Extending Agents

Cognition uses a convention-over-configuration model. Most extensions require zero code changes — drop a file in the right directory and the server picks it up automatically (via the file watcher). More powerful extensions require Python.

Level	Mechanism	Code Required	Hot-Reload
Memory	AGENTS.md	No	Yes
Skills	.cognition/skills/ SKILL.md files	No	Yes
Agents	.cognition/agents/ YAML or Markdown	No	Yes
Tools	Python functions	Yes	Yes
Middleware	Python classes	Yes	No
MCP servers	Remote HTTP/SSE endpoints	No	Yes
A2A exposure	a2a_exposed: true on agent definition	No	Yes
Custom LLM providers	Python factories	Yes	No

---

1. Memory (AGENTS.md)

Place an AGENTS.md file in your project root. It is automatically injected into the agent's system prompt for every session in that project.

Use memory for: - Project-specific rules and conventions - Architecture decisions - Code style guidelines - Workflow instructions

# My Project

This is a Django REST API. All models live in `myapp/models/`.
Use Python 3.11 type hints everywhere. Tests run with pytest.
The database is PostgreSQL — never use SQLite in tests.

## Conventions
- Prefer `select_related` over multiple queries
- All API views inherit from `BaseAPIView`
- Migrations must be reviewed before merging

Configure which memory files to load in .cognition/config.yaml:

agent:
  memory:
    - "AGENTS.md"
    - "docs/architecture.md"    # additional files

---

2. Skills (SKILL.md)

Skills are modular instruction sets for domain-specific tasks. The agent sees a skill's name and description and loads the full content only when it is relevant to the current task (progressive disclosure).

Directory Structure

.cognition/skills/
  deploy-app/
    SKILL.md           # instructions for deploying the application
    references/        # optional supporting files
      checklist.md
  run-migrations/
    SKILL.md

SKILL.md Format

# Deploy App

Use this skill when the user asks to deploy the application or push changes to production.

## Prerequisites
- Docker must be running
- AWS credentials must be configured

## Steps
1. Run the test suite: `uv run pytest`
2. Build the Docker image: `docker build -t myapp:latest .`
3. Push to ECR: `docker push <account>.dkr.ecr.us-east-1.amazonaws.com/myapp:latest`
4. Update the ECS service: `aws ecs update-service --cluster prod --service myapp --force-new-deployment`

Attach skills by registry name (seeded from skill_sources directories):

agent:
  skills:
    - "my-skill-name"

---

3. Custom Agents

Place agent definitions in .cognition/agents/ as Markdown or YAML files. The file watcher reloads them automatically on change.

Markdown Format

The filename (without extension) becomes the agent name. The YAML frontmatter provides fields; the Markdown body becomes the system prompt.

---
# .cognition/agents/security-auditor.md
mode: subagent
description: Audits code for security vulnerabilities and reports findings with severity ratings
tools:
  - "run_semgrep"
config:
  model: gpt-4o
  temperature: 0.1
---

You are a security expert specialising in Python web applications.

When asked to audit code:
1. Check for SQL injection, XSS, CSRF, and path traversal vulnerabilities
2. Review dependency versions for known CVEs
3. Report findings with severity (Critical/High/Medium/Low) and remediation steps

YAML Format

# .cognition/agents/data-analyst.yaml
name: data-analyst
mode: primary
description: Analyses datasets and generates statistical reports
system_prompt: |
  You are a data analyst. Use pandas and matplotlib for analysis.
  Always validate data quality before drawing conclusions.
tools:
  - "load_csv"
  - "plot_chart"
config:
  model: gpt-4o
  temperature: 0.2

Agent Modes

Mode	Can own a session	Can be delegated to
primary	Yes	No
subagent	No	Yes
all	Yes	Yes

Sessions are created with agent_name:

curl -X POST http://localhost:8000/sessions \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "data-analyst"}'

Primary agents can delegate to subagents via the task tool. The delegation appears as a delegation SSE event.

---

4. Custom Tools

Tools are Python callables that the agent can invoke. Cognition converts them to LangChain tools automatically.

Simple Function Tool

# myapp/tools/analysis.py
import subprocess

def run_linter(file_path: str) -> str:
    """Run ruff linter on a Python file and return the findings.

    Args:
        file_path: Path to the Python file to lint.

    Returns:
        Linter output as a string.
    """
    result = subprocess.run(
        ["ruff", "check", file_path],
        capture_output=True,
        text=True,
    )
    return result.stdout or "No issues found."

The docstring becomes the tool description shown to the agent. Type annotations become the argument schema.

Register via Config

# .cognition/config.yaml
skill_sources:
  - .cognition/skills/

tool_sources:
  - .cognition/tools/

agent:
  tools:
    - "run_linter"
    - "query_database"

Auto-Discovery

Drop Python files into .cognition/tools/ and they are discovered automatically. Each public function in the file becomes a tool. The file watcher reloads them on change.

# .cognition/tools/my_tools.py

def fetch_ticket(ticket_id: str) -> str:
    """Fetch a Jira ticket by ID and return its summary and status."""
    ...

def post_comment(ticket_id: str, comment: str) -> str:
    """Post a comment to a Jira ticket."""
    ...

Register via API (Source-in-DB)

When Cognition runs in a separate container from your builder application, you cannot write files into .cognition/tools/. Use the REST API to register tools with inline Python source code instead:

curl -X POST http://localhost:8000/tools \
  -H "Content-Type: application/json" \
  -d '{
    "name": "search-jira",
    "code": "from langchain_core.tools import tool\nimport httpx\n\n@tool\ndef search_jira(query: str) -> str:\n    \"\"\"Search Jira issues by query string.\"\"\"\n    resp = httpx.get(f\"https://jira.example.com/search?q={query}\")\n    return resp.text"
  }'

The tool is stored in the ConfigRegistry (Postgres or SQLite) and loaded on every agent invocation — no restart required.

# The code field contains a complete Python module as a string.
# @tool-decorated functions and BaseTool subclasses are extracted automatically.
code = """
from langchain_core.tools import tool

@tool
def search_jira(query: str) -> str:
    \"\"\"Search Jira issues by query string.\"\"\"
    import httpx
    resp = httpx.get(f"https://jira.example.com/search?q={query}")
    return resp.text
"""

import httpx
httpx.post("http://localhost:8000/tools", json={"name": "search-jira", "code": code})

Security: Tool code executes with full Python privileges inside the sandbox backend. Restrict POST /tools to authorized administrators at your Gateway/proxy layer.

Alternatively, register by module path if the module is already importable in the server's Python environment:

curl -X POST http://localhost:8000/tools \
  -H "Content-Type: application/json" \
  -d '{"name": "jira-tools", "path": "mycompany.cognition_tools.jira"}'

To see all registered tools (both file-discovered and API-registered):

curl http://localhost:8000/tools

Response includes a source_type field: "file" for auto-discovered tools, "api_code" for source-in-DB tools, and "api_path" for module-path tools.

Async Tools

Async functions are supported natively:

async def call_api(endpoint: str, payload: dict) -> str:
    """Call an internal API endpoint with a JSON payload."""
    async with httpx.AsyncClient() as client:
        response = await client.post(endpoint, json=payload)
        return response.text

Programmatic Registration

from server.app.agent.cognition_agent import create_cognition_agent
from server.app.agent.definition import AgentDefinition

definition = AgentDefinition(
    name="my-agent",
    system_prompt="You are a helpful assistant.",
    tools=["run_linter"],
)

agent = await create_cognition_agent(definition, settings)

Testing Tools

# tests/unit/test_my_tools.py
from myapp.tools.analysis import run_linter
from unittest.mock import patch, MagicMock

def test_run_linter_clean_file():
    with patch("subprocess.run") as mock_run:
        mock_run.return_value = MagicMock(stdout="", returncode=0)
        result = run_linter("clean.py")
    assert result == "No issues found."

def test_run_linter_with_issues():
    with patch("subprocess.run") as mock_run:
        mock_run.return_value = MagicMock(
            stdout="clean.py:1:1: E501 Line too long", returncode=1
        )
        result = run_linter("messy.py")
    assert "E501" in result

---

5. Middleware

Middleware intercepts the agent's processing loop. Use middleware for cross-cutting concerns: approval gates, custom telemetry, PII detection, retry logic.

Upstream Middleware (No Code)

Four upstream middleware components are available by name in agent.middleware:

agent:
  middleware:
    # Retry failed tool calls with exponential backoff
    - name: tool_retry
      max_retries: 3
      backoff_factor: 2.0

    # Hard cap on total tool invocations
    - name: tool_call_limit
      run_limit: 50
      per_tool_limits:
        execute_bash: 10

    # Detect and redact PII before sending to the LLM
    - name: pii
      pii_types:
        - email
        - phone
        - credit_card
        - ip
        - ssn
      strategy: redact   # or "mask"

    # Require human approval before specific tools execute
    - name: human_in_the_loop
      approve_tools:
        - execute_bash
        - file_write

Custom Middleware

Implement deepagents.middleware.AgentMiddleware and register it in .cognition/config.yaml as a dotted import path.

# myapp/middleware/audit.py
from deepagents.middleware import AgentMiddleware
from myapp.audit_log import write_audit_event

class AuditMiddleware(AgentMiddleware):
    """Writes every tool call to an immutable audit log."""

    async def awrap_tool_call(self, tool_call, handler):
        # Called before the tool executes
        write_audit_event(
            event_type="tool_call",
            tool=tool_call.name,
            args=tool_call.args,
        )
        result = await handler(tool_call)
        # Called after the tool executes
        write_audit_event(
            event_type="tool_result",
            tool=tool_call.name,
            exit_code=result.exit_code,
        )
        return result

Register in config:

agent:
  middleware:
    - "myapp.middleware.audit.AuditMiddleware"
    - name: tool_retry
      max_retries: 2

String entries are imported directly; dict entries with a name key are treated as upstream middleware.

---

6. MCP Tool Servers

Connect to any remote Model Context Protocol (MCP) server. MCP servers expose tools over HTTP.

# .cognition/config.yaml
mcp:
  servers:
    - name: github-tools
      url: https://mcp.github.example.com/sse
      transport: sse  # or "streamable_http"
    - name: internal-db
      url: http://db-tools.internal:8080/sse

All tools exposed by the MCP server become available to the agent under the server name as a namespace prefix (e.g. github-tools/create_pr). The tool_name_prefix=True setting on MultiServerMCPClient prevents tool name collisions between servers.

How It Works

Cognition uses langchain-mcp-adapters to connect to MCP servers. The adapter:

1. Connects to each configured remote server using SSE or Streamable HTTP transport
2. Converts MCP tools into LangChain BaseTool instances
3. Applies a scope injection interceptor that adds X-Cognition-Scope-* headers to every MCP request
4. Registers progress and logging callbacks for observability
5. Returns tools that participate in the full Deep Agents middleware stack (tool safety, HITL, permissions)

Transport Options

Transport	Description
sse	Server-Sent Events (default). Best for long-lived connections.
streamable_http	HTTP with streaming. Best for serverless or short-lived connections.

Managing MCP Servers via API

MCP servers can also be managed at runtime via the REST API:

# List registered servers
curl http://localhost:8000/mcp-servers

# Register a new server
curl -X POST http://localhost:8000/mcp-servers \
  -H "Content-Type: application/json" \
  -d '{"name": "my-tools", "url": "https://tools.example.com/sse", "transport": "sse"}'

# Update a server
curl -X PATCH http://localhost:8000/mcp-servers/my-tools \
  -H "Content-Type: application/json" \
  -d '{"enabled": false}'

# Delete a server
curl -X DELETE http://localhost:8000/mcp-servers/my-tools

Note: MCP server headers (containing credentials) are redacted in API responses — GET /mcp-servers returns an empty headers dict to prevent credential leakage. File-managed servers (from .cognition/config.yaml) are read-only via the API — mutation attempts return 409 Conflict.

Only HTTP/HTTPS URLs are accepted — stdio-based MCP servers are not supported for security reasons.

---

7. Exposing Agents via A2A

Cognition can expose agents via the Agent-to-Agent (A2A) protocol, allowing external systems to discover and invoke your agents.

Opting In

Set a2a_exposed: true on any agent definition:

# .cognition/agents/deploy-agent.yaml
name: deploy-agent
mode: primary
a2a_exposed: true
description: Handles deployment workflows
system_prompt: |
  You are a deployment agent. Deploy applications safely and report results.

Or via the API:

curl -X POST http://localhost:8000/agents \
  -H "Content-Type: application/json" \
  -d '{"name": "deploy-agent", "system_prompt": "...", "a2a_exposed": true}'

How It Works

1. Agent card discovery — GET /a2a/{agent_name}/.well-known/agent-card.json returns the A2A AgentCard for that agent. GET /.well-known/agent-card.json also lists exposed agents visible to the caller's scope.
2. JSON-RPC endpoint — Each agent gets a dedicated endpoint at POST /a2a/{agent_name}. The agent is resolved at request time, so agents created after server startup are immediately available.
3. Scope-aware — A2A requests must include X-Cognition-Scope-* headers. Only agents visible in the caller's scope are discoverable and invocable.
4. Bridging — The CognitionA2AExecutor bridges A2A requests to Cognition's service.stream_response(), reusing the full agent runtime, tools, middleware, and persistence.

A2A Client Example

import httpx

# Discover a specific agent card from the agent's canonical A2A URL
resp = httpx.get(
    "http://localhost:8000/a2a/deploy-agent/.well-known/agent-card.json",
    headers={"X-Cognition-Scope-User": "alice"},
)
card = resp.json()

# Send a message to an agent
resp = httpx.post(
    "http://localhost:8000/a2a/deploy-agent",
    json={
        "jsonrpc": "2.0",
        "id": "1",
        "method": "message/send",
        "params": {
            "message": {
                "role": "user",
                "parts": [{"kind": "text", "text": "Deploy staging"}],
            }
        },
    },
    headers={
        "X-Cognition-Scope-User": "alice",
    },
)

Constraints

• Built-in agents (default, readonly) have a2a_exposed=False by default
• Only primary and all mode agents can be exposed via A2A
• If A2A-Version is omitted, Cognition treats the request as the current supported A2A version
• Dynamically registered agents are scope-bound. Use the same X-Cognition-Scope-* headers when creating, discovering, and invoking an agent.
• A2A does not add any additional services — endpoints are part of the main Cognition server

For full A2A protocol details, see the A2A SDK documentation.

---

8. Custom LLM Providers

Cognition uses LangChain's init_chat_model() under the hood, which supports any provider that has a LangChain integration. The built-in provider types are:

Type	LangChain Package	Credentials
openai	langchain-openai	OPENAI_API_KEY
anthropic	langchain-anthropic	ANTHROPIC_API_KEY
bedrock	langchain-aws	AWS IAM credentials
google_genai	langchain-google-genai	GOOGLE_API_KEY
google_vertexai	langchain-google-vertexai	Google ADC
openai_compatible	langchain-openai + custom base_url	COGNITION_OPENAI_COMPATIBLE_API_KEY

To add a provider, create a ProviderConfig entry via the REST API:

curl -X POST http://localhost:8000/models/providers \
  -H "Content-Type: application/json" \
  -d '{
    "id": "my-provider",
    "provider": "openai_compatible",
    "model": "my-model",
    "base_url": "https://my-provider.example.com/v1",
    "api_key_env": "MY_PROVIDER_API_KEY",
    "enabled": true,
    "priority": 0
  }'

Or define it in .cognition/config.yaml (bootstrapped on first startup):

llm:
  provider: openai_compatible
  model: my-model
  base_url: https://my-provider.example.com/v1
  api_key_env: MY_PROVIDER_API_KEY

Test connectivity:

curl -X POST http://localhost:8000/models/providers/my-provider/test

For providers not supported by init_chat_model, wrap them in a LangChain BaseChatModel and use openai_compatible with a local proxy, or contribute a LangChain integration upstream.

---

Hot-Reload

The file watcher (server/app/file_watcher.py) monitors .cognition/tools/, .cognition/middleware/, and .cognition/agents/ using watchdog. When any file in these directories changes:

1. Tool registry is reloaded (new tools available, removed tools gone)
2. Agent definition registry is reloaded (new/updated agents loaded)
3. Agent cache is invalidated so the next session uses the updated definition

No server restart required. Changes typically take effect within 1 second.