Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Overview
The Interactive Runtime provides a unified core runtime that powers all interactive modes in PraisonAI:
praisonai chat - Interactive chat modepraisonai chat - Interactive terminal chat modepraisonai tui launch - Full-screen TUI mode
All modes share the same:
- Session storage and continuation
- Tool dispatch and loading
- Permission/approval semantics
- Event-based architecture
Installation
Quick Start
import asyncio
from praisonai.cli.features import InteractiveRuntime, RuntimeConfig
async def main():
# Configure runtime
config = RuntimeConfig(
workspace="./my_project",
lsp_enabled=True,
acp_enabled=True,
approval_mode="auto",
trace_enabled=True
)
# Create and start runtime
runtime = InteractiveRuntime(config)
status = await runtime.start()
print(f"LSP ready: {runtime.lsp_ready}")
print(f"ACP ready: {runtime.acp_ready}")
print(f"Read-only: {runtime.read_only}")
# Use runtime for operations...
await runtime.stop()
asyncio.run(main())
Configuration
RuntimeConfig
| Parameter | Type | Default | Description |
|---|
workspace | str | ”.” | Workspace root directory |
lsp_enabled | bool | True | Enable LSP code intelligence |
acp_enabled | bool | True | Enable ACP action orchestration |
approval_mode | str | ”manual” | Approval mode: manual, auto, scoped |
trace_enabled | bool | False | Enable trace logging |
trace_file | str | None | Path to save trace file |
json_output | bool | False | Output JSON format |
timeout | float | 60.0 | Operation timeout in seconds |
model | str | None | LLM model to use |
verbose | bool | False | Verbose output |
Runtime Status
status = runtime.get_status()
{
"started": true,
"workspace": "/path/to/project",
"lsp": {
"enabled": true,
"status": "ready",
"ready": true,
"error": null
},
"acp": {
"enabled": true,
"status": "ready",
"ready": true,
"error": null
},
"read_only": false,
"approval_mode": "auto"
}
Subsystem States
| Status | Description |
|---|
not_started | Subsystem not initialized |
starting | Subsystem is starting |
ready | Subsystem is ready for use |
failed | Subsystem failed to start |
stopped | Subsystem has been stopped |
LSP Operations
When LSP is ready, you can use code intelligence:
# Get symbols in a file
symbols = await runtime.lsp_get_symbols("main.py")
# Get definition location
definitions = await runtime.lsp_get_definition("main.py", line=10, col=5)
# Get references
references = await runtime.lsp_get_references("main.py", line=10, col=5)
# Get diagnostics
diagnostics = await runtime.lsp_get_diagnostics("main.py")
ACP Operations
When ACP is ready, you can create and apply action plans:
# Create a plan
plan = await runtime.acp_create_plan("Create a new file")
# Apply a plan
result = await runtime.acp_apply_plan(plan, auto_approve=True)
Tracing
Enable tracing to capture all operations:
config = RuntimeConfig(
workspace="./project",
trace_enabled=True,
trace_file="trace.json"
)
runtime = InteractiveRuntime(config)
await runtime.start()
# ... perform operations ...
# Save trace
runtime.save_trace("my_trace.json")
# Get trace object
trace = runtime.get_trace()
print(trace.to_dict())
Graceful Degradation
The runtime handles subsystem failures gracefully:
- LSP fails: Code intelligence falls back to regex-based extraction
- ACP fails: Runtime enters read-only mode (no file modifications)
if runtime.read_only:
print("Warning: ACP unavailable, read-only mode")
CLI Integration
The runtime integrates with CLI flags:
# Enable LSP
praisonai chat --lsp
# Enable ACP with auto-approval
praisonai chat --acp --approval auto
# Enable tracing
praisonai chat --trace --trace-file session.json
Operational Notes
- Subsystems start in parallel for faster initialization
- LSP client is lazy-loaded only when enabled
- ACP session is lightweight (in-process)
Dependencies
pylsp (optional) - For Python LSP supportpyright (optional) - Alternative Python LSP
Production Caveats
- LSP startup may take a few seconds for large workspaces
- Trace files can grow large for long sessions
- ACP session is in-memory; external storage needed for persistence
InteractiveCore (Unified Runtime)
The new InteractiveCore provides a unified runtime for all interactive modes:
from praisonai.cli.interactive import InteractiveCore, InteractiveConfig
# Create config
config = InteractiveConfig(
model="gpt-4o-mini",
continue_session=True, # Resume last session
files=["README.md"], # Attach files
)
# Create core
core = InteractiveCore(config=config)
# Create or continue session
if config.continue_session:
session_id = core.continue_session()
else:
session_id = core.create_session(title="My Session")
# Execute prompt
import asyncio
response = asyncio.run(core.prompt("Hello!"))
print(response)
CLI Flags
| Flag | Short | Description |
|---|
--model | -m | LLM model to use |
--session | -s | Session ID to resume |
--continue | -c | Continue last session |
--file | -f | Attach file(s) to prompt |
--workspace | -w | Workspace directory |
--verbose | -v | Verbose output |
--memory | | Enable memory |
Session Management
# List sessions
praisonai session list
# Export session
praisonai session export <session_id> --output session.json
# Import session
praisonai session import session.json
# Continue last session
praisonai chat --continue "What was my last question?"
Approval Modes
The runtime supports three approval modes:
| Mode | Description |
|---|
prompt | Ask user for each action (default) |
auto | Auto-approve all actions |
reject | Reject all actions requiring approval |
config = InteractiveConfig(approval_mode="auto")
