feat: deep-agent canvas, live observability, and multi-environment tooling

Self-hosted platform for building, testing, and shipping LangChain/LangGraph agents. Deep-agent sub-agents on the canvas, a live tracing/observability timeline, auto-provisioned built-in tools with import/export, per-environment tool variables, streamed evaluations, and per-user auth token forwarding.

apps/api/tests/test_embedding_spans.py

@@ -0,0 +1,33 @@
+"""Embedding spans (item 9): embed calls run through the Embedder are timed + priced as
+`kind="embedding"` spans on the active run tracer, so RAG/memory embedding latency and cost
+show up in traces. Wrapping at the Embedder level covers every call site (knowledge/*,
+nodes/rag.py, services/memory.py) at once.
+"""
+
+from __future__ import annotations
+
+from forge.knowledge.embeddings import _est_tokens, resolve_embedder
+from forge.tracing.tracer import ForgeTracer, embedding_span
+
+
+def test_est_tokens():
+    assert _est_tokens(["aaaa"]) == 1  # 4 chars / 4
+    assert _est_tokens(["", None]) == 0  # handles empties
+    assert _est_tokens(["a" * 40, "b" * 40]) == 20
+
+
+def test_embedding_span_noop_off_run():
+    # No active tracer bound -> the context manager is a harmless no-op.
+    with embedding_span("some:model", n_texts=3):
+        pass
+
+
+async def test_embed_call_records_embedding_span():
+    tr = ForgeTracer()  # __init__ binds this as the active tracer for this async context
+    embedder = resolve_embedder(None)  # default local fastembed
+    await embedder.aembed_query("hello world")
+    spans = [s for s in tr.ordered() if s.kind == "embedding"]
+    assert spans, "an embed call should record an embedding span on the active tracer"
+    assert spans[0].model == embedder.name
+    assert spans[0].attributes.get("n_texts") == 1
+    assert spans[0].end is not None and spans[0].end >= spans[0].start  # latency captured