diff --git a/CHANGELOG.md b/CHANGELOG.md index bd6fddc..7d9a518 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,14 @@ # Changelog +## [2025-10-15T01:13:29-04:00 (America/New_York)] +### Added +- Authored `GOALS.md`, `PLANNING_THOUGHTS.md`, and `ROADMAP.md` to capture competitor-informed strategic goals, planning + options, and a prioritized feature roadmap aligning MeshMind with Mem0, Graphiti, and Zep capabilities. + +### Updated +- Refreshed planning collateral (`PLAN.md`, `SOT.md`, `RECOMMENDATIONS.md`, `TODO.md`, `ISSUES.md`, `RESUME_NOTES.md`) to + reference the new strategic documents and surface roadmap-aligned backlog items. + ## [2025-10-14T22:51:20-04:00 (America/New_York)] ### Changed - Documented LLM override precedence in `README.md`, expanded service documentation in `docs/api.md` and diff --git a/GOALS.md b/GOALS.md new file mode 100644 index 0000000..9070b7b --- /dev/null +++ b/GOALS.md @@ -0,0 +1,36 @@ +# MeshMind Strategic Goals + +## Platform and Interface Parity +- Deliver an official MeshMind MCP server with tooling coverage for memory CRUD, fact search, triplet queries, and graph cleanup so agent runtimes can treat MeshMind as a drop-in replacement for Mem0, Graphiti, and Zep offerings.【F:research/meshmind_exceed_recommendations.md†L4-L9】【F:research/ai_memory_features_catalog.csv†L2-L27】 +- Publish stable REST, gRPC, and MCP contracts accompanied by SDKs (Python, TypeScript) and example integrations for LangGraph, CrewAI, and other orchestration frameworks.【F:research/ai_memory_features_catalog.csv†L21-L59】 +- Provide command-line tooling that mirrors hosted competitors by covering provisioning, maintenance, reindexing, export/import, and evaluation flows.【F:research/meshmind_exceed_recommendations.md†L16-L23】 + +## Graph Excellence and Temporal Intelligence +- Add bi-temporal edge modeling (valid/transaction windows) with `as_of` querying semantics, invalidation hooks, and historical replay utilities to match Graphiti/Zep capabilities.【F:research/meshmind_exceed_recommendations.md†L5-L9】【F:research/meshmind_gap_table.csv†L2-L5】 +- Implement node-distance and focal-entity rerankers alongside multi-hop traversal recipes that combine BM25, embeddings, RRF/MMR, and BFS prioritization.【F:research/meshmind_exceed_recommendations.md†L5-L9】【F:research/meshmind_gap_table.csv†L6-L12】 +- Support graph-personalization features such as persona nodes, implicit preference graphs, and configurable rerank boosts for first-party experiences.【F:research/meshmind_exceed_recommendations.md†L24-L27】 + +## Scope, Tenancy, and Governance +- Introduce multi-level scoping primitives for user, agent, session, and run identifiers across storage, retrieval, APIs, and tooling so MeshMind can mirror Mem0 and Zep tenancy models.【F:research/meshmind_gap_table.csv†L3-L4】【F:research/ai_memory_features_catalog.csv†L3-L33】 +- Harden auth with API keys/JWT, per-scope quotas, and tenant isolation checks that execute in graph drivers and service layers by default.【F:research/meshmind_exceed_recommendations.md†L9-L15】【F:research/ai_memory_features_catalog.csv†L20-L59】 +- Ship data-governance controls including PII detection, redaction, retention policies, and encryption-at-rest toggles with audit reporting for compliance-sensitive deployments.【F:research/meshmind_exceed_recommendations.md†L27-L32】【F:research/meshmind_gap_table.csv†L15-L17】 + +## Pipeline Reliability and Maintenance Intelligence +- Build a consolidation planner that reasons about ADD/UPDATE/DELETE operations, contradiction detection, and human-in-the-loop review, complementing existing dedupe heuristics.【F:research/meshmind_exceed_recommendations.md†L16-L20】【F:research/meshmind_gap_table.csv†L5-L7】 +- Expand maintenance automation with TTL enforcement, scope-level resets, replayable change logs, and backpressure-aware scheduling across Celery tasks and admin tooling.【F:research/meshmind_exceed_recommendations.md†L10-L23】【F:research/ai_memory_features_catalog.csv†L14-L31】 +- Offer change-data-capture hooks and event streams so downstream analytics or cache layers can react to graph updates in real time.【F:research/ai_memory_features_catalog.csv†L14-L59】 + +## Retrieval Quality and Evaluation +- Publish an open evaluation harness that benchmarks Recall@k, MRR, NDCG, latency, and token costs for each retrieval recipe across vector, hybrid, and graph-traversal scenarios.【F:research/meshmind_exceed_recommendations.md†L31-L33】【F:research/ai_memory_features_catalog.csv†L24-L27】 +- Provide curated datasets and synthetic corpora that stress-test consolidation, summarization, and scoping logic for reproducible validation runs.【F:research/meshmind_exceed_recommendations.md†L16-L23】【F:research/ai_memory_features_catalog.csv†L24-L29】 +- Integrate online feedback loops (implicit clicks, rerank overrides) to continuously tune scoring weights and LLM-assisted rerank prompts.【F:research/meshmind_exceed_recommendations.md†L7-L9】【F:research/ai_memory_features_catalog.csv†L11-L18】 + +## Experience and Ecosystem Expansion +- Release browser ingestion extensions and MCP-compatible capture workflows that preserve provenance metadata while streaming memories into MeshMind.【F:research/meshmind_exceed_recommendations.md†L32-L33】【F:research/ai_memory_features_catalog.csv†L25-L29】 +- Launch hosted and managed MeshMind tiers with tenancy isolation, billing telemetry, and migration paths from self-hosted installations.【F:research/meshmind_gap_table.csv†L10-L13】【F:research/ai_memory_features_catalog.csv†L16-L21】 +- Deliver turnkey starter kits (demo agents, notebooks, LangGraph templates) demonstrating best practices for each feature tier and vertical use case.【F:research/meshmind_exceed_recommendations.md†L19-L23】【F:research/ai_memory_features_catalog.csv†L21-L59】 + +## Observability, Safety, and Operations +- Instrument full OpenTelemetry traces, metrics, and structured logs across ingestion, maintenance, retrieval, and LLM calls with dashboards for latency, recall, and cost monitoring.【F:research/meshmind_exceed_recommendations.md†L16-L23】【F:research/meshmind_gap_table.csv†L12-L15】 +- Add safety guardrails: rate limiting, anomaly detection on ingestion payloads, abuse monitoring, and configurable moderation pipelines integrated with governance tooling.【F:research/meshmind_gap_table.csv†L10-L17】【F:research/ai_memory_features_catalog.csv†L20-L39】 +- Provide disaster-recovery playbooks including automated backups, time-travel restores (leveraging bi-temporal data), and chaos-testing scenarios for graph backends.【F:research/meshmind_exceed_recommendations.md†L4-L9】【F:research/meshmind_gap_table.csv†L2-L5】 diff --git a/ISSUES.md b/ISSUES.md index 229f55b..be70018 100644 --- a/ISSUES.md +++ b/ISSUES.md @@ -17,6 +17,7 @@ - [ ] Document Neo4j driver requirements and verify connectivity against a live cluster (CLI connectivity checks exist but still need validation against a real instance). - [ ] Exercise the new namespace/entity-label filtering against live Memgraph/Neo4j datasets to confirm Cypher predicates behave as expected. - [ ] Regenerate `uv.lock` to reflect the updated dependency set (`pymgclient`, `fastapi`, `uvicorn`, extras) so CI tooling stays in sync. +- [ ] Break down the competitive roadmap (MCP parity, multi-level scoping, bi-temporal edges, advanced rerankers, governance) into executable epics with owners and timelines referencing `GOALS.md`/`ROADMAP.md`. ## Medium Priority - [x] Persist results from consolidation and compression tasks back to the database (currently in-memory only). - [x] Refine `Memory.importance` scoring to reflect actual ranking heuristics instead of a constant. diff --git a/PLAN.md b/PLAN.md index ac9c42a..5d808cd 100644 --- a/PLAN.md +++ b/PLAN.md @@ -43,3 +43,8 @@ 2. **Operational Observability** – Export telemetry to Prometheus/OpenTelemetry and surface dashboards/alerts. 3. **Celery Hardening** – Stress test consolidation/compression heuristics at scale and codify retry/backoff policies. 4. **Model Fidelity** – Replace compatibility shims with production-ready Pydantic models once dependency support catches up. + +## Phase 6 – Competitive Roadmap Alignment (New) +1. **Strategic Goals** – Track `GOALS.md` as the repository for parity, governance, and differentiation targets derived from the latest competitor analyses. +2. **Planning Options** – Reference `PLANNING_THOUGHTS.md` during iteration reviews to select the sequencing strategy (parity-first, reliability-first, differentiator-led) that matches current constraints and staffing. +3. **Execution Roadmap** – Groom tasks against `ROADMAP.md`, prioritizing MCP parity, multi-level scoping, bi-temporal edges, advanced rerankers, and governance items before lower-impact enhancements. diff --git a/PLANNING_THOUGHTS.md b/PLANNING_THOUGHTS.md new file mode 100644 index 0000000..0bbe39e --- /dev/null +++ b/PLANNING_THOUGHTS.md @@ -0,0 +1,24 @@ +# Planning Thoughts + +## Option A – Parity-First Sprints +- **Concept**: Deliver the competitor feature set (MCP server, bi-temporal graph, scoped tenancy, RRF/MMR rerankers) before layering differentiators so MeshMind can immediately replace Mem0, Graphiti, and Zep in pilot programs.【F:research/meshmind_exceed_recommendations.md†L4-L15】【F:research/meshmind_gap_table.csv†L2-L13】 +- **Why it works**: Rapidly closes critical capability gaps, simplifies messaging (“everything they have, plus more”), and unlocks co-marketing with ecosystem partners reliant on MCP integrations and hosted tenancy workflows.【F:research/ai_memory_features_catalog.csv†L16-L59】 +- **Risks**: Compresses bandwidth for experimentation, leaving differentiators (personalization, governance, evaluation harness) for later and risking burnout if parity demands exceed available engineering cycles.【F:research/meshmind_exceed_recommendations.md†L24-L33】 +- **Mitigations**: Parallelize observability and evaluation harness groundwork so readiness reviews keep quality high, and schedule design spikes for differentiator features while parity builds progress.【F:research/meshmind_exceed_recommendations.md†L16-L33】 + +## Option B – Reliability and Observability First +- **Concept**: Fortify graph durability, telemetry, tenancy, and governance before shipping parity features to ensure every new capability launches with enterprise-grade reliability and compliance hooks.【F:research/meshmind_exceed_recommendations.md†L9-L32】【F:research/meshmind_gap_table.csv†L3-L17】 +- **Why it works**: Positions MeshMind as the safest, most trustworthy platform, attracting regulated customers and enabling paid tiers/SLAs sooner than pure feature parity could.【F:research/meshmind_gap_table.csv†L10-L17】 +- **Risks**: Competitive demos may still highlight missing MCP tooling or advanced retrieval features, slowing adoption within open-source agent ecosystems that expect immediate compatibility.【F:research/meshmind_gap_table.csv†L6-L13】 +- **Mitigations**: Release public roadmap updates, partner with early adopters on co-developed MCP pilots, and provide interim adapters or compatibility layers until full parity arrives.【F:research/meshmind_exceed_recommendations.md†L4-L15】 + +## Option C – Differentiator-Led Sequencing +- **Concept**: Invest early in personalization, governance analytics, evaluation harnesses, and hosted offerings to leapfrog competitors while continuing incremental parity work in parallel tracks.【F:research/meshmind_exceed_recommendations.md†L24-L33】【F:research/meshmind_gap_table.csv†L11-L17】 +- **Why it works**: Creates a compelling “MeshMind advantage” narrative (adaptive retrieval, safety, hosted tier) that can justify premium pricing or open new verticals even if some parity items arrive later.【F:research/ai_memory_features_catalog.csv†L24-L59】 +- **Risks**: Without MCP parity or multi-level scoping, integrators might face friction onboarding, reducing the immediate utility of differentiation investments.【F:research/meshmind_gap_table.csv†L3-L13】 +- **Mitigations**: Define must-have parity milestones (MCP server beta, scoping primitives) as release gates for differentiator GA and staff shared teams to maintain progress across both tracks.【F:research/meshmind_exceed_recommendations.md†L4-L23】 + +## Cross-Cutting Considerations +- Maintain a living roadmap that sequences parity, reliability, and differentiation work with clear dependencies so contributors can volunteer for the highest-leverage streams.【F:research/meshmind_exceed_recommendations.md†L4-L33】 +- Prioritize documentation updates (SDK guides, governance policies, evaluation harness instructions) alongside feature work to keep MeshMind’s onboarding advantage intact.【F:research/ai_memory_features_catalog.csv†L21-L59】 +- Schedule recurring competitive reviews to ingest new Mem0/Zep/Graphiti releases and adjust priority ordering before each planning increment.【F:research/meshmind_gap_table.csv†L2-L17】 diff --git a/RECOMMENDATIONS.md b/RECOMMENDATIONS.md index 2a7c484..ef52308 100644 --- a/RECOMMENDATIONS.md +++ b/RECOMMENDATIONS.md @@ -31,6 +31,8 @@ the Makefile and expand it as new developer utilities are introduced. Keep `SETUP.md` synchronized when dependencies change. - Provide walkthroughs for configuring LLM reranking, including sample prompts and response expectations. - Add onboarding notes for the REST/gRPC service layers with sample payloads and curl/grpcurl snippets. +- Keep `GOALS.md`, `PLANNING_THOUGHTS.md`, and `ROADMAP.md` refreshed each planning cycle so contributors have a single + reference for competitive priorities, sequencing strategies, and upcoming feature commitments. ## Future Enhancements - Export telemetry to Prometheus/OpenTelemetry and wire alerts/dashboards around ingestion and maintenance. diff --git a/RESUME_NOTES.md b/RESUME_NOTES.md index fc30d97..6466021 100644 --- a/RESUME_NOTES.md +++ b/RESUME_NOTES.md @@ -22,6 +22,8 @@ - Extended `DUMMIES.md` and `docs/testing.md` to capture the `FakeLLMClient` behaviour and the setup script smoke-test coverage; updated `ENVIRONMENT_NEEDS.md` and `NEEDED_FOR_TESTING.md` to acknowledge that optional packages now install with network access. +- Authored competitor-aligned planning collateral (`GOALS.md`, `PLANNING_THOUGHTS.md`, `ROADMAP.md`) and refreshed planning + artifacts (`PLAN.md`, `SOT.md`, `RECOMMENDATIONS.md`, `TODO.md`, `ISSUES.md`) to surface roadmap-aligned follow-up work. ## Environment State @@ -34,12 +36,14 @@ ## Next Session Starting Points -1. Work through the remaining `TODO.md` priority items that are unblocked by missing infrastructure (e.g., research tasks may +1. Break down roadmap initiatives (MCP parity, multi-level scoping, bi-temporal edges, advanced rerankers, governance) into + executable specs and tickets referencing `GOALS.md`, `ROADMAP.md`, and the new `TODO.md` items. +2. Work through the remaining `TODO.md` priority items that are unblocked by missing infrastructure (e.g., research tasks may remain pending until live services exist). -2. Validate Neo4j connectivity end-to-end once a reachable instance is available, using `meshmind admin graph --backend neo4j`. -3. Plan integration tests for the LLM override payloads against a real provider when credentials are provisioned; update +3. Validate Neo4j connectivity end-to-end once a reachable instance is available, using `meshmind admin graph --backend neo4j`. +4. Plan integration tests for the LLM override payloads against a real provider when credentials are provisioned; update `docs/testing.md` accordingly. -4. Continue chipping away at shim retirements documented in `DUMMIES.md`, starting with replacing the Pydantic compatibility +5. Continue chipping away at shim retirements documented in `DUMMIES.md`, starting with replacing the Pydantic compatibility layer when production targets allow the real dependency. ## Helpful References diff --git a/ROADMAP.md b/ROADMAP.md new file mode 100644 index 0000000..909bba8 --- /dev/null +++ b/ROADMAP.md @@ -0,0 +1,18 @@ +# MeshMind Feature Roadmap + +1. **Ship official MCP server and tool suite** – Implement authenticated MCP endpoints for adding, querying, and managing memories, triplets, and graph hygiene so MeshMind reaches feature parity with Mem0, Graphiti, and Zep integrations.【F:research/meshmind_exceed_recommendations.md†L4-L9】【F:research/ai_memory_features_catalog.csv†L18-L37】 +2. **Introduce multi-level scoping and tenancy** – Add user, agent, session, and run identifiers across storage, retrieval, SDKs, and admin tooling with tenant-aware rate limits and purge/reset flows.【F:research/meshmind_gap_table.csv†L3-L4】【F:research/ai_memory_features_catalog.csv†L3-L33】 +3. **Adopt bi-temporal graph edges** – Support valid/transaction time windows, `as_of` queries, and automated invalidation semantics to unlock temporal analytics parity with Graphiti/Zep.【F:research/meshmind_exceed_recommendations.md†L5-L9】【F:research/meshmind_gap_table.csv†L2-L5】 +4. **Expand hybrid retrieval and reranking** – Deliver BM25 + embedding ensembles with configurable RRF/MMR fusion, node-distance boosts, and BFS traversal recipes for focal-entity relevance gains.【F:research/meshmind_exceed_recommendations.md†L5-L9】【F:research/meshmind_gap_table.csv†L6-L12】 +5. **Harden auth and governance** – Enable API keys/JWT, tenant isolation enforcement, PII scrubbing, retention policies, encryption controls, and moderation/audit logs baked into service layers.【F:research/meshmind_exceed_recommendations.md†L9-L15】【F:research/meshmind_gap_table.csv†L10-L17】 +6. **Deliver consolidation planner and maintenance intelligence** – Build LLM-assisted ADD/UPDATE/DELETE planners, contradiction detection, replayable change logs, and scope-level reset tooling.【F:research/meshmind_exceed_recommendations.md†L16-L20】【F:research/meshmind_gap_table.csv†L5-L7】 +7. **Stand up full OpenTelemetry observability** – Emit traces, metrics, dashboards, and per-route recall/latency/cost panels covering ingestion, retrieval, LLM calls, and maintenance flows.【F:research/meshmind_exceed_recommendations.md†L16-L23】【F:research/meshmind_gap_table.csv†L12-L15】 +8. **Broaden provider and backend flexibility** – Add first-class drivers for Qdrant/Chroma/Azure AI Search, expose latency/cost analytics, and document backend selection guides for production workloads.【F:research/meshmind_exceed_recommendations.md†L16-L23】【F:research/ai_memory_features_catalog.csv†L14-L33】 +9. **Launch SDKs and ecosystem starter kits** – Ship production-ready Python and TypeScript SDKs, notebooks, LangGraph/CrewAI templates, and CLI/admin enhancements for reindex, compact, prune, export, and import flows.【F:research/meshmind_exceed_recommendations.md†L16-L23】【F:research/ai_memory_features_catalog.csv†L21-L59】 +10. **Publish evaluation harness and benchmarks** – Release reusable datasets, scoring scripts (Recall@k, MRR, NDCG, latency, token cost), and regression dashboards comparing retrieval recipes and backends.【F:research/meshmind_exceed_recommendations.md†L31-L33】【F:research/ai_memory_features_catalog.csv†L24-L27】 +11. **Enable graph-aware personalization** – Introduce persona graphs, focal-node boosting, and adaptive rerank strategies that learn from implicit feedback and stored preferences.【F:research/meshmind_exceed_recommendations.md†L24-L27】 +12. **Provide browser and MCP ingestion workflows** – Build extensions and capture tools that push annotated content into MeshMind with provenance metadata and scoping defaults.【F:research/meshmind_exceed_recommendations.md†L32-L33】【F:research/ai_memory_features_catalog.csv†L25-L29】 +13. **Offer hosted MeshMind tiers** – Deliver managed deployments with tenancy isolation, billing metrics, SSO, quotas, and migration support from self-hosted instances.【F:research/meshmind_gap_table.csv†L10-L13】【F:research/ai_memory_features_catalog.csv†L16-L21】 +14. **Roll out data governance and compliance tooling** – Bundle PII detection, redaction, retention policy management, encryption configuration, and compliance reporting dashboards.【F:research/meshmind_exceed_recommendations.md†L27-L32】【F:research/meshmind_gap_table.csv†L15-L17】 +15. **Activate advanced safety and resilience** – Implement abuse monitoring, anomaly detection, rate limiting, automated backups, time-travel restores, and chaos-testing scenarios leveraging temporal graph features.【F:research/meshmind_exceed_recommendations.md†L4-L9】【F:research/meshmind_gap_table.csv†L10-L17】 +16. **Sustain ongoing competitive analysis** – Schedule quarterly research reviews, update feature matrices, and adjust priorities as Mem0, Graphiti, Zep, and emerging platforms evolve.【F:research/meshmind_gap_table.csv†L2-L17】【F:research/ai_memory_features_catalog.csv†L2-L59】 diff --git a/SOT.md b/SOT.md index aae1829..8d5e7ef 100644 --- a/SOT.md +++ b/SOT.md @@ -31,6 +31,8 @@ Supporting assets: - Documentation (`PROJECT.md`, `PLAN.md`, `SOT.md`, `README.md`, etc.) describing the system and roadmap. - `DUMMIES.md`: Catalog of temporary shims (Pydantic fallback, REST/gRPC stubs, Celery dummies, fake drivers) with removal guidance now that dependencies can be installed. +- `GOALS.md`, `PLANNING_THOUGHTS.md`, `ROADMAP.md`: Strategic planning artifacts aligning MeshMind workstreams with + competitor-informed goals, sequencing options, and prioritized delivery order. ## Configuration (`meshmind/core/config.py`) - Loads environment variables for the active graph backend (`GRAPH_BACKEND`), Memgraph (`MEMGRAPH_URI`, `MEMGRAPH_USERNAME`, diff --git a/TODO.md b/TODO.md index 6ced283..6fa14a8 100644 --- a/TODO.md +++ b/TODO.md @@ -65,6 +65,8 @@ - [ ] Add gRPC proto definitions and generated clients so the Python stubs align with production servers (including `MemoryCounts`). - [ ] Benchmark driver-side pagination/filtering on large datasets to tune default candidate limits and document recommended overrides. - [ ] Implement backend-native vector similarity queries for Memgraph/Neo4j to eliminate Python-side scoring when embeddings are present. +- [ ] Draft a detailed MCP server specification (tools, auth, tenancy flows) aligned with `GOALS.md` and the roadmap for engineering breakdown. +- [ ] Design the multi-level scoping data model (user/agent/session/run) and persistence changes required for roadmap parity before implementation begins. ## Recommended Waiting for Approval Tasks