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_channels.py

@@ -0,0 +1,23 @@
+"""Email channel parsing and reply construction."""
+
+from __future__ import annotations
+
+from forge.channels import email as email_ch
+
+# --- email parsing ---
+
+
+def test_email_parse_provider_dict():
+    p = email_ch.parse_inbound({"from": "Jane <jane@acme.com>", "subject": "Help", "text": "  my order is late  "})
+    assert p["from_addr"] == "jane@acme.com" and p["from_name"] == "Jane" and p["text"] == "my order is late"
+
+
+def test_email_parse_raw_mime():
+    raw = b"From: Bob <bob@x.com>\r\nSubject: Hi\r\nMessage-ID: <m1>\r\nContent-Type: text/plain\r\n\r\nHello body\r\n"
+    p = email_ch.parse_inbound(raw)
+    assert p["from_addr"] == "bob@x.com" and "Hello body" in p["text"] and p["message_id"] == "<m1>"
+
+
+def test_email_reply_threads_subject():
+    msg = email_ch.build_reply(to_addr="a@b.com", subject="Order", body="done", from_addr="bot@x.com", in_reply_to="<m1>")
+    assert msg["Subject"] == "Re: Order" and msg["In-Reply-To"] == "<m1>" and msg["To"] == "a@b.com"