Research Langchain agent runner integration

README.md

@@ -1,8 +1,8 @@
 # `agentexec`
 
-**Production-ready orchestration for OpenAI Agents SDK** with Redis-backed task queues, SQLAlchemy activity tracking, and multiprocessing worker pools.
+**Production-ready orchestration for AI agents** with Redis-backed task queues, SQLAlchemy activity tracking, and multiprocessing worker pools.
 
-Build reliable, scalable AI agent applications with automatic lifecycle management, progress tracking, and fault tolerance.
+Supports **OpenAI Agents SDK** and **LangChain** with automatic lifecycle management, progress tracking, and fault tolerance.
 
 Running AI agents in production requires more than just the SDK. You need:
 
@@ -22,6 +22,7 @@ Running AI agents in production requires more than just the SDK. You need:
 - **Redis task queue** - Reliable job distribution with priority support
 - **Automatic activity tracking** - Full lifecycle management (QUEUED → RUNNING → COMPLETE/ERROR)
 - **OpenAI Agents integration** - Drop-in runner with max turns recovery
+- **LangChain integration** - Support for ReAct, tool-calling, and LangGraph agents
 - **Agent self-reporting** - Built-in tools for agents to report progress
 - **SQLAlchemy-based storage** - Flexible database support (PostgreSQL, MySQL, SQLite)
 - **Type-safe** - Full type annotations with Pydantic schemas
@@ -31,10 +32,18 @@ Running AI agents in production requires more than just the SDK. You need:
 
 ## Installation
 
+**For OpenAI Agents SDK:**
 ```bash
 uv add agentexec
 ```
 
+**For LangChain:**
+```bash
+uv add "agentexec[langchain]"
+# Also install your LLM provider, e.g.:
+uv add langchain-openai  # or langchain-anthropic, etc.
+```
+
 **Requirements:**
 - Python 3.11+
 - Redis (for task queue)
@@ -229,7 +238,9 @@ See **[examples/openai-agents-fastapi/pipeline.py](examples/openai-agents-fastap
 
 ---
 
-## Full Example: FastAPI Integration
+## Full Examples: FastAPI Integration
+
+### OpenAI Agents SDK Example
 
 See **[examples/openai-agents-fastapi/](examples/openai-agents-fastapi/)** for a complete production application showing:
 
@@ -240,6 +251,16 @@ See **[examples/openai-agents-fastapi/](examples/openai-agents-fastapi/)** for a
 - Real-time progress monitoring
 - Graceful shutdown with cleanup
 
+### LangChain Example
+
+See **[examples/langchain-agents-fastapi/](examples/langchain-agents-fastapi/)** for a LangChain-based application demonstrating:
+
+- ReAct agent pattern with LangChain
+- Integration with multiple LLM providers
+- Streaming support via astream_events
+- Custom tools with @tool decorator
+- Same orchestration patterns as OpenAI example
+
 ---
 
 ## Configuration
@@ -320,6 +341,34 @@ result = await runner.run(agent, input="...", max_turns=15)
 result = await runner.run_streamed(agent, input="...", max_turns=15)
 ```
 
+### LangChain Runner
+
+```python
+from langchain.agents import AgentExecutor, create_react_agent
+from langchain_openai import ChatOpenAI
+
+# Create LangChain agent
+llm = ChatOpenAI(model="gpt-4o-mini")
+agent = create_react_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+# Wrap with agentexec runner
+runner = ax.LangChainRunner(
+    agent_id=agent_id,
+    agent_executor=agent_executor,
+    max_turns_recovery=True,
+    wrap_up_prompt="Summarize...",
+)
+
+# Run agent
+result = await runner.run(input="...", max_iterations=15)
+
+# Streaming
+async for event in runner.run_streamed(input="...", max_iterations=15):
+    if event["event"] == "on_chat_model_stream":
+        print(event["data"]["chunk"].content, end="")
+```
+
 ---
 
 ## Architecture
@@ -445,7 +494,9 @@ MIT License - see [LICENSE](LICENSE) for details
 
 ## Links
 
-- **Documentation**: See example application in `examples/openai-agents-fastapi/`
+- **Documentation**: See example applications
+  - OpenAI Agents SDK: `examples/openai-agents-fastapi/`
+  - LangChain: `examples/langchain-agents-fastapi/`
 - **Issues**: [GitHub Issues](https://github.com/Agent-CI/agentexec/issues)
 - **PyPI**: [agentexec](https://pypi.org/project/agentexec/)
 

examples/langchain-agents-fastapi/README.md

@@ -0,0 +1,170 @@
+# LangChain Agents FastAPI Example
+
+This example demonstrates a complete FastAPI application using **agentexec** to orchestrate LangChain agents in production.
+
+## What This Example Demonstrates
+
+### Core Features
+
+- **Background worker pool** (`worker.py`) - Multi-process task execution with Redis queue
+- **LangChainRunner integration** - Automatic activity tracking for agent lifecycle
+- **ReAct agent pattern** - Using LangChain's create_react_agent for reasoning
+- **Custom FastAPI routes** (`views.py`) - Building your own API on agentexec's public API
+- **Database session management** (`main.py`) - Standard SQLAlchemy patterns with full control
+- **Agent self-reporting** - Agents report progress via built-in `report_activity` tool
+- **Max iterations recovery** - Automatic handling of conversation limits with wrap-up prompts
+
+### Key Patterns Shown
+
+**Task Registration:**
+```python
+@pool.task("research_company")
+async def research_company(
+    agent_id: UUID,
+    context: ResearchCompanyContext,
+) -> ResearchCompanyResult:
+    # Create LangChain agent
+    llm = ChatOpenAI(model="gpt-4o-mini")
+    agent = create_react_agent(llm, tools, prompt)
+    agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+    # Wrap with agentexec runner
+    runner = ax.LangChainRunner(
+        agent_id=agent_id,
+        agent_executor=agent_executor,
+        max_turns_recovery=True
+    )
+
+    # Execute with activity tracking
+    result = await runner.run(context.input_prompt, max_iterations=15)
+    return ResearchCompanyResult(summary=result["output"])
+```
+
+**Activity Tracking API:**
+```python
+# List activities with pagination
+ax.activity.list(db, page=1, page_size=50)
+
+# Get detailed activity with full log history
+ax.activity.detail(db, agent_id)
+
+# Cleanup on shutdown
+ax.activity.cancel_pending(db)
+```
+
+**Queueing Tasks:**
+```python
+context = ResearchCompanyContext(company_name="Acme")
+task = await ax.enqueue(
+    "research_company",
+    context,
+    priority=ax.Priority.HIGH,
+)
+```
+
+## Quick Start
+
+```bash
+# Install dependencies
+cd examples/langchain-agents-fastapi
+uv sync
+
+# Start Redis
+docker run -d -p 6379:6379 redis:latest
+
+# Set API key
+export OPENAI_API_KEY="your-key"
+
+# Run migrations
+alembic upgrade head
+
+# Start worker (terminal 1)
+python -m langchain_agents_fastapi.worker
+
+# Start API server (terminal 2)
+uvicorn langchain_agents_fastapi.main:app --reload
+```
+
+## Try It
+
+Queue a task:
+```bash
+curl -X POST "http://localhost:8000/api/tasks/research_company" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "company_name": "Anthropic",
+    "input_prompt": "Focus on their AI safety research"
+  }'
+```
+
+Monitor progress:
+```bash
+# List all activities
+curl "http://localhost:8000/api/agents/activity"
+
+# Get specific agent details
+curl "http://localhost:8000/api/agents/activity/{agent_id}"
+```
+
+## Configuration
+
+Set via environment variables:
+
+```bash
+DATABASE_URL="sqlite:///agents.db"              # or postgresql://...
+REDIS_URL="redis://localhost:6379/0"
+QUEUE_NAME="agentexec:tasks"
+NUM_WORKERS="4"
+OPENAI_API_KEY="sk-..."
+```
+
+## LangChain-Specific Features
+
+### Agent Types Supported
+
+This example demonstrates the **ReAct agent** pattern, but agentexec's LangChainRunner supports:
+
+- **ReAct agents** (`create_react_agent`) - Reasoning and acting
+- **Tool-calling agents** (`create_tool_calling_agent`) - Structured tool use
+- **Structured chat agents** (`create_structured_chat_agent`) - Multi-turn conversations
+- **LangGraph agents** - Durable execution with state persistence
+
+### Streaming Support
+
+LangChain agents support streaming via `astream_events`:
+
+```python
+async for event in runner.run_streamed(input_prompt, max_iterations=15):
+    if event["event"] == "on_chat_model_stream":
+        print(event["data"]["chunk"].content, end="")
+```
+
+### Custom Tools
+
+Tools in LangChain use the `@tool` decorator:
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def search_company_info(company_name: str, query_type: str) -> str:
+    """Search for company information."""
+    return f"Information about {company_name}..."
+```
+
+## Differences from OpenAI Agents SDK
+
+| Feature | OpenAI Agents SDK | LangChain |
+|---------|------------------|-----------|
+| Agent creation | `Agent(name, instructions, tools)` | `create_react_agent(llm, tools, prompt)` |
+| Tool decorator | `@function_tool` | `@tool` |
+| Execution | `Runner.run()` | `AgentExecutor.invoke()` |
+| Streaming | `run_streamed()` | `astream_events()` |
+| Max iterations | `MaxTurnsExceeded` exception | Max iterations in config |
+| LLM providers | OpenAI only | OpenAI, Anthropic, local models, etc. |
+
+## Learn More
+
+- [LangChain Documentation](https://python.langchain.com/)
+- [AgentExec Documentation](https://github.com/Agent-CI/agentexec)
+- [ReAct Agent Pattern](https://python.langchain.com/docs/modules/agents/agent_types/react/)