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Observational Memory — Full Code Flow

What it is

Observational Memory (OM) is a two-stage compression system that gives agents unlimited conversation length. As messages accumulate beyond a token threshold, an Observer agent extracts structured observations. When observations themselves grow too large, a Reflector agent compresses them further. The main agent (the "Actor") only ever sees: compressed observations + recent unobserved messages.

Three agents are involved:

• Actor — the main agent the user talks to
• Observer — extracts observations from raw messages (default: gemini-2.5-flash, temp 0.3)
• Reflector — compresses observations when they get too large (default: gemini-2.5-flash, temp 0)

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Local instructions

Welcome, if you're reading this we have started a new conversation or coding session, the following sections provide important context for you to get started. Any TODOs and FILLOUT you find in this file should be completed after doing the relevant research.

things you should know starting a new conversation

• this is a worktree of the Pydantic AI repository ("upstream") Pydantic's AI Agents framework to create and orchestrate agents.
• I am dsfaccini, a contributor, and you're my coding agent.
• the main branch is checked out at /Users/david/projects/forks/pydantic-ai-main (check what folder you're in)

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# you can add this to your ~/.zshrc

alias claude-otel='CLAUDE_CODE_ENABLE_TELEMETRY=1 \
  OTEL_METRICS_EXPORTER=otlp \
  OTEL_LOGS_EXPORTER=otlp \
  OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf \
  OTEL_EXPORTER_OTLP_ENDPOINT=https://logfire-us.pydantic.dev \
  OTEL_EXPORTER_OTLP_HEADERS="Authorization=pylf_v1_us_yourtoken" \
  OTEL_LOG_USER_PROMPTS=1 \
  OTEL_LOG_TOOL_DETAILS=1 \

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"""Example: Fallback on validation errors.

The FallbackModel only falls back on API errors (4xx/5xx), not validation errors.
This example shows how to fall back to another model when validation retries are exhausted.
"""
import asyncio
from collections.abc import Sequence

import logfire
from pydantic import BaseModel, Field, ValidationError

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#!/usr/bin/env python
"""Quick script to verify base64 encoding/decoding for MCP content using actual classes."""

from pydantic_ai._mcp import map_from_pai_messages
from pydantic_ai.messages import BinaryContent, ModelRequest, UserPromptPart

# Test image content
raw_image_bytes = b'some image data here'
image_message = ModelRequest(
    parts=[

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"""
Compare LiteLLMProvider and GroqProvider for Groq-routed models.
"""

from pydantic_ai.providers.litellm import LiteLLMProvider
from pydantic_ai.providers.groq import GroqProvider

def main():
    litellm = LiteLLMProvider(api_base='http://localhost:4000', api_key='test')
    groq = GroqProvider(api_key='test')

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# add this line to you .zshrc to make the command globally available:
# source ~/.shell-functions/clone-git-folder.zsh
# you'll need to open a new shell or run source ~/.zshrc to reload your shell config
#
# usage example: the template for deploying payloadcms on cloudflare
# running `clone-git-folder https://github.com/payloadcms/payload/tree/main/templates/with-cloudflare-d1`
# will create a new folder called "with-cloudflare-d1"
# running `clone-git-folder https://github.com/payloadcms/payload/tree/main/templates/with-cloudflare-d1 payloadcms-on-cloudflare`
# will create a new folder called "payloadcms-on-cloudflare"
clone-git-folder() {

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// Source: https://github.com/sveltejs/kit/blob/environment-api/packages/kit/src/core/sync/utils.js
/**
 * Allows indenting template strings without the extra indentation ending up in the result.
 * Still allows indentation of lines relative to one another in the template string.
 * @param {TemplateStringsArray} strings
 * @param {any[]} values
 */
export function dedent(strings, ...values) {
	let dedented = dedent_map.get(strings);

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n8n Custom Nodes Coding Agent

You are an expert n8n coding agent that translates API specifications to custom n8n nodes.

You are working in a cloned repository for an already existing and working n8n nodes community package.

You should use the existing code as a guide to implement the new nodes, following best practices, such as putting optional fields behind dropdowns and similar UI elements and grouping related fields.

You also make sure to understand how the API behaves under errors, such that you set up appropriate timeouts and bubble up any information about the errors returned by the API so the user understands what's going on behind the surface and can take appropriate action.