Agent-Body
A DeepSeek Harness plugin layer that loads task-relevant tools and adds memory, reflexes, and attributed recovery.
- Source repo
- 1420079678-ctrl/agent-body
- Stars
- ★ 15
- Last updated
- 5d ago
- License
- MIT
- Primary language
- TypeScript
- FA score
- 78/100 · Good
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free, no paid service needed
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- DeepSeek Harness operators whose growing tool catalog should expose only schemas relevant to the current task.
- Not a fit if
- Teams that do not run DeepSeek Harness
- Production teams requiring a stable third-party API or SLA
- Users requiring Linux or macOS parity with Windows
- Source review
- 78/100 · Good
What does this agent do, and when should you use it?
Agent-Body is a plugin layer for DeepSeek Harness rather than a standalone, general-purpose agent framework. Its components include the organism kernel, the Cortex memory organ, the Zero-residence context engine, and optional organ plugins coordinated through a nerve bus, heartbeat, and deterministic routing rules. In its reproducible 48-command cold-start benchmark, average estimated tool-schema usage falls from 55,154 to 8,433 tokens, a 84.71% reduction limited strictly to tool schemas rather than the full prompt or bill. The runtime records outcomes, adjusts routing synapses, attributes failures before selecting remedies, and consolidates long-term memory with deterministic rules while idle. The core demo and checks run without a host, network, model, or API key, but mounted organs and the body_* interfaces require DeepSeek Harness; the project is early-stage and Windows-first.
At agent/pre-step, the runtime converts command text into a nerve impulse. body_nerve applies deterministic intent rules, selects organs and named capabilities, and publishes the result through organism/impulse. After execution, tools/result updates learned routing: successful delivery strengthens a command-class-to-organ synapse, while failure weakens it. body_heal attributes errors to tool_missing, arg_error, permission, timeout, network, not_found, conflict, or unknown before choosing a remedy; arg_error is never automatically retried, and a wound closes only after that organ later succeeds. The heartbeat writes directives, proprioception, and homeostasis warnings to bloodstream.json and broadcasts organism/heartbeat. During deep sleep, Cortex derives pitfall, playbook, hotspot, unresolved, and fact memory cards from recorded experience. Zero-residence exposes zr_compact, zr_recall, zr_ledger, and zr_fast to replace resident content with pointers, reconstruct masked payloads verbatim from session logs, measure context cost, and run long commands asynchronously. body_status, body_map, body_cell, body_pulse, and body_tokens expose runtime health, ownership, capability units, event history, and tool-schema token accounting.
- DeepSeek Harness operators whose growing tool catalog should expose only schemas relevant to the current task.
- Framework maintainers who need an offline, model-free reproduction of routing, failure attribution, and reflex execution.
- Local-agent users who want outcomes retained across sessions and consolidated into deterministic long-term memory while idle.
- Automation teams that need to distinguish argument, permission, timeout, network, and other failures before retrying.
- Plugin authors building custom DeepSeek Harness organs with defineOrgan declarations for capabilities, permissions, signals, and fallbacks.
How do you install or deploy this agent?
To verify the core without installing the host, use Git and Node.js 22.19 or 24. No npm install, model, or API key is required.
git clone https://github.com/1420079678-ctrl/agent-body && cd agent-body
npm run demo
npm run checkOperational use requires a working DeepSeek Harness installation. The supported command below installs the organism, Cortex, and Zero-residence packages from the latest GitHub Release; restart the harness afterward.
rel=https://github.com/1420079678-ctrl/agent-body/releases/latest/download
dsh plugin --profile web add \
"$rel/dsh-external-dsh-organism-0.1.1.tgz" \
"$rel/dsh-external-dsh-cortex-0.1.1.tgz" \
"$rel/dsh-external-dsh-zero-residence-0.1.0.tgz"If the GitHub asset redirect is blocked, the documentation provides a jsDelivr path:
rel=https://cdn.jsdelivr.net/gh/1420079678-ctrl/[email protected]/dist
dsh plugin --profile web add \
"$rel/dsh-external-dsh-organism-0.1.1.tgz" \
"$rel/dsh-external-dsh-cortex-0.1.1.tgz" \
"$rel/dsh-external-dsh-zero-residence-0.1.0.tgz"The packages are not published to the npm registry. Some organs require a compiler, browser, or external binaries and must be installed from source with their replay scripts. The supplied material does not document DeepSeek Harness model credentials, so host credential requirements cannot be established here.
How do you use this agent?
Run the end-to-end demo, repository gate, and schema benchmark locally:
npm run demo
npm run check
npm run bench
npm run bench:checkAfter installing the plugins and restarting DeepSeek Harness, inspect health and ownership, then route a command without executing it:
body_status
body_map
body_nerve action=send text="Crawl this site and extract structured data"body_status reports organs, heartbeat, fatigue, and integrity; body_map shows capability ownership; body_nerve returns the organs and capabilities selected for the command. A capability excluded by the initial gate remains retrievable through body_call. Use npm run verify for repository structure, JSON, link, and secret-hygiene checks. npm run verify:organs runs organ regressions and explicitly reports SKIP when the host runtime is unavailable.
What are this agent's strengths and limitations?
- Schema gating has a reproducible in-repository benchmark: 48 cold-start commands average 55,154 to 8,433 estimated tokens, with bench:check detecting drift.
- Routing, attribution, reflex handling, and sleep-time consolidation use deterministic rules, allowing core verification without a model or API key.
- Recovery is cause-specific: argument errors are not blindly retried, and a wound remains open until the affected organ later succeeds.
- defineOrgan gives plugins an explicit contract for capabilities, permissions, signals, handled failures, and fallbacks, while the kernels avoid dependence on one organ.
- Vitals, wounds, synapses, memory cards, and the pulse stream are represented as inspectable runtime data.
- Core operation is tied to DeepSeek Harness; without that host, users get the offline demonstration rather than a deployable complete agent.
- The project is explicitly early-stage and Windows-first; Linux CI covers the zero-dependency core more thoroughly than the organ integrations.
- Schema savings decline as history keeps organs hot: the documented live measurement is 58%, so 84.71% is not a steady-state guarantee.
- Ten of 48 benchmark commands require a second body_call, and large organs are affected by a ten-capability-per-organ exposure cap.
- Only five of the 24 plugin packages currently have offline regression suites; the rest rely mainly on replay scripts.
- Packages are not yet published to npm, and some organs require host-side compilers, browsers, or external binaries.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Agent-Body This agent | 78 · Good | CLIFree | ★ 15 | 5d ago | TypeScript | — |
| Auto Company | 70 · Some gaps | CLIFree + model costs | ★ 3.1k | 2d ago | Python | Codex · Claude Code |
| Hive Agent Harness | 46 · Major gaps | CLIFree + model costs | ★ 11k | 21d ago | Python | OpenAI API · Claude API |
| ANOLISA Agent OS Layer | 45 · Major gaps | CLIFree + model costs | ★ 657 | today | Rust | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Trust: The source says read-only remedies may run automatically while side-effecting remedies await a model decision, and CI grants only contents:read. The security policy also states plainly that the local GUI can read files, execute commands, and load plugins, and that plugins inherit host privileges. This supports ordinary privilege and confirmation controls, but no fine-grained sandbox, per-action authorization, or comprehensive undo path is shown, so least_privilege, user_confirmation, external_effects, and rollback do not receive full marks. Heartbeat files, session recovery, credential location, and telemetry/log leakage risks are described concretely, justifying full data_flow_transparency. Sensitive-data protection rests mainly on .gitignore, a repository check, and operator practices; encryption, redaction, and key isolation are not evidenced, so sensitive_data_handling is 2. Runtime requirements are documented, but deep regressions install the host from main, Actions use movable major tags, and the supplied files show no dependency audit, lockfile, or vulnerability scanning, leaving dependency_security at 1. The MIT copyright holder, repository, issue path, and private vulnerability channel provide attribution, but the maintainer is only an unverified account with limited ownership detail, so source_attribution is 2; unknown identity is not treated as suspicious.
Reliability: The README, scripts, and workflows form a mostly coherent offline demo, test, benchmark, and failure-attribution story. Failure classes, no-retry treatment for bad arguments, chronic-wound termination, and installation failure symptoms are unusually specific, so failure_messages earns 3. The text carefully distinguishes cold-start from historical state and the 256- versus 332-capability populations. However, package.json reports 0.1.0 while the README mixes v0.1.1 and v0.1.2, and bare-clone parity can skip the real kernel, reducing self_consistency to 2. Node, DeepSeek Harness, Windows-first scope, absent npm publication, GitHub/CDN alternatives, and an offline kit are disclosed, but plugin regressions require a separately installed host and are manual-only, so dependency_availability is 2. This static review neither penalizes unexecuted tests nor treats their claimed outcomes as independently observed.
Adaptability: Documentation addresses host-free evaluators, existing Harness users, offline installers, and plugin authors, and it defines the local single-user, Windows-first, Node-version, host-dependency, and tool-schema boundaries. This supports full audience_and_scenarios and capability_boundaries scores. Deterministic intent routing, per-organ caps, and the body_call fallback are well explained, but the benchmark itself says 10 of 48 commands need a second hop and identifies capped or unrouted cases, so trigger_precision is 2. Windows and shell installation paths, two-platform repository checks, and an offline bundle help portability, while full deep regressions remain Windows- and Harness-dependent; environment_fit is therefore 2.
Convention: The README provides strong navigation and clear entry points for architecture, layout, quick start, SDK material, demos, FAQ, and limitations; generated catalog and documentation checks reinforce this, earning 3 for information_architecture, install_notes, examples_and_faq, and known_limitations. License metadata matches the complete MIT text, so license is 3. The biological names map to concrete components, but organ, package, capability, and project/package version counts use several different scopes that readers must reconcile, so naming_stability is 2. Release-note and roadmap links exist, but no complete changelog is included in the supplied material and package/install asset versions diverge, making versioning_changelog 2. Issues, private security reporting, response expectations, the supported branch, and the one-person maintenance fact establish a usable path, but no verified organization, defined team, or succession arrangement is shown; maintenance_responsibility is 2.
Effectiveness: The framework combines routing, schema gating, failure attribution, memory consolidation, and reflex behavior into concrete status, invocation, recovery, and verification commands. That is clear incremental value over an undifferentiated tool collection, so marginal_value is 3. The outputs and installation path appear usable for the target audience, but the extensive biological vocabulary, host integration, and occasional second body_call add operational and cognitive overhead, limiting output_usability to 2. The documentation quantifies schema-token savings and openly reports the cold-start 84.71%, live 58%, and 10-of-48 second-hop trade-off. The supplied evidence does not establish net savings for the whole prompt, latency, billing, or maintenance effort, so cost_benefit is 2.
Verifiability: Major claims are tied to named commands, a benchmark report, frozen corpus, raw capture, renderer, table/catalog synchronization checks, and CI drift gates. The 84.71% headline is repeatedly limited to the cold-start tool-schema block, supporting claim_traceability at 3. README statements are supported across package scripts, workflows, and sample unit tests, but the decisive benchmark report, implementation sources, lockfile, and captured output are absent from this evidence set; third-party listing claims also do not validate performance, so cross_source_corroboration is 2. The documentation clearly labels reproducible benchmarks, development-install observations, historical snapshots, estimates, and real-world caveats, earning 3 for fact_inference_separation. No commands were executed for this assessment.
- The unauthenticated local Web GUI can read files, execute commands, and load plugins. Keep it bound to 127.0.0.1 and never expose it through port forwarding or a reverse proxy.
- Plugins run with Harness privileges. Review each new organ before activation and use provider-specific credentials with spending limits.
- Do not interpret 84.71% as whole-prompt, billing, or sustained production savings. The documented history-bearing observation is 58%, and 10 of 48 benchmark commands require a second hop.
- Deep organ regressions are manual and depend on DeepSeek Harness main. Run them against a fixed host revision before release and add dependency locking and vulnerability auditing.
- Reconcile the 0.1.0, v0.1.1, and v0.1.2 version references and tarball combination before installation to avoid incompatible assets.