What would you build with Spector Cognitive Memory? 💡

[sbharatjoshi]

Share Your Vision: Building with Spector Memory 🧠

Spector is our zero-overhead, off-heap cognitive memory engine featuring SIMD-accelerated hybrid search (integrating vector, graph, and text search) and ACT-R-inspired structured recall.

We want to know: What are you planning to build or integrate with Spector?

• Are you building persistent conversational agents?
• Are you integrating it with Spring AI or a custom framework?
• Do you want to try our Python bindings or multi-modal capabilities?

Share your ideas, suggest features, or ask for architectural feedback here!

[ofekron]

I maintain Better Agent, a source-available/non-commercial local workspace for supervising native Claude, Codex, and Gemini coding-agent sessions: https://github.com/ofekron/better-agent

A Spector integration I would explore is durable, provenance-aware memory for parallel coding agents. Each memory should carry repository, branch, commit, provider, and session scope, so retrieval can exclude stale architectural assumptions before hybrid ranking begins. The graph side could connect decisions to the files, tests, and worker sessions that produced them; text/vector search could then recover the relevant rationale when another agent reaches the same subsystem.

For concurrent workers, I would also want conflicting memories to remain distinct until a human or deterministic merge policy resolves them. Last-write-wins is risky when one agent has inspected newer code than another. ACT-R-inspired activation could help prioritize frequently reused decisions, while explicit invalidation on changed files or commits would keep durable memory from becoming durable misinformation.

I have not integrated Spector yet, so this is the concrete architecture and evaluation scenario I would bring to an experiment, not a claim about its current behavior.

AI helped draft this comment. I reviewed it for accuracy before posting.

[jarvispectrayan]

This is an exceptional set of insights, @ofekron! You've hit on some of the most critical challenges in multi-agent collaboration systems.

Here is how your suggestions align with our architectural roadmap for Spector Memory and how we can realize them:

1. Provenance-Aware Memory (Git + Session Scoping)

Having memories carry explicit metadata—repository, branch, commit, provider, and session—is a game changer.

• Synaptic Filtering: In Spector's retrieval pipeline, we can leverage this metadata as hard or soft Synaptic Filters before vector/hybrid ranking begins.
• Excluding Stale Assumptions: If an agent is working on branch-A at commit-X, retrieval can automatically filter out architectural assumptions stored from a divergent branch-B or an ancestor commit that has since been invalidated by a refactor.

2. Graph-Connected Decision Provenance

Spector's 3-Layer Cognitive Graph (specifically the EntityGraph and TemporalChain) is perfectly suited for this:

• The Entity Graph: We can model relationships linking CodeEntity nodes (files, classes, test cases) to AgentSession and DecisionNode nodes.
• Traceable Rationale: When a test fails or a developer/agent opens a subsystem, spreading activation can traverse the graph from the code/test node to retrieve the exact design decision and rationale that created it, even if pure vector similarity is low.

3. Concurrency and Conflict Resolution (Anti-Misinformation)

Your point about the dangers of last-write-wins in concurrent multi-agent environments is spot-on.

• Discrete Episodic Nodes: Instead of destructive updates, conflicting memories will be stored as distinct episodic nodes linked to their respective session/commit provenance.
• Lineage-Based Merging: We can design deterministic merge policies that evaluate Git commit ancestry to resolve conflicts (i.e., verifying if one memory is based on a newer codebase state).
• Decay & Invalidation: By coupling ACT-R base-level activation (which naturally strengthens frequently retrieved decisions) with explicit, event-driven invalidation triggers (e.g., when a file is modified or a commit is pushed), we can prevent stale context from becoming durable misinformation.

We would love to collaborate on defining the JSON schema for this provenance metadata and the filter/merge policy logic. If you are interested, we can open a dedicated issue to start mapping out the implementation details!

[cedendahlkim]

Interesting question! We built an Ebbinghaus-based memory system for Bride (a cognitive AI at bride.gracestack.se) that models forgetting curves — concepts decay over time unless reinforced through use.

The key insight we learned: raw storage isn"t the hard part. The hard part is knowing what to remember. Our system uses Active Inference — it prioritizes memories that reduce prediction error. If a fact surprises the agent (high prediction error), it gets stronger encoding. If it confirms existing beliefs, it decays faster.

This creates a natural attention mechanism: the system remembers what challenges it, not what comforts it.

If you"re building cognitive memory, I"d suggest starting with: (1) a forgetting curve model (not just LRU), (2) surprise-weighted encoding, (3) a decay/reinforcement loop that runs continuously, not just on query.