contextual_request + contextual_scope_update are the only WS
flows for ad-hoc contextual chat now. Floating system prompt
constant removed; Langfuse 'floating_system' is deleted in a
separate manual step. Also removes floating-agent LLM slot from
llm.py and the associated LLM_MODEL_FLOATING_AGENT setting entry.
Smoke trace 0b46841484ba7d024ed9f8d5ac8b1df0 showed the agent
defaulting to list_projects + get_project for a 'summarize
project Nexus' query, returning a shallow row without aiSummary
or tasks/notes. The legacy read tools were exposed via
*PROJECT_TOOLS / *TASK_TOOLS spreading.
Now _contextual_tools exposes exactly:
- get_page_details (sole read; supports per-entity + list views)
- create_task, update_task
- create_note
- create_timeline
Prompt rule 2 explicitly forbids the legacy reads, and the test
asserts they are excluded from the palette.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Use _build_system_prompt helper so the contextual agent gets the
same system-prompt slots as home/floating runners — most importantly
{date_context} so the agent can reason about due dates when
creating/updating tasks.
Also makes the session_id contract on run_contextual_stream explicit
(was reading via context['_debug']) and tightens the tool-list test.
New agent runner. Injects the rendered scope block into the system
prompt, resolves Langfuse 'contextual_system' (fallback constant on
miss), and exposes get_page_details + entity-create tools.
Note-edit tools (propose_note_edit) intentionally excluded — next sprint.
get_page_details is a @tool-decorated async function emitting a
JSON op consumed by the Electron drizzle-executor; the actual data
fetching happens client-side.
_contextual_tools() assembles the safe tool palette. Tools follow the
existing @tool decorator pattern from langchain_core.tools.
NOTE: test_run_contextual.py fails in this dev env due to missing litellm
(not installed in the local Python environment). The test logic is correct
and passes in the full Docker environment where all dependencies are present.
Add build_brief_multi_project_manifest() to deep_agent.py that fetches
all project folder manifests via execute_on_client and keeps the top 5
most-recently-modified files per project. Wire into run_home_brief in
brief_agent.py, injecting the <linked_folders> block into the system
prompt alongside FOLDER_TOOLS.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add optional project_id param to run_home_stream. When set, fetch the linked
folder manifest via _fetch_project_manifest and prepend the <linked_folder>
block to the system prompt. Also build an explicit tools list that extends
_all_tools_for_user with FOLDER_TOOLS so the home agent can read folder
files. device_ws._handle_home_request extracts project_id / projectId from
the home_request frame and forwards it to the runner.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add _fetch_project_manifest helper that calls read_project_folder_manifest
via execute_on_client. Wire it into run_task_brief_research_stream (new
optional project_id param) so the <linked_folder> block is prepended to the
system prompt when the task belongs to a linked project. Also bind
FOLDER_TOOLS into the task-brief tool palette so the agent can read folder
files. device_ws extracts project_id / projectId from the task_brief_request
frame and forwards it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add _language_instruction() to deep_agent.py, reads language from core memory
- Append language directive to all 4 run_* functions (task/project/checkpoint/note)
- Minor fixes: alembic env, route imports, test cleanup
Langfuse V3 does not accept user_id/session_id on observation-level calls.
Moved to metadata dict in agent_runner, deep_agent, and agent_setup.
refactor(tests): fixture-based pattern for agent_runner_v2 eval tests
- cases.yaml + data/ fixtures under tests/fixtures/agent_runner_v2/
- pytest_generate_tests parametrizes test_eval_runner from YAML
- _resolve_projects() handles symbolic names and inline dicts
- _evaluate_case() centralizes all assertion logic
- --runner-dir CLI option for custom fixture folders
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- New app/core/langfuse_client.py: lazy singleton client, get_prompt_or_fallback()
helper (returns raw template + prompt obj for linking), extract_usage() for token
counts. No-ops when LANGFUSE_* env vars are not set.
- deep_agent.py: home-agent and floating-agent runs wrapped in spans; each ainvoke
wrapped in a generation with model/input/output/usage; prompts fetched from
Langfuse (adiuva-home-agent, adiuva-floating-agent, adiuva-floating-classifier)
with hardcoded fallback.
- agent_runner.py: step1-classifier and step2-processor LLM calls traced; batch
agent _run_agent_with_tools spans + generations; cloud-processor included.
Prompts: adiuva-step1-classifier, adiuva-step2-processor, adiuva-cloud-processor.
- agent_setup.py: journey-setup span + generation per ainvoke; prompt_obj stored
on JourneySession and reused across turns. Prompt: journey_system.
- settings.py: LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, LANGFUSE_HOST added.
- .env.example: Langfuse section with EU/US/self-hosted host comments.
- requirements.txt: langfuse>=2.0.0.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
