Context Bridging

Here are some strategies for bridging context between ChatGPT (web) and other platforms or interfaces: 1. Manual Prompt Injection 1. Save and Load Memory 1. Use Federated Memory 1. Personal Memory Service

# Manual Prompt Injection Before using Open Interpreter or CrewAI, copy a summary of your current project from ChatGPT and paste it as an initial system prompt. Example: I’m building a system called Hitchhiker Agents, combining CrewAI, Claude, and Federated Wiki. We’ve defined agent roles like Deep Thought, Scribe, and Slartibartfast. My goal is to run an agentic stack locally and interact with Federated Wiki ghost pages using JSON. This gives the agent enough memory to continue meaningfully.

# Save and Load Memory You can persist memory manually or with tools: - Save session logs as `.md`, `.json`, or `.yam` files in your `assets/` or `memory/` folder - Let your agents read these files before responding - Append each new exchange to a local log file This mimics conversation memory even when using stateless APIs.

# Integrate with Federated Wiki Federated Wiki supports: - Reading and writing structured page JSON - Exposing content via HTTP from the `assets/` folder - Using ghost pages for draft AI output and review Agents can use wiki pages as both inputs (plans, prompts) and outputs (summaries, Vibelets, snapshots), building a persistent, peer-reviewed memory structure.

# Use a Personal Memory Service You may optionally set up a local or shared memory layer using: - Git-tracked Markdown or YAML files - A local vector store (e.g. Chroma or Weaviate) - A CrewAI Librarian agent that indexes and retrieves prompts and plans This enables shared context between ChatGPT and terminal-based agents.

# Summary

You cannot sync memory between ChatGPT and the API directly. But with prompts, logging, and structured wiki storage, you can simulate persistent agent memory across tools and workflows.

# See - Chatgpt Memory - openai.com - Open Interpreter - github - CrewAI - github