Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/managedcode/dotpilot/memorygit clone --depth 1 https://github.com/managedcode/dotPilotWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00013 | $0.03484 |
| Opus 5 | $0.00006 | $0.01742 |
| Sonnet 5 | $0.00003 | $0.00697 |
| Haiku 4.5 | $0.00001 | $0.00348 |
Grade A, and why
memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to memory — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adding Memory to an Agent
::: zone pivot="programming-language-csharp"
This tutorial shows how to add memory to an agent by implementing an AIContextProvider and attaching it to the agent.
[!IMPORTANT] Not all agent types support
AIContextProvider. This step uses aChatClientAgent, which does supportAIContextProvider.
Prerequisites
For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.
Create an AIContextProvider
AIContextProvider is an abstract class that you can inherit from, and which can be associated with the AgentThread for a ChatClientAgent.
It allows you to:
- Run custom logic before and after the agent invokes the underlying inference service.
- Provide additional context to the agent before it invokes the underlying inference service.
- Inspect all messages provided to and produced by the agent.
Pre and post invocation events
The AIContextProvider class has two methods that you can override to run custom logic before and after the agent invokes the underlying inference service:
InvokingAsync- called before the agent invokes the underlying inference service. You can provide additional context to the agent by returning anAIContextobject. This context will be merged with the agent's existing context before invoking the underlying service. It is possible to provide instructions, tools, and messages to add to the request.InvokedAsync- called after the agent has received a response from the underlying inference service. You can inspect the request and response messages, and update the state of the context provider.
Serialization
AIContextProvider instances are created and attached to an AgentThread when the thread is created, and when a thread is resumed from a serialized state.
The AIContextProvider instance might have its own state that needs to be persisted between invocations of the agent. For example, a memory component that remembers information about the user might have memories as part of its state.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 386 lines · 13 tokens per session scan A 0da9bfaceac0
memory is an agent published in the GitHub repository managedcode/dotPilot (23 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 3,484 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to memory, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
knowledge-ops
Executes knowledge management operations under CKO direction. Audits consistency, distributes learnings, optimizes memory files, and detects knowledge gaps across the agent fleet.
Explore
Fast read-only codebase & docs exploration. Returns structured findings, never raw file dumps.
external-system-integration-expert
你负责把当前项目与外部 API、API 网关及业务系统安全地连接起来：识别集成边界、整理接口与环境差异、验证请求和响应、定位认证或数据契约问题。.
Audit
Deep security + performance audit of a specific diff. Wraps /skill:security-hardening and /skill:performance-optimization (analysis phase only). Use when a change touches auth, untrusted input, secrets, webhooks, PII, or a latency/throughput budget — a focused, read-only risk pass that returns findings the parent…
registry
InitRunner's role registry lets you install, share, and discover roles from InitHub and OCI registries. Roles are downloaded, validated, and saved to /.initrunner/roles/ where they integrate automatically with the CLI.
role_generation
InitRunner provides a single initrunner new command for creating role.yaml files. It supports multiple seed modes (templates, AI generation, examples, hub bundles, or local files) and an interactive refinement loop for iterating on the YAML before saving. Run with no arguments in a terminal and it shows a guided start…