Frappucc1no/recall-loom — Project memory for long-running AI work across agents, models, and sessions.

Description and use

**Brief Summary**
Recall‑Loom (Frappucc1no/recall-loom) is a Python library that gives long‑running AI projects a persistent, file‑based memory layer, letting agents, models, and sessions share context, decisions, progress, and next‑step instructions. By storing this information in local project files, it turns ad‑hoc prompts and tool calls into repeatable, orchestrated workflows that can be version‑controlled and reused across teams.

**Value**

• **Continuity of context** – agents can retrieve prior reasoning, outcomes, and pending actions, eliminating the “cold‑start” problem that plagues multi‑step AI pipelines.
• **Workflow repeatability** – the same memory files can be checked into source control, enabling deterministic re‑runs, debugging, and audit trails.
• **Cross‑agent coordination** – multiple agents (or model versions) can read/write a shared state, making complex orchestration (e.g., data‑prep → model‑train → evaluation loops) straightforward.

**Practical Adoption Path**

1. **Install the SDK/CLI** (pip install recall-loom) and add the provided loom.yaml configuration to an existing project.
2. **Instrument prompts** by wrapping calls with loom.record() and loom.retrieve() to persist inputs, outputs, and meta‑data.
3. **Integrate with orchestration tools** (e.g., Airflow, Prefect, or LangChain) by invoking the CLI or SDK inside task definitions, thereby turning each step into a memory‑aware node.
4. **Version‑control the generated .loom/ directory** so that collaborators can replay or branch workflows without losing state.

**Production Readiness**
Recall‑Loom scores high for pilot deployment: it has recent commits (last updated 2026‑07‑07), active community interest (144 ★, 5 forks), clear Python API/CLI, and extensive topic tagging that eases integration. While a final review of licensing, security posture, and maintainer activity is still advisable, the project shows strong ecosystem signals and is ready for controlled production use in multi‑agent AI pipelines.

Problem

Frappucc1no/recall-loom helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

Project memory for long-running AI work across agents, models, and sessions. Keep context, decisions, progress, and next steps in local project files.

Source