Loomline

The next agent shouldn't have to start over

One agent investigates. Another challenges its conclusion. A third picks up the work tomorrow. Chat history alone doesn't tell them which claims still hold, where the evidence came from, or who owns the next step.

Loomline gives those agents one evolving memory. Observations, hypotheses, procedures, source turns and handoffs persist as connected records in FalkorDB, with separate identities and project-scoped access.

Actual Studio capture from a packaged acceptance run using independent MCP test clients. Every displayed relationship comes from stored graph data.

Related MCP server: CPG-Radio

What Loomline brings to the team

Why FalkorDB

FalkorDB is the authoritative memory and coordination engine. Loomline traverses real relationships to answer who recorded this, what supports it, what challenged it, and what work depends on it.

flowchart LR
  A[Independent agents] -->|MCP / Python SDK| G[Authenticated gateway]
  S[Developer Studio] --> G
  G --> F[(FalkorDB)]
  F --- M[Typed memories]
  F --- E[Sources & evidence]
  F --- H[Claims & handoffs]

Memories link to source turns through DERIVED_FROM, to evidence through SUPPORTED_BY and CHALLENGED_BY, and to handoffs through REFERS_TO. Version checks protect coordination changes; operation receipts make write retries recoverable. Tenant graphs and authenticated task membership bound access. Explore the graph model →

Run locally

You need Docker with Compose v2. The core walkthrough needs no host Python, Node, AI application or provider key. Use Linux containers on Docker Desktop.

git clone https://github.com/abhinavg08/loomline.git
cd loomline

# Generate fresh private configuration. Existing configuration is preserved.
docker run --rm --mount "type=bind,source=$($PWD.Path),target=/work" --workdir /work python:3.12.10-slim@sha256:fd95fa221297a88e1cf49c55ec1828edd7c5a428187e67b5d1805692d11588db python scripts/init_local.py
docker compose --profile app up -d --build --wait
docker compose --profile app run --rm operator python -m scripts.onboard bootstrap

docker run --rm --user "$(id -u):$(id -g)" --mount "type=bind,source=$(pwd),target=/work" --workdir /work python:3.12.10-slim@sha256:fd95fa221297a88e1cf49c55ec1828edd7c5a428187e67b5d1805692d11588db python scripts/init_local.py
docker compose --profile app up -d --build --wait
docker compose --profile app run --rm operator python -m scripts.onboard bootstrap

Bootstrap reports READY, your Studio URL and a private token-file path. Open localhost:8000 and follow the first-run guide to connect privately, create a project and commit your first shared memory. Each new installation gets fresh credentials and its own persistent volume.

Evaluating the project? The demonstration guide walks through two independent identities, shared recall, graph provenance and session recovery.

Bring your own agents

Your coding application keeps its own model runtime. Loomline provides shared memory and coordination. You register the generated MCP configuration in your own application. Connection instructions →

Verification

The release baseline passed 61 backend/SDK tests and 21 MCP tests with one Windows symlink skip, using real FalkorDB. Packaged clean-room verification covered fresh credentials, independent clients, tenant isolation, work contention, handoffs and database restart recovery.

The owner confirmed a Codex CLI + Antigravity CLI collaboration and fresh-session recovery. Automated MCP test clients are labelled separately. Claude Code, Antigravity IDE and a genuine three-application run still need owner verification.

Verification evidence, reproducible checks and known limits →

Documentation

Core source lives in backend/, frontend/ and packages/. Runnable workflows live in agents/ and examples/; tests/ and mcp_tests/ retain the regression suite. Private credentials, databases and engineering artifacts are excluded.

The default deployment is local and uses one gateway worker. MCP records explicit turns and tool activity; it doesn't capture an application's entire conversation. Recall uses bounded graph and text filters. The optional CrewAI environment has documented dependency advisories; review security and verification before extending deployment. Licensed under MIT.