OpenClaw & Hermes
Give agent frameworks an operating discipline.
OpenClaw and Nous Research's Hermes Agent are open-source agent frameworks that can read files, run commands, browse and talk to people through chat apps. ColdAI helps teams turn one of them into a bounded workflow for a specialised task, such as literature review or document analysis, with the tools, data access and review points that task needs and nothing more.
The opportunity
Start with a decision
worth improving.
A research assistant needs to gather information, keep task state and produce a reviewable result. Its tools should be powerful enough to help without inheriting unrestricted access to everything.
From possibility to a working system
Follow the flow.
Three connected decisions. One considered architecture.
Conceptual flow, not a live system or measured result.
Inspect the foundation
Review the selected framework, dependencies and deployment model. Map its capabilities against the task and identify features that require additional engineering rather than assuming built-in coverage.
Constrain the tools
Provide only the tools and credentials required for the workflow. Validate inputs, isolate execution where appropriate and require approval for actions with material consequences.
Make runs reviewable
Preserve useful state, traces and outputs across task attempts. Exercise cancellation, timeout and recovery paths so operators can understand what happened when a run does not complete.
The work, made concrete
What we can build
with your team.
We agree scope, dependencies and acceptance criteria before delivery. Your project can start with an assessment, a pilot or an integration into an existing system.
Framework and dependency assessment
Clarify the system boundary, the responsible owners and the decisions the architecture needs to support.
A restricted tool-enabled pilot
Turn the agreed design into reviewable work, evaluated against representative inputs and explicit success criteria.
Run tracing and recovery procedures
Make the next step operable: document responsibilities, known limitations and the path from pilot to ongoing use.
In depth
Go deeper into agent framework deployments
An even-handed comparison of the two projects, and a step-by-step hardening procedure for running either with real data and tools.
- 01ComparisonOpenClaw vs Hermes Agent: which open-source framework?OpenClaw vs Hermes Agent compared from each project's own documentation: architecture, trust model, skills, memory, hosting and fit for specialised tasks.
- 02ProcessHardening an OpenClaw or Hermes Agent deploymentA step-by-step hardening procedure for self-hosted OpenClaw or Hermes Agent: pinned versions, sandboxing, gateway exposure, vetted skills and scoped secrets.
Why an impressive agent demo is not yet a deployment
Both frameworks are designed to be capable out of the box. OpenClaw runs a gateway on your own machine or server and connects an assistant to messaging channels, models and tools1. Hermes Agent adds a learning loop in which the agent writes reusable skills after complex tasks and keeps memory across sessions2. On a developer's laptop, with the developer as the only user, that breadth is the point.
An organisation changes the problem. The agent now sees shared documents and real credentials, several people may be able to instruct it, and its outputs feed decisions someone has to answer for. A framework's defaults were chosen for one trusted operator, and OpenClaw's own security guidance assumes one trust boundary per gateway3. The deployment work is to decide what the agent may touch for this task, enforce that in the runtime and make every run something a reviewer can follow afterwards.
The tasks that suit this approach are narrow and document-heavy: assembling a literature review from an approved corpus, comparing clauses across a set of contracts, checking the assumptions in a financial model against its source data. In each, the agent does the reading and drafting, and a person owns the conclusion.
Six surfaces every agent framework deployment has to settle
Each surface is a separate decision. Leaving one at its default is still a decision, usually the least restrictive one.
- Agent runtime
The framework process, pinned to a reviewed version and running under its own identity.
- Model providers
Which hosted or local models it may call, and what data may be sent to each.
- Channels and users
Who can instruct the agent, through which chat apps or interfaces, and how unknown senders are handled.
- Tools, shell and browser
The commands, files and sites it can reach, ideally inside an isolated sandbox.
- Skills and plug-ins
Third-party or self-generated extensions, each of which is code or instructions to review.
- Memory and state
What persists between sessions, where it is stored and who can read or delete it.
- Credentials
Scoped keys for each task, kept out of prompts, transcripts and long-term memory.
Setting the operating envelope for a specialised task
A bounded deployment starts by writing down, per task, what the agent does and what stays with people.
| Task | The agent may | People keep |
|---|---|---|
| Scientific literature review | Search approved databases and an internal library, extract findings and draft a summary that cites each source | Inclusion criteria, judgement on study quality and the conclusions drawn |
| Legal document analysis | Read a defined set of contracts, extract clauses into a table and flag departures from a playbook | Legal interpretation, advice and anything sent to a counterparty |
| Financial model review | Trace inputs to source files, recompute figures in a sandbox and list inconsistencies | Changes to the model, sign-off and any figure used externally |
| Internal research requests | Gather material from approved sources and keep task notes between sessions | Decisions about what to share beyond the requesting team |
Rows are illustrative envelopes, not a catalogue of supported use cases. The right envelope depends on your data, obligations and reviewers.
When an open-source agent framework is the right foundation
- If
You need an assistant that works through chat channels, keeps task state and uses a shell or browser, and you can run it in your own environment.
ThenOpenClaw or Hermes Agent is a reasonable foundation, provided the deployment work on this page is done.
Both projects provide that breadth already, and self-hosting keeps data and logs under your control.
- If
The steps are fixed and the judgement is limited to extraction or classification.
ThenUse a workflow platform instead; see n8n and Zapier workflows.
A fixed path is easier to test and operate than a general-purpose agent.
- If
You want a managed runtime with cloud identity, policy and observability services around the agent.
ThenConsider a managed agent runtime such as Bedrock AgentCore.
Managed services take on parts of the isolation and tracing work you would otherwise build.
- If
The task genuinely decomposes into specialist roles that check each other's work.
ThenLook at multi-agent designs before stretching one framework instance.
Independent review between roles is a design property, not something a single assistant adds by itself.
A hypothetical literature-review assistant with a narrow brief
Risks particular to open-source agent frameworks
Fast-moving releases
Early signalConfiguration keys, defaults and plug-in interfaces change between versions you have not reviewed.
MitigationPin an exact version, read release notes before upgrading and retest the envelope on each upgrade.
Untrusted extensions
Early signalSkills or plug-ins are installed from a community registry because they looked useful in a demo.
MitigationTreat every extension as third-party code: read it, allow-list it and run it inside the sandbox.
Memory that keeps too much
Early signalPersistent memory or generated skills contain client names, credentials or confidential findings.
MitigationDecide what may persist, review memory and skill files periodically and keep secrets in a separate store.
Single-user defaults in a multi-user setting
Early signalSeveral colleagues share one gateway and one set of credentials with different levels of trust.
MitigationRun separate instances per trust boundary, with separate credentials and, ideally, separate hosts.
What a ColdAI engagement on these frameworks includes
We start from three inputs: the exact framework and version you want to use, a concrete task with its runtime environment, and a decision on which actions and data the agent is allowed. The work produces a framework and dependency assessment, a pilot with a restricted set of tools, and procedures for tracing runs and recovering when one does not complete4.
We assess the implementation in front of us rather than the project's reputation. The framework comparison sets out how the two projects differ, and the hardening procedure walks through the host-level controls step by step. The design-level threat model for any agent sits with the AI agents practice.
Frequently asked questions
Can OpenClaw or Hermes Agent run entirely inside our own network?
Largely, yes. Both run on infrastructure you choose, and both can be pointed at a model endpoint inside your network instead of a hosted provider. What leaves your environment then depends on the channels and tools you enable. Map every outbound connection during the pilot and block anything the task does not need.
Are these frameworks suitable for regulated work such as legal or clinical research?
The framework alone does not make a deployment suitable or unsuitable. Suitability depends on the envelope you enforce, the data you expose, how runs are logged and who reviews outputs. For regulated work, keep the agent in a drafting and analysis role, keep professional judgement and sign-off with qualified people, and document the controls so they can be examined.
Which models work with OpenClaw and Hermes Agent?
Both support several hosted providers and local models, and both let you switch between them. The choice matters for data handling as much as for quality, so decide which data each provider may receive and test the specific task on candidate models before settling. Results on one task do not transfer automatically to another.
How long does it take to move from a demo to a bounded pilot?
It depends mostly on how quickly the allowed actions, data and reviewers can be agreed, rather than on installation. Once those decisions are made, the work is configuration, isolation, tracing and testing against real task examples. We agree the scope and timetable for a pilot after reviewing the task and the environment.
Do you modify or fork the frameworks?
We prefer configuration, sandboxing and wrapping over forking, because a fork has to be maintained against a fast-moving upstream project. Where a gap needs code, we keep the change small, documented and separable, and we tell you plainly when a different foundation would serve the task better than adapting this one.
Sources
- OpenClaw repository and README — OpenClaw Foundation
- Hermes Agent repository and README — Nous Research
- Gateway security — OpenClaw Docs
- OpenClaw & Hermes: deliverables and readiness — ColdAI
Connect the architecture
The next connection.
Explore the complementary capabilities that can turn an individual technology into a complete workflow.
Your next move
Bring us the
real problem.
A workflow that takes too long. A system that cannot connect. An idea that needs a technical path. Start there, and we can define what to investigate, build and measure.
- The exact framework and version
- A concrete task and runtime environment
- A decision on allowed actions and data