projectkate 0.3.1

KATE SDK — auto-eval, observability, and knowledge marketplace for AI agents

KATE SDK

Auto-eval, observability, and knowledge marketplace for AI agents. Trace every LLM call, run evaluations, discover and use marketplace tools, and catch regressions before they ship.

Install

pip install projectkate

Optional instrumentation extras

pip install projectkate[openai]                # OpenAI
pip install projectkate[anthropic-instrument]  # Anthropic
pip install projectkate[langchain]             # LangChain / LangGraph
pip install projectkate[mistral]               # Mistral
pip install projectkate[vertexai]              # Vertex AI
pip install projectkate[google-genai]          # Google GenAI
pip install projectkate[crewai]                # CrewAI
pip install projectkate[all]                   # All supported providers

Quick Start

Trace mode — instrument your agent

import projectkate

# Initialize — reads KATE_API_URL, KATE_API_KEY, KATE_AGENT_ID from env
projectkate.init()

@projectkate.trace("summarize")
def summarize(text: str) -> str:
    return client.messages.create(
        model="claude-sonnet-4-20250514",
        messages=[{"role": "user", "content": f"Summarize: {text}"}],
    ).content[0].text

async with projectkate.run():
    result = summarize("Today's top news stories...")
    print(result)

Management client — programmatic platform access

from projectkate import KateClient

async with KateClient(api_key="kate_...") as kate:
    # List your agents
    agents = await kate.agents.list()

    # Check eval results for a run
    evals = await kate.evals.get_run_evals(run_id="...")

    # Publish an artifact
    await kate.artifacts.publish(artifact_id="...")

    # Check wallet balance
    balance = await kate.wallet.get_balance()

Tools — discover and use marketplace tools

KATE agents can discover and execute tools from the marketplace. The SDK provides a tool loop that handles the LLM ↔ tool-call cycle automatically.

Agentic tool loop

Wire up your LLM client and let the SDK handle tool discovery, execution, and chaining:

import projectkate
from openai import AsyncOpenAI

projectkate.init()
llm = AsyncOpenAI()

messages = [
    {"role": "system", "content": "You are a helpful assistant with access to tools."},
    {"role": "user", "content": "Find SEO keywords for 'AI observability'"},
]

result = await projectkate.tool_loop(
    llm,
    model="gpt-4o",
    messages=messages,
    max_rounds=10,
)

print(result.content)          # Final LLM response
print(result.tool_calls_made)  # Number of tool calls executed

Works with both OpenAI and Anthropic clients — the SDK detects the provider automatically.

Local tools

You can register your own tools alongside marketplace tools. The SDK merges them and routes calls to the right handler:

from projectkate import LocalTool

def get_current_date() -> str:
    from datetime import date
    return date.today().isoformat()

result = await projectkate.tool_loop(
    llm,
    model="gpt-4o",
    messages=messages,
    local_tools=[
        LocalTool(
            name="get_current_date",
            description="Returns today's date in ISO format",
            parameters={"type": "object", "properties": {}},
            fn=get_current_date,
        ),
    ],
)

Local tools run in-process. Async functions are supported.

Direct tool management

Use the management client for lower-level control:

async with KateClient(api_key="kate_...") as kate:
    # List tools available to your agent
    tools = await kate.tools.list(agent_id="...")

    # Execute a specific tool
    result = await kate.tools.execute(
        agent_id="...",
        tool_name="seo_keyword_research",
        input_data={"query": "AI observability"},
    )
    print(result.output)

    # Check credential status for subscribed tools
    statuses = await kate.tools.status(agent_id="...")

Local Eval (no server needed)

Run evaluations locally against your agent with zero infrastructure:

from projectkate.local import LocalEvalRunner

runner = LocalEvalRunner(agent_fn=my_agent)
results = await runner.run(test_cases=[
    {"input": "Summarize the news", "expected": "A concise summary..."},
])
runner.print_results(results)

Documentation

• Docs — guides, API reference, and examples

License

Apache 2.0 — see LICENSE.