Wandering Nomad: multi-agent architecture

1.8.25

Wide Research: Manus Unleashes 100-Agent Parallel Processing for Lightning-Fast, Large-Scale Insight

Manus—the Singapore-based startup behind the namesake autonomous AI agent—has flipped the research workflow on its head with Wide Research, a system-level mechanism that sends hundreds of parallel agents after every angle of a complex question. Whether you want a side-by-side on 500 MBA programs or a 360° scan of GenAI tools, Wide Research chews through the workload in a fraction of the time sequential agents would take.

From Deep to Wide

Most “deep research” agents operate like meticulous librarians: a single high-capacity model crawls source after source, sequentially synthesising answers. It’s thorough—but agonisingly slow at scale. Wide Research replaces that linear approach with an agent-cluster collaboration protocol. Each sub-agent is a full Manus instance, not a narrow specialist, so any of them can read, reason and write. The orchestration layer splinters a task into sub-queries, distributes them, then merges the results into one coherent report.

Why general-purpose sub-agents matter

Traditional multi-agent designs hard-code roles—“planner,” “coder,” “critic.” Those rigid templates break when a project veers off script. Because every Wide Research worker is general-purpose, task boundaries dissolve: one sub-agent might scrape SEC filings, another might summarise IEEE papers, and a third could draft executive bullets—then hand the baton seamlessly.

Inside the Architecture

Layer	Function	Default Tech
Task Decomposer	Splits the master query into 100-plus granular prompts	LLM-based planner
Agent Fabric	Launches isolated, cloud-hosted Manus instances; scales elastically	K8s + Firecracker VMs
Coordination Protocol	Routes intermediate results, resolves duplicates, merges insights	Proprietary RPC
Aggregator & Formatter	Synthesises final doc, slides, or CSV	Manus core model

The entire pipeline is asynchronous; users can park a query (“compare 1 000 stocks”) and return later to a ready-made dashboard—no tab babysitting required.

Performance Snapshot

Scenario	Deep-style Single Agent	Wide Research (100+ agents)
Analyse 100 sneakers for price, reviews, specs	~70 min	< 7 min
Rank Fortune 500 by AI spend, ESG score	~3 h	18 min
Cross-compare 1 000 GenAI startups	Time-out	45 min

(Internal Manus demo data shown during launch.)

Early Use Cases

Competitive Intelligence – Product teams ingest hundreds of rival SKUs, markets and patents overnight.
Financial Screening – Analysts filter thousands of equities or tokens with bespoke metrics—faster than spreadsheet macros can update.
Academic Surveys – Researchers pull citations across disciplines, summarising 200+ papers into thematic clusters in a single afternoon.

Because Wide Research is model-agnostic, enterprises can plug in Anthropic Claude, Qwen, or local Llama checkpoints to meet data-sovereignty rules.

Pricing & Roll-Out

Today: Wide Research is live for Pro subscribers (US $199/month).
Q3 2025: Gradual access for Plus and Basic tiers.
Future: Manus hints at an on-prem “WideKit” for regulated industries that can’t leave their firewall.

Limitations & Trade-Offs

Compute Cost: Hundreds of VM-backed agents aren’t cheap; budget accordingly for very large jobs.
Cold-Start Results: Until sub-agents gather enough signal, early outputs can be uneven—iteration helps.
Benchmark Transparency: Manus hasn’t yet published formal speed/quality benchmarks vs. sequential baselines, though third-party analyses are emerging.

The Bigger Picture

Wide Research is less a one-off feature than a proof-of-concept for “scaling laws of agentic AI.” Manus argues that throwing more capable agents—not merely larger context windows—can yield super-linear gains in throughput and idea diversity. It’s a thesis with broad implications for everything from autonomous coding swarms to AI-driven drug pipelines.

As parallel agent frameworks proliferate (think IBM’s MCP Gateway, Baidu’s AI Search Paradigm, Anthropic’s Claude tool plugins), context engineering and agent coordination will rival model size as the key levers of performance.

Key Takeaway

Wide Research reframes high-volume, messy analysis as a parallel rather than serial challenge—turning hours of manual slog into minutes of delegated computation. For teams drowning in data and deadlines, Manus just opened a wormhole to faster, broader insight—no prompt cajoling required.

21.7.25

Mirix: A Modular Memory Layer that Gives AI Agents Long-Term Recall and Personalized Reasoning

1 | Why “Memory” Is the Next AI Bottleneck

Large-language-model agents excel at single-turn answers, but forget everything once the context window scrolls out of sight. That results in repetitive conversations, lost project state, and brittle multi-step plans. Mirix, introduced by researchers from Carnegie Mellon and Tsinghua University, tackles the problem with a drop-in, modular memory layer that any agent framework (LangGraph, Autogen, IBM MCP, etc.) can call.

2 | How Mirix Works under the Hood

Layer	Purpose	Default Tech Stack
Ingestors	Capture raw events (chat turns, tool outputs, sensors).	Web-hooks, Kafka, Postgres logical decode
Canonicalizer	Convert heterogeneous events to a common MemoryEvent schema with type, timestamp, and embeddings.	Pydantic, OpenAI `embeddings-3-small`
Memory Stores	Pluggable persistence engines. Ship with: • VectorDB (FAISS / Milvus) • Knowledge Graph (Neo4j) • Document Store (Weaviate hybrid).	Drivers for each
Retrievers	Route agent queries to the right store; merge and de-dupe results; compress into 2-3 k tokens.	Hybrid BM25 + vector; Rank-fusion
Reasoners	Optional small models that label sentiment, importance, or user identity to prioritize what is stored or surfaced.	DistilRoBERTa sentiment, MiniLM ranker

Key insight: memory need not live in a single DB; Mirix treats it as an orchestrated ensemble of stores, each optimised for a particular signal (facts vs. tasks vs. social cues).

3 | What It Enables

Capability	Example
Long-Horizon Planning	A code-review agent tracks open pull-requests and test failures for weeks, not hours.
True Personalization	A tutoring bot recalls a student’s weak areas and preferred explanations.
Contextual Tool Use	An enterprise helper chooses between Jira, Confluence, or GitLab based on past success rates with the same user.

Benchmarks on WikiChat-Memory (multi-episode conversations) show 58 % fewer repetitions vs. vanilla RAG and 3.4 × higher success on 15-step task chains.

4 | Plugging Mirix into an Existing Agent


from mirix.memory import MemoryClient
from agentic import Agent

mem = MemoryClient(
    stores=[
        "faiss://embeddings",
        "neo4j://graph",
        "weaviate://docs"
    ]
)

agent = Agent(llm="mistral-small-3.2", memory=mem)

response = agent.chat("Where did we leave the migration script last week?")
print(response)

The memory layer runs async, so ingest and retrieval add <50 ms latency, even with three stores in parallel.

5 | Governance & Cost Controls

Policy Filters: PII redaction rules determine what is persisted.
TTL & Eviction: Events expire after a configurable horizon (default 90 days) or when embedding budget is hit.
Audit Log: Every retrieval is stamped for compliance, easing SOC 2 / GDPR audits.

6 | Limitations & Roadmap

Cold-start: Until enough signal accumulates, Mirix falls back to generic prompts.
Cross-user Contamination: Requires careful namespace isolation in multi-tenant deployments.
Upcoming: Graph-based reasoning (path-finding across memory) and a “Memory-as-Service” managed version on Azure.

Final Takeaway

Mirix turns stateless LLM calls into stateful, personalised experiences—without locking you into a single database or vendor. If your chatbot forgets what happened yesterday or your autonomous agent loses track of a multi-day workflow, Mirix may be the missing memory you need.