dispersl-sdk 0.1.4

Production Python SDK for Dispersl API

Dispersl Python SDK

Flexible workflow automation with plug-and-play agents for Python.

Install

pip install dispersl-sdk

Requirements

• Python >=3.9
• Async runtime (asyncio)

Quick Start

import asyncio

from dispersl import AgenticExecutor, AsyncDisperslClient


async def main() -> None:
    client = AsyncDisperslClient(
        base_url="https://api.dispersl.com/v1",
        api_key="YOUR_API_KEY",
        timeout_s=120.0,
        retry_attempts=3,
    )

    executor = AgenticExecutor(client)
    out = await executor.run_plan_and_agent_loop(
        prompt="Plan and implement a production webhook pipeline",
        agent_choices="auto",  # or ["architect", "security-auditor", "release-manager"]
        execution_sequence="sequential",  # or "parallel" for concurrent agent execution
    )

    print(out["task_id"], len(out["events"]), len(out["tool_results"]))
    await client.aclose()


asyncio.run(main())

SDK Capabilities

• Async HTTP client with retries, timeout support, and structured error mapping.
• Full endpoint coverage for agent/completion, agent/plan, and agent lifecycle APIs.
• NDJSON async stream parsing for long-running multi-agent responses.
• Sequential and parallel agent execution modes for flexible workflow orchestration.
• Handover extraction for handover_task, end_session, and finish_task semantics.
• Task continuation support via task_id for multi-phase workflows.
• MCP config and runtime tool registry support for custom tool execution.
• Agentic loop orchestration with continuation prompts and max-loop safety.

Client API Surface

Agent execution endpoints

Method	Request	Endpoint
agent_completion	dict[str, Any]	POST /agent/completion
agent_plan	dict[str, Any]	POST /agent/plan

Agent plan choices

agent_plan accepts:

• "auto" for automatic custom-agent selection
• list[str] for explicit custom agent name_id values

When "auto" is passed, the SDK normalizes the request payload to ["auto"] for API compatibility.

Execution modes

agent_plan and run_plan_and_agent_loop support execution sequence control:

• "sequential" (default): agents execute one after another
• "parallel": multiple agents execute concurrently when handed over from plan

For parallel execution, use parallel_concurrency to limit simultaneous agent runs.

Resource endpoints

Domain	Method	Endpoint
Agents	agents(limit=20, next_token=None)	GET /agents?limit&nextToken
Agents	agents_create(body)	POST /agents/create
Agents	agents_edit(agent_id, body)	POST /agents/edit/{id}
Agents	agent_by_id(agent_id)	GET /agents/{agent_id}
Agents	agent_delete(agent_id)	DELETE /agents/{agent_id}

Agent lifecycle fields and stats

agents() returns paginated data with:

• pagination: limit, hasNext, hasPrev, nextToken, prevToken
• per-agent lifecycle + stats fields such as stars_count, clone_count, and created_at

agent_by_id() returns richer lifecycle fields:

• public, active, updated_at

Create/edit request payload support:

• create: name, prompt, optional description, model, category, public
• edit: optional name, prompt, description, model, category, public, active

Execution Loop Behavior

AgenticExecutor.run_plan_and_agent_loop includes:

• plan-first execution and automatic transition to selected specialist agent
• execution sequence control (execution_sequence: "sequential" or "parallel", detected from plan metadata)
• parallel concurrency limit (parallel_concurrency for controlling simultaneous agent runs)
• handover propagation across turns
• end detection using end_session and finish_task
• task continuation support via task_id for multi-phase workflows
• optional custom tool runner (tool_executor)
• deterministic termination with max_loops
• returned payload:
  • task_id (use for task continuation)
  • events (parsed NDJSON chunks)
  • tool_results (custom tool execution outcomes)

Direct Single-Agent Completion Loop

Use run_agent_completion_loop to execute POST /agent/completion directly for one name_id until end_session.

executor = AgenticExecutor(client)
result = await executor.run_agent_completion_loop(
    name_id="architect",
    prompt="Review this backend design and produce a migration plan",
    max_loops=50,
)

Behavior:

• fixed agent identity across turns (name_id)
• no handover transition to other agents
• continues until end_session, no tool calls, or max_loops reached

Task Continuation

Pass task_id to resume work on an existing task and retain context across invocations:

# First run: initial execution
first_run = await executor.run_plan_and_agent_loop(
    prompt="Design the system architecture",
    agent_choices="auto",
)

# Continue after initial completion
result = await executor.run_plan_and_agent_loop(
    prompt="Now implement the core modules",
    agent_choices="auto",
    task_id=first_run["task_id"],
)

Core Models and Helpers

Component	Purpose
NDJSONChunk	typed stream chunk structure
ToolCall and ToolFunction	tool invocation envelope
PaginationInfo	cursor pagination metadata
MCPConfigLoader	load and merge MCP config
MCPRegistry	runtime MCP tool registration
parse_ndjson_stream	robust async NDJSON parser

Error Model

Error	Trigger
AuthenticationError	auth failures
NotFoundError	404
ConflictError	409
RateLimitError	429
ValidationError	invalid request payload
ServerError	upstream 5xx
RequestTimeoutError	timeout or deadline exceeded
StreamParseError	invalid stream payload
ToolExecutionError	tool callback failed
HandoverError	malformed handover contract

Development

python -m pip install -e ".[dev]"
python -m ruff check src tests
python -m ruff format --check src tests
python -m mypy src
python -m pytest -q
python -m build

Example Quickstarts

End-to-end quickstarts live in root examples/py:

• examples/py/plan_handover_loop.py
• examples/py/single_agent_completion.py
• examples/py/task_insight_progress.py
• examples/py/agent_lifecycle_and_stats.py
• examples/py/mcp_custom_agent_flow.py

Release

• Package name: dispersl-sdk
• Python release workflow: .github/workflows/release-python.yml
• Trigger: push tag py-v*