cendor-core 1.11.1

Wrap your LLM client once and capture exact token counts and cost on every call — the shared foundation the other Cendor tools build on.

cendor-core

The shared foundation for the Cendor stack: canonical types, provider-aware token counting, an offline price table, one instrument() interception point, an in-process event bus, and OpenTelemetry GenAI emitters. Tiny on purpose — it's the blast radius for every other tool.

One instrument() call, every sibling tool observes the stream — no per-call wiring, offline by default.

· usually installed transitively · import cendor.core

Using an AI coding assistant? npx @cendor/init (TS) / uvx cendor-init (Python) wires it up — or point it at cendor.ai/docs/for-ai-assistants.

from cendor.core import tokens, prices, instrument, bus

# Count tokens and price a call — fully offline, no API key, no network:
n = tokens.count([{"role": "user", "content": "Summarize the attached report in 3 bullets."}],
                 model="claude-opus-4-8")
cost = prices.estimate("claude-opus-4-8", input_tokens=n, output_tokens=200)

# Instrument any client once; tools subscribe to the normalized event stream:
@bus.subscribe
def on_call(call):                   # normalized LLMCall with usage + cost
    print(call.provider, call.model, call.cost)

client = instrument(openai_or_anthropic_client)   # idempotent, additive · sync · async · streaming

Highlights

• instrument() — wrap any client once: OpenAI (Chat Completions + Responses API + Embeddings, since 1.6.0) · Anthropic · Hugging Face (InferenceClient) · AWS Bedrock · Google Gemini (google-genai + legacy google-generativeai) · Ollama, detected by shape; sync, async, and streaming; idempotent + additive. Embedding calls carry metadata["embedding"] and ride the same pre-flight interceptor pass (budgets can block, guards can redact). instrument_tool() does the same for tools.
• Streaming is a context manager and an iterator — the streamed value supports both for chunk in stream / async for and with client…create(stream=True) as stream: / async with, matching the SDK's own stream and unbreaking frameworks (e.g. LangChain) that consume streams via with. Usage/cost finalize exactly once.
• Event bus — subscribe / emit; thread-safe within a process; one failing subscriber never starves another.
• Interceptor seam — add_interceptor + Reroute / MISS powers replay (cassette) and reroute / block (tokenguard) without a second patch point.
• Token counting, exact by default — tiktoken is a required dependency, so OpenAI counts are exact out of the box (Claude/Gemini use its o200k BPE as a close estimate); a character heuristic remains only as a defensive fallback if tiktoken fails to import. tokens.method(model) reports which tier is active; tokens.register() plugs in a precise counter.
• Reasoning-token accounting — Usage.reasoning_tokens breaks out a reasoning/thinking model's internal reasoning (OpenAI reasoning_tokens, Gemini thoughts_token_count), non-streaming and streaming. A subset of output_tokens, so cost is unchanged; Gemini's separately-reported thoughts are folded into the output total.
• Offline-first, refreshable prices — bundled dated snapshot; estimate() -> Decimal Money (never float); optional refresh(source="litellm"|"openrouter"|"azure") from live no-auth sources, with age_days()/is_stale() staleness signals. Cached tokens are billed once (cached ⊆ input, normalized across providers), not at both the input and cached rate. A gateway-reported cost (e.g. OpenRouter's usage.cost) is preferred over the estimate and labeled cost_reported vs cost_estimated.
• OpenTelemetry — emit gen_ai.* spans, or otel.ingest() a managed runtime's spans onto the bus. Structural protocols (Compressor / EvictionStrategy / Sink / Subscriber / Handle) let the tools interlock without coupling. Sink now has optional flush()/close() lifecycle methods (write-only sinks still valid).
• Framework adapters — when your app runs under a third-party framework, a small adapter carries the framework's agent name onto the bus (core carries no identity of its own): LangChain / LangGraph cendor.core.langchain.CendorCallbackHandler (extra [langchain], recording-only — usage + reasoning + tools + root-run trace_id); the OpenAI Agents SDK cendor.core.openai_agents.CendorAgentHooks (extra [openai-agents] — the agent's calls ride the standard OpenAI client, so instrument() still captures tokens/cost/streaming; this supplies only the name); and Azure AI Foundry Agents cendor.core.foundry (extra [foundry] — stamps agent + conversation_id, attribution-only since the model runs server-side). For direct-SDK agents, core.trace("run-id") sets the same ambient trace_id.

Exact OpenAI token counts ship by default (tiktoken is a required dependency — truthful counts are the product, not an add-on). Optional extras: [otel] to emit spans, [langchain] for the LangChain/LangGraph callback handler, [openai-agents] for the OpenAI Agents SDK hooks, [foundry] for the Azure AI Foundry correlation adapter; provider SDKs are always optional extras.

A rendered architecture diagram lives in docs/core.md (GitHub renders Mermaid; PyPI shows code as text).

See docs/core.md · CHANGELOG. Part of the Cendor stack — github.com/cendorhq/cendor-libs. Powered by PowerAI Labs. Apache-2.0; provided "as is", without warranty — use at your own risk (LICENSE §7–8).