kattalai 0.4.5.9

Sirapana Ai Engine

PyRuntime — Python API Reference

PyRuntime is the Python-facing async interface to the soulengine Rust core. Exposed via a PyO3 extension module, it manages the full lifecycle of users, agents, topic threads, and message memory.

---

Contents

• Installation & Import
• Initialisation
• User Management
• Topic Threads
• Agent Management
• Agent Episodes
• Block Format
• Configuration Reference
  • inference_config.toml
  • agents_config.toml
  • App TOML Schema
• Typical Usage Pattern
• Error Handling
• Thread Safety

---

Installation & Import

from soulengine import PyRuntime

Windows: Ensure PyTorch's DLL directory is added before importing.

import torch, os
os.add_dll_directory(os.path.join(os.path.dirname(torch.__file__), "lib"))
from soulengine import PyRuntime

---

Initialisation

PyRuntime.create(bind="127.0.0.1:3077") → PyRuntime

Creates and returns a new PyRuntime instance. This is the only constructor — do not call PyRuntime() directly. Also launches the HTTP API server at the given bind address.

runtime = await PyRuntime.create(bind="127.0.0.1:3077")

This is a staticmethod and must be awaited. It initialises the internal Rust Runtime wrapped in Arc<RwLock<>>.

Reads two config files at startup — both must exist and be valid:

configs/
├── inference_config.toml
└── agents_config.toml

---

User Management

create_user(user_name: str) → str

Registers a new user identity and returns a unique user_id.

user_id = await runtime.create_user("kattalaiUser")

Parameter	Type	Description
user_name	str	Display name for the user

Returns: str — opaque user ID, required for insert_message.

---

Topic Threads

A topic thread is the shared conversation context that users and agents read from and write to.

create_topic_thread() → str

Creates a new topic thread and returns its topic_id.

topic_id = await runtime.create_topic_thread()

---

topic_history_len(topic_id: str) → int

Returns the total number of messages in the thread. Use this for polling — compare before and after insert_message to detect new activity.

length = await runtime.topic_history_len(topic_id)

---

iter_topic(topic_id: str, start_index: int) → str (JSON)

Returns a JSON-serialised list of messages from start_index onward.

raw     = await runtime.iter_topic(topic_id, cursor)
entries = json.loads(raw)   # list of dicts

Each entry contains:

Key	Type	Description
name	str	Source name (agent name or user handle)
role	str	"user" or "assistant"
content	str	Raw message text (may contain block markers)

Incremental polling pattern:

# Snapshot length before sending
cursor = await runtime.topic_history_len(topic_id)

# ... send message, wait ...

# Fetch only new entries
new_entries = json.loads(await runtime.iter_topic(topic_id, cursor))

---

insert_message(topic_id: str, user_id: str, message: str) → str

Inserts a user message into the thread. This triggers attached agents to begin processing.

await runtime.insert_message(topic_id, user_id, "Summarise my unread emails")

Parameter	Type	Description
topic_id	str	Target topic thread
user_id	str	Sender identity (from create_user)
message	str	Message text

Returns: str — confirmation from the Rust layer.
Raises: ValueError if the insert fails.

---

Agent Management

get_agent_list() → list[str]

Returns the list of agent names registered in the config.

agents = await runtime.get_agent_list()
# e.g. ["Researcher", "Coder", "DIA"]

---

deploy_agent(agent_name: str) → str

Deploys a named agent and returns its agent_id.

agent_id = await runtime.deploy_agent("Researcher")

Parameter	Type	Description
agent_name	str	Must match a name in get_agent_list()

Returns: str — opaque agent ID used in topic and episode calls.

---

add_agent_to_topic(topic_id: str, agent_id: str,agent_topic_backstory:str) → str

Attaches an agent to a topic thread. The agent begins responding to new messages inserted into that topic.

await runtime.add_agent_to_topic(topic_id, agent_id,agent_topic_backstory)

Only one agent should be active on a topic at a time. Call remove_agent_from_topic before switching agents.

Returns: str — confirmation. Raises: ValueError on failure.

---

remove_agent_from_topic(topic_id: str, agent_id: str) → str

Detaches an agent from a topic thread.

await runtime.remove_agent_from_topic(topic_id, agent_id)

Returns: str — confirmation. Raises: ValueError on failure.

---

is_agent_working_on_topic(topic_id: str, agent_id: str) → bool

Returns True if the agent is currently processing a message. Use this to drive a "thinking…" indicator.

thinking = await runtime.is_agent_working_on_topic(topic_id, agent_id)

---

Agent Episodes (Internal Memory)

An episode is the agent's internal working scratchpad for a topic — thoughts, tool calls, and intermediate outputs, separate from the shared topic history.

agent_episode_len(topic_id: str, agent_id: str) → int

Returns the number of entries in the agent's episode memory.

ep_len = await runtime.agent_episode_len(topic_id, agent_id)

---

iter_agent_episode(topic_id: str, agent_id: str, start_index: int) → str (JSON)

Returns a JSON-serialised list of the agent's internal episode entries from start_index onward. Same shape as iter_topic entries (name, role, content). The content field contains structured response blocks.

raw     = await runtime.iter_agent_episode(topic_id, agent_id, cursor)
entries = json.loads(raw)

---

Block Format

Agent content fields use fenced-block conventions that the UI layer parses:

```thoughts
Agent reasoning goes here...
```

```terminal
&app_handle command arg
-> result
```

```output
Final response to the user.
```

```validation
Cross-check notes.
```

```followup_context
Handoff state for the next turn.
```

Parser:

import re

def parse_se_content(content: str) -> list[tuple[str, str]]:
    pattern = re.compile(
        r'```(thoughts|terminal|output|validation|followup_context)\s*\n(.*?)```',
        re.DOTALL
    )
    matches = pattern.findall(content)
    if matches:
        return [(kind.strip(), body.strip()) for kind, body in matches if body.strip()]
    return [("output", content.strip())]  # plain-text fallback

---

Configuration Reference

Both files are read once inside PyRuntime.create(). Changes require a runtime restart — there is no hot-reload.

inference_config.toml

[[ollama_config]]
chat_api_url     = "http://localhost:11434/api/chat"
generate_api_url = "http://localhost:11434/api/generate"
temperature      = 0.1

[[gemini_config]]
api_key     = "YOUR_GEMINI_API_KEY"
temperature = 0.1

[[huggingface_config]]
api_key        = "YOUR_HF_API_KEY"
max_new_tokens = 8000
temperature    = 0.1

[[sarvam_config]]
api_key          = "YOUR_SARVAM_API_KEY"
max_new_tokens   = 8000
temperature      = 0.1
reasoning_effort = "high"

inference_provider	Notes
ollama	Local inference, no API key. Requires Ollama on localhost:11434.
gemini	Google Gemini API. Requires api_key.
huggingface	HuggingFace Inference Router. Requires api_key.
sarvam	Sarvam AI API. Requires api_key.

Minimum local-only config:

[[ollama_config]]
chat_api_url     = "http://localhost:11434/api/chat"
generate_api_url = "http://localhost:11434/api/generate"
temperature      = 0.1

---

agents_config.toml

[[agent_config]]
agent_name = "DIA"
agent_goal = "To assist user with their queries"
backstory  = "You are an AI assistant"

reasoning_model = { inference_provider = "ollama", model_id = "qwen3:4b" }
nlp_model       = { inference_provider = "ollama", model_id = "qwen3:0.6b" }
default_apps    = ["clock_app"]

Field	Type	Description
agent_name	str	Name passed to deploy_agent() and returned by get_agent_list()
agent_goal	str	Injected into the agent's system prompt as its objective
backstory	str	Additional persona context in the system prompt
reasoning_model	inline table	Provider + model ID for the main thinking loop
nlp_model	inline table	Provider + model ID for fast NLP/routing decisions
default_apps	list[str]	App handles loaded on deploy — must match app_handle_name in app TOMLs

Mixing providers per agent:

reasoning_model = { inference_provider = "gemini", model_id = "gemini-2.5-flash" }
nlp_model       = { inference_provider = "ollama", model_id = "qwen3:0.6b" }

---

App TOML Schema

The app_handle_name in an app's TOML is the string referenced in default_apps.

app_name          = "Clock App"
app_path          = "./apps/core_apps/clock_app/clock_app.py"
app_start_command = "python"
app_start_args    = "./apps/core_apps/clock_app/clock_app.py"
app_handle_name   = "clock_app"
app_launch_mode   = "REPL"

app_usage_guideline = """
Use to get the current time or set timed reminders.
"""

[[app_command_signatures]]
command  = "get_time"
consumes = []
produces = ["current_time"]
action   = "read"

Built-in handle names (available after kattalai-setup):

Handle	Description
clock_app	Current time and alarms
notes_app	Note CRUD and search
grep_app	File and stdin search
calculator_app	Arithmetic and variables
stock_tracker	Live quotes via yfinance
webpage_reader	Web extraction via Playwright

---

Typical Usage Pattern

import asyncio, json
from soulengine import PyRuntime

async def main():
    # 1. Bootstrap
    runtime  = await PyRuntime.create()
    user_id  = await runtime.create_user("alice")
    topic_id = await runtime.create_topic_thread()

    # 2. Deploy and attach an agent
    agents   = await runtime.get_agent_list()
    agent_id = await runtime.deploy_agent(agents[0])
    agent_topic_backstory = "You are the first agent for this topic"
    await runtime.add_agent_to_topic(topic_id, agent_id)

    # 3. Snapshot cursor, send message
    cursor = await runtime.topic_history_len(topic_id)
    await runtime.insert_message(topic_id, user_id, "Hello!")

    # 4. Poll until the agent finishes
    while await runtime.is_agent_working_on_topic(topic_id, agent_id):
        await asyncio.sleep(0.5)

    # 5. Fetch new messages
    entries = json.loads(await runtime.iter_topic(topic_id, cursor))
    for entry in entries:
        if entry["role"] != "user":
            print(f"{entry['name']}: {entry['content']}")

asyncio.run(main())

---

Error Handling

All PyRuntime methods raise ValueError on internal Rust errors.

try:
    await runtime.insert_message(topic_id, user_id, text)
except ValueError as e:
    print(f"SoulEngine error: {e}")

---

Thread Safety

The Rust runtime uses Arc<RwLock<Runtime>> internally.

Operations	Lock type
create, deploy_agent, create_user, create_topic_thread	Write lock
insert_message, iter_topic, topic_history_len, all reads	Read lock

All methods are async — use with asyncio or an async framework like textual. Do not share a PyRuntime instance across threads without an async executor.