tahrix-agent

name	tahrix-agent
description	Build an AI agent with LLM streaming, tool calling, and flow-based frontend rendering. This skill should be used when building chat-based AI agents that need real-time streaming responses with reasoning, tool execution, and dynamic UI updates. Covers hybrid polling architecture (Redis + background coroutine), Tool dataclass registry pattern, and flow-ordered DOM rendering.

Tahrix Agent

Overview

Build a production-grade AI agent chat interface with three core capabilities: (1) hybrid LLM streaming via Redis-backed polling, (2) structured tool calling with a dataclass registry, and (3) flow-based frontend rendering that displays reasoning, tool calls, and content in the exact order produced by the LLM.

Architecture

The agent uses a hybrid streaming pattern to avoid SSE/reverse-proxy timeouts:

1. POST /chat-stream-job — Creates a background streaming job, returns job_id
2. Background coroutine (_run_stream_job) — Streams LLM response, executes tool calls, stores incremental chunks in Redis
3. GET /chat-stream-job/{job_id} — Frontend polls every 500ms for incremental chunks

This avoids long-lived HTTP connections that get killed by reverse proxies (Nginx, Cloudflare).

Backend: Streaming Job

Job Creation Endpoint

POST /api/v1/agent/chat-stream-job
Body: { message: str, case_id: str|null, history: list }
Response: { job_id: str, meta: dict }

• Build context messages from case data + history
• Prepare tool schemas from REGISTRY
• Generate unique job_id, store initial state in Redis (chatjob:{job_id})
• Launch asyncio.create_task(_run_stream_job(...)) — fire and forget
• Return job_id immediately

Background Coroutine

The _run_stream_job coroutine:

1. Calls llm.chat_stream(messages, tools=tools_schema) — async generator yielding events
2. For each event, pushes a chunk to Redis state:
   • reasoning — LLM thinking text (pushed incrementally)
   • content — LLM response text (pushed incrementally)
   • tool_call — Tool name + args (pushed once, status set to "running")
   • tool_result — Tool result (pushed once, status set to "done")
   • follow_up — Signals start of post-tool synthesis LLM call
3. On done event with tool_calls_executed:
   • Append assistant tool_call messages + tool result messages to conversation
   • Reset full_content and full_reasoning to empty strings
   • Push follow_up chunk
   • Add user instruction: "Based on the tool results above, provide your final analysis. Do NOT repeat your earlier assessment."
   • Stream follow-up LLM call, pushing reasoning/content chunks
4. On final done, set status: "done", clear chunks, persist to Redis

Polling Endpoint

GET /api/v1/agent/chat-stream-job/{job_id}
Response: { status, content, reasoning, chunks[], tool_calls[], usage }

• Read from Redis, return current state
• chunks array contains incremental events since last poll (frontend tracks lastChunkIdx)
• When status: "done", chunks is empty — frontend uses content/reasoning fields

Redis State Schema

{
  "status": "streaming" | "done" | "error",
  "content": "accumulated content text",
  "reasoning": "accumulated reasoning text",
  "chunks": [
    {"type": "reasoning", "text": "..."},
    {"type": "content", "text": "..."},
    {"type": "tool_call", "name": "...", "args": {...}},
    {"type": "tool_result", "name"   
       
  
      |
    N  N

TTL: 180 seconds (auto-cleanup after streaming completes).

Backend: Tool Registry

Tool Dataclass

@dataclass
class Tool:
    name: str
    description: str
    parameters: dict[str, Any]  # JSON schema
    fn: ToolFn  # async callable: (args, ctx) -> dict

    def to_openai_schema(self) -> dict[str, Any]:
        return {
            "type": "function",
            "function": {
                "name": self.name,
                "description": self.description,
                "parameters": self.parameters,
            },
        }

Registry Pattern

REGISTRY: dict[str, Tool] = {}

def _register(name, description, parameters, fn):
    REGISTRY[name] = Tool(name=name, description=description, parameters=parameters, fn=fn)

Critical: Tool Execution

The Tool object is NOT callable. Always invoke via .fn:

tool_func = REGISTRY.get(tool_name)
if tool_func:
    result = await tool_func.fn(tool_args, ctx)  # NOT tool_func(tool_args, ctx)

ToolContext

@dataclass
class ToolContext:
    case_id: str
    address: str
    chain: str
    seen_addresses: set[str]
    transactions: list
    bridge_events: list
    anomaly_flags: list

Frontend: Flow-Based Rendering

Core Concept

Events are appended to a flow container in the exact order received from the LLM, not grouped by type. This means reasoning, tool calls, and content interleave naturally.

Stream Message Structure

function createStreamMessage() {
  // Returns: { el, flowEl, badgeEl, fullContent, fullReasoning,
  //            activeReasoningEl, activeContentEl, activeToolName, activeToolStatusEl }
}

The flowEl is a single container. All event blocks are appended as children.

Loading Dots

• Created on send, appended to flowEl
• Never removed during streaming — moved to end of flow on each new event
• Use insertBefore(newEl, loadingDots) to insert events before dots
• Then appendChild(loadingDots) to move dots to end
• Hidden via display:none only on done, error, or timeout

Event Rendering Logic

Chunk Type	Action
reasoning	Create .ta-reasoning block if none active. Collapse previous content block. Append text.
content	Create .ta-msg-content.ta-typing block if none active. Collapse previous reasoning block. Render markdown.
tool_call	Close active content/reasoning. Create .ta-tool-call with "running..." status.
tool_result	Update matching tool_call status to "done".
follow_up	Reset fullContent/fullReasoning. Remove all old content/reasoning DOM blocks. Create synthesizing indicator.

Follow-Up Handling

When follow_up chunk arrives:

1. Reset stream.fullContent = '' and stream.fullReasoning = ''
2. Remove all .ta-msg-content and .ta-reasoning DOM elements from flow (tool calls remain)
3. Show synthesizing indicator (removed when new reasoning/content starts)

This prevents duplicate content — the first LLM call's content is discarded, replaced by the follow-up synthesis.

Fallback Sync

After processing chunks, check status.content vs stream.fullContent. If Redis has more text than what chunks provided (e.g., chunks were trimmed), create a new content block and render the full text. This handles edge cases where polling misses chunks.

CSS Classes

Class	Purpose
.ta-loading-dots	Animated pulse dots container
.ldot	Individual dot with staggered animation
.ta-reasoning	Reasoning block, auto-collapses when next phase starts
.ta-reasoning.collapsed	Truncated to one line with ellipsis
.ta-tool-call	Tool execution indicator with name + status
.tc-status.done	Green checkmark for completed tools
.ta-typing::after	Blinking cursor on active content block
.ta-token-badge	Token usage display (In/Out)

Common Pitfalls

1. 'Tool' object is not callable — Always use tool_func.fn(args, ctx), never tool_func(args, ctx)
2. Duplicate content after tool calls — Reset full_content/full_reasoning before follow-up LLM call, remove old DOM blocks
3. Reasoning appears at wrong position — Must remove first call's DOM elements when follow_up starts, otherwise they stay and push new content below them
4. Loading dots disappear — Never .remove() dots during streaming; use insertBefore + appendChild to keep them at flow end
5. SSE timeout behind reverse proxy — Use hybrid polling instead of long-lived SSE connections
6. Missing import — Ensure REGISTRY and ToolContext are imported in endpoint files

Resources

references/

• streaming-api.md — Detailed API endpoint specifications and Redis state schema
• tool-registry.md — Tool dataclass, registry pattern, and ToolContext specification

assets/

• streaming-frontend.js — Complete frontend streaming implementation (createStreamMessage, streamTaMessage, event handlers)
• streaming-backend.py — Complete backend implementation (_run_stream_job, job endpoints)