OneContext: Seamless Unified Context Layer for AI Agents

Artificial Intelligence is no longer a one‑shot conversation. Modern workflows involve a swarm of agents—from chatbots and LLM assistants to automation bots—that must remember past interactions and context to deliver high‑quality responses. OneContext solves this pain point by providing a self‑managed context layer that can be shared across any number of agents, devices, or services.

What is OneContext?

• Unified context hub – A single context store that can be loaded by any agent.
• Trajectory recording – Every interaction is logged, giving you a full history of how an agent reached its current state.
• Cross‑platform – Works on Node.js and Python. The CLI is a thin wrapper that proxies commands to Python via a local package.
• Slack integration – Context can be shared over Slack, allowing collaborators to "talk" to the context directly.
• No service cost – It’s a local library; run it locally without cloud infra.

It’s the missing glue for teams that build complex AI pipelines and need one source of truth for all agents.

Quick start

# 1️⃣ Install the OneContext wrapper globally
npm i -g onecontext-ai

# 2️⃣ Verify the installation
onecontext version

# 3️⃣ Start the context service
onecontext

The installer will fetch the latest Python package automatically using your preferred Python package manager (uv, pipx, pip3, or pip). Make sure you have Node ≥ 16 and Python 3.11+ on your path.

Available commands

Using OneContext in Your AI Pipeline

1. Record a session – Wrap your agent calls with onecontext-ai record to automatically log prompts, completions, and any metadata.
2. Export context – Dump the history to disk or send it to Slack:

   onecontext export --format json > context.json
   onecontext slack --channel #ai-context
   

3. Load context – At any point in the future or on a different machine, simply import the context:

   onecontext import context.json
   

   The agent will continue from where it left off.
4. Share in real time – Other developers can query the context via a simple REST endpoint exposed by the CLI.

This workflow turns the context into a living artifact, ensuring continuity, reducing hallucination, and improving team collaboration.

Key Features Highlighted

• Trajectory Visualization – The onecontext visualize command generates a timeline of interactions, great for debugging.
• Context Migration – Supports importing old Codex/Claude sessions.
• Multiple Agents – Different agents can connect to the same context; no need to copy data.
• Environment-agnostic – Works on Windows, macOS, and Linux.
• Extensible Plugin System – Future releases will allow custom context storage backends.

Advanced Tips

Updating the Core

If the wrapper gets out of sync, run:

onecontext doctor --fix-upgrade && onecontext update

Repairing Corrupted Links

npm rebuild onecontext-ai

Running Without Node

The underlying Python package can be used directly:

python -m onecontext_ai run

Community & Support

• Github Repo – https://github.com/TheAgentContextLab/OneContext
• Issues & Feedback – Contribute by filing issues or PRs.
• Slack – Invite the bot to your workspace and use @OneContext to ping context directly.

Conclusion

OneContext brings a disciplined approach to AI agent development. By centralizing context, teams can collaborate more effectively, reduce redundancies, and maintain a clear audit trail. Whether you’re building a single chatbot or a network of autonomous agents, OneContext gives you the backbone you need to keep everything in sync. Try it out today, and see how a unified context can change the way you build AI.