Files

Roberto Musso 333bba6fdd feat(batch-agent): extract Batch Agent Service (Step 3)

- agent_runner: local directory + cloud agent orchestration via Redis
- 5 domain agents: filesystem, task, note, project, timeline
- integrations: Gmail, MS Graph (Outlook + Teams)
- journey: guided chatbot conversation to build prompt_template
- routes: REST endpoints (catalog, can-create, trigger)
- redis_consumer: subscribes to batch:request:* pattern
- ws_context: Redis-based execute_on_client for tool round-trip
- Dockerfile with 300s timeout for long-running batch jobs

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

app

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

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

Dockerfile

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

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README.md

docs: add Langfuse integration TODO for batch-agent service

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requirements.txt

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

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

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.