Agentmemory vs Voyage AI

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-09-01
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At a glance

DimensionAgentmemoryVoyage AI
PricingFree (open-source)Contact sales (paid)
Primary FunctionPersistent memory for AI coding agentsDomain-specialized embedding & reranker for RAG
DeploymentSelf-hosted (Node.js, no external DB)Cloud API (managed)
Key FeatureTriple-stream retrieval (BM25 + vector + KG)Domain-specific models (finance, legal, code)
Best ForDevelopers using Claude Code, Cursor, etc.Enterprise RAG on specialized domains
Latest NewsTransitioning from demo to production infrastructure (2026-06-29)No recent news captured

Agentmemory is the no-brainer choice for developers needing persistent agent memory — it's free, self-hosted, and integrates directly with coding agents like Claude Code. Voyage AI wins if you need high-accuracy embeddings for domain-specific RAG (finance, legal), but its enterprise pricing and lack of transparency make it unsuitable for smaller teams. Choose by need: agent memory vs. search retrieval.

Agentmemory
Agentmemory

Open-source persistent memory runtime for AI coding agents — zero external databases.

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Voyage AI
Voyage AI

Specialized embedding models and rerankers for high-accuracy enterprise RAG, with 32K-token context and multimodal support.

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Pricing
Free
Contact Sales
Plans
$0/mo
Popularity
14 views
7.4k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
CLIPluginAPI
WebAPI
Categories
🧠 Agent Memory & Runtimes🔌 MCP Servers & Agent Tooling
🗄️ Vector Databases & Retrieval
Features
12 auto-hooks capture session start, prompt, tool call, and stop
Hybrid recall: BM25 + vector + knowledge graph with on-device reranking
95.2% R@5 recall on LongMemEval-S benchmark
Sub-20ms P50 latency on a laptop
92% fewer input tokens per session
54 MCP tools (memory_save, memory_recall, memory_smart_search, etc.)
130 REST endpoints under /agentmemory/*
Hourly consolidation sweeps: compress, merge duplicates, decay stale rows, emit audit rows
Knowledge graph extraction with temporal edges
Mesh federation: peer-to-peer sync over authenticated HTTPS
Obsidian export with frontmatter-tagged markdown
JSONL session import (Claude Code transcripts) for replay and crystal extraction
OTEL observability with traces and logs (Jaeger/Honeycomb/Tempo export)
Runs as single Node.js process, zero external databases (no Redis, Kafka, Postgres)
Real-time viewer on port 3113 and engine console on port 3114
General-purpose embedding models: voyage-3.5, voyage-3.5 lite
Domain-specific models for finance, legal, and code
Company-specific fine-tuned models for proprietary data
Voyage 4 model series for improved retrieval quality
voyage-multimodal-3.5 for multimodal retrieval (images + text)
Low-dimensional embeddings (3x-8x shorter vectors) reduce storage costs
Long-context support up to 32K tokens
rerank-2.5 and rerank-2.5-lite with instruction following
Batch API for large-scale embedding workloads
voyage-context-3 provides chunk-level details with global document context
Low-latency inference with 4x smaller model
2x cheaper inference than previous models
SOC 2 and HIPAA compliance
Modular design: plug-and-play with any vector DB and LLM
Integrations
Claude Code
Copilot CLI
Codex CLI
OpenClaw
Hermes
pi
Cursor
OpenCode
Claude Desktop
OpenHuman
Warp
Continue
Cline
Zed
Gemini CLI

What real users say: Agentmemory vs Voyage AI

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

Agentmemory

60 mentions across 6 sources · 61% positive — mixed

Hacker News, YouTube, Product Hunt, Bluesky, GitHub, Lemmy

What users praise

  • Free and open-source (MIT) with no external databases required.
  • Excellent benchmark: 95.2% recall on LongMemEval-S.
  • Triple-stream retrieval (BM25, vector, knowledge graph) is unique and effective.
  • Works with 10+ coding agents via MCP and REST.

What frustrates them

  • Crashes under large datasets (370K+ observations reported).
  • Some early adopters retracted after production use.
  • Tier 1 cap may limit truly infinite memory scenarios.
  • Manual pruning needed for contradictory or stale memories.

Researched Jul 6, 2026

Voyage AI

41 mentions across 4 sources · 48% positive — mixed

Hacker News, YouTube, Stack Overflow, Lemmy

What users praise

  • High accuracy for RAG retrieval, especially with the reranker models.
  • Domain-specific models for finance, legal, and code deliver better results.
  • Low-dimensional embeddings cut vector storage costs by up to 8x.
  • Supports long contexts up to 32K tokens, useful for large documents.

What frustrates them

  • Data-training clause in terms raises privacy red flags for enterprises.
  • Pricing is opaque, requiring contact with sales.
  • Community support is sparse — few Stack Overflow answers or forum threads.
  • No clear free tier, so trying it costs time with sales or API credits.

Researched Aug 26, 2026

Who should pick which

  • Solo developer using Claude Code
    Pick: Agentmemory

    Free, easy self-hosted setup, and directly integrates with Claude Code via MCP hooks. Eliminates context loss across sessions.

  • Enterprise RAG team in finance
    Pick: Voyage AI

    Voyage's finance-specific embedding model, long 32K context, and low-dimensional vectors optimize accuracy and cost for financial document retrieval.

  • Open-source contributor team
    Pick: Agentmemory

    Open-source, self-hosted, with mesh federation for team sync. No vendor lock-in, community-driven development.

  • Legal tech startup building document review
    Pick: Voyage AI

    Legal domain model and high recall from rerankers improve accuracy in legal document retrieval, despite higher cost.

  • AI agent memory researcher
    Pick: Agentmemory

    Supports graph extraction, Obsidian export, and OTEL observability — ideal for experimenting with memory architectures. Free and extensible.

Frequently Asked Questions

Agentmemory vs Voyage AI: which should you choose?

Agentmemory is the no-brainer choice for developers needing persistent agent memory — it's free, self-hosted, and integrates directly with coding agents like Claude Code. Voyage AI wins if you need high-accuracy embeddings for domain-specific RAG (finance, legal), but its enterprise pricing and lack of transparency make it unsuitable for smaller teams. Choose by need: agent memory vs. search retrieval.

Can Agentmemory and Voyage AI be used together?

Yes. Agentmemory's vector search could use Voyage's embeddings for improved recall, though it's not a built-in integration. You'd need to configure the vector store component.

Does Agentmemory require an external database?

No. It runs as a single Node.js process with no external dependencies. All memory is stored locally.

Is Voyage AI compliant with SOC 2 and HIPAA?

Yes, according to the description. Voyage AI supports SOC 2 and HIPAA compliance, suitable for regulated industries.

What is the latest news about Agentmemory?

As of 2026-06-29, Agentmemory is transitioning from a demo to production-grade infrastructure, indicating increased reliability and scalability.

Does Voyage AI offer a free tier?

The pricing is listed as 'contact', suggesting no free tier. Users must contact sales for access and pricing.

Which coding agents does Agentmemory support?

It supports Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI, Cline, and any MCP-compatible agent.

What makes Voyage AI's embeddings special?

Domain-specific models (finance, legal, code), up to 32K token context, low-dimensional vectors (3x-8x shorter), and instruction-following rerankers.

Can I self-host Agentmemory?

Yes. It is designed as a self-hosted solution, requiring only Node.js with no external services.

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Last reviewed: July 3, 2026