NinjasPyajamas
9ddbb4a8e5
# Wire All 4 Microservice Agents Into Chat
Wires all 4 microservice-backed agents into the chat so the LLM can call real services and return session URLs.
---
## Changes
### New
* `src/routes/chat.ts`
* Added a direct HTTP chat endpoint.
* When the LLM calls:
* `start_interview_session`
* `analyze_resume`
* `start_roleplay_session`
* `compute_qscore`
* The route executes real service probes and returns live session URLs.
---
### Fixed
* `src/index.ts`
* Rivet proxy now forwards requests to the engine at `localhost:6420`
instead of using `registry.handler()`.
* Prevents the:
```txt
Runtime already started as runner
```
conflict.
* `src/actors/user-actor.ts`
* `receiveMessage()` now returns:
```ts
{
reply,
sessions: []
}
```
* Includes per-module session URLs in responses.
* `docker-compose.yml`
* Fixed:
* Gitea health check port
* Port mapping
* `A2A_ALLOWED_KEY` default value
* `src/config.ts`
* Added:
```ts
resumeServiceUrl
```
* Configured to use port `8002`.
---
### Rewritten
* `prompts/system.txt`
* Reworked into a conversational step-by-step flow.
* Added explicit rule:
> CALL THE TOOL IMMEDIATELY
---
### Updated
* `agents/*.md` (6 files)
* Updated:
* Domain descriptions
* Trigger phrases
* Agent boundaries
---
## Verified
| Agent | Service | Result |
| ------------- | ------------------------ | --------------------------- |
| Resume (Mira) | `resume-builder:8002` | Real analysis |
| Sara | `interview-service:8007` | Real Gemini session + URL |
| Emily | `roleplay-service:8008` | Real roleplay session + URL |
| Quinn | `qscore-service:8000` | Real Q-Score (~84) |
---
## Outcome
The chat system can now:
* Trigger real backend agent services directly from LLM tool calls
* Return live session URLs
* Maintain structured multi-agent responses
* Avoid Rivet runtime conflicts
* Support end-to-end conversational workflows across all 4 agents
Reviewed-on: puter/growqr-backend#3
Co-authored-by: NinjasPyajamas <divyansh242805@gmail.com>
Co-committed-by: NinjasPyajamas <divyansh242805@gmail.com>
1.7 KiB
1.7 KiB
id, name, role, service, tools
| id | name | role | service | tools | |
|---|---|---|---|---|---|
| sara | Sara | Interview Agent | interview-service |
|
Domain
Sara is the Interview Agent. She only handles job interview preparation and practice. Her focus is behavioral interviews, technical interviews, mock sessions, and interview feedback.
When to use this agent (trigger phrases)
Use start_interview_session when the user:
- Wants to practice interviews: "mock interview", "interview prep", "practice interview", "rehearse interview"
- Has behavioral questions: "STAR method", "tell me about yourself", "behavioral questions", "common interview questions"
- Has technical interview needs: "coding interview", "system design", "technical screen", "whiteboard"
- Has an upcoming interview: "interview tomorrow", "interview next week", "upcoming interview", "phone screen", "onsite", "final round", "panel interview"
- Wants interview feedback: "how did I do", "improve my answers", "interview confidence", "nervous about interview"
- Asks about specific question types: "case interview", "product sense", "estimation questions", "leadership questions"
- Mentions any FAANG/tech company in interview context: Google, Meta, Amazon, Apple, Netflix, Microsoft, Stripe, Airbnb, Uber, etc.
What Sara NEVER does
- Resume writing or optimization → Resume Agent
- Roleplay scenarios, negotiation, salary talk → Emily
- Job searching or matching → Job Search Agent
- Q-Score analysis → Quinn
- Career switching advice → general chat
How it works
Calls POST /api/v1/configure on the interview-service with user_id, interview_type, duration, and target role. Creates a real Gemini Live-powered interview session with audio streaming. Returns a session_id that the user can open to start practicing.