windhover 0.9.0

Self-hosted, mobile-friendly observability for LangGraph — traces, run history, cost, and a living graph view.

Windhover

Windhover — the old poetic name for the kestrel, the falcon that hangs motionless in the wind, watching everything below. This tool does the same for your agent graphs.

Self-hosted, mobile-friendly observability for LangGraph. Trace depth like LangSmith (LLM prompts, tokens, cost, latency — plus retrievers and human-in-the-loop interrupts), run history, a timing waterfall, per-node stats, error forensics down to the throwing source line — and a living graph view that auto-updates when your code's topology changes. Point it at any compiled graph, or trace runs in from your own app. No LangSmith account, no cloud tunnel, no fragile websocket. HTTP + SSE, MIT.

Nothing about your graph's domain is baked in. Topology, the input form, and run outputs all come from the graph itself. Windhover observes — it never edits your graph.

Living graph (parallel fan-out)	Trace drawer — retrievers, LLM calls, cost, state

Runs — search, tags, sessions, interrupts	Dashboards — per-day, per-model

Quick start

pip install windhover langgraph
WINDHOVER_GRAPH=windhover.demo_graph:graph windhover   # -> :8090

Open http://<host>:8090. New run (input pre-filled from the graph's schema) → watch it execute → Runs for history, span trees, and replay → Stats for cost/latency. Edit the graph file while it runs and the canvas updates itself.

Your own graph: WINDHOVER_GRAPH="myapp.graphs:g" WINDHOVER_GRAPH_DIR=/path python -m windhover.server

Trace runs from any app

from windhover import WindhoverTracer
graph.invoke(input, config={"callbacks": [WindhoverTracer("http://HOST:8090")]})

Node spans, LLM calls (model/prompt/response/tokens/cost), and tools show up in Runs — wherever your app runs. Non-blocking, best-effort; never raises into your graph.

Sessions and tags use standard LangChain config — no Windhover imports needed beyond the tracer:

graph.invoke(input, config={
    "callbacks": [WindhoverTracer("http://HOST:8090")],
    "metadata": {"windhover_session": "chat-42", "windhover_tags": ["prod"]},
    "tags": ["also-captured"],          # langgraph-internal tags are filtered out
})

Features

• Any graph — topology from graph.get_graph(); input form from its state schema.
• Full trace tree — nodes → nested LLM / tool / retriever spans: prompts, responses, tokens, cost, latency, retrieved documents with their metadata.
• Clickable graph — tap a node for health, latency, wiring, its source code, and recent executions with payloads.
• Error forensics — failed runs show the full traceback; the failing node turns red on the graph, and the node's source renders with the throwing line highlighted.
• Human-in-the-loop aware — a graph paused on interrupt() shows an amber interrupted status plus the payload it's asking a human about.
• State evolution — every trace shows which state keys each node wrote, in order.
• X-ray — graphs with subgraphs get a canvas toggle that expands composite nodes (get_graph(xray=True)).
• Search & filters — full-text over prompts/payloads/errors (FTS5, LIKE fallback), status/tag/session filters, bookmarks, pagination, CSV/JSON export.
• Sessions — group runs into threads/batches; roll-up tokens, cost, errors.
• Scores — attach numeric evals to runs (API or UI): eval harnesses, LLM-as-judge, human review.
• Live tail — open a running run and watch its spans arrive in real time; nodes can push progress via LangGraph's get_stream_writer().
• Custom events — dispatch_custom_event("name", {...}) anywhere in your app lands as an event marker in the trace, parented to the node that fired it.
• Retries + TTFT — tenacity retries badge the span (↻2); streaming LLM calls record time-to-first-token; cache-read / reasoning token details show on the model line.
• Memory browser — graphs compiled with a LangGraph Store get a Memory view: browse namespaces and search long-term memory items.
• Time-travel — checkpointed graphs get a per-thread checkpoint browser: state, writes, and next-nodes at every superstep (get_state_history).
• Run diff — compare any two runs node-by-node: identical vs differing outputs, duration and token deltas.
• Datasets / batch eval — store golden input sets, run the graph over them, and get an expected_match score per item (see Datasets on the Stats page).
• Run history + replay — SQLite; runs persist even if the browser closes (worker thread).
• Living graph — file watcher re-extracts topology in a subprocess and pushes it to the UI.
• Dashboards — runs/tokens per day, per-model usage and latency, per-node latency, error rate.
• Mobile-first PWA, light/dark. Fully local (FastAPI + Cytoscape.js).

Datasets API

curl -X POST :8090/api/datasets -H 'Content-Type: application/json' -d '{
  "name": "golden", "items": [
    {"input": {"n": 2},  "expected": 6},
    {"input": {"n": 40}, "expected": "big"}]}'
curl -X POST :8090/api/datasets/golden/run   # -> runs land in an eval:golden:<ts> session

Scores API

curl -X POST :8090/api/runs/RUN_ID/scores -H 'Content-Type: application/json' \
     -d '{"name": "accuracy", "value": 0.92, "comment": "vs golden set"}'

Config (env)

WINDHOVER_GRAPH (module:attr; unset = ingest-only) · WINDHOVER_GRAPH_DIR · WINDHOVER_DB · WINDHOVER_HOST/WINDHOVER_PORT (0.0.0.0/8090) · WINDHOVER_WATCH (1) · WINDHOVER_PRICING · WINDHOVER_RETENTION_DAYS (0 = keep forever; else prune older runs on startup + every 6h) · WINDHOVER_TOKEN (set to require Authorization: Bearer <token> — or ?token= — on all /api routes; the UI prompts once and remembers it). Edit windhover/pricing.json for your models' $/1M rates (unknown model → cost null).

Notes

Runs use the imported graph (restart to run new code); the view always reflects current-on-disk topology. All frontend assets are vendored — no CDN, works fully offline. Deep links: #runs, #sessions, #stats, #run=<id>.

License

MIT.