Pallas — FastAgent MCP Bridge

Pallas is the generic runtime that turns fast-agent agent definitions into StreamableHTTP MCP servers.

It is completely deployment-agnostic: all environment-specific values (agent names, ports, hosts, model) live in the calling project's agents.yaml and fastagent.config.yaml.

---

Installation

pip install git+ssh://git@git.helu.ca:22022/r/pallas.git

Or as a project dependency in pyproject.toml:

dependencies = [
    "pallas-mcp @ git+ssh://git@git.helu.ca:22022/r/pallas.git",
]

---

Usage

Pallas reads configuration from the working directory at runtime.

my-project/
├── agents/
│   ├── __init__.py
│   └── jarvis.py          # FastAgent definitions
├── agents.yaml            # Deployment topology
├── fastagent.config.yaml  # FastAgent + model config
└── fastagent.secrets.yaml # API keys (gitignored)

Run from your project root:

pallas                     # start all agents + registry
pallas --agent jarvis      # start a single agent

Or via python -m:

python -m pallas.server

---

agents.yaml format

name: my-project           # used in log prefixes and registry names
version: "1.0.0"
host: my-host.example.com  # hostname for registry URLs
namespace: com.example.my-project
registry_port: 8200

agents:
  jarvis:
    module: agents.jarvis  # importable Python module path
    port: 8201
    title: Jarvis
    description: "My assistant agent"
    depends_on: [research]  # optional: start these first

  research:
    module: agents.research
    port: 8250
    title: Research Agent
    description: "Web search and knowledge graph"

Loop safeguards

Three optional fields bound how long an agent's tool-call loop can run:

Field	Type	Default	Purpose
max_iterations	int	15	Maximum tool calls in a single agent turn
streaming_timeout	float	120	Max idle seconds between streaming events
turn_timeout	float	300	Hard wall-clock limit for a full turn (seconds)

All three are optional. Agents that omit them use the defaults shown above.

agents:
  research:
    module: agents.research
    port: 8250
    max_iterations: 10      # this agent only needs a few search calls
    streaming_timeout: 60   # fail fast on a slow search MCP
    turn_timeout: 120       # research turns should not take more than 2 min

---

fastagent.config.yaml extensions

Pallas reads two extra keys beyond the standard fast-agent config:

default_model: openai.my-custom-model-name

# Explicit capability declarations — avoids brittle name-regex heuristics
model_capabilities:
  vision: false
  context_window: 200000
  max_output_tokens: 32000

Capabilities are published in the registry and used to register unknown models with fast-agent's ModelDatabase.

AWS Bedrock Mantle — automatic shims

When anthropic.base_url points at a Bedrock Mantle endpoint (https://bedrock-mantle.{region}.api.aws/anthropic), Pallas auto-detects it at startup and installs two compatibility shims via pallas.mantle_shims. No config flag is required.

Shim 1 — wire-name prefix. Mantle requires the full anthropic.<name> wire id (e.g. anthropic.claude-opus-4-7). Fast-agent's model-spec parser would otherwise strip the anthropic. prefix, causing a misleading 404 "The model '...' does not exist". The shim registers the prefixed forms in ModelDatabase._PROVIDER_WIRE_MODEL_NAMES.

Shim 2 — strip caller: null from replayed tool_use blocks. Anthropic SDK 0.100.x leaks caller: null onto serialised BetaToolUseBlock params (upstream issue #1454). api.anthropic.com silently tolerates the extra field; Mantle rejects it with tool_use.caller: Input should be a valid dictionary or object, which breaks the MCP tool-use loop on the second turn. The shim monkeypatches AnthropicConverter._deserialize_assistant_raw_blocks and _append_server_tool_channel_blocks to pop the field before history is re-sent.

See docs/bedrock.md for the full configuration walkthrough.

---

Environment variable

Variable	Default	Purpose
PALLAS_AGENTS_CONFIG	agents.yaml	Override path to deployment config

---

What Pallas provides

Module	Purpose
pallas.server	CLI entry point and agent orchestration
pallas.registry	GET /.well-known/mcp/server.json registry server
pallas.multimodal_server	MultimodalAgentMCPServer — AgentMCPServer subclass with image + history support
pallas.health	LLM preflight validation + get_health MCP tool
pallas._fastagent_patch	Traceback-capture wrappers around three opaque fast-agent catch-sites (debug-only)

---

Authentication

Pallas is transparent to downstream authentication. Whatever the operator places under each downstream MCP server's headers: block in fastagent.config.yaml (typically loaded from fastagent.secrets.yaml) is what fast-agent sends — Pallas does not intercept, rewrite, or forward the inbound Authorization header of the MCP request that triggered the agent turn.

For agents that talk to Mnemosyne, the convention is a long-lived team JWT minted from Mnemosyne's admin UI and pasted into the agent project's fastagent.secrets.yaml:

mcp:
  servers:
    mnemosyne:
      transport: http
      url: https://mnemosyne.example.com/mcp/
      headers:
        Authorization: "Bearer eyJ…team-jwt…"

See mnemosyne/docs/DAEDALUS_PALLAS_INTEGRATION_v1.md for the three credential types Mnemosyne recognises, how team JWTs are minted and rotated, and the data model that ties a team to a set of libraries.

Earlier versions of Pallas shipped a forward_inbound_auth: true mechanism that captured the per-turn Authorization header and propagated it to opted-in downstream servers. That mechanism has been retired — opt-in flags in old fastagent.config.yaml files are now silently ignored and can be removed at your convenience.