agentx-kit 0.5.0

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.

📊 Prompt dashboard (observability + optimization)

A Streamlit workbench to understand and refine how your prompts talk to the LLM — launch it any time:

pip install "agentx-kit[dashboard]"
agentx dashboard                 # opens http://localhost:8501
agentx prompt set assistant -d   # edit a prompt AND open the dashboard

It gives you, live as you edit:

• Token count, context-window utilization gauge, and cost estimate (tiktoken-accurate).
• Quality score (0–100) with a checklist (role / goal / output-format / examples / constraints / specificity) and concrete suggestions + limit warnings.
• ✨ One-click LLM optimization — refines the prompt while preserving intent, shows a diff + rationale + token delta, and can apply the result straight back to prompts.json.
• ▶️ Test run — send the prompt to the model and see the response with tokens in/out, latency, and cost.
• 📈 Usage trends — tokens, cost, and latency over time, logged locally to .agentx/insights.jsonl.

Run it inside a generated AgentX project and it reads/writes that project's prompts.json; run it anywhere else for a free-form prompt scratchpad.

🔌 Use as a connector (Claude / Copilot / Codex)

AgentX-Kit ships an MCP server, so any MCP-capable assistant can scaffold a complete project from a single prompt with your problem statement.

pip install "agentx-kit[connector]"
agentx mcp --print-config        # prints the client config below

Add it to your client (then restart it):

// Claude Desktop / Codex / Copilot — under "mcpServers"
{ "mcpServers": { "agentx-kit": { "command": "agentx", "args": ["mcp"] } } }

# Claude Code one-liner
claude mcp add agentx-kit -- agentx mcp

Now just ask, in plain language:

“Build a customer-support agent that answers from our product docs and serves a REST API.”

The assistant calls AgentX-Kit's tools and you get a complete, runnable project:

• recommend_project(problem_statement) — suggests framework, provider, agent count, and features.
• create_agent_project(problem_statement, …) — generates the project (infers RAG/serve/memory/etc. from the statement, or take explicit overrides / enterprise=true) and returns the file tree + key file contents + run steps.
• list_providers, analyze_prompt, optimize_prompt — provider list + prompt insights.

So from one sentence the assistant produces a pre-wired project (prompts already seeded from your use case), ready to uv sync && uv run.

🧩 Editor & assistant integrations

The same connector powers ready-made integrations (see integrations/):

• VS Code extension (integrations/vscode) — commands for New Agent Project, Open Prompt Dashboard, Add Prompt, Cache Stats, and Register MCP Server for Copilot (writes .vscode/mcp.json). Build with vsce package.
• GitHub Copilot (agent mode) — add the MCP server via .vscode/mcp.json:

  { "servers": { "agentx-kit": { "command": "agentx", "args": ["mcp"] } } }
  

  (the VS Code command above writes this for you), then ask Copilot to build an agent.
• Claude Code plugin (integrations/claude-plugin):

  /plugin marketplace add muhammadyahiya/agentx-kit
  /plugin install agentx-kit@agentx-kit
  /agentx-kit:new-agent a support agent that answers from our docs and serves an API
  

• Claude Desktop / Codex — add the connector config from agentx mcp --print-config.

💾 Response caching (cost & latency saver)

Caching is the top 2026 token-optimization lever. Turn on a global LLM response cache and every provider call is served from a local store on repeat — no code changes:

from agentx import enable_caching, cache_stats
enable_caching()                 # all get_chat_model(...) calls are cached
...
print(cache_stats())             # {'hit_rate': 0.6, 'tokens_saved': 12000, 'est_usd_saved': 0.024, ...}

agentx cache stats               # hit rate + estimated tokens/$ saved
agentx cache clear

Generated projects can enable it automatically (it's part of --enterprise), and the dashboard's Trends tab shows live hit-rate and $ saved. TTL-capable, SQLite-backed at .agentx/llm_cache.sqlite.

🏢 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
dashboard	streamlit, tiktoken, pandas	prompt observability dashboard
connector	mcp	MCP server for Claude/Copilot/Codex
all	everything above	kitchen sink

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

License

MIT