axc-agent-engine 2.0

Agent execution engine with Plan-Observe-Replan (POR) and plugin architecture

AxcAgentEngine

Agent execution engine with Plan-Observe-Replan, tool calling, and a plugin system

Quick Start · Features · Architecture · Plugins · Examples

🇨🇳 Chinese

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Most agent frameworks rely on a ReAct loop: think, call a tool, observe, repeat. As tasks grow more complex, plain ReAct tends to drift.

AxcAgentEngine adds POR (Plan-Observe-Replan) on top of ReAct: the agent produces a structured plan, schedules steps by dependency, observes outcomes, and replans when needed.

🚀 Quick Start

pip install axc-agent-engine

from axc_agent_engine import Engine, LLMConfig, PluginRegistry
from axc_agent_engine.plugins.builtin import BuiltinToolsPlugin

registry = PluginRegistry()
registry.register(BuiltinToolsPlugin)

engine = Engine(
    default_llm=LLMConfig(
        base_url="https://api.openai.com/v1",
        api_key="sk-xxx",
        model="gpt-4o",
    ),
    plugin_registry=registry,
)

agent = engine.load_agent("./agents/my_agent.yaml")

# Non-streaming
result = await agent.chat("Analyze last month's sales data")

# Streaming
async for event in agent.stream("Build a REST API for user management"):
    if event.type == "stream_delta":
        print(event.content, end="")
    elif event.type == "tool_call":
        print(f"\n[Tool: {event.tool_name}]")
    elif event.type == "plan_created":
        print(f"\n[Plan: {event.content}, {len(event.steps)} steps]")

✨ Features

• POR planning - structured plans, dependency scheduling, replanning; routes via auto / react_only / por_first
• ReAct executor - standard think / call / observe loop
• Plugin system - built-in spec registry, YAML-driven loading; optional capabilities live in plugins
• Tool protocol - every tool returns ToolOutput; read-only runs concurrent, write serial; safe function-name mapping
• Durable workflow - WorkflowRuntime + CheckpointStore + Agent resume API
• OpenAI compatible - provider protocol + OpenAI-compatible HTTP client and API subset
• Memory & knowledge - four-layer memory (KV, dedup, decay, graph hooks) + semantic chunking + vector/BM25 hybrid retrieval
• MCP - stdio, JSON-RPC HTTP, official SDK transports
• Human-in-the-loop - approval queue and ask_human tool
• Sidecar suite - multi-agent, simulation, eval, cost, failure mining, trace distillation

Full capability matrix

Capability	Implementation
ReAct loop	Executor
POR planning	auto / react_only / por_first
Durable workflow	WorkflowRuntime + CheckpointStore + Agent resume
Plugin system	spec registry + YAML-driven loading
LLM provider	provider protocol + OpenAI-compatible HTTP
Parallel tools	read concurrent, write serial
Tool output	enforced ToolOutput
Tool name mapping	provider-side model-safe mapping
Context compression	built-in compress plugin
Memory	four layers + KV fallback + dedup + decay + graph hooks
Knowledge	semantic chunking + embeddings + BM25/vector + optional rerank
MCP	stdio / JSON-RPC HTTP / official SDK
Human approval	approval queue + ask_human
Sidecar	multi-agent / simulation / eval / cost / failure mining / distillation
API server	OpenAI Chat Completions compatible subset

📦 Docs

Architecture	Engine and plugin boundaries
API	HTTP API subset notes
Plugin development	Build your own plugin
Security model	Capabilities, risk, workspace
Examples	7 end-to-end demos
Contributing / Security / Changelog / LICENSE	Apache-2.0

Agent YAML

name: "data-analyst"
description: "Data analysis assistant"

runtime:
  max_rounds: 50
  thinking: "auto"
  workspace: "/tmp/agent-workspace"
  allowed_capabilities:
    - "file_read"
    - "file_write"
    - "http_request"

system_prompt: |
  You are a data analysis assistant...

plugins:
  builtin_tools:
    enabled: true
    load: ["get_time", "file_read", "file_write", "http_request", "result_read"]
    defer: ["file_write", "http_request"]

  knowledge:
    enabled: true
    sources: ["./docs"]
    namespace: "default"
    embedding:
      base_url: "https://api.openai.com/v1"
      api_key: "sk-xxx"
      model: "text-embedding-3-small"

  memory:
    enabled: true
    namespace: "default"
    scope_keys: ["tenant_id", "user_id", "agent_name"]
    sensitive_policy: "redact"

  compress:
    enabled: true
    summary_after_rounds: 8

  risk_guard:
    enabled: true

Notes:

• Plugin registration is explicit host code via PluginRegistry. Agent YAML only enables and configures already-registered plugins.
• builtin_tools loads only get_time when load is omitted; other built-ins must be explicitly enabled.
• Tools with a non-empty capability are denied by default; list them in runtime.allowed_capabilities.
• File and command tools require runtime.workspace by default.
• LLM configuration is provided in code, not in Agent YAML.

Provider Configuration

Engine accepts an LLMConfig or any object implementing the full LLMProvider protocol (model, tool_name_mapping, chat, stream, ask, close).

from axc_agent_engine import ConcurrencyConfig, Engine, LLMConfig
from axc_agent_engine.tools.name_mapping import ToolNameMappingConfig

default_llm = LLMConfig(
    base_url="https://api.openai.com/v1",
    api_key="sk-xxx",
    model="gpt-4o",
    timeout=120,
    max_concurrent_requests=32,
    requests_per_minute=0,
    rate_limit_queue_timeout=10,
    tool_name_mapping=ToolNameMappingConfig(),
)

engine = Engine(
    default_llm=default_llm,
    concurrency=ConcurrencyConfig(
        max_engine_concurrent_runs=128,
        queue_timeout=30,
    ),
)

Multiple named providers can be registered on engine.provider_registry and selected by name:

engine.provider_registry.register("fast", fast_provider)
agent = engine.load_agent("./agents/my_agent.yaml", default_llm="fast")

Tool-name mapping is the provider's job. Internal tool names are encoded to model-safe function names before the LLM call and decoded before hooks and tool execution.

API

The HTTP API is an OpenAI Chat Completions compatible subset.

• POST /v1/chat/completions
• GET /v1/agents
• GET /v1/capabilities

Request-level tools and tool_choice are intentionally unsupported. Tools come from Agent YAML and plugins so the engine can enforce capabilities, risk metadata, plugin hooks, workspace policy, and audit events.

Clients should not assume full OpenAI parity; call /v1/capabilities first. See docs/API.md.

Built-in Plugins

Capabilities not required by a basic agent live in plugins. The default Engine.plugin_registry is empty; both built-in and custom plugins must be registered explicitly.

from axc_agent_engine import Engine, LLMConfig, PluginRegistry
from axc_agent_engine.plugins.builtin import BuiltinToolsPlugin, MemoryPlugin
from my_project.plugins import MyCustomPlugin

registry = PluginRegistry()
registry.register_many([BuiltinToolsPlugin, MemoryPlugin, MyCustomPlugin])
engine = Engine(default_llm=llm, plugin_registry=registry)

Plugin	Purpose
builtin_tools	Basic tools and artifact paging
knowledge	Ingestion, semantic chunking, hybrid retrieval, citations, rerank
memory	Memory, governance tools, sensitive-data policy, decay, TTL
output_format	Final output contracts, validation, repair, audit
graph	Entity/relation graph search and CRUD
skill	Load skills, run scripts through sandbox
mcp	MCP server tool loading and guarding
hooks	Declarative LLM/tool hook rules
compress	Context window management, summaries, recall, file restore
human_in_the_loop	Human approval and ask_human
risk_guard	Dynamic tool risk classification
safety	Input sanitization, prompt-injection checks, PII masking
tracing	Trace/span collection, audit mode, query tools
reflexion	End-of-round and end-of-run self-reflection
repetition_guard	Repeated tool / response / result detection
cost_statistics	Token and tool-call accounting
collaboration	Agent-to-agent calls and host orchestration entry
swarm	Lightweight parallel fan-out

Sidecar Capabilities

Sidecars live under axc_agent_engine.sidecar and are invoked explicitly by the host, not part of the agent's core execution path. See axc_agent_engine/sidecar/README.md.

Package	Purpose
sidecar.multi_agent	Multi-agent sessions, schedulers, stop conditions, shared context
sidecar.simulation	Structured simulation kernel
sidecar.eval	Evaluation cases, annotation stores, matcher, runner, reports
sidecar.agent_selector	Host-side agent routing and candidate scoring
sidecar.distiller	Distill rules, tool preferences, and skill candidates from traces
sidecar.failure_miner	Cluster failures and suggest remediation / eval coverage
sidecar.cost_optimizer	Cost estimation and optimization findings

from axc_agent_engine.sidecar import OrchestrationTaskService
from axc_agent_engine.storage.in_memory import InMemoryMessageBus

engine = Engine(default_llm=default_llm, message_bus=InMemoryMessageBus())
red = engine.load_agent("./agents/red.yaml")
blue = engine.load_agent("./agents/blue.yaml")

service = OrchestrationTaskService(
    agent_getter=engine.get_agent,
    agent_lister=engine.list_agents,
    dispatcher=engine._dispatcher,
    utility_llm=utility_llm,
)

task = await service.run_task(
    agent_names=[red.name, blue.name],
    mode="redblue",
    topic="Plugin marketplace security tabletop",
    max_rounds=3,
)

Runtime Flow

Load time

flowchart TD
    A["Application creates Engine"] --> B["Inject providers and services"]
    B --> C["Engine.load_agent(agent.yaml)"]
    C --> D["Parse AgentConfig"]
    D --> E["Build PluginContext"]
    E --> F["Load enabled plugins"]
    F --> G["Plugin.initialize()"]
    G --> H["Plugin.get_tools()"]
    H --> I["Register ToolDefinition"]
    I --> J["Create Agent"]

One agent run

flowchart TD
    A["User message"] --> B["Agent.chat() / Agent.stream()"]
    B --> C["ExecutionContext"]
    C --> D["Executor"]
    D --> E["MessageStore"]
    E --> F["Plugin hooks"]
    F --> G["LLM call"]
    G --> H["TransactionRouter"]
    H -->|final answer| I["done event"]
    H -->|tool calls| J["Tool pipeline"]
    J --> E
    H -->|plan| K["PORRunner"]
    K --> I

Plugin Development

from axc_agent_engine import BasePlugin, ToolDefinition, ToolOutput

class MyPlugin(BasePlugin):
    name = "my_plugin"
    display_name = "My Plugin"
    priority = 30
    phase = "core"

    def initialize(self, config: dict, ctx) -> None:
        super().initialize(config, ctx)
        self.api_url = config["api_url"]

    def get_tools(self) -> list[ToolDefinition]:
        return [ToolDefinition(
            name="my_tool",
            description="Does something useful",
            parameters={"type": "object", "properties": {"query": {"type": "string"}}},
            execute=self._execute,
        )]

    async def pre_tool_call(self, exec_ctx, tool_name, arguments):
        return True, arguments

    async def _execute(self, args: dict, context: dict) -> ToolOutput:
        return ToolOutput.text(f"Result for {args['query']}")

Plugins must inherit BasePlugin, and tools must be returned as ToolDefinition instances. ToolRegistry does not accept dicts.

plugins:
  my_plugin:
    enabled: true
    api_url: "http://localhost:5000"

CLI

export AXC_LLM_BASE_URL="https://api.openai.com/v1"
export AXC_LLM_API_KEY="sk-xxx"
export AXC_LLM_MODEL="gpt-4o"

axc chat --agent ./agents/my_agent.yaml
axc serve --agent ./agents/my_agent.yaml --port 8000
axc --log-level DEBUG --json-logs chat --agent ./agents/my_agent.yaml

CLI logging flags are global and must be placed before the subcommand.

Design Decisions

• Engine core = Executor + LLMCaller. Reads Agent YAML, calls LLM providers, runs the loop, emits events/results.
• Plugins are the runtime extension boundary. Knowledge, memory, graph, MCP, output repair, skills belong in plugins.
• Orchestration is sidecar. Multi-agent sessions, simulation, mode adapters are host-driven.
• Evaluation is sidecar. EvalRunner, stores, matchers, reports are host-driven test framework pieces.
• Registration is not loading. The spec registry is the full plugin table; agents load only YAML-enabled plugins.
• Tools come from plugins. The engine core embeds no business tools.
• Tools must return ToolOutput. Non-ToolOutput returns are rejected.
• Tool definitions must be ToolDefinition. No dicts.
• Business protocols stay out. Internal APIs, private DBs, company auth belong in private plugins.
• LLM config lives in code. Agent YAML describes runtime limits, capabilities, and plugins.
• The API is a subset. Request-level tools, tool_choice, n > 1 are rejected.

Tests

python3 -m pytest -q