG, short/long-term memory, MCP tools, and a skills registry — each optional and gracefully degrading.
• Scaffolder, not a black box. The generated project is readable, idiomatic code you own, pre-wired to your selections, in a fresh .venv.

Use as a library

from agentx import get_chat_model, list_providers

llm = get_chat_model("openai", "gpt-4o-mini")
print(llm.invoke("Say hi in 3 words").content)

for spec in list_providers():
    print(spec.id, "→", spec.label)

CrewAI:

from agentx import get_crewai_llm
llm = get_crewai_llm("openrouter", "anthropic/claude-3.5-sonnet")

Scaffold a project

agentx new                         # fully interactive
agentx new --name my-bot --yes     # accept sensible defaults
agentx providers                   # list providers + required env vars

The wizard asks, one option at a time:

1. Project name & target directory
2. Framework — LangGraph or CrewAI
3. LLM provider and model
4. Number of agents (and their roles)
5. RAG module? (vector store)
6. Memory? (short-term / long-term / both)
7. MCP tools?
8. Skills integration?
9. Prompt style (defaults or scaffolded custom prompts)
10. Create .venv and uv sync now?

It then renders the project, writes a feature-aware pyproject.toml + .env.example, and runs uv venv to create .venv.

Prompts: add at creation, or any time after

Prompts are not baked into code — every generated project keeps them in a prompts.json that agents.py loads dynamically. Add an entry and the project runs it on next start, no code changes.

# at creation
agentx new --yes -n chatops --prompt "You are a senior DevOps engineer. Be terse."

# after creation (run inside the project)
agentx prompt list
agentx prompt set assistant --text "You are an SRE. Prioritise reliability."
agentx prompt add reviewer --role "Code Reviewer" --goal "Review diffs" \
    --text "You review code for bugs and security."
agentx prompt remove reviewer

prompts.json:

{
  "with_rag": false,
  "agents": {
    "assistant": {"role": "...", "goal": "...", "system_prompt": "You are ..."}
  }
}

A blank system_prompt is auto-derived from the agent's role + goal. You can also just open prompts.json in an editor — the CLI is a convenience, not a gate.

🏢 Enterprise pack

Generate a production-shaped project with one flag — informed by a survey of CrewAI/LangGraph/create-llama/AgentStack/agno/pydantic-ai (see RESEARCH.md):

agentx new --yes -n my-bot --enterprise        # everything below
# or pick individually:
agentx new --yes -n my-bot --observability --guardrails --serve --docker --ci --evals

What --enterprise adds to the generated project:

• Observability — OpenTelemetry GenAI tracing + optional Langfuse (observability.py), opt-out via AGENTX_TELEMETRY=false.
• Guardrails — input/output validation + PII redaction (guardrails.py).
• FastAPI server — server.py with /health, /chat, and SSE /chat/stream.
• Docker — Dockerfile + docker-compose.yml (+ .dockerignore).
• CI — .github/workflows/ci.yml (lint + compile + tests, optional eval gate).
• Evals — evals/ LLM-as-judge harness runnable locally and in CI.
• Typed config — config.py via pydantic-settings (12-factor).
• Manifest — agentx.json declaring framework, provider, features (à la langgraph.json).

These are also usable as a library in any project:

from agentx import (
    setup_tracing, get_callbacks,          # observability
    build_resilient_chat,                  # retries + provider fallbacks
    UsageLimits, UsageTracker,             # token/cost budgets
    apply_guards, structured_model,        # guardrails + typed outputs
)
setup_tracing("my-service")
llm = build_resilient_chat("openai", "gpt-4o-mini", fallbacks=[("anthropic", "claude-3-5-sonnet-latest")])

Installation extras

Extra	Installs	For
openai / azure / openrouter	langchain-openai	OpenAI-compatible
anthropic	langchain-anthropic	Claude
google	langchain-google-genai	Gemini (AI Studio)
vertex	langchain-google-vertexai	Vertex AI
bedrock	langchain-aws	Amazon Bedrock
groq	langchain-groq	Groq
ollama	langchain-ollama	local
langgraph	langgraph, langchain	LangGraph agents
crewai	crewai	CrewAI crews
rag	langchain-community, chromadb	RAG
mcp	langchain-mcp-adapters	MCP tools
observability	opentelemetry-*, openinference-*	tracing
server	fastapi, uvicorn	serving
all	everything above	kitchen sink

See DESIGN.md for the architecture and RESEARCH.md for the competitive analysis behind these features.

License

MIT