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# tRPC-Agent-Python
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**A production-grade Agent framework deeply integrated with the Python AI ecosystem.**
tRPC-Agent-Python provides an end-to-end foundation for agent building, orchestration, tool integration, session and long-term memory, service deployment, and observability, so you can ship reliable and extensible AI applications faster.
## Why Choose tRPC-Agent-Python
- **Multi-paradigm agent orchestration**: Built-in orchestration supports `ChainAgent` / `ParallelAgent` / `CycleAgent` / `TransferAgent`, with `GraphAgent` for graph-based orchestration.
- **Graph orchestration capability (`GraphAgent`)**: Use DSL to orchestrate `Agent` / `Tool` / `MCP` / `Knowledge` / `CodeExecutor` in one unified flow.
- **Efficient integration with Python AI ecosystems**: Agent ecosystem extensions (`claude-agent-sdk` / `LangGraph`, etc.) / Tool ecosystem extensions (`mcp`, etc.) / Knowledge ecosystem extensions (`LangChain`, etc.) / Model ecosystem extensions (`LiteLLM`, etc.) / Memory ecosystem extensions (`Mem0`, `Mempalace`, etc.).
- **Agent ecosystem extensions**: Supports `LangGraphAgent` / `ClaudeAgent` / `TeamAgent` (Agno-Like).
- **Tool ecosystem extensions**: `FunctionTool` / File tools / `MCPToolset` / LangChain Tool / Agent-as-Tool.
- **Complete memory capability (`Session` / `Memory`)**: `Session` manages messages and state within a single session, while `Memory` manages cross-session long-term memory and personalization. Persistence supports `InMemory` / `Redis` / `SQL`; `Memory` also supports `Mem0`、`Mempalace`.
- **Production-grade knowledge capability**: Built on LangChain components with first-class RAG support.
- **CodeExecutor extension capability**: Supports local / container executors for code execution and task grounding.
- **Skills extension capability**: Supports `SKILL.md`-based skill systems for reusable capabilities and dynamic tooling.
- **Connect to multiple LLM providers**: OpenAI-like / Anthropic / LiteLLM routing.
- **Serving and observability**: Expose HTTP / A2A / AG-UI services through FastAPI, with built-in OpenTelemetry tracing.
- **trpc-claw (OpenClaw-like personal agent)**: Built on [nanobot](https://github.com/HKUDS/nanobot), tRPC-Agent ships trpc-claw so you can quickly build an OpenClaw-like personal AI agent with Telegram, WeCom, and other channel support.
## Use Cases
- Intelligent customer support and knowledge QA (RAG + session memory)
- Code generation and engineering automation (`ClaudeAgent`)
- Code execution and automated task grounding (`CodeExecutor`)
- Agent Skills for reusable capabilities
- Multi-role collaborative workflows (`TeamAgent` / multi-agent)
- Cross-protocol agent service integration (`A2A` / `AG-UI`)
- MCP tool protocol integration and tool ecosystem expansion
- Unified gateway access and protocol conversion
- Component-based workflow orchestration using `GraphAgent`
- Reusing existing LangGraph workflows in this runtime
- Build an OpenClaw-like personal AI agent quickly with trpc-claw
## Table of Contents
- [tRPC-Agent-Python](#trpc-agent-python)
- [Why Choose tRPC-Agent-Python](#why-choose-trpc-agent-python)
- [Use Cases](#use-cases)
- [Table of Contents](#table-of-contents)
- [Quick Start](#quick-start)
- [trpc-claw Usage](#trpc-claw-usage)
- [Documentation](#documentation)
- [Examples](#examples)
- [1. Getting Started and Basic Agents](#1-getting-started-and-basic-agents)
- [2. Preset Multi-Agent Orchestration](#2-preset-multi-agent-orchestration)
- [3. Team Collaboration](#3-team-collaboration)
- [4. Graph Orchestration](#4-graph-orchestration)
- [5. Agent Ecosystem Extensions](#5-agent-ecosystem-extensions)
- [6. Tools and MCP](#6-tools-and-mcp)
- [7. Skills](#7-skills)
- [8. CodeExecutor](#8-codeexecutor)
- [9. Session, Memory, and Knowledge](#9-session-memory-and-knowledge)
- [10. Serving and Protocols](#10-serving-and-protocols)
- [11. Filters and Execution Control](#11-filters-and-execution-control)
- [12. Advanced LlmAgent Capabilities](#12-advanced-llmagent-capabilities)
- [13. LlmAgent Tool Calling and Interaction](#13-llmagent-tool-calling-and-interaction)
- [Architecture Overview](#architecture-overview)
- [Contributing](#contributing)
- [Acknowledgements](#acknowledgements)
## Quick Start
### Prerequisites
- Python3.10+ (Python3.12 recommended)
- Available model API key (OpenAI-like / Anthropic, or route via LiteLLM)
### Installation
#### Install with pip
```bash
pip install trpc-agent-py
```
Install optional capabilities as needed:
```bash
pip install "trpc-agent-py[graph,a2a,ag-ui,knowledge,knowledge-hf,agent-claude,mem0,mempalace,langfuse]"
```
#### Install with uv
[uv](https://docs.astral.sh/uv/) provides fast, reproducible installs:
```bash
# Install uv if it is not already available
python -m pip install uv
# Install the published package in a virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv pip install trpc-agent-py
# Install optional capabilities as needed
# uv pip install "trpc-agent-py[graph,a2a,ag-ui,knowledge,knowledge-hf]"
```
For a project managed by uv:
```bash
# Core package only
uv add trpc-agent-py
# Or: core + optional extras (one command is enough)
# uv add "trpc-agent-py[graph,a2a,ag-ui,knowledge,knowledge-hf]"
```
When developing this repository:
```bash
uv sync --extra dev
./build.sh # uv (default; installed automatically if missing)
# ./build.sh "[graph]" # uv by default, extras only
# ./build.sh pip # use pip instead
# ./build.sh uv "[dev,graph]" # explicit uv with selected extras
# Install dependencies required by feature-specific test suites
./build.sh uv "[dev,graph,ag-ui,agent-claude,a2a]"
uv run python -c "from trpc_agent_sdk.version import __version__; print(__version__)"
```
On macOS, use the same cross-platform build script inside a virtual environment:
```bash
./build.sh # creates .venv automatically; default uv
#./build.sh "[dev,graph,knowledge]" # uv by default with selected extras
#./build.sh pip "[dev]" # use pip
#./build.sh uv "[dev,graph,knowledge]" # explicit uv
source .venv/bin/activate # activate it in the current shell
```
### Develop Weather Agent
```python
import asyncio
import os
import uuid
from trpc_agent_sdk.agents import LlmAgent
from trpc_agent_sdk.models import OpenAIModel
from trpc_agent_sdk.runners import Runner
from trpc_agent_sdk.sessions import InMemorySessionService
from trpc_agent_sdk.tools import FunctionTool
from trpc_agent_sdk.types import Content, Part
async def get_weather_report(city: str) -> dict:
return {"city": city, "temperature": "25°C", "condition": "Sunny", "humidity": "60%"}
async def main():
model = OpenAIModel(
model_name=os.environ["TRPC_AGENT_MODEL_NAME"],
api_key=os.environ["TRPC_AGENT_API_KEY"],
base_url=os.environ.get("TRPC_AGENT_BASE_URL", ""),
)
agent = LlmAgent(
name="assistant",
description="A helpful assistant",
model=model,
instruction="You are a helpful assistant.",
tools=[FunctionTool(get_weather_report)],
)
session_service = InMemorySessionService()
runner = Runner(app_name="demo_app", agent=agent, session_service=session_service)
user_id = "demo_user"
session_id = str(uuid.uuid4())
user_content = Content(parts=[Part.from_text(text="What's the weather in Beijing?")])
async for event in runner.run_async(user_id=user_id, session_id=session_id, new_message=user_content):
if not event.content or not event.content.parts:
continue
for part in event.content.parts:
if part.text and event.partial:
print(part.text, end="", flush=True)
elif part.function_call:
print(f"\n🔧 [{part.function_call.name}({part.function_call.args})]", flush=True)
elif part.function_response:
print(f"📊 [{part.function_response.response}]", flush=True)
print()
if __name__ == "__main__":
asyncio.run(main())
```
### Run the Agent
```bash
export TRPC_AGENT_API_KEY=xxx
export TRPC_AGENT_BASE_URL=xxxx
export TRPC_AGENT_MODEL_NAME=xxxx
python quickstart.py
```
## trpc-claw Usage
tRPC-Agent ships trpc-claw (`trpc_agent_cmd openclaw`), built on [nanobot](https://github.com/HKUDS/nanobot), so you can quickly build an OpenClaw-like personal AI agent. Start it with a single command and it runs 24/7 — chat through Telegram, WeCom, or any other IM, or use it locally via CLI / UI.
For full configuration and advanced features, see: [openclaw.md](./docs/mkdocs/en/openclaw.md)
### Quick Start
**1. Generate config**
```bash
mkdir -p ~/.trpc_claw
trpc_agent_cmd openclaw conf_temp > ~/.trpc_claw/config.yaml
```
**2. Set environment variables**
```bash
export TRPC_AGENT_API_KEY=your_api_key
export TRPC_AGENT_BASE_URL=your_base_url
export TRPC_AGENT_MODEL_NAME=your_model
```
**3. Run locally**
```bash
# Force local CLI mode
trpc_agent_cmd openclaw chat -c ~/.trpc_claw/config.yaml
# Local UI
trpc_agent_cmd openclaw ui -c ~/.trpc_claw/config.yaml
```
**4. Connect WeCom / Telegram**
Enable the channel in `config.yaml`, then launch with `run`:
```yaml
channels:
wecom:
enabled: true
bot_id: ${WECOM_BOT_ID}
secret: ${WECOM_BOT_SECRET}
# or Telegram:
# telegram:
# enabled: true
# token: ${TELEGRAM_BOT_TOKEN}
```
```bash
trpc_agent_cmd openclaw run -c ~/.trpc_claw/config.yaml
```
If no channel is available, `run` automatically falls back to local CLI for easy debugging.
## Documentation
- See directory: [`docs/mkdocs/en`](./docs/mkdocs/en)
## Examples
All examples in the `examples` directory are runnable. The groups below organize recommended starting points by capability, with short guidance so you can quickly pick what to read first for your scenario.
### 1. Getting Started and Basic Agents
Recommended first:
- [examples/quickstart](./examples/quickstart/README.md) - Minimal runnable demo
- [examples/llmagent](./examples/llmagent/README.md) - Basic `LlmAgent` usage
- [examples/litellm](./examples/litellm/README.md) - LiteLLM backend routing with `LiteLLMModel`
- [examples/llmagent_with_custom_prompt](./examples/llmagent_with_custom_prompt/README.md) - Custom prompts
- [examples/llmagent_with_schema](./examples/llmagent_with_schema/README.md) - Structured outputs
Related docs: [llm_agent.md](./docs/mkdocs/en/llm_agent.md) / [model.md](./docs/mkdocs/en/model.md)
This group helps you:
- Run a full end-to-end path from user input to tool call to model output
- Understand how to consume `function_call` / `function_response` events in streaming output
- Learn baseline patterns for prompts and structured responses
Start with this snippet (`Runner` + streaming events):
```python
runner = Runner(app_name=app_name, agent=root_agent, session_service=session_service)
async for event in runner.run_async(user_id=user_id, session_id=current_session_id, new_message=user_content):
if event.partial and event.content:
...
```
### 2. Preset Multi-Agent Orchestration
Recommended first:
- [examples/multi_agent_chain](./examples/multi_agent_chain/README.md) - `ChainAgent`
- [examples/multi_agent_parallel](./examples/multi_agent_parallel/README.md) - `ParallelAgent`
- [examples/multi_agent_cycle](./examples/multi_agent_cycle/README.md) - `CycleAgent`
- [examples/transfer_agent](./examples/transfer_agent/README.md) - `TransferAgent` handoff (`run_agent_v1.py` for `[a2a-v1]`, `run_agent.py` for `[a2a]`)
- [examples/multi_agent_subagent](./examples/multi_agent_subagent/README.md) - Sub-agent delegation
- [examples/multi_agent_compose](./examples/multi_agent_compose/README.md) - Composed orchestration
- [examples/multi_agent_start_from_last](./examples/multi_agent_start_from_last/README.md) - Resume from last agent state
Related docs: [multi_agents.md](./docs/mkdocs/en/multi_agents.md)
This group helps you:
- Understand the role differences among Chain / Parallel / Cycle / Transfer
- Pick serial, parallel, loop, or handoff orchestration by task shape
- Learn how to resume and compose flows from existing outputs
Start with this snippet (`ChainAgent`):
```python
pipeline = ChainAgent(
name="document_processor",
sub_agents=[extractor_agent, translator_agent],
)
```
### 3. Team Collaboration
Recommended first:
- [examples/team](./examples/team/README.md) - Team coordination mode
- [examples/team_parallel_execution](./examples/team_parallel_execution/README.md) - Team parallel execution
- [examples/team_with_skill](./examples/team_with_skill/README.md) - Team + Skills
- [examples/team_human_in_the_loop](./examples/team_human_in_the_loop/README.md) - Team with human-in-the-loop
- [examples/team_as_sub_agent](./examples/team_as_sub_agent/README.md) - Team as a sub-agent
- [examples/team_member_message_filter](./examples/team_member_message_filter/README.md) - Team member message filtering
- [examples/team_member_agent_claude](./examples/team_member_agent_claude/README.md) - Team member using `ClaudeAgent`
- [examples/team_member_agent_langgraph](./examples/team_member_agent_langgraph/README.md) - Team member using `LangGraphAgent`
- [examples/team_member_agent_team](./examples/team_member_agent_team/README.md) - Nested Team members
- [examples/team_with_cancel](./examples/team_with_cancel/README.md) - Team task cancellation
Related docs: [team.md](./docs/mkdocs/en/team.md) / [human_in_the_loop.md](./docs/mkdocs/en/human_in_the_loop.md) / [cancel.md](./docs/mkdocs/en/cancel.md)
This group helps you:
- Understand the Leader / Member collaboration model in Team
- Combine Skills, sub-teams, and external agents in one workflow
- Cover practical concerns like filtering, human approval, and cancellation
Start with this snippet (`TeamAgent`):
```python
content_team = TeamAgent(
name="content_team",
model=model,
members=[researcher, writer],
instruction=LEADER_INSTRUCTION,
share_member_interactions=True,
)
```
### 4. Graph Orchestration
Recommended first:
- [examples/graph](./examples/graph/README.md) - `GraphAgent` with function / llm / agent / code / mcp / knowledge nodes
- [examples/graph_multi_turns](./examples/graph_multi_turns/README.md) - Multi-turn graph execution
- [examples/graph_with_interrupt](./examples/graph_with_interrupt/README.md) - Graph execution interruption
- [examples/dsl](./examples/dsl/README.md) - DSL orchestration basics
- [examples/dsl/classifier_mcp](./examples/dsl/classifier_mcp/README.md) - DSL + MCP classification routing
Related docs: [graph.md](./docs/mkdocs/en/graph.md) / [dsl.md](./docs/mkdocs/en/dsl.md)
This group helps you:
- Build explicit, controllable workflows (branching, merging, interruption, resuming)
- Mix `Agent` / `Tool` / `MCP` / `CodeExecutor` / `Knowledge` in a single graph
- Use DSL for workflows that stay readable and maintainable
Start with this snippet (conditional routing):
```python
graph.add_conditional_edges(
"decide",
create_route_choice(set(path_map.keys())),
path_map,
)
```
### 5. Agent Ecosystem Extensions
Recommended first:
- [examples/langgraph_agent](./examples/langgraph_agent/README.md) - Integrate pre-built and compiled LangGraph workflows
- [examples/langgraph_agent_with_cancel](./examples/langgraph_agent_with_cancel/README.md) - `LangGraphAgent` cancellation
- [examples/langgraph_agent_with_HITL](./examples/langgraph_agent_with_HITL/README.md) - `LangGraphAgent` human-in-the-loop
- [examples/claude_agent](./examples/claude_agent/README.md) - `ClaudeAgent` basics
- [examples/claude_agent_with_streaming_tool](./examples/claude_agent_with_streaming_tool/README.md) - `ClaudeAgent` streaming tools
- [examples/claude_agent_with_skills](./examples/claude_agent_with_skills/README.md) - `ClaudeAgent` + Skills
- [examples/claude_agent_with_code_writer](./examples/claude_agent_with_code_writer/README.md) - `ClaudeAgent` for code generation
- [examples/claude_agent_with_travel_planner](./examples/claude_agent_with_travel_planner/README.md) - `ClaudeAgent` task planning
- [examples/claude_agent_with_cancel](./examples/claude_agent_with_cancel/README.md) - `ClaudeAgent` cancellation
Related docs: [langgraph_agent.md](./docs/mkdocs/en/langgraph_agent.md) / [claude_agent.md](./docs/mkdocs/en/claude_agent.md) / [human_in_the_loop.md](./docs/mkdocs/en/human_in_the_loop.md) / [cancel.md](./docs/mkdocs/en/cancel.md)
This group helps you:
- Reuse existing LangGraph assets in the current runtime with `LangGraphAgent`
- Use `ClaudeAgent` for code generation, engineering automation, and streaming tools
- Cover production-ready patterns like human-in-the-loop and cancellation
Start with this snippet (`ClaudeAgent`):
```python
root_agent = ClaudeAgent(
name="claude_weather_agent",
model=_create_model(),
instruction=INSTRUCTION,
tools=[FunctionTool(get_weather)],
enable_session=True,
)
```
### 6. Tools and MCP
Recommended first:
- [examples/function_tools](./examples/function_tools/README.md) - `FunctionTool`
- [examples/file_tools](./examples/file_tools/README.md) - File tools
- [examples/tools](./examples/tools/README.md) - Basic tool combinations
- [examples/toolsets](./examples/toolsets/README.md) - ToolSet composition
- [examples/streaming_tools](./examples/streaming_tools/README.md) - Streaming tool calling
- [examples/mcp_tools](./examples/mcp_tools/README.md) - `MCPToolset` (`stdio` / `sse` / `streamable-http`)
- [examples/langchain_tools](./examples/langchain_tools/README.md) - LangChain tools integration
- [examples/agent_tools](./examples/agent_tools/README.md) - Agent as a Tool
Related docs: [tool.md](./docs/mkdocs/en/tool.md)
This group helps you:
- Cover the full tool access path from function tools to MCP to composed toolsets
- Learn advanced modes such as streaming tools and Agent-as-Tool
- Reuse existing tool implementations in multi-agent scenarios
Start with this snippet (`MCPToolset`):
```python
class StdioMCPToolset(MCPToolset):
def __init__(self):
super().__init__()
self._connection_params = StdioConnectionParams(
server_params=McpStdioServerParameters(command="python3", args=["mcp_server.py"]),
timeout=5,
)
```
### 7. Skills
Recommended first:
- [examples/skills](./examples/skills/README.md) - `SkillToolSet` basics
- [examples/skills_with_container](./examples/skills_with_container/README.md) - Skills in containers
- [examples/skills_with_dynamic_tools](./examples/skills_with_dynamic_tools/README.md) - Dynamic tool skills
Related docs: [skill.md](./docs/mkdocs/en/skill.md)
This group helps you:
- Package reusable capabilities into Skills
- Support scenario-based dynamic tool composition
- Build reusable business skill modules
Start with this snippet (`SkillToolSet`):
```python
workspace_runtime = create_local_workspace_runtime()
repository = create_default_skill_repository(skill_paths, workspace_runtime=workspace_runtime)
skill_tool_set = SkillToolSet(repository=repository, run_tool_kwargs=tool_kwargs)
```
### 8. CodeExecutor
Recommended first:
- [examples/code_executors](./examples/code_executors/README.md) - `UnsafeLocalCodeExecutor` / `ContainerCodeExecutor`
Related docs: [code_executor.md](./docs/mkdocs/en/code_executor.md)
This group helps you:
- Choose local or containerized executors by runtime constraints
- Let agents execute code and ground tasks within controlled boundaries
- Combine with Skills/Tools for planning-and-execution loops
### 9. Session, Memory, and Knowledge
Recommended first:
- Session: [examples/session_service_with_in_memory](./examples/session_service_with_in_memory/README.md) / [examples/session_service_with_redis](./examples/session_service_with_redis/README.md) / [examples/session_service_with_sql](./examples/session_service_with_sql/README.md) / [examples/session_summarizer](./examples/session_summarizer/README.md) / [examples/session_state](./examples/session_state/README.md)
- Memory: [examples/memory_service_with_in_memory](./examples/memory_service_with_in_memory/README.md) / [examples/memory_service_with_redis](./examples/memory_service_with_redis/README.md) / [examples/memory_service_with_sql](./examples/memory_service_with_sql/README.md) / [examples/memory_service_with_mem0](./examples/memory_service_with_mem0/README.md) / [examples/memory_service_with_mempalace](./examples/memory_service_with_mempalace/README.md)
- Knowledge: [examples/knowledge_with_documentloader](./examples/knowledge_with_documentloader/README.md) / [examples/knowledge_with_vectorstore](./examples/knowledge_with_vectorstore/README.md) / [examples/knowledge_with_rag_agent](./examples/knowledge_with_rag_agent/README.md) / [examples/knowledge_with_searchtool_rag_agent](./examples/knowledge_with_searchtool_rag_agent/README.md) / [examples/knowledge_with_prompt_template](./examples/knowledge_with_prompt_template/README.md) / [examples/knowledge_with_custom_components](./examples/knowledge_with_custom_components/README.md)
Related docs:
- Session: [session.md](./docs/mkdocs/en/session.md) / [session_redis.md](./docs/mkdocs/en/session_redis.md) / [session_sql.md](./docs/mkdocs/en/session_sql.md) / [session_summary.md](./docs/mkdocs/en/session_summary.md)
- Memory: [memory.md](./docs/mkdocs/en/memory.md)
- Knowledge: [knowledge.md](./docs/mkdocs/en/knowledge.md) / [knowledge_document_loader.md](./docs/mkdocs/en/knowledge_document_loader.md) / [knowledge_retrievers.md](./docs/mkdocs/en/knowledge_retrievers.md) / [knowledge_vectorstore.md](./docs/mkdocs/en/knowledge_vectorstore.md) / [knowledge_prompt_template.md](./docs/mkdocs/en/knowledge_prompt_template.md) / [knowledge_custom_components.md](./docs/mkdocs/en/knowledge_custom_components.md)
This group helps you:
- Session: manage per-session messages, summaries, and state
- Memory: manage cross-session long-term memory (including Mem0, Mempalace)
- Knowledge: cover document loading, retrieval, RAG, and prompt templates
### 10. Serving and Protocols
Recommended first:
- [examples/fastapi_server](./examples/fastapi_server/README.md) - HTTP service (sync + SSE)
- [examples/a2a](./examples/a2a/README.md) / [examples/a2a_with_cancel](./examples/a2a_with_cancel/README.md) - A2A service and cancellation (a2a-sdk 0.3)
- [examples/a2a_v1](./examples/a2a_v1/README.md) / [examples/a2a_v1_with_cancel](./examples/a2a_v1_with_cancel/README.md) - A2A service and cancellation (a2a-sdk 1.x)
- [examples/agui](./examples/agui/README.md) / [examples/agui_with_cancel](./examples/agui_with_cancel/README.md) - AG-UI service and cancellation
Related docs: [a2a.md](./docs/mkdocs/en/a2a.md) / [agui.md](./docs/mkdocs/en/agui.md) / [cancel.md](./docs/mkdocs/en/cancel.md)
This group helps you:
- Expose services through HTTP / A2A / AG-UI
- Integrate streaming responses and cancellation into real applications
- Use minimal templates for production service rollout
### 11. Filters and Execution Control
Recommended first:
- [examples/filter_with_model](./examples/filter_with_model/README.md) - Model-level filters
- [examples/filter_with_tool](./examples/filter_with_tool/README.md) - Tool-level filters
- [examples/filter_with_agent](./examples/filter_with_agent/README.md) - Agent-level filters
- [examples/llmagent_with_branch_filtering](./examples/llmagent_with_branch_filtering/README.md) - Branch filtering
- [examples/llmagent_with_timeline_filtering](./examples/llmagent_with_timeline_filtering/README.md) - Timeline filtering
- [examples/llmagent_with_cancel](./examples/llmagent_with_cancel/README.md) - Execution cancellation
Related docs: [filter.md](./docs/mkdocs/en/filter.md) / [cancel.md](./docs/mkdocs/en/cancel.md)
This group helps you:
- Apply control policies at model, tool, and agent layers
- Cover branch filtering, timeline filtering, and cancellation
- Build strong governance and risk-control constraints
### 12. Advanced LlmAgent Capabilities
Recommended first:
- [examples/llmagent_with_tool_prompt](./examples/llmagent_with_tool_prompt/README.md) - Tool-call prompt enhancement
- [examples/llmagent_with_thinking](./examples/llmagent_with_thinking/README.md) - Thinking mode
- [examples/llmagent_with_user_history](./examples/llmagent_with_user_history/README.md) - User history management
- [examples/llmagent_with_max_history_messages](./examples/llmagent_with_max_history_messages/README.md) - History window limits
- [examples/llmagent_with_model_create_fn](./examples/llmagent_with_model_create_fn/README.md) - Dynamic model factory
- [examples/llmagent_with_custom_agent](./examples/llmagent_with_custom_agent/README.md) - Custom agent extension
Related docs: [llm_agent.md](./docs/mkdocs/en/llm_agent.md) / [model.md](./docs/mkdocs/en/model.md) / [custom_agent.md](./docs/mkdocs/en/custom_agent.md)
This group helps you:
- Focus on `LlmAgent` extension points for context, prompting, and model routing
- Adapt a general-purpose agent to domain-specific business policies
- Build reusable behavior templates for repeated scenarios
### 13. LlmAgent Tool Calling and Interaction
Recommended first:
- [examples/llmagent_with_streaming_tool_simple](./examples/llmagent_with_streaming_tool_simple/README.md) - Simple streaming tool calls
- [examples/llmagent_with_streaming_tool_complex](./examples/llmagent_with_streaming_tool_complex/README.md) - Complex streaming tool calls
- [examples/llmagent_with_parallal_tools](./examples/llmagent_with_parallal_tools/README.md) - Parallel tool calling (directory name intentionally uses `parallal`)
- [examples/llmagent_with_human_in_the_loop](./examples/llmagent_with_human_in_the_loop/README.md) - Human-in-the-loop decisions
Related docs: [llm_agent.md](./docs/mkdocs/en/llm_agent.md) / [tool.md](./docs/mkdocs/en/tool.md) / [human_in_the_loop.md](./docs/mkdocs/en/human_in_the_loop.md)
This group helps you:
- Cover both simple and complex streaming tool interaction patterns
- Orchestrate parallel tool calls with human confirmation nodes
- Combine with filters and cancellation for more reliable execution chains
> For more examples, see each subdirectory README.md under [examples](./examples).
## Architecture Overview
The framework is organized in an event-driven architecture where each layer can evolve independently:
- **Agent layer**: LlmAgent / ChainAgent / ParallelAgent / CycleAgent / TransferAgent
- **Agent ecosystem extension layer**: LangGraphAgent / ClaudeAgent / TeamAgent
- **Graph capability layer**: GraphAgent / trpc_agent_sdk.dsl.graph (DSL-based orchestration)
- **Runner layer**: Unified execution entry, coordinating Session / Memory / Artifact services
- **Tool layer**: FunctionTool / file tools / MCPToolset / Skill tools
- **Model layer**: OpenAIModel / AnthropicModel / LiteLLMModel
- **Memory layer**: SessionService / MemoryService / SessionSummarizer / Mem0MemoryService / MempalaceMemoryService
- **Knowledge layer**: Production-grade LangChain-based knowledge and RAG capability
- **Execution and skill layer**: CodeExecutor (local / container) / Skills
- **Service layer**: FastAPI / A2A / AG-UI
- **Observability layer**: OpenTelemetry tracing/metrics, integrable with platforms like Langfuse
- **Ecosystem adapter layer**: claude-agent-sdk / mcp / LangChain / LiteLLM / Mem0 / Mempalace plugged into the main chain through model/tool/memory adapters
Key packages:
| Package | Responsibility |
| --- | --- |
| trpc_agent_sdk.agents | Agent abstractions, multi-agent orchestration, ecosystem extensions (LangGraphAgent / ClaudeAgent / TeamAgent) |
| trpc_agent_sdk.runners | Unified execution and event output |
| trpc_agent_sdk.models | Model adapter layer |
| trpc_agent_sdk.tools | Tooling system and MCP support |
| trpc_agent_sdk.sessions | Session management and summarization |
| trpc_agent_sdk.memory | Long-term memory services |
| trpc_agent_sdk.dsl.graph | DSL graph orchestration engine |
| trpc_agent_sdk.teams | Team collaboration mode |
| trpc_agent_sdk.code_executors | Code execution and workspace runtime |
| trpc_agent_sdk.skills | Skill repository and Skill tools |
| trpc_agent_sdk.server | FastAPI / A2A / AG-UI serving capabilities |
## Contributing
We love contributions! Join our growing developer community and help build the future of AI Agents.
### **Ways to Contribute**
- **Report bugs** or suggest new features through [Issues](https://github.com/trpc-group/trpc-agent-python/issues)
- **Improve documentation** to help others onboard faster
- **Submit PRs** for bug fixes, new features, or examples
- **Share your use cases** to inspire other builders
### **Quick Contribution Setup**
```bash
# Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/trpc-agent-python.git
cd trpc-agent-python
# Install development and feature-test dependencies, then run tests
pip install -e ".[dev,graph,ag-ui,agent-claude,a2a]"
pytest
# Make your changes and open a PR!
```
**Please read** [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines and coding standards.
**Please follow** [CODE-OF-CONDUCT.md](CODE-OF-CONDUCT.md) to keep our community friendly, respectful, and inclusive.
## Acknowledgements
### **Enterprise Validation**
We sincerely thank Tencent Licaitong, Tencent Ads, and other business teams for continuous validation and feedback in real production scenarios, which helps us keep improving the framework.
### **Open-source Inspiration**
We are also inspired by outstanding open-source frameworks including **ADK**, **Agno**, **CrewAI**, and **AutoGen**. We keep moving forward on the shoulders of giants.
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