Agent-to-Agent (A2A) Protocol

The A2A server exposes haiku.rag as a conversational agent using the Agent-to-Agent protocol. Unlike the MCP server which provides stateless tools, the A2A agent maintains conversation history and context across multiple turns.

Features

Conversational Context: Maintains full conversation history including tool calls and results
Multi-turn Dialogue: Supports follow-up questions with pronoun resolution ("he", "it", "that document")
Intelligent Search: Performs single or multiple searches depending on question complexity
Source Citations: Always includes sources with both titles and URIs
Full Document Retrieval: Can fetch complete documents on request
Document Discovery: Lists available documents to help users explore the knowledge base

Starting A2A Server

haiku-rag serve --a2a

Server options:

--a2a-host - Host to bind to (default: 127.0.0.1)
--a2a-port - Port to bind to (default: 8000)

Example:

haiku-rag serve --a2a --a2a-host 0.0.0.0 --a2a-port 8080

Requirements

A2A support requires the a2a extra:

uv pip install 'haiku.rag[a2a]'

Python Usage

from pathlib import Path
from haiku.rag.a2a import create_a2a_app
import uvicorn

# Create A2A app
app = create_a2a_app(Path("database.lancedb"))

# Run with uvicorn
uvicorn.run(app, host="127.0.0.1", port=8000)

This installs the fasta2a package and its dependencies.

Architecture

The A2A agent uses:

FastA2A: Python framework implementing the A2A protocol
Pydantic AI: Agent framework with tool support
In-Memory Storage: Context and message history storage (persists during server lifetime)
Conversation State: Full pydantic-ai message history serialized in A2A context

Message History

The agent stores the complete conversation state including:

User prompts
Agent responses
Tool calls and their arguments
Tool return values

This enables the agent to:

Reference previous searches
Understand pronouns and context
Maintain coherent multi-turn conversations

Context Management

Each conversation is identified by a context_id. All messages within the same context share conversation history. This allows the agent to:

Remember what was discussed
Track which documents were already found
Provide contextual follow-up answers

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