cogency 3.3.0

Streaming agents

Cogency

Streaming agents with stateless context assembly.

Install

pip install cogency
export OPENAI_API_KEY="your-key"

Quickstart

from cogency import Agent

agent = Agent(llm="openai")
async for event in agent("What files are in this directory?"):
    if event["type"] == "respond":
        print(event["content"])

Core Design

1. Persist-then-rebuild: Events written to storage immediately, context rebuilt each execution
2. Protocol/storage separation: XML delimiters for LLM I/O, clean events in storage
3. Stateless execution: Agent and context are pure functions, all state in storage

Result: no state corruption, crash recovery, concurrent safety.

Execution Modes

Mode	Method	Token Usage	Providers
Resume	WebSocket	Constant	OpenAI, Gemini
Replay	HTTP	Grows with conversation	All
Auto	WebSocket with HTTP fallback	Optimal	All

agent = Agent(llm="openai", mode="auto")  # Default

Token efficiency (Resume vs Replay):

Turns	Replay	Resume	Savings
16	100,800	10,800	9.3x
32	355,200	20,400	17.4x

Streaming

Event mode (default): Complete semantic units

async for event in agent("Debug this code", stream="event"):
    if event["type"] == "think":
        print(f"~ {event['content']}")
    elif event["type"] == "respond":
        print(f"> {event['content']}")

Token mode: Real-time streaming

async for event in agent("Debug this code", stream="token"):
    if event["type"] == "respond":
        print(event["content"], end="", flush=True)

Conversations

Stateless (default):

async for event in agent("What's in this directory?"):
    if event["type"] == "respond":
        print(event["content"])

Stateful with profile learning:

async for event in agent(
    "Continue our code review",
    conversation_id="review_session",
    user_id="developer"  # For profile learning and multi-tenancy
):
    if event["type"] == "respond":
        print(event["content"])

Built-in Tools

Tool	Description
read	Read file (with optional pagination)
write	Write file (overwrite protection)
edit	Replace exact text in file
list	Tree view of directory
find	Find files by pattern or content
replace	Find-and-replace across files
shell	Execute shell command
search	Web search
scrape	Extract webpage text
recall	Search past conversations

Custom Tools

from dataclasses import dataclass
from typing import Annotated
from cogency import ToolResult
from cogency.core.tool import tool
from cogency.core.protocols import ToolParam

@dataclass
class QueryParams:
    sql: Annotated[str, ToolParam(description="SQL query")]

@tool("Execute SQL queries")
async def query_db(params: QueryParams, **kwargs) -> ToolResult:
    result = db.execute(params.sql)
    return ToolResult(outcome="Query executed", content=result)

agent = Agent(llm="openai", tools=[query_db])

Configuration

agent = Agent(
    llm="openai",                    # or "gemini", "anthropic"
    mode="auto",                     # "resume", "replay", or "auto"
    storage=custom_storage,          # Custom Storage implementation
    identity="Custom agent identity",
    instructions="Additional context",
    tools=[CustomTool()],
    max_iterations=10,
    history_window=None,             # None = full history, int = sliding window
    history_transform=compress,      # Optional history compression callable
    profile=True,                    # Enable automatic user learning
    security=Security(access="project", shell_timeout=60),  # Security policies
    notifications=notification_source,  # Mid-execution context injection
    debug=False
)

History compression: For long conversations, pass history_transform to compress context:

async def compress(messages: list[dict]) -> list[dict]:
    if len(messages) <= 20:
        return messages
    return [{"role": "system", "content": f"[{len(messages)-10} earlier messages]"}] + messages[-10:]

agent = Agent(llm="openai", history_transform=compress)

Documentation

• architecture.md - Core pipeline and design decisions
• execution.md - Tool execution protocol specification
• protocol.md - Wire format, event stream, storage
• tools.md - Built-in tool reference
• memory.md - Profile, recall, history window
• proof.md - Mathematical efficiency analysis

License

Apache 2.0