atakanelik34/Bilinc

**Hosted memory infrastructure for AI agents: commit, recall, and inspect agent state through one API key, with verification, provenance, and recovery around every write.**
Retrieval answers *"what is similar to this?"*. Long-running agents also need to answer *"who wrote this state, was it verified, did it contradict what we already knew, and can we undo it?"* — that is the layer Bilinc provides.
Bilinc 2.3.9 on PyPI is the public cloud-only package: a thin Python SDK, CLI, and MCP adapter for Bilinc Cloud. It does not ship the local StatePlane, storage backends, eval, observability, integrations, or server runtime internals.
> **Frozen regression receipt** — LongMemEval-s cleaned retrieval fixture, 500 questions: **Hit@5 98.0%**, **NDCG@5 0.913**, no LLM reranker, no paid API. This is an isolated retrieval guardrail, not a current hosted SLA, end-to-end agent score, or competitor ranking — see Benchmark receipt for the full scope and qualification.
The short version
Bilinc is the state layer between an agent and the things it must remember. It keeps memory writes attributable,
correctable, and recoverable instead of treating retrieval as a bag of similar text.
| If your agent needs to... | Bilinc gives it... |
| --- | --- |
| Recall a decision before acting | Key-scoped recall with explicit profiles and evidence metadata |
| Correct a bad memory | revise, contradiction-aware state, and provenance-preserving updates |
| Recover from an unsafe run | Snapshots, diffs, and confirmed rollback |
| Work across any MCP-compatible agent | A Python SDK, CLI, and stdio MCP adapter |
The fastest path is pip install -U bilinc, bilinc login, then bilinc quicktest against Bilinc Cloud.
Use Bilinc when
- A long-running agent — coding, support, research, or a personal assistant — needs to recall prior decisions before a risky action.
- You need to know which run, tool, or operator produced a piece of agent state.
- A bad agent run wrote incorrect state and you need a recovery path, not a manual cleanup.
- Several agents or teammates share one memory surface and you need key-scoped access and usage visibility.
Do not use Bilinc when
- You only need semantic search over documents — a vector database is the simpler primitive.
- You require an Apache-2.0 licensed, fully self-hosted runtime. The public package is cloud-only and licensed BUSL-1.1.
- You want the memory layer to also be your agent framework. Bilinc is the state layer your runtime calls; it does not orchestrate agents.
Choose your surface
| You want... | Use... |
| --- | --- |
| A hosted memory API for an agent or MCP client | The public cloud-only package from PyPI |
| Local StatePlane, SQLite/PostgreSQL, benchmarks, or internals | This repository and the architecture guide |
| A hosted MCP connection without a Python install | The remote MCP setup guide |
The public package is intentionally smaller than this repository. It does not bundle the internal StatePlane or local
storage runtime.
Start in 60 Seconds
Claude connector, without installing Python
Open **Customize → Connectors → Add custom connector** in Claude, name it Bilinc,
and paste https://mcp.bilinc.space/mcp. Sign in and choose read-only or read and
write access. The setup guide is bilinc.space/mcp.
For the remote connector in Claude Code:
Then run /mcp and authenticate. The remote connector has ten tools; the Python
stdio adapter below has eleven. Both connect to hosted Bilinc memory.
Recall in the remote connector shows how long ago each memory was last confirmed
and marks entries that are past their review window (90 days for facts, decisions,
how-tos and places; 1 day for working notes; events are never marked) as possibly
outdated. Nothing is hidden or deleted. Revising a memory with the same value
confirms it.
| Remote tool | Access | What it does |