> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
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# Agent Frameworks
> How AI agent concepts map from LangGraph, CrewAI, and similar frameworks to Pixeltable
If you've been building AI agents with LangGraph or CrewAI — defining state graphs, tool nodes, conditional edges, and bolting on separate memory stores — this guide shows how Pixeltable replaces the graph DSL with declarative tables.
**Related use case:** [Agents & MCP](/use-cases/agents-mcp)
***
## Concept Mapping
| Agent Framework | Pixeltable Equivalent |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
| `StateGraph` / `AgentExecutor` | [`pxt.create_table()`](/tutorials/tables-and-data-operations) with [computed columns](/tutorials/computed-columns) |
| Graph nodes (functions) | Computed columns — dependencies resolved automatically |
| Graph edges / conditional routing | Column references — Pixeltable infers the DAG |
| `ToolNode` / `@tool` | [`pxt.tools()` + `invoke_tools()`](/howto/cookbooks/agents/llm-tool-calling) |
| `MemorySaver` / checkpointer | Tables are persistent by default |
| Separate vector DB for RAG | [`add_embedding_index()`](/platform/embedding-indexes) + [`@pxt.query`](/platform/udfs-in-pixeltable) |
| LangSmith for observability | `t.select()` on any column — every step is [queryable](/tutorials/queries-and-expressions) |
***
## Side by Side: Tool-Calling Agent
An agent that picks tools, calls them, and answers based on the results.
```python theme={null}
from typing import Annotated, Sequence, TypedDict
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END, add_messages
from langgraph.prebuilt import ToolNode
from langchain_core.tools import tool
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], add_messages]
@tool
def get_weather(city: str) -> str:
"""Get current weather for a city."""
return f'Weather in {city}: 72°F, sunny'
@tool
def search_docs(query: str) -> str:
"""Search internal documents."""
return f'Results for: {query}'
tools = [get_weather, search_docs]
model = ChatOpenAI(model='gpt-4o-mini').bind_tools(tools)
def call_model(state):
return {'messages': [model.invoke(state['messages'])]}
def should_continue(state):
last = state['messages'][-1]
return 'tools' if last.tool_calls else END
workflow = StateGraph(AgentState)
workflow.add_node('agent', call_model)
workflow.add_node('tools', ToolNode(tools))
workflow.set_entry_point('agent')
workflow.add_conditional_edges(
'agent', should_continue, {'tools': 'tools', END: END})
workflow.add_edge('tools', 'agent')
graph = workflow.compile()
result = graph.invoke(
{'messages': [HumanMessage(content='Weather in SF?')]})
print(result['messages'][-1].content)
```
**Packages:** `langgraph`, `langchain-openai`, `langchain-core`, plus a vector DB client for RAG
```python theme={null}
import pixeltable as pxt
from pixeltable.functions.openai import chat_completions, invoke_tools
@pxt.udf
def get_weather(city: str) -> str:
"""Get current weather for a city."""
return f'Weather in {city}: 72°F, sunny'
@pxt.udf
def search_docs(query: str) -> str:
"""Search internal documents."""
return f'Results for: {query}'
tools = pxt.tools(get_weather, search_docs)
agent = pxt.create_table('agents.assistant', {'message': pxt.String})
agent.add_computed_column(response=chat_completions(
messages=[{'role': 'user', 'content': agent.message}],
model='gpt-4o-mini', tools=tools))
agent.add_computed_column(
tool_output=invoke_tools(tools, agent.response))
@pxt.udf
def build_followup(message: str, tool_output: dict) -> list[dict]:
results = [
str(r) for vals in (tool_output or {}).values()
if vals for r in vals
]
return [
{'role': 'user', 'content': message},
{'role': 'assistant', 'content': '\n'.join(results)},
{'role': 'user', 'content':
'Answer my original question using that information.'},
]
agent.add_computed_column(
followup=build_followup(agent.message, agent.tool_output))
agent.add_computed_column(
final=chat_completions(messages=agent.followup, model='gpt-4o-mini'))
agent.add_computed_column(
answer=agent.final.choices[0].message.content)
agent.insert([{'message': 'What is the weather in SF?'}])
agent.select(agent.message, agent.answer).collect()
```
**Packages:** `pixeltable`, `openai`
### What Changes
| | LangGraph / CrewAI | Pixeltable |
| -------------------- | -------------------------------------- | --------------------------------------------------------- |
| **State** | Ephemeral — lost when the process ends | Persistent — every row survives restarts |
| **Caching** | No built-in caching of tool results | Same input returns cached result |
| **Observability** | LangSmith (separate service + API key) | `agent.select(agent.tool_output).collect()` |
| **Adding RAG** | Separate vector DB integration | `add_embedding_index()` + `@pxt.query` — no extra service |
| **Graph definition** | Nodes, edges, conditional routing DSL | Computed columns — Pixeltable infers the DAG |
| **MCP tools** | Custom integration | `pxt.mcp_udfs()` loads tools from any MCP server |
***
## Common Patterns
### Adding persistent memory
```python theme={null}
from langgraph.checkpoint.memory import MemorySaver
checkpointer = MemorySaver()
graph = workflow.compile(checkpointer=checkpointer)
# In-process only — lost on restart
```
```python theme={null}
from pixeltable.functions.openai import embeddings
memories = pxt.create_table('agents.memories', {
'content': pxt.String, 'timestamp': pxt.Timestamp})
memories.add_embedding_index('content',
string_embed=embeddings.using(model='text-embedding-3-small'))
@pxt.query
def recall(query: str, top_k: int = 5) -> pxt.Query:
sim = memories.content.similarity(string=query)
return memories.order_by(sim, asc=False) \
.limit(top_k).select(memories.content)
```
### Adding RAG to an agent
```python theme={null}
from langchain_pinecone import PineconeVectorStore
vector_store = PineconeVectorStore(
index_name='docs', embedding=embeddings)
@tool
def search_kb(query: str) -> str:
"""Search the knowledge base."""
docs = vector_store.as_retriever() \
.get_relevant_documents(query)
return '\n'.join(d.page_content for d in docs)
# Must add tool to graph, re-compile...
```
```python theme={null}
@pxt.query
def search_kb(query: str) -> pxt.Query:
"""Search the knowledge base."""
sim = chunks.text.similarity(string=query)
return chunks.order_by(sim, asc=False) \
.limit(5).select(chunks.text)
tools = pxt.tools(get_weather, search_kb)
```
### Inspecting agent behavior
```python theme={null}
# Requires LangSmith: set LANGSMITH_API_KEY,
# LANGSMITH_PROJECT, then view traces in dashboard
```
```python theme={null}
agent.select(
agent.message,
agent.tool_output,
agent.answer
).collect()
```
***
## Next Steps
Full use case walkthrough
All 8 agentic patterns as Pixeltable tables
Register UDFs and queries as LLM tools
Lightweight agent framework built on Pixeltable
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