Files

Roberto Musso 55500cc818 feat(batch-agent): add Langfuse prompt management

- _get_system_prompt helper: fetches managed prompts from Langfuse
  with hardcoded fallback (same pattern as chat service)
- journey.py: journey_system prompt manageable via Langfuse
- agent_runner.py: batch_file_classifier, batch_processing,
  batch_cloud_processing prompts all manageable via Langfuse
- redis_consumer.py: link_prompt_to_trace for all three handlers

2026-03-23 22:30:36 +01:00

app

feat(batch-agent): add Langfuse prompt management

2026-03-23 22:30:36 +01:00

eval

feat(batch-agent): add E2E evaluation harness with Langfuse integration

2026-03-23 08:54:19 +01:00

Dockerfile

feat(batch-agent): extract Batch Agent Service (Step 3)

2026-03-23 07:19:02 +01:00

README.md

docs: add Langfuse integration TODO for batch-agent service

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requirements.txt

feat(batch-agent): integrate Langfuse tracing

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README.md

Batch Agent Service

Owns: agent_runner, journey builder, filesystem_agent, integrations (Gmail, MS Graph).

Tables owned

local_agent_configs
cloud_agent_configs
agent_run_logs

Endpoints

GET /agents/catalog
POST /agents/can-create
POST /agents/trigger
GET /agents/{id}/history

Redis channels

Subscribe: batch:request:{user_id}
Publish: ws:out:{user_id} (journey replies + tool calls)
BRPOP: tool:result:{call_id} (30s timeout)
SET+EX: journey:{user_id} (session state, TTL 1800s)

TODO

Integrate Langfuse tracing (reuse services/chat/app/tracing.py pattern — trace_span(), get_langfuse_callback(), prompt management). Each batch agent run should create a trace with input/output, link prompts, and pass the LangChain CallbackHandler to LLM calls.