aws-samples / aws-samples/sample-autonomous-cloud-coding-agents

feat(memory): structured knowledge graph (tier 4, optional)

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agent-runtime enhancement P2
Dominant language
TypeScript
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143
Forks
46
Avg merge
3d 9h
Merged PRs (30d)
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Description

**Context:** ROADMAP.md → Memory security → Structured knowledge graph (tier 4)

---

## Component

Agent (Python runtime)

## Describe the feature

Optional **long-term** direction: structured knowledge graph if semantic + episodic AgentCore Memory proves insufficient for repo-specific query patterns (e.g. cross-file API dependency queries).

## Use case

Some repos need relationship-aware retrieval ("what calls this endpoint?") beyond vector similarity. Defer until semantic/episodic limits are measured in production.

## Proposed solution

1. RFC spike: graph schema, query API, sync from codebase indexing.
2. Integration point with hydration budget.
3. Explicitly **optional**—ship only if evaluation pipeline shows retrieval gaps.
4. May leverage external store (Neptune, or embedded graph) — TBD in RFC.

## Other information

- Lowest priority in memory epic; do not block other memory security work.
- Design context: `docs/design/MEMORY.md`.

- [ ] This might be a breaking change

Contributor guide

Open the contributing guide

Research direction

Read ROADMAP.md and docs/design/MEMORY.md to understand the memory-security context and existing semantic and episodic limits. Begin with the proposed RFC spike, documenting the graph schema, query API, codebase-index synchronization, and hydration-budget integration. Done means the evaluation pipeline justifies the optional feature and the RFC resolves the external-store choice.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws, python
Domain
ai, databases
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
25/100

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