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How to carry lessons from past runs into a new run with Aibuildai LLM Posttrain Agent

Fold earlier runs of a task into an editable memory document and inject it into new runs.

Who it is forA team that has already run the agent on a task and wants the next run to start from what the earlier runs learned.

The problemEach new run starts from scratch, so earlier findings about what worked are lost.

The outcomeAn editable memory document built from past runs, used by every agent in the next run when memory is enabled.

What you need first

  • Linux x86_64 with a systemd user session and Python 3.11 to 3.13
  • A C toolchain (gcc, make) with zlib and readline headers, plus bwrap (bubblewrap) with unprivileged user namespaces enabled
  • A Claude Code login or an Anthropic API key, or an Anthropic-compatible endpoint and its key
  • A CUDA GPU is recommended
  • Network access to GitHub releases and ftp.postgresql.org

Steps

  1. Run aibuildai memorize task.yaml to fold past runs of the task into a memory document
  2. Edit the generated memory document to keep the findings you trust
  3. Set memory.enable to true in task.yaml
  4. Start the next run with aibuildai run task.yaml

Prompt for your agent

In the aibuildai-llm-posttrain-agent repo, run `aibuildai memorize task.yaml`, then set memory.enable to true and run `aibuildai run task.yaml`. Expected output: a memory document and a new run that uses it. Verify that memory.enable is true in the config before starting the run.

Caveat. The README does not say how the memory document is formatted or where it is stored, so check the generated file before relying on it.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for aibuildai-inc/aibuildai-llm-posttrain-agent and "Carry lessons from past runs into a new run"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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