cendor-sdk 1.1.1

Build an LLM agent with spending limits, a tamper-evident audit trail, PII redaction, and record/replay testing built in from the start — a governed agent in about 10 lines.

cendor-sdk

A governed agent in 10 lines — cost budgets, tamper-evident audit, and PII redaction built in.

provider-agnostic · local-first · offline by default · sync and async

The only agent SDK where cost budgets, tamper-evident audit, PII redaction, context governance, and record/replay testing are the foundation, not plugins.

cendor-sdk owns the agent loop, so every governance concern that is best-effort beneath a framework becomes first-class here: usage is never lost, budgets enforce before the model call, PII is redacted before send, and the whole run correlates under one trace_id. It's the simple, batteries-included door into the Cendor stack — you don't need to pick a framework or wire the libraries. (Already have a framework? Compose the libraries beneath it: pip install cendor-libs.)

Install

pip install "cendor-sdk[openai,anthropic]"     # provider SDKs are optional extras
pip install "cendor-sdk[all]"                  # every provider + interop, batteries included

The install bundles the whole Cendor stack (cendor-core, tokenguard, acttrace, contextkit, squeeze, cassette) by dependency — you install once and import only from cendor.sdk. Provider SDKs stay optional extras: [openai], [anthropic], [google], [bedrock], [ollama], [huggingface], [azure], [foundry-local], plus [mcp] and [otel].

A governed agent in 10 lines

from cendor.sdk import Agent, tool, run, budget, guard, Policy, AuditLog

@tool
def get_weather(city: str) -> str:
    """Current weather for a city."""
    return f"Sunny in {city}"

agent = Agent(name="assistant", model="gpt-4o", tools=[get_weather],
              instructions="Answer using tools when helpful.")

log = AuditLog(system="support", risk_tier="limited", path="audit.jsonl")
with budget(usd=0.25, on_exceed="block"), guard(Policy.default(), audit=log):
    result = run(agent, "What's the weather in Paris?", audit=log)

print(result.output)                        # -> "It's sunny in Paris."
print(result.cost, result.usage)            # priced in Decimal, budgeted
print([s.name for s in result.tool_steps])  # -> ["get_weather"]
# audit.jsonl: audit_open -> decision -> llm_call -> tool_call -> llm_call, hash-chained &
# verify()-able, all correlated by one trace_id. Wrap in cassette.using("run.json") to replay it.

Ungoverned still works — on cendor-core alone. Every governance layer is optional and removable; drop the with block and run(agent, ...) runs bare:

from cendor.sdk import Agent, run
result = run(Agent(name="a", model="gpt-4o", instructions="Be brief."), "Hi")
result = await run.aio(agent, "Hi")   # same call, async

run.aio is natively async for OpenAI (Chat + Responses), Anthropic, Ollama, and Hugging Face. Gemini and Bedrock have no native async client, so run.aio runs them synchronously for now.

Why it's different

	Provider lock	Cost budgets	Tamper-evident audit	PII redaction	Record/replay tests	Local-first
OpenAI Agents SDK	OpenAI-centric	✗	✗	✗	✗	lib
LangGraph	agnostic	DIY	DIY	DIY	DIY	lib
Anthropic Agent SDK	Anthropic-centric	✗	✗	✗	✗	lib
CrewAI / Pydantic AI / ADK	varies	✗/DIY	✗	✗	✗	lib
cendor-sdk	agnostic	built-in	built-in	built-in	built-in	yes

Governance is composed through Cendor's existing bus / interceptor / Sink / Compressor seams, correlated by trace() — zero SDK-specific glue. Budgets, audit, redaction, and record/replay all ride the agent loop through those seams, so removing any one is just not entering its context.

Multi-agent, one correlated tree

Handoff, supervisor/router, and sequential/parallel pipelines — with the correlation that was impossible beneath frameworks. A whole multi-agent run is one governed, trace_id-correlated tree, on one verifiable audit chain. Handoff even works across providers:

from cendor.sdk import Agent, run

writer  = Agent(name="writer",  model="claude-opus-4-8", instructions="Write the brief.")
planner = Agent(name="planner", model="gpt-4o", instructions="Plan, then hand off.",
                handoffs=["writer"])

result = run([planner, writer], "Research X and write a brief")   # OpenAI -> Anthropic handoff
print(result.agents)     # ["planner", "writer"]

Every major provider — one canonical loop

The provider is inferred from the model id (override with provider=). History is held in one canonical shape, so a run can hand off between providers without rewriting it.

Provider	Models	Extra
OpenAI	Chat Completions + Responses API	[openai]
Anthropic	Messages API	[anthropic]
Google Gemini	google-genai	[google]
AWS Bedrock	Converse API	[bedrock]
Ollama	local models	[ollama]
Hugging Face	Inference / endpoints	[huggingface]
Azure AI Foundry	deployments via the OpenAI v1 endpoint (Chat + Responses)	[azure]
Foundry Local	on-device, OpenAI-compatible	[foundry-local]

More in the box

Everything a real agent needs — all governed through the same seams:

• Streaming — run.stream / run.astream yield text deltas + tool events (native for the OpenAI family + Ollama).
• Structured output — a dataclass / Pydantic / JSON-schema output_type uses each provider's native schema mode.
• Reasoning & control — Agent.extra passes tool_choice, reasoning_effort, top_p, stop, …; o-series temperature is handled for you.
• RAG — VectorIndex + Agent(retriever=…) inject governed retrieval, or expose your store as a @tool.
• Memory — Session (conversation), SummarizingSession (rolling summary), SQLiteSessionStore (durable), context_budget (fit the window).
• Embeddings — embed() / aembed() capture RAG calls on the same cost/audit tree.
• Cost governance for any model — register_model_price(...) so budgets bind on custom / deployment-named ids.

Docs

• Quickstart & reference
• Multi-agent orchestration
• Interop — MCP, A2A, Foundry, OTel, HITL
• Production hardening · Governed eval
• Runnable, network-free examples
• Changelog

License

Apache-2.0.