Exogram vs local-deep-research

Exogram and local-deep-research are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Exogram

Exogram is an execution governance layer that intercepts AI agent actions — payments, database writes, customer emails, record updates — and applies a policy decision before anything hits your infrastructure. The vendor describes a four-way enforcement decision: allow, deny, escalate, or log. Policy rules are checked at runtime, not after the fact, which means a $25,000 invoice approval blocked against a $1,000 limit never reaches your payment system. The immutable audit trail is positioned for SOC 2, HIPAA, and financial compliance workflows. The tool is not itself an agent runner — it assumes you already have an agent; it governs what that agent is allowed to touch.

local-deep-research

The tool autonomously plans and executes multi-step research tasks: it queries sources, follows citations, synthesizes findings, and returns results with full attribution — all without a cloud handoff. The vendor reports ~95% on SimpleQA benchmarks using models like Qwen3-27B on a single RTX 3090, which gives you a concrete hardware target. It pulls from 10+ search backends including arXiv, PubMed, and private document collections. Where it breaks: running capable local models demands real GPU headroom, and teams without that hardware will either throttle to weaker models or route queries to cloud LLMs — at which point the privacy guarantee depends entirely on which cloud endpoint they configure. The 109 open issues and 210 open pull requests on GitHub signal an active but fast-moving codebase; production stability requires version pinning.

Attribute	Exogram	local-deep-research
Pricing	Paid	Free
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	Yes
Platforms	SaaS, Cloud	Linux, macOS, Windows (via Docker, WSL2, or direct installation)
Released	2025-05	2024
Pros	Runtime policy enforcement at the tool-call boundary, so unauthorized payments and database mutations are blocked before they execute rather than flagged after the damage is done. Four-way enforcement decisions — allow, deny, escalate, log — which means regulated workflows get a human review step without building a custom approval queue on top of your agent stack. Immutable audit logs positioned for SOC 2 and HIPAA compliance, so teams in regulated industries have a defensible record of every action an agent attempted and what decision was returned. Pre-built integrations with LangChain, CrewAI, AutoGen, Vercel AI SDK, and LlamaIndex, so teams already running these frameworks add a governance layer without rewriting their agent code. An open protocol spec (EAAP) published as RFC-0001, so teams who need to audit, extend, or independently verify the governance model are not working against a black-box contract.	Encrypted, fully local processing means documents never leave your infrastructure, so regulated or confidential data can be fed directly into research workflows without legal review of a vendor's data handling terms. Provider-agnostic model routing — llama.cpp, Ollama, OpenAI, Google, and others through a single config — so migrating from cloud to local inference when privacy requirements tighten is a configuration change, not a rewrite. 10+ search backends including arXiv and PubMed alongside private document collections, so a single research query can span published literature and internal proprietary data in one agent run rather than requiring two separate tools. Full source citations on every synthesized output, which means research results arrive with attribution intact — no manual provenance chase before you can use the findings in a paper or internal report. MIT license with self-hosted deployment means no vendor lock-in and no per-query costs as research volume scales, so teams running high-throughput literature reviews are not watching an API bill grow with every job.
Cons	Exogram governs actions but does not orchestrate agents — teams that need branching logic, memory, or coordination between multiple agents still maintain a separate orchestration layer, which means adding Exogram adds a second system to debug when an escalation fires unexpectedly. No self-hosted deployment option is described on the vendor page, which means teams whose compliance requirements mandate on-premises data residency — common in financial services and healthcare — cannot use Exogram without routing agent traffic through external infrastructure; those teams move to building policy enforcement into their own API gateway instead. The tool launched in approximately May 2025, so production case studies at scale are not yet publicly available; teams evaluating for high-volume payment workflows are working from architecture documentation and demos rather than documented incident records from comparable deployments.	Benchmark-level accuracy (~95% on SimpleQA) is tied to running Qwen3-27B on a GPU like the RTX 3090; teams without comparable hardware that fall back to smaller models or CPU inference will see meaningfully lower result quality, and the gap is not documented per-model in the scraped source. With 109 open issues and 210 open pull requests, the codebase changes fast — teams that deploy this into production pipelines without pinning to a specific release version will encounter breaking changes between upgrades, and there is no paid support tier to escalate when something breaks. The project has no commercial backing, only donations and grants; teams that need SLA-backed uptime, security patches on a defined schedule, or vendor-supported integrations will eventually migrate to a commercial research agent — the community-only support model is the condition that triggers that switch.

Bottom line

Exogram is paid while local-deep-research is free; local-deep-research is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Exogram and local-deep-research?

Exogram is Paid, while local-deep-research is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Exogram better than local-deep-research?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.