clustertrace 0.7.0

Local-first LLM agent observability — decorator + OpenTelemetry ingestion, traces grouped by execution pattern, cost per call, search, replay, shareable HTML snapshots.

clustertrace

Local-first LLM agent observability that tells you which clusters of traces are failing — not which individual ones.

Drop in a decorator, an SDK wrapper, or your existing OpenTelemetry setup. Get traces grouped by execution pattern, cost per call, full-text search, and replay of failing runs — all running off a single SQLite file on your laptop.

Two clusters explain 87% of all failures in the bundled demo. That's the kind of diagnosis the clusters page hands you in one screen instead of 47 stack traces.

---

30-second trial — no API key needed

pip install clustertrace
clustertrace demo

60 pre-recorded traces of three agents (research, RAG, tool-use), dashboard auto-launches, no API spend. Pre-PyPI: pip install "clustertrace @ git+https://github.com/harrywinter06-code/clustertrace".

---

When you're ready to use it for real

1. Native decorator

import clustertrace

@clustertrace.trace(tags={"agent": "research"})
async def plan(query): ...

with clustertrace.span("retrieval", k=5):
    ...

clustertrace.tool_call("web_search", args={"q": query}, result=hits)
clustertrace.tag("user_tier", "pro")
clustertrace.metric("score", 0.85)        # numeric — aggregated to a time-series chart

Async-safe — concurrent asyncio.gather calls produce separate traces; nesting tracks the parent via contextvars.

2. SDK wrappers (no decorator needed)

from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex
from openai import OpenAI
import clustertrace

client  = clustertrace.wrap_anthropic(Anthropic())          # direct API
bedrock = clustertrace.wrap_anthropic(AnthropicBedrock())   # AWS Bedrock
vertex  = clustertrace.wrap_anthropic(AnthropicVertex())    # Google Vertex
oai     = clustertrace.wrap_openai(OpenAI())                # OpenAI

Explicit wrap — no global monkey-patching. Async clients (AsyncAnthropic, AsyncOpenAI) are detected automatically.

3. OpenTelemetry exporter (use your existing instrumentation)

If you already have OTel set up — LangChain, LlamaIndex, Bedrock auto-instrumentation, your own custom spans — add clustertrace as an exporter:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from clustertrace.otel import ClustertraceSpanExporter

provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(ClustertraceSpanExporter()))

The clusters page, cost view, and search work on OTel-sourced traces too. gen_ai.* and llm.* attribute conventions are mapped onto clustertrace's schema.

---

What you get

Page	What
/	filterable trace list (status, tag, name search) with live polling and per-trace cost
/clusters	distinct execution patterns — count, failure rate, sample trace, longest common failure prefix, top failing nodes
/search	FTS5 search across span name + input + output + error_message; supports phrases, OR, NEAR
/metrics	per-metric aggregates + rolling-mean sparklines for everything you've passed to clustertrace.metric()
/failures	per-span error-rate bars, step-of-failure histogram, force-directed call graph
/trace/<id>	Gantt timeline + expandable I/O + tags + metrics + per-span cost

See examples/sample-trace.html for a self-contained shareable snapshot of one failing trace — 16 KB single file with embedded data and renderer, no external assets.

CLI

clustertrace demo                                      # one-step trial with bundled data
clustertrace dashboard                                 # launch local server
clustertrace stats                                     # one-screen DB summary
clustertrace backfill-cost                             # compute $ for every LLM call
clustertrace backfill-signatures                       # signatures for older traces
clustertrace snapshot <trace_id> -o trace.html         # self-contained shareable HTML
clustertrace export <trace_id>                         # JSONL to stdout
clustertrace export --all > backup.jsonl               # everything
clustertrace import < backup.jsonl                     # merge (skips existing IDs)
clustertrace replay <trace_id> --entry mod:fn          # re-run with captured args
clustertrace db-path                                   # print SQLite path

Configuration

Var	Default	Purpose
CLUSTERTRACE_DB	~/.clustertrace/traces.db	SQLite file path
CLUSTERTRACE_MAX_PAYLOAD_BYTES	32768	Per-field cap on serialized span I/O
CLUSTERTRACE_PRICING_JSON	(none)	Override or extend the model price table

---

How does this compare to Langfuse / Phoenix / LangSmith?

	clustertrace	Langfuse OSS	Arize Phoenix	LangSmith
Local-first (one binary / SQLite)	yes	no (Postgres + worker)	yes (in-memory or Postgres)	no (SaaS)
Clusters traces by execution pattern	yes — the differentiator	no	partial (groupings by ID, not signature)	no
Longest common failure prefix	yes	no	no	no
OpenTelemetry ingestion	yes (exporter)	yes	yes	partial
Cost tracking	yes (built-in pricing)	yes	yes	yes
Full-text search	yes (FTS5)	yes	yes	yes
Replay with captured args	yes	partial	partial	yes
Self-contained shareable trace HTML	yes (no other tool ships this)	no	no	no
Decorator + OTel + SDK wrappers	all three	OTel + wrappers	OTel	wrappers
Single-file install, no server setup	yes	no	yes (for in-mem)	n/a
Multi-user / teams	no	yes	yes	yes
Production retention / sampling	no	yes	yes	yes

Pick clustertrace when: you're debugging a single agent or running a small eval suite on your laptop, you want clustering + failure-prefix mining as a first-class view, and you'd rather pip install than docker compose up.

Pick Langfuse / Phoenix / LangSmith when: you're running in production, need teams, need retention policies, need PII redaction, or want a managed dashboard. clustertrace is intentionally simpler.

---

FAQ

Why not just use Langfuse OSS? Langfuse is more capable for production deployment — multi-user, Postgres-backed, fully featured. It's also a four-container Docker stack that needs a workers process and a separate web service. clustertrace is one Python package and one SQLite file. If you want to debug an agent on your laptop tonight, clustertrace is faster to set up; if you want to deploy a tracing service for a team, Langfuse is the right answer.

Why "clustering" instead of just listing traces? Because at 200+ traces, eyeballing the list doesn't find the pattern. The demo data has 29 distinct execution patterns; the top 2 account for 87% of all failures. That's the kind of structural signal you can't see from a list — and it's the diagnosis that points you at the actual fix.

Why local-only / no auth? Trade-off: keeps the binary small and the trial frictionless. Single-user is the right default for a debug tool. The README is explicit that production observability with retention and teams is a different tool's job.

Does it work with LangChain / LlamaIndex / DSPy? Yes, via the OpenTelemetry path. Anything emitting OTel spans flows into clustertrace. We map gen_ai.* / llm.* attribute conventions onto our schema so cost and clustering still work.

Does it support streaming? The span is logged on completion. Chunk-by-chunk capture isn't implemented yet (v0.4 target).

What's the algorithmic depth? Cluster signatures use exact-string equality on a normalized, run-length-collapsed span sequence. Reorderings split clusters today (A→B→C and A→C→B are two clusters). Reorder-insensitive matching via set-of-edges or tree-edit-distance is the v0.4 algorithmic move. The README doesn't oversell the implementation — see ARCHITECTURE.md for the full design trade-offs.

How much does the demo cost? $0. The bundled 60 traces are pre-recorded. The full reproduction script (examples/generate_demo_data.py, 240 traces) costs ~$2-3 in Haiku.

---

Overhead

@clustertrace.trace adds ~35 µs of pure-Python overhead per call on modern hardware; the SQLite write that follows is the real cost (~5 ms on Linux/macOS, ~30 ms on Windows NTFS). For a debug tool on a laptop this is fine — you don't trace 100/sec. For production:

@clustertrace.trace(sample=0.01)   # log 1% of calls
def hot_path(): ...

@clustertrace.trace(skip=True)     # zero overhead — returns the function unwrapped
def loop_body(): ...

Run python examples/benchmark.py to see the numbers on your hardware.

Known limitations

• Streaming responses are logged on completion only, not chunk-by-chunk. The streaming: true attribute is recorded so you can filter — but the intermediate chunks aren't captured. v0.5 target.
• Replay with prompt diff is half-built — clustertrace replay re-runs with captured args; modifying the prompt before re-invocation is not yet exposed. v0.5.
• Native wrappers only for Anthropic and OpenAI. Bedrock + Vertex work through wrap_anthropic (shared .messages.create interface). Gemini works through OpenTelemetry.
• Single-user, no auth. Dashboard is intended for 127.0.0.1. See SECURITY.md.

Contributing

Read ARCHITECTURE.md for the design choices, CONTRIBUTING.md for the setup and the step-by-step recipe for adding a new SDK wrapper. Real gaps that would meaningfully help users are listed at the bottom of CONTRIBUTING.md.

License

MIT.